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1. A worldwide study of white matter microstructural alterations in people living with Parkinson’s disease

Author: Conor Owens-Walton, Talia M. Nir, Sarah Al-Bachari, Sonia Ambrogi, Tim J. Anderson, Ítalo Karmann Aventurato, Fernando Cendes, Yao-Liang Chen, Valentina Ciullo, Phil Cook, John C. Dalrymple-Alford, Michiel F. Dirkx, Jason Druzgal, Hedley C. A. Emsley, Rachel Guimarães, Hamied A. Haroon, Rick C. Helmich, Michele T. Hu, Martin E. Johansson, Ho Bin Kim, Johannes C. Klein, Max Laansma, Katherine E. Lawrence, Christine Lochner, Clare Mackay, Corey T. McMillan, Tracy R. Melzer, Leila Nabulsi, Ben Newman, Peter Opriessnig, Laura M. Parkes, Clelia Pellicano, Fabrizio Piras, Federica Piras, Lukas Pirpamer, Toni L. Pitcher, Kathleen L. Poston, Annerine Roos, Lucas Scárdua Silva, Reinhold Schmidt, Petra Schwingenschuh, Marian Shahid-Besanti, Gianfranco Spalletta, Dan J. Stein, Sophia I. Thomopoulos, Duygu Tosun, Chih-Chien Tsai, Odile A. van den Heuvel, Eva van Heese, Daniela Vecchio, Julio E. Villalón-Reina, Chris Vriend, Jiun-Jie Wang, Yih-Ru Wu, Clarissa Lin Yasuda, Paul M. Thompson, Neda Jahanshad, and Ysbrand van der Werf
Subjects: Neurology. Diseases of the nervous system, RC346-429
Abstract: Abstract The progression of Parkinson’s disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20–89 years; 33% female) and 885 controls (age: 19–84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen’s d effect sizes reached d = −1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder.
Published: 2024
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2. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm

Author: Yuchao Jiang, Cheng Luo, Jijun Wang, Lena Palaniyappan, Xiao Chang, Shitong Xiang, Jie Zhang, Mingjun Duan, Huan Huang, Christian Gaser, Kiyotaka Nemoto, Kenichiro Miura, Ryota Hashimoto, Lars T. Westlye, Genevieve Richard, Sara Fernandez-Cabello, Nadine Parker, Ole A. Andreassen, Tilo Kircher, Igor Nenadić, Frederike Stein, Florian Thomas-Odenthal, Lea Teutenberg, Paula Usemann, Udo Dannlowski, Tim Hahn, Dominik Grotegerd, Susanne Meinert, Rebekka Lencer, Yingying Tang, Tianhong Zhang, Chunbo Li, Weihua Yue, Yuyanan Zhang, Xin Yu, Enpeng Zhou, Ching-Po Lin, Shih-Jen Tsai, Amanda L. Rodrigue, David Glahn, Godfrey Pearlson, John Blangero, Andriana Karuk, Edith Pomarol-Clotet, Raymond Salvador, Paola Fuentes-Claramonte, María Ángeles Garcia-León, Gianfranco Spalletta, Fabrizio Piras, Daniela Vecchio, Nerisa Banaj, Jingliang Cheng, Zhening Liu, Jie Yang, Ali Saffet Gonul, Ozgul Uslu, Birce Begum Burhanoglu, Aslihan Uyar Demir, Kelly Rootes-Murdy, Vince D. Calhoun, Kang Sim, Melissa Green, Yann Quidé, Young Chul Chung, Woo-Sung Kim, Scott R. Sponheim, Caroline Demro, Ian S. Ramsay, Felice Iasevoli, Andrea de Bartolomeis, Annarita Barone, Mariateresa Ciccarelli, Arturo Brunetti, Sirio Cocozza, Giuseppe Pontillo, Mario Tranfa, Min Tae M. Park, Matthias Kirschner, Foivos Georgiadis, Stefan Kaiser, Tamsyn E. Van Rheenen, Susan L. Rossell, Matthew Hughes, William Woods, Sean P. Carruthers, Philip Sumner, Elysha Ringin, Filip Spaniel, Antonin Skoch, David Tomecek, Philipp Homan, Stephanie Homan, Wolfgang Omlor, Giacomo Cecere, Dana D. Nguyen, Adrian Preda, Sophia I. Thomopoulos, Neda Jahanshad, Long-Biao Cui, Dezhong Yao, Paul M. Thompson, Jessica A. Turner, Theo G. M. van Erp, Wei Cheng, ENIGMA Schizophrenia Consortium, Jianfeng Feng, and ZIB Consortium
Subjects: Science
Abstract: Abstract Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal ‘trajectory’ of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
Published: 2024
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3. Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer’s disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI

Author: Tamoghna Chattopadhyay, Saket S. Ozarkar, Ketaki Buwa, Neha Ann Joshy, Dheeraj Komandur, Jayati Naik, Sophia I. Thomopoulos, Greg Ver Steeg, Jose Luis Ambite, and Paul M. Thompson
Subjects: Alzheimer’s disease, amyloid, 3D convolutional neural networks, deep learning, transfer learning, vision transformers, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer’s disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer’s treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer’s Disease Neuroimaging Initiative. We evaluated each model’s balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.
Published: 2024
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4. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author: Vladimir Belov, Tracy Erwin-Grabner, Moji Aghajani, Andre Aleman, Alyssa R. Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Robin Bülow, Christopher R. K. Ching, Colm G. Connolly, Kathryn Cullen, Christopher G. Davey, Danai Dima, Annemiek Dols, Jennifer W. Evans, Cynthia H. Y. Fu, Ali Saffet Gonul, Ian H. Gotlib, Hans J. Grabe, Nynke Groenewold, J Paul Hamilton, Ben J. Harrison, Tiffany C. Ho, Benson Mwangi, Natalia Jaworska, Neda Jahanshad, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Meng Li, David E. J. Linden, Frank P. MacMaster, David M. A. Mehler, Elisa Melloni, Bryon A. Mueller, Amar Ojha, Mardien L. Oudega, Brenda W. J. H. Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J. Portella, Elena Pozzi, Liesbeth Reneman, Matthew D. Sacchet, Philipp G. Sämann, Anouk Schrantee, Kang Sim, Jair C. Soares, Dan J. Stein, Sophia I. Thomopoulos, Aslihan Uyar-Demir, Nic J. A. van der Wee, Steven J. A. van der Werff, Henry Völzke, Sarah Whittle, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson, Roberto Goya-Maldonado, and the ENIGMA Major Depressive Disorder working group
Subjects: Medicine, Science
Abstract: Abstract Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
Published: 2024
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5. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis

Author: Eamonn Kennedy, Spencer W. Liebel, Hannah M. Lindsey, Shashank Vadlamani, Pui-Wa Lei, Maheen M. Adamson, Martin Alda, Silvia Alonso-Lana, Tim J. Anderson, Celso Arango, Robert F. Asarnow, Mihai Avram, Rosa Ayesa-Arriola, Talin Babikian, Nerisa Banaj, Laura J. Bird, Stefan Borgwardt, Amy Brodtmann, Katharina Brosch, Karen Caeyenberghs, Vince D. Calhoun, Nancy D. Chiaravalloti, David X. Cifu, Benedicto Crespo-Facorro, John C. Dalrymple-Alford, Kristen Dams-O’Connor, Udo Dannlowski, David Darby, Nicholas Davenport, John DeLuca, Covadonga M. Diaz-Caneja, Seth G. Disner, Ekaterina Dobryakova, Stefan Ehrlich, Carrie Esopenko, Fabio Ferrarelli, Lea E. Frank, Carol E. Franz, Paola Fuentes-Claramonte, Helen Genova, Christopher C. Giza, Janik Goltermann, Dominik Grotegerd, Marius Gruber, Alfonso Gutierrez-Zotes, Minji Ha, Jan Haavik, Charles Hinkin, Kristen R. Hoskinson, Daniela Hubl, Andrei Irimia, Andreas Jansen, Michael Kaess, Xiaojian Kang, Kimbra Kenney, Barbora Keřková, Mohamed Salah Khlif, Minah Kim, Jochen Kindler, Tilo Kircher, Karolina Knížková, Knut K. Kolskår, Denise Krch, William S. Kremen, Taylor Kuhn, Veena Kumari, Junsoo Kwon, Roberto Langella, Sarah Laskowitz, Jungha Lee, Jean Lengenfelder, Victoria Liou-Johnson, Sara M. Lippa, Marianne Løvstad, Astri J. Lundervold, Cassandra Marotta, Craig A. Marquardt, Paulo Mattos, Ahmad Mayeli, Carrie R. McDonald, Susanne Meinert, Tracy R. Melzer, Jessica Merchán-Naranjo, Chantal Michel, Rajendra A. Morey, Benson Mwangi, Daniel J. Myall, Igor Nenadić, Mary R. Newsome, Abraham Nunes, Terence O’Brien, Viola Oertel, John Ollinger, Alexander Olsen, Victor Ortiz García de la Foz, Mustafa Ozmen, Heath Pardoe, Marise Parent, Fabrizio Piras, Federica Piras, Edith Pomarol-Clotet, Jonathan Repple, Geneviève Richard, Jonathan Rodriguez, Mabel Rodriguez, Kelly Rootes-Murdy, Jared Rowland, Nicholas P. Ryan, Raymond Salvador, Anne-Marthe Sanders, Andre Schmidt, Jair C. Soares, Gianfranco Spalleta, Filip Španiel, Scott R. Sponheim, Alena Stasenko, Frederike Stein, Benjamin Straube, April Thames, Florian Thomas-Odenthal, Sophia I. Thomopoulos, Erin B. Tone, Ivan Torres, Maya Troyanskaya, Jessica A. Turner, Kristine M. Ulrichsen, Guillermo Umpierrez, Daniela Vecchio, Elisabet Vilella, Lucy Vivash, William C. Walker, Emilio Werden, Lars T. Westlye, Krista Wild, Adrian Wroblewski, Mon-Ju Wu, Glenn R. Wylie, Lakshmi N. Yatham, Giovana B. Zunta-Soares, Paul M. Thompson, Mary Jo Pugh, David F. Tate, Frank G. Hillary, Elisabeth A. Wilde, and Emily L. Dennis
Subjects: verbal learning, memory, dementia, depression, Parkinson’s disease, schizophrenia, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores (p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.
Published: 2024
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6. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Author: Xi Zhu, Yoojean Kim, Orren Ravid, Xiaofu He, Benjamin Suarez-Jimenez, Sigal Zilcha-Mano, Amit Lazarov, Seonjoo Lee, Chadi G. Abdallah, Michael Angstadt, Christopher L. Averill, C. Lexi Baird, Lee A. Baugh, Jennifer U. Blackford, Jessica Bomyea, Steven E. Bruce, Richard A. Bryant, Zhihong Cao, Kyle Choi, Josh Cisler, Andrew S. Cotton, Judith K. Daniels, Nicholas D. Davenport, Richard J. Davidson, Michael D. DeBellis, Emily L. Dennis, Maria Densmore, Terri deRoon-Cassini, Seth G. Disner, Wissam El Hage, Amit Etkin, Negar Fani, Kelene A. Fercho, Jacklynn Fitzgerald, Gina L. Forster, Jessie L. Frijling, Elbert Geuze, Atilla Gonenc, Evan M. Gordon, Staci Gruber, Daniel W Grupe, Jeffrey P. Guenette, Courtney C. Haswell, Ryan J. Herringa, Julia Herzog, David Bernd Hofmann, Bobak Hosseini, Anna R. Hudson, Ashley A. Huggins, Jonathan C. Ipser, Neda Jahanshad, Meilin Jia-Richards, Tanja Jovanovic, Milissa L. Kaufman, Mitzy Kennis, Anthony King, Philipp Kinzel, Saskia B.J. Koch, Inga K. Koerte, Sheri M. Koopowitz, Mayuresh S. Korgaonkar, John H. Krystal, Ruth Lanius, Christine L. Larson, Lauren A.M. Lebois, Gen Li, Israel Liberzon, Guang Ming Lu, Yifeng Luo, Vincent A. Magnotta, Antje Manthey, Adi Maron-Katz, Geoffery May, Katie McLaughlin, Sven C. Mueller, Laura Nawijn, Steven M. Nelson, Richard W.J. Neufeld, Jack B Nitschke, Erin M. O'Leary, Bunmi O. Olatunji, Miranda Olff, Matthew Peverill, K. Luan Phan, Rongfeng Qi, Yann Quidé, Ivan Rektor, Kerry Ressler, Pavel Riha, Marisa Ross, Isabelle M. Rosso, Lauren E. Salminen, Kelly Sambrook, Christian Schmahl, Martha E. Shenton, Margaret Sheridan, Chiahao Shih, Maurizio Sicorello, Anika Sierk, Alan N. Simmons, Raluca M. Simons, Jeffrey S. Simons, Scott R. Sponheim, Murray B. Stein, Dan J. Stein, Jennifer S. Stevens, Thomas Straube, Delin Sun, Jean Théberge, Paul M. Thompson, Sophia I. Thomopoulos, Nic J.A. van der Wee, Steven J.A. van der Werff, Theo G.M. van Erp, Sanne J.H. van Rooij, Mirjam van Zuiden, Tim Varkevisser, Dick J. Veltman, Robert R.J.M. Vermeiren, Henrik Walter, Li Wang, Xin Wang, Carissa Weis, Sherry Winternitz, Hong Xie, Ye Zhu, Melanie Wall, Yuval Neria, and Rajendra A. Morey
Subjects: Posttraumatic stress disorder, Multimodal MRI, Machine learning, Deep learning, Classification, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.
Published: 2023
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7. Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

Author: Bo-yong Park, Valeria Kebets, Sara Larivière, Meike D. Hettwer, Casey Paquola, Daan van Rooij, Jan Buitelaar, Barbara Franke, Martine Hoogman, Lianne Schmaal, Dick J. Veltman, Odile A. van den Heuvel, Dan J. Stein, Ole A. Andreassen, Christopher R. K. Ching, Jessica A. Turner, Theo G. M. van Erp, Alan C. Evans, Alain Dagher, Sophia I. Thomopoulos, Paul M. Thompson, Sofie L. Valk, Matthias Kirschner, and Boris C. Bernhardt
Subjects: Biology (General), QH301-705.5
Abstract: Individuals across six major psychiatric conditions from the ENIGMA consortium reveals a shared morphological effect, following a sensory-fugal axis, which is related to microstructural gradient and neurotransmitter axes.
Published: 2022
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8. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Author: Justine Y. Hansen, Golia Shafiei, Jacob W. Vogel, Kelly Smart, Carrie E. Bearden, Martine Hoogman, Barbara Franke, Daan van Rooij, Jan Buitelaar, Carrie R. McDonald, Sanjay M. Sisodiya, Lianne Schmaal, Dick J. Veltman, Odile A. van den Heuvel, Dan J. Stein, Theo G. M. van Erp, Christopher R. K. Ching, Ole A. Andreassen, Tomas Hajek, Nils Opel, Gemma Modinos, André Aleman, Ysbrand van der Werf, Neda Jahanshad, Sophia I. Thomopoulos, Paul M. Thompson, Richard E. Carson, Alain Dagher, and Bratislav Misic
Subjects: Science
Abstract: Changes to structural and functional connectivity can give rise to neurodegeneration and neurodevelopmental diseases. Here the authors investigate molecular and connectomic patterns in 13 different neurological, psychiatric and neurodevelopmental diseases from the ENIGMA consortium.
Published: 2022
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9. Neuroanatomical heterogeneity and homogeneity in individuals at clinical high risk for psychosis

Author: Helen Baldwin, Joaquim Radua, Mathilde Antoniades, Shalaila S. Haas, Sophia Frangou, Ingrid Agartz, Paul Allen, Ole A. Andreassen, Kimberley Atkinson, Peter Bachman, Inmaculada Baeza, Cali F. Bartholomeusz, Michael W. L. Chee, Tiziano Colibazzi, Rebecca E. Cooper, Cheryl M. Corcoran, Vanessa L. Cropley, Bjørn H. Ebdrup, Adriana Fortea, Louise Birkedal Glenthøj, Holly K. Hamilton, Kristen M. Haut, Rebecca A. Hayes, Ying He, Karsten Heekeren, Michael Kaess, Kiyoto Kasai, Naoyuki Katagiri, Minah Kim, Jochen Kindler, Mallory J. Klaunig, Shinsuke Koike, Alex Koppel, Tina D. Kristensen, Yoo Bin Kwak, Jun Soo Kwon, Stephen M. Lawrie, Irina Lebedeva, Jimmy Lee, Ashleigh Lin, Rachel L. Loewy, Daniel H. Mathalon, Chantal Michel, Romina Mizrahi, Paul Møller, Barnaby Nelson, Takahiro Nemoto, Dorte Nordholm, Maria A. Omelchenko, Christos Pantelis, Jayachandra M. Raghava, Jan I. Røssberg, Wulf Rössler, Dean F. Salisbury, Daiki Sasabayashi, Ulrich Schall, Lukasz Smigielski, Gisela Sugranyes, Michio Suzuki, Tsutomu Takahashi, Christian K. Tamnes, Jinsong Tang, Anastasia Theodoridou, Sophia I. Thomopoulos, Alexander S. Tomyshev, Peter J. Uhlhaas, Tor G. Værnes, Therese A. M. J. van Amelsvoort, Theo G. M. Van Erp, James A. Waltz, Lars T. Westlye, Stephen J. Wood, Juan H. Zhou, Philip McGuire, Paul M. Thompson, Maria Jalbrzikowski, Dennis Hernaus, Paolo Fusar-Poli, and the ENIGMA Clinical High Risk for Psychosis Working Group
Subjects: Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Abstract Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the ‘normativeness’ of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.
Published: 2022
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10. Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline

Author: Gavin T. Kress, Emily S. Popa, Paul M. Thompson, Susan Y. Bookheimer, Sophia I. Thomopoulos, Christopher R.K. Ching, Hong Zheng, Daniel A. Hirsh, David A. Merrill, Stella E. Panos, Cyrus A. Raji, Prabha Siddarth, and Jennifer E. Bramen
Subjects: Mild cognitive impairment, Alzheimer’s disease, Dementia, Magnetic resonance imaging, Biomarker, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption.In this study, we introduce a novel index which we call an “AD-NeuroScore,” that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1–91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses.Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics.In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.
Published: 2023
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11. Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group

Author: Anita Harrewijn, Elise M. Cardinale, Nynke A. Groenewold, Janna Marie Bas-Hoogendam, Moji Aghajani, Kevin Hilbert, Narcis Cardoner, Daniel Porta-Casteràs, Savannah Gosnell, Ramiro Salas, Andrea P. Jackowski, Pedro M. Pan, Giovanni A. Salum, Karina S. Blair, James R. Blair, Mira Z. Hammoud, Mohammed R. Milad, Katie L. Burkhouse, K. Luan Phan, Heidi K. Schroeder, Jeffrey R. Strawn, Katja Beesdo-Baum, Neda Jahanshad, Sophia I. Thomopoulos, Randy Buckner, Jared A. Nielsen, Jordan W. Smoller, Jair C. Soares, Benson Mwangi, Mon-Ju Wu, Giovana B. Zunta-Soares, Michal Assaf, Gretchen J. Diefenbach, Paolo Brambilla, Eleonora Maggioni, David Hofmann, Thomas Straube, Carmen Andreescu, Rachel Berta, Erica Tamburo, Rebecca B. Price, Gisele G. Manfro, Federica Agosta, Elisa Canu, Camilla Cividini, Massimo Filippi, Milutin Kostić, Ana Munjiza Jovanovic, Bianca A. V. Alberton, Brenda Benson, Gabrielle F. Freitag, Courtney A. Filippi, Andrea L. Gold, Ellen Leibenluft, Grace V. Ringlein, Kathryn E. Werwath, Hannah Zwiebel, André Zugman, Hans J. Grabe, Sandra Van der Auwera, Katharina Wittfeld, Henry Völzke, Robin Bülow, Nicholas L. Balderston, Monique Ernst, Christian Grillon, Lilianne R. Mujica-Parodi, Helena van Nieuwenhuizen, Hugo D. Critchley, Elena Makovac, Matteo Mancini, Frances Meeten, Cristina Ottaviani, Tali M. Ball, Gregory A. Fonzo, Martin P. Paulus, Murray B. Stein, Raquel E. Gur, Ruben C. Gur, Antonia N. Kaczkurkin, Bart Larsen, Theodore D. Satterthwaite, Jennifer Harper, Michael Myers, Michael T. Perino, Chad M. Sylvester, Qiongru Yu, Ulrike Lueken, Dick J. Veltman, Paul M. Thompson, Dan J. Stein, Nic J. A. Van der Wee, Anderson M. Winkler, and Daniel S. Pine
Subjects: Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Abstract The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.
Published: 2021
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12. A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Author: Delin Sun, Gopalkumar Rakesh, Courtney C. Haswell, Mark Logue, C. Lexi Baird, Erin N. O'Leary, Andrew S. Cotton, Hong Xie, Marijo Tamburrino, Tian Chen, Emily L. Dennis, Neda Jahanshad, Lauren E. Salminen, Sophia I. Thomopoulos, Faisal Rashid, Christopher R.K. Ching, Saskia B.J. Koch, Jessie L. Frijling, Laura Nawijn, Mirjam van Zuiden, Xi Zhu, Benjamin Suarez-Jimenez, Anika Sierk, Henrik Walter, Antje Manthey, Jennifer S. Stevens, Negar Fani, Sanne J.H. van Rooij, Murray Stein, Jessica Bomyea, Inga K. Koerte, Kyle Choi, Steven J.A. van der Werff, Robert R.J.M. Vermeiren, Julia Herzog, Lauren A.M. Lebois, Justin T. Baker, Elizabeth A. Olson, Thomas Straube, Mayuresh S. Korgaonkar, Elpiniki Andrew, Ye Zhu, Gen Li, Jonathan Ipser, Anna R. Hudson, Matthew Peverill, Kelly Sambrook, Evan Gordon, Lee Baugh, Gina Forster, Raluca M. Simons, Jeffrey S. Simons, Vincent Magnotta, Adi Maron-Katz, Stefan du Plessis, Seth G. Disner, Nicholas Davenport, Daniel W. Grupe, Jack B. Nitschke, Terri A. deRoon-Cassini, Jacklynn M. Fitzgerald, John H. Krystal, Ifat Levy, Miranda Olff, Dick J. Veltman, Li Wang, Yuval Neria, Michael D. De Bellis, Tanja Jovanovic, Judith K. Daniels, Martha Shenton, Nic J.A. van de Wee, Christian Schmahl, Milissa L. Kaufman, Isabelle M. Rosso, Scott R. Sponheim, David Bernd Hofmann, Richard A. Bryant, Kelene A. Fercho, Dan J. Stein, Sven C. Mueller, Bobak Hosseini, K. Luan Phan, Katie A. McLaughlin, Richard J. Davidson, Christine L. Larson, Geoffrey May, Steven M. Nelson, Chadi G. Abdallah, Hassaan Gomaa, Amit Etkin, Soraya Seedat, Ilan Harpaz-Rotem, Israel Liberzon, Theo G.M. van Erp, Yann Quidé, Xin Wang, Paul M. Thompson, and Rajendra A. Morey
Subjects: Data Harmonization, Scanner Effects, Site Effects, Cortical Thickness, ComBat, ComBat-GAM, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.
Published: 2022
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13. Enhancing neuroimaging genetics through meta-analysis for Tourette syndrome (ENIGMA-TS): A worldwide platform for collaboration

Author: Peristera Paschou, Yin Jin, Kirsten Müller-Vahl, Harald E. Möller, Renata Rizzo, Pieter J. Hoekstra, Veit Roessner, Nanette Mol Debes, Yulia Worbe, Andreas Hartmann, Pablo Mir, Danielle Cath, Irene Neuner, Heike Eichele, Chencheng Zhang, Katarzyna Lewandowska, Alexander Munchau, Julius Verrel, Richard Musil, Tim J. Silk, Colleen A. Hanlon, Emily D. Bihun, Valerie Brandt, Andrea Dietrich, Natalie Forde, Christos Ganos, Deanna J. Greene, Chunguang Chu, Michel J. Grothe, Tamara Hershey, Piotr Janik, Jonathan M. Koller, Juan Francisco Martin-Rodriguez, Karsten Müller, Stefano Palmucci, Adriana Prato, Shukti Ramkiran, Federica Saia, Natalia Szejko, Renzo Torrecuso, Zeynep Tumer, Anne Uhlmann, Tanja Veselinovic, Tomasz Wolańczyk, Jade-Jocelyne Zouki, Pritesh Jain, Apostolia Topaloudi, Mary Kaka, Zhiyu Yang, Petros Drineas, Sophia I. Thomopoulos, Tonya White, Dick J. Veltman, Lianne Schmaal, Dan J. Stein, Jan Buitelaar, Barbara Franke, Odile van den Heuvel, Neda Jahanshad, Paul M. Thompson, and Kevin J. Black
Subjects: Tourette syndrome, neuroimaging, genetics, ENIGMA, brain MRI, Psychiatry, RC435-571
Abstract: Tourette syndrome (TS) is characterized by multiple motor and vocal tics, and high-comorbidity rates with other neuropsychiatric disorders. Obsessive compulsive disorder (OCD), attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), major depressive disorder (MDD), and anxiety disorders (AXDs) are among the most prevalent TS comorbidities. To date, studies on TS brain structure and function have been limited in size with efforts mostly fragmented. This leads to low-statistical power, discordant results due to differences in approaches, and hinders the ability to stratify patients according to clinical parameters and investigate comorbidity patterns. Here, we present the scientific premise, perspectives, and key goals that have motivated the establishment of the Enhancing Neuroimaging Genetics through Meta-Analysis for TS (ENIGMA-TS) working group. The ENIGMA-TS working group is an international collaborative effort bringing together a large network of investigators who aim to understand brain structure and function in TS and dissect the underlying neurobiology that leads to observed comorbidity patterns and clinical heterogeneity. Previously collected TS neuroimaging data will be analyzed jointly and integrated with TS genomic data, as well as equivalently large and already existing studies of highly comorbid OCD, ADHD, ASD, MDD, and AXD. Our work highlights the power of collaborative efforts and transdiagnostic approaches, and points to the existence of different TS subtypes. ENIGMA-TS will offer large-scale, high-powered studies that will lead to important insights toward understanding brain structure and function and genetic effects in TS and related disorders, and the identification of biomarkers that could help inform improved clinical practice.
Published: 2022
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14. Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data

Author: Peter Kochunov, Meghann C. Ryan, Qifan Yang, Kathryn S. Hatch, Alyssa Zhu, Sophia I. Thomopoulos, Neda Jahanshad, Lianne Schmaal, Paul M. Thompson, Shuo Chen, Xiaoming Du, Bhim M. Adhikari, Heather Bruce, Stephanie Hare, Eric L. Goldwaser, Mark D. Kvarta, Thomas E. Nichols, and L. Elliot Hong
Subjects: Big data, Meta-analysis, DTI, Structural deficit patterns, ENIGMA, RVI, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract: Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed to quantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10−8), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10−5) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.
Published: 2021
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15. Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Author: Ezequiel Gleichgerrcht, Brent C. Munsell, Saud Alhusaini, Marina K.M. Alvim, Núria Bargalló, Benjamin Bender, Andrea Bernasconi, Neda Bernasconi, Boris Bernhardt, Karen Blackmon, Maria Eugenia Caligiuri, Fernando Cendes, Luis Concha, Patricia M. Desmond, Orrin Devinsky, Colin P. Doherty, Martin Domin, John S. Duncan, Niels K. Focke, Antonio Gambardella, Bo Gong, Renzo Guerrini, Sean N. Hatton, Reetta Kälviäinen, Simon S. Keller, Peter Kochunov, Raviteja Kotikalapudi, Barbara A.K. Kreilkamp, Angelo Labate, Soenke Langner, Sara Larivière, Matteo Lenge, Elaine Lui, Pascal Martin, Mario Mascalchi, Stefano Meletti, Terence J. O'Brien, Heath R. Pardoe, Jose C. Pariente, Jun Xian Rao, Mark P. Richardson, Raúl Rodríguez-Cruces, Theodor Rüber, Ben Sinclair, Hamid Soltanian-Zadeh, Dan J. Stein, Pasquale Striano, Peter N. Taylor, Rhys H. Thomas, Anna Elisabetta Vaudano, Lucy Vivash, Felix von Podewills, Sjoerd B. Vos, Bernd Weber, Yi Yao, Clarissa Lin Yasuda, Junsong Zhang, Paul M. Thompson, Sanjay M. Sisodiya, Carrie R. McDonald, Leonardo Bonilha, Andre Altmann, Chantal Depondt, Marian Galovic, Sophia I. Thomopoulos, and Roland Wiest
Subjects: Epilepsy, Temporal lobe epilepsy, Machine learning, Artificial inteligence, Computer applications to medicine. Medical informatics, R858-859.7, Neurology. Diseases of the nervous system, RC346-429
Abstract: Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.
Published: 2021
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16. Associations of medication with subcortical morphology across the lifespan in OCD: Results from the international ENIGMA Consortium

Author: Iliyan Ivanov, Premika S.W. Boedhoe, Yoshinari Abe, Pino Alonso, Stephanie H. Ameis, Paul D. Arnold, Srinivas Balachander, Justin T. Baker, Nerisa Banaj, Nuria Bargalló, Marcelo C. Batistuzzo, Francesco Benedetti, Jan C. Beucke, Irene Bollettini, Silvia Brem, Brian P. Brennan, Jan Buitelaar, Rosa Calvo, Yuqi Cheng, Kang Ik K. Cho, Sara Dallaspezia, Damiaan Denys, Juliana B. Diniz, Benjamin A. Ely, Jamie D. Feusner, Sónia Ferreira, Kate D. Fitzgerald, Martine Fontaine, Patricia Gruner, Gregory L. Hanna, Yoshiyuki Hirano, Marcelo Q. Hoexter, Chaim Huyser, Keisuke Ikari, Anthony James, Fern Jaspers-Fayer, Hongyan Jiang, Norbert Kathmann, Christian Kaufmann, Minah Kim, Kathrin Koch, Jun Soo Kwon, Luisa Lázaro, Yanni Liu, Christine Lochner, Rachel Marsh, Ignacio Martínez-Zalacaín, David Mataix-Cols, José M. Menchón, Luciano Minuzzi, Astrid Morer, Pedro Morgado, Akiko Nakagawa, Takashi Nakamae, Tomohiro Nakao, Janardhanan C. Narayanaswamy, Erika L. Nurmi, Sanghoon Oh, Chris Perriello, John C. Piacentini, Maria Picó-Pérez, Fabrizio Piras, Federica Piras, Y.C. Janardhan Reddy, Daniela Rodriguez Manrique, Yuki Sakai, Eiji Shimizu, H. Blair Simpson, Noam Soreni, Carles Soriano-Mas, Gianfranco Spalletta, Emily R. Stern, Michael C. Stevens, S. Evelyn Stewart, Philip R. Szeszko, David F. Tolin, Daan van Rooij, Dick J. Veltman, Ysbrand D. van der Werf, Guido A. van Wingen, Ganesan Venkatasubramanian, Susanne Walitza, Zhen Wang, Anri Watanabe, Lidewij H. Wolters, Xiufeng Xu, Je-Yeon Yun, Mojtaba Zarei, Fengrui Zhang, Qing Zhao, Neda Jahanshad, Sophia I. Thomopoulos, Paul M. Thompson, Dan J. Stein, Odile A. van den Heuvel, Joseph O'Neill, Sara Poletti, Egill Axfjord Fridgeirsson, Toshikazu Ikuta, Stella J. de Wit, Chris Vriend, Selina Kasprzak, Masaru Kuno, Jumpei Takahashi, Euripedes C. Miguel, Roseli G. Shavitt, Morgan Hough, Jose C. Pariente, Ana E. Ortiz, Sara Bertolín, Eva Real, Cinto Segalàs, Pedro Silva Moreira, Nuno Sousa, Jin Narumoto, Kei Yamada, Jinsong Tang, Jean-Paul Fouche, Taekwan Kim, Sunah Choi, Minji Ha, Sunghyun Park, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Systems & Network Neuroscience, Amsterdam Neuroscience - Neurodegeneration, Adult Psychiatry, Child Psychiatry, Paediatric Psychosocial Care, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Graduate School, Ivanov, Iliyan, Boedhoe, Premika S W, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Arnold, Paul D, Balachander, Sriniva, Baker, Justin T, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Brem, Silvia, Brennan, Brian P, Buitelaar, Jan, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Dallaspezia, Sara, Denys, Damiaan, Diniz, Juliana B, Ely, Benjamin A, Feusner, Jamie D, Ferreira, Sónia, Fitzgerald, Kate D, Fontaine, Martine, Gruner, Patricia, Hanna, Gregory L, Hirano, Yoshiyuki, Hoexter, Marcelo Q, Huyser, Chaim, Ikari, Keisuke, James, Anthony, Jaspers-Fayer, Fern, Jiang, Hongyan, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kwon, Jun Soo, Lázaro, Luisa, Liu, Yanni, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morer, Astrid, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, Oh, Sanghoon, Perriello, Chri, Piacentini, John C, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Reddy, Y C Janardhan, Manrique, Daniela Rodriguez, Sakai, Yuki, Shimizu, Eiji, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carle, Spalletta, Gianfranco, Stern, Emily R, Stevens, Michael C, Stewart, S Evelyn, Szeszko, Philip R, Tolin, David F, van Rooij, Daan, Veltman, Dick J, van der Werf, Ysbrand D, van Wingen, Guido A, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij H, Xu, Xiufeng, Yun, Je-Yeon, Zarei, Mojtaba, Zhang, Fengrui, Zhao, Qing, Jahanshad, Neda, Thomopoulos, Sophia I, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, and O'Neill, Joseph
Subjects: Obsessive-Compulsive Disorder, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], OCD, Psychotropics, Longevity, SRIs, Magnetic Resonance Imaging, Benzodiazepines, Psychiatry and Mental health, Clinical Psychology, Age, Cross-Sectional Studies, Subcortical volumes, 130 000 Cognitive Neurology & Memory, Child, Preschool, Serotonin Uptake Inhibitors, Humans, Child, Selective Serotonin Reuptake Inhibitors, Aged, Antipsychotic Agents
Abstract: Background: Widely used psychotropic medications for obsessive-compulsive disorder (OCD) may change the volumes of subcortical brain structures, and differently in children vs. adults. We measured subcortical volumes cross-sectionally in patients finely stratified for age taking various common classes of OCD drugs. Methods: The ENIGMA-OCD consortium sample (1081 medicated/1159 unmedicated OCD patients and 2057 healthy controls aged 6–65) was divided into six successive 6–10-year age-groups. Individual structural MRIs were parcellated automatically using FreeSurfer into 8 regions-of-interest (ROIs). ROI volumes were compared between unmedicated and medicated patients and controls, and between patients taking serotonin reuptake inhibitors (SRIs), tricyclics (TCs), antipsychotics (APs), or benzodiazepines (BZs) and unmedicated patients. Results: Compared to unmedicated patients, volumes of accumbens, caudate, and/or putamen were lower in children aged 6–13 and adults aged 50–65 with OCD taking SRIs (Cohen's d = −0.24 to −0.74). Volumes of putamen, pallidum (d = 0.18–0.40), and ventricles (d = 0.31–0.66) were greater in patients aged 20–29 receiving APs. Hippocampal volumes were smaller in patients aged 20 and older taking TCs and/or BZs (d = −0.27 to −1.31). Conclusions: Results suggest that TCs and BZs could potentially aggravate hippocampal atrophy of normal aging in older adults with OCD, whereas SRIs may reduce striatal volumes in young children and older adults. Similar to patients with psychotic disorders, OCD patients aged 20–29 may experience subcortical nuclear and ventricular hypertrophy in relation to APs. Although cross-sectional, present results suggest that commonly prescribed agents exert macroscopic effects on subcortical nuclei of unknown relation to therapeutic response.
Published: 2022

17. Event‐based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross‐sectional data

Author: Seymour M, Lopez, Leon M, Aksman, Neil P, Oxtoby, Sjoerd B, Vos, Jun, Rao, Erik, Kaestner, Saud, Alhusaini, Marina, Alvim, Benjamin, Bender, Andrea, Bernasconi, Neda, Bernasconi, Boris, Bernhardt, Leonardo, Bonilha, Lorenzo, Caciagli, Benoit, Caldairou, Maria Eugenia, Caligiuri, Angels, Calvet, Fernando, Cendes, Luis, Concha, Estefania, Conde-Blanco, Esmaeil, Davoodi-Bojd, Christophe, de Bézenac, Norman, Delanty, Patricia M, Desmond, Orrin, Devinsky, Martin, Domin, John S, Duncan, Niels K, Focke, Sonya, Foley, Francesco, Fortunato, Marian, Galovic, Antonio, Gambardella, Ezequiel, Gleichgerrcht, Renzo, Guerrini, Khalid, Hamandi, Victoria, Ives-Deliperi, Graeme D, Jackson, Neda, Jahanshad, Simon S, Keller, Peter, Kochunov, Raviteja, Kotikalapudi, Barbara A K, Kreilkamp, Angelo, Labate, Sara, Larivière, Matteo, Lenge, Elaine, Lui, Charles, Malpas, Pascal, Martin, Mario, Mascalchi, Sarah E, Medland, Stefano, Meletti, Marcia E, Morita-Sherman, Thomas W, Owen, Mark, Richardson, Antonella, Riva, Theodor, Rüber, Ben, Sinclair, Hamid, Soltanian-Zadeh, Dan J, Stein, Pasquale, Striano, Peter N, Taylor, Sophia I, Thomopoulos, Paul M, Thompson, Manuela, Tondelli, Anna Elisabetta, Vaudano, Lucy, Vivash, Yujiang, Wang, Bernd, Weber, Christopher D, Whelan, Roland, Wiest, Gavin P, Winston, Clarissa Lin, Yasuda, Carrie R, McDonald, Daniel C, Alexander, Sanjay M, Sisodiya, Andre, Altmann, and Rhys H, Thomas
Subjects: Epilepsy, Sclerosis, 3,979, disease progression, duration of illness, event-based model, patient staging [Key Points, MTLE, Word Count], 3 [Word Count], 610 Medicine & health, Key Points, Hippocampus, Magnetic Resonance Imaging, Cross-Sectional Studies, Epilepsy, Temporal Lobe, Neurology, Humans, Neurology (clinical), Atrophy, patient staging, Biomarkers
Abstract: Objective: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross‐sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE‐HS) correlate with clinical features. Methods: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1‐weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA‐Epilepsy consortium, comprising 804 people with MTLE‐HS and 1625 healthy controls from 25 centers. Features with a moderate case–control effect size (Cohen d ≥ .5) were used to train an event‐based model (EBM), which estimates a sequence of disease‐specific biomarker changes from cross‐sectional data and assigns a biomarker‐based fine‐grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. Results: In MTLE‐HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10−16), age at onset (ρ = −.18, p = 9.82 × 10−7), and ASM resistance (area under the curve = .59, p = .043, Mann–Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE‐HS with mild or nondetectable abnormality on T1W MRI. Significance: From cross‐sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE‐HS subjects in other cohorts and help establish connections between imaging‐based progression staging and clinical features.
Published: 2022

18. Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3

Author: Artemis Zavaliangos-Petropulu, Talia M. Nir, Sophia I. Thomopoulos, Robert I. Reid, Matt A. Bernstein, Bret Borowski, Clifford R. Jack Jr., Michael W. Weiner, Neda Jahanshad, and Paul M. Thompson
Subjects: Alzheimer’s disease, ADNI3, white matter, DTI, multi-site, harmonization, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract: Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.
Published: 2019
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19. In vivo white matter microstructure in adolescents with early-onset psychosis: a multi-site mega-analysis

Author: Claudia Barth, Sinead Kelly, Stener Nerland, Neda Jahanshad, Clara Alloza, Sonia Ambrogi, Ole A. Andreassen, Dimitrios Andreou, Celso Arango, Inmaculada Baeza, Nerisa Banaj, Carrie E. Bearden, Michael Berk, Hannes Bohman, Josefina Castro-Fornieles, Yann Chye, Benedicto Crespo-Facorro, Elena de la Serna, Covadonga M. Díaz-Caneja, Tiril P. Gurholt, Catherine E. Hegarty, Anthony James, Joost Janssen, Cecilie Johannessen, Erik G. Jönsson, Katherine H. Karlsgodt, Peter Kochunov, Noemi G. Lois, Mathias Lundberg, Anne M. Myhre, Saül Pascual-Diaz, Fabrizio Piras, Runar E. Smelror, Gianfranco Spalletta, Therese S. Stokkan, Gisela Sugranyes, Chao Suo, Sophia I. Thomopoulos, Diana Tordesillas-Gutiérrez, Daniela Vecchio, Kirsten Wedervang-Resell, Laura A. Wortinger, Paul M. Thompson, and Ingrid Agartz
Subjects: Male, Pediatric, Psychiatry, Adolescent, Psychology and Cognitive Sciences, Neurosciences, Brain, Biological Sciences, Serious Mental Illness, White Matter, Medical and Health Sciences, Brain Disorders, Cellular and Molecular Neuroscience, Psychiatry and Mental health, Diffusion Tensor Imaging, Mental Health, Good Health and Well Being, Psychotic Disorders, Clinical Research, Schizophrenia, Humans, Anisotropy, Biomedical Imaging, Female, Molecular Biology
Abstract: Emerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset d = 0.37), posterior corona radiata (d = 0.32), and superior fronto‐occipital fasciculus (d = 0.31). We also found widespread higher RD and more localized higher MD and AD. We detected significant effects of diagnostic subgroup, sex, and duration of illness, but not medication status. Using the largest EOP DTI sample to date, our findings suggest a profile of widespread white matter microstructure alterations in adolescents with EOP, most prominently in male individuals with early-onset schizophrenia and individuals with a shorter duration of illness.
Published: 2023

20. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of <scp>ENIGMA</scp> findings

Author: Eun‐Jin Cheon, Carrie E. Bearden, Daqiang Sun, Christopher R. K. Ching, Ole A. Andreassen, Lianne Schmaal, Dick J. Veltman, Sophia I. Thomopoulos, Peter Kochunov, Neda Jahanshad, Paul M. Thompson, Jessica A. Turner, Theo G.M. van Erp, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, and Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep
Subjects: Bipolar Disorder, Clinical Sciences, Article, Clinical Research, 2.3 Psychological, Genetics, DiGeorge Syndrome, 2.1 Biological and endogenous factors, Humans, Aetiology, Depressive Disorder, Depressive Disorder, Major, major depressive disorder, Depression, General Neuroscience, ENIGMA, Neurosciences, Major, Brain, General Medicine, velocardiofacial, Serious Mental Illness, Magnetic Resonance Imaging, Brain Disorders, schizophrenia, Psychiatry and Mental health, Mental Health, Diffusion Tensor Imaging, Neurology, Schizophrenia, Biomedical Imaging, Cognitive Sciences, Neurology (clinical), social and economic factors
Abstract: This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.
Published: 2022

21. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals

Author: Sean R, McWhinney, Christoph, Abé, Martin, Alda, Francesco, Benedetti, Erlend, Bøen, Caterina, Del Mar Bonnin, Tiana, Borgers, Katharina, Brosch, Erick J, Canales-Rodríguez, Dara M, Cannon, Udo, Dannlowski, Ana M, Diaz-Zuluaga, Lorielle, Dietze, Torbjørn, Elvsåshagen, Lisa T, Eyler, Janice M, Fullerton, Jose M, Goikolea, Janik, Goltermann, Dominik, Grotegerd, Bartholomeus C M, Haarman, Tim, Hahn, Fleur M, Howells, Martin, Ingvar, Tilo T J, Kircher, Axel, Krug, Rayus T, Kuplicki, Mikael, Landén, Hannah, Lemke, Benny, Liberg, Carlos, Lopez-Jaramillo, Ulrik F, Malt, Fiona M, Martyn, Elena, Mazza, Colm, McDonald, Genevieve, McPhilemy, Sandra, Meier, Susanne, Meinert, Tina, Meller, Elisa M T, Melloni, Philip B, Mitchell, Leila, Nabulsi, Igor, Nenadic, Nils, Opel, Roel A, Ophoff, Bronwyn J, Overs, Julia-Katharina, Pfarr, Julian A, Pineda-Zapata, Edith, Pomarol-Clotet, Joaquim, Raduà, Jonathan, Repple, Maike, Richter, Kai G, Ringwald, Gloria, Roberts, Alex, Ross, Raymond, Salvador, Jonathan, Savitz, Simon, Schmitt, Peter R, Schofield, Kang, Sim, Dan J, Stein, Frederike, Stein, Henk S, Temmingh, Katharina, Thiel, Sophia I, Thomopoulos, Neeltje E M, van Haren, Holly, Van Gestel, Cristian, Vargas, Eduard, Vieta, Annabel, Vreeker, Lena, Waltemate, Lakshmi N, Yatham, Christopher R K, Ching, Ole A, Andreassen, Paul M, Thompson, Tomas, Hajek, Mcwhinney, Sean R, Abé, Christoph, Alda, Martin, Benedetti, Francesco, Bøen, Erlend, Del Mar Bonnin, Caterina, Borgers, Tiana, Brosch, Katharina, Canales-Rodríguez, Erick J, Cannon, Dara M, Dannlowski, Udo, Diaz-Zuluaga, Ana M, Dietze, Lorielle, Elvsåshagen, Torbjørn, Eyler, Lisa T, Fullerton, Janice M, Goikolea, Jose M, Goltermann, Janik, Grotegerd, Dominik, Haarman, Bartholomeus C M, Hahn, Tim, Howells, Fleur M, Ingvar, Martin, Kircher, Tilo T J, Krug, Axel, Kuplicki, Rayus T, Landén, Mikael, Lemke, Hannah, Liberg, Benny, Lopez-Jaramillo, Carlo, Malt, Ulrik F, Martyn, Fiona M, Mazza, Elena, Mcdonald, Colm, Mcphilemy, Genevieve, Meier, Sandra, Meinert, Susanne, Meller, Tina, Melloni, Elisa M T, Mitchell, Philip B, Nabulsi, Leila, Nenadic, Igor, Opel, Nil, Ophoff, Roel A, Overs, Bronwyn J, Pfarr, Julia-Katharina, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Raduà, Joaquim, Repple, Jonathan, Richter, Maike, Ringwald, Kai G, Roberts, Gloria, Ross, Alex, Salvador, Raymond, Savitz, Jonathan, Schmitt, Simon, Schofield, Peter R, Sim, Kang, Stein, Dan J, Stein, Frederike, Temmingh, Henk S, Thiel, Katharina, Thomopoulos, Sophia I, van Haren, Neeltje E M, Van Gestel, Holly, Vargas, Cristian, Vieta, Eduard, Vreeker, Annabel, Waltemate, Lena, Yatham, Lakshmi N, Ching, Christopher R K, Andreassen, Ole, Thompson, Paul M, Hajek, Tomas, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Psychiatry, Child and Adolescent Psychiatry / Psychology, and Clinical Child and Family Studies
Subjects: obesity, Bipolar Disorder, body mass index, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, SCHIZOPHRENIA, Cluster Analysis, Humans, BRAIN, Biological Psychiatry, Body mass index, GRAY-MATTER VOLUME, METABOLIC SYNDROME, 2. Zero hunger, MAJOR DEPRESSIVE DISORDER, INSULIN-RESISTANCE, ABNORMALITIES, 1ST-EPISODE, heterogeneit, surface area, cortical thickness, Magnetic Resonance Imaging, Temporal Lobe, 3. Good health, 030227 psychiatry, Psychiatry and Mental health, bipolar disorders, WHITE, heterogeneity, 030217 neurology & neurosurgery
Abstract: Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.
Published: 2022

22. Cortical thickness across the lifespan

Author: Simon E. Fisher, Eveline A. Crone, Dominik Grotegerd, Jilly Naaijen, Anders M. Dale, Sean N. Hatton, Ramona Baur-Streubel, Anthony A. James, Daniel Brandeis, Andrew J. Kalnin, Andreas Reif, Hans-Jörgen Grabe, Pieter J. Hoekstra, Lars Nyberg, Fleur M. Howells, Moji Aghajani, Randy L. Buckner, Daniel A. Rinker, Steven G. Potkin, Dennis van 't Ent, Rachel M. Brouwer, Sophia Frangou, Yang Wang, Nhat Trung Doan, Theodore D. Satterthwaite, Christine Lochner, Geraldo F. Busatto, Lars T. Westlye, Lara M. Wierenga, Calhoun Vd, Henry Brodaty, Carles Soriano-Mas, Annette Conzelmann, Christian K. Tamnes, Julian N. Trollor, Nicholas G. Martin, Neeltje E.M. van Haren, René S. Kahn, Irina Lebedeva, Philip Asherson, Suzanne C. Swagerman, John A. Joska, Theophilus N. Akudjedu, Kang Sim, Lachlan T. Strike, Patricia Gruner, Brenna C. McDonald, Thomas Frodl, Edith Pomarol-Clotet, Víctor Ortiz-García de la Foz, Margaret J. Wright, Norbert Hosten, Jean-Paul Fouche, Bernd Weber, Salvador Sarró, Wei Wen, Dag Alnæs, Greig I. de Zubicaray, Iris E. C. Sommer, Marise W. J. Machielsen, Knut Schnell, Dara M. Cannon, Paola Fuentes-Claramonte, Josiane Bourque, Andreas Meyer-Lindenberg, Anton Albajes-Eizagirre, Sarah Hohmann, Erin W. Dickie, Theo G.M. van Erp, Micael Andersson, Paul Pauli, Thomas Espeseth, Heather C. Whalley, Victoria Chubar, Ruben C. Gur, Tomohiro Nakao, Xavier Caseras, Alessandro Bertolino, Ignacio Martínez-Zalacaín, Katharina Wittfeld, Erick J. Canales-Rodríguez, David C. Glahn, Neda Jahanshad, Jiyang Jiang, Katie L. McMahon, Stefan Borgwardt, Erlend S. Dørum, Jaap Oosterlaan, Won Hee Lee, Alan Breier, Steven Williams, Aristotle N. Voineskos, Bernard Mazoyer, Jordan W. Smoller, Nancy C. Andreasen, Ilya M. Veer, Tiffany M. Chaim-Avancini, Sophie Maingault, Paul M. Thompson, Eco J. C. de Geus, Luisa Lázaro, Giulio Pergola, Efstathios Papachristou, Beng-Choon Ho, David Mataix-Cols, Esther Walton, Ben J. Harrison, Dirk J. Heslenfeld, Pablo Najt, Helena Fatouros-Bergman, Derrek P. Hibar, Gunter Schumann, Raymond Salvador, Lieuwe de Haan, Henry Völzke, Joaquim Radua, Henk Temmingh, Lianne Schmaal, Martine Hoogman, Daniel H. Wolf, Georg C. Ziegler, Marieke Klein, Barbara Franke, Erik G. Jönsson, Laura Koenders, Stefan Ehrlich, Oliver Gruber, Ingrid Agartz, Kun Yang, Ryota Kanai, Sarah Baumeister, Colm McDonald, Annabella Di Giorgio, Amanda Worker, Anne Uhlmann, Marcus V. Zanetti, Danai Dima, Matthew D. Sacchet, Sarah E. Medland, Aurora Bonvino, Benedicto Crespo-Facorro, Jan Egil Nordvik, Joshua L. Roffman, Yannis Paloyelis, Jessica A. Turner, T. P. Klyushnik, Christopher G. Davey, Rachel E. Gur, Ian B. Hickie, Christopher R.K. Ching, Jonna Kuntsi, Tobias Banaschewski, Chaim Huyser, Amirhossein Modabbernia, John D. West, Fabrice Crivello, Núria Bargalló, Patricia J. Conrod, Nic J.A. van der Wee, Mauricio H. Serpa, Thomas H. Wassink, Kathryn I. Alpert, Dick J. Veltman, Andrew J. Saykin, Genevieve McPhilemy, Perminder S. Sachdev, Vincent P. Clark, Ian H. Gotlib, Susanne Erk, Henrik Walter, Dennis van den Meer, Simon Cervenka, Oliver Grimm, Andrew M. McIntosh, Alexander Tomyshev, Francisco X. Castellanos, Bernd Kramer, Klaus-Peter Lesch, Odile A. van den Heuvel, Sophia I. Thomopoulos, Diana Tordesillas-Gutiérrez, Terry L. Jernigan, Yulyia Yoncheva, Anouk den Braber, Jim Lagopoulos, Maria J. Portella, Ole A. Andreassen, Gaelle E. Doucet, Avram J. Holmes, Nynke A. Groenewold, Pedro G.P. Rosa, Hilleke E. Hulshoff Pol, Sanne Koops, José M. Menchón, Jan K. Buitelaar, Dan J. Stein, Dorret I. Boomsma, Lei Wang, C.A. Hartman, Pascual Sánchez-Juan, Andreas Heinz, European Commission, National Institute of Child Health and Human Development (US), QIMR Berghofer Medical Research Institute (Australia), University of Queensland, National Cancer Institute (US), Dutch Research Council, Netherlands Organisation for Health Research and Development, National Institute of Mental Health (US), European Research Council, National Center for Advancing Translational Sciences (US), Medical Research Council (UK), Fundación Marques de Valdecilla, Instituto de Salud Carlos III, Swedish Research Council, South-Eastern Norway Regional Health Authority, Research Council of Norway, Icahn School of Medicine at Mount Sinai, South London and Maudsley NHS Foundation Trust, NHS Foundation Trust, National Institute for Health Research (UK), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Movement Disorder (MD), Developmental Neuroscience in Society, Child and Adolescent Psychiatry / Psychology, Adult Psychiatry, APH - Mental Health, ANS - Complex Trait Genetics, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, Child Psychiatry, ANS - Cellular & Molecular Mechanisms, General Paediatrics, ARD - Amsterdam Reproduction and Development, Karolinska Schizophrenia Project (KaSP), Ontwikkelingspsychologie (Psychologie, FMG), Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Epidemiology and Data Science, Neurology, Amsterdam Neuroscience - Neurodegeneration, Pediatric surgery, Anatomy and neurosciences, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Brain Imaging, RS: MHeNs - R2 - Mental Health, Psychiatrie & Neuropsychologie, Biological Psychology, APH - Methodology, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, Educational and Family Studies, Cognitive Psychology, IBBA, Clinical Neuropsychology, Faculty of Behavioural and Movement Sciences, and APH - Personalized Medicine
Subjects: Male, Aging, Neurologi, Audiology, Trajectories, 0302 clinical medicine, 130 000 Cognitive Neurology & Memory, diagnostic imaging [Cerebral Cortex], Child, Research Articles, Cerebral Cortex, Psychiatry, Aged, 80 and over, Radiological and Ultrasound Technology, Fractional polynomial, 05 social sciences, Radiology, Nuclear Medicine & Medical Imaging, 1. No poverty, Cognition, Middle Aged, Cerebral cortex, Regression, 3. Good health, Escorça cerebral, Neurology, Radiology Nuclear Medicine and imaging, Healthy individuals, Child, Preschool, anatomy & histology [Cerebral Cortex], Female, Analysis of variance, Anatomy, Life Sciences & Biomedicine, Trajectorie, Research Article, Neuroinformatics, Adult, medicine.medical_specialty, Adolescent, Human Development, Clinical Neurology, BF, Neuroimaging, Biology, Development, 050105 experimental psychology, Psykiatri, Cortical thickness, 03 medical and health sciences, Young Adult, Neuroimaging genetics, Envelliment, medicine, Humans, trajectories, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, ddc:610, development, Aged, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Science & Technology, Brain morphometry, aging, Neurosciences, cortical thickness, Cross-Sectional Studies, RC0321, Neurology (clinical), Neurosciences & Neurology, 030217 neurology & neurosurgery, physiology [Human Development]
Abstract: Special Issue: The ENIGMA Consortium: the first 10 years., Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., European Community's Seventh Framework Programme, Grant/Award Numbers: 278948, 602450, 603016, 602805; US National Institute of Child Health and Human Development, Grant/Award Numbers: RO1HD050735, 1009064, 496682; QIMR Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland; ICTSI NIH/NCRR, Grant/Award Number: RR025761; European Community's Horizon 2020 Programme, Grant/Award Numbers: 667302, 643051; Vici Innovation Program, Grant/Award Numbers: #91619115, 016-130-669; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09-229; Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL); Spinozapremie, Grant/Award Number: NWO-56-464-14192; Biobanking and Biomolecular Resources Research Infrastructure, Grant/Award Numbers: 184.033.111, 184.021.007; Netherlands Organization for Health Research and Development (ZonMW), Grant/Award Numbers: 480-15-001/674, 024.001.003, 911-09-032, 056-32-010, 481-08-011, 016-115-035, 31160008, 400-07-080, 400-05-717, 451-04-034, 463-06-001, 480-04-004, 904-61-193, 912-10-020, 985-10-002, 904-61-090; NIMH, Grant/Award Number: R01 MH090553; Geestkracht programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: NWO/SPI 56-464-14192, NWO-MagW 480-04-004, 433-09-220, NWO 51.02.062, NWO 51.02.061; National Center for Advancing Translational Sciences, National Institutes of Health, Grant/Award Number: UL1 TR000153; National Center for Research Resources; National Center for Research Resources at the National Institutes of Health, Grant/Award Numbers: NIH 1U24 RR025736-01, NIH 1U24 RR021992; NIH Institutes contributing to the Big Data to Knowledge; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Numbers: U54EB020403, G0500092; National Institute of Mental Health, Grant/Award Numbers: R01MH117014, R01MH042191; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Swedish Research Council, Grant/Award Numbers: 523-2014-3467, 2017-00949, 521-2014-3487; South-Eastern Norway Health Authority; the Research Council of Norway, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Icahn School of Medicine at Mount Sinai; Seventh Framework Programme (FP7/2007-2013), Grant/Award Number: 602450; National Institutes of Health, Grant/Award Numbers: R01 MH116147, R01 MH113619, R01 MH104284; South London and Maudsley NHS Foundation Trust; the National Institute for Health Research (NIHR)
Published: 2022

23. Greater male than female variability in regional brain structure across the lifespan

Author: Erick J. Canales-Rodríguez, Hans J. Grabe, Dirk J. Heslenfeld, Erik G. Jönsson, Oliver Gruber, Daniel Brandeis, Yang Wang, Henry Brodaty, Ruben C. Gur, Iris E. C. Sommer, Paul M. Thompson, Knut K. Kolskår, Christopher G. Davey, Dick J. Veltman, Eco J. C. de Geus, Tobias Banaschewski, Greig I. Zubicaray, Xavier Caseras, Sarah Baumeister, Raquel E. Gur, Vincent P. Clark, Maria J. Portella, Simon E. Fisher, Christopher R.K. Ching, Lars T. Westlye, Laura Koenders, Vince D. Calhoun, Carles Soriano-Mas, Nicholas G. Martin, Stefan Ehrlich, Fleur M. Howells, Catharina A. Hartman, Matthew D. Sacchet, Ole A. Andreassen, Josiane Bourque, Fabrice Crivello, Annette Conzelmann, Jaap Oosterlaan, Brenna C. McDonald, Gaelle E. Doucet, Avram J. Holmes, José M. Menchón, Danai Dima, Moji Aghajani, Joshua L. Roffman, Steven Williams, Lei Wang, David Mataix-Cols, Philip R. Szeszko, Bernd Weber, Tiril P. Gurholt, Sarah Hohmann, Ian H. Gotlib, Patricia Gruner, Anthony C. James, Paul Pauli, Lara M. Wierenga, Andrew M. McIntosh, Andrew J. Kalnin, Jim Lagopoulos, Henrik Walter, Andreas Reif, Andrew Simmons, Norbert Hosten, Pieter J. Hoekstra, Aristotle Voineskos, Alexander Tomyshev, Anton Albajes-Eizagirre, Jean-Paul Fouche, Dara M. Cannon, Ignacio Martínez‐Zalacaín, Geneviève Richard, Theophilus N. Akudjedu, David C. Glahn, Patricia J. Conrod, Ben J. Harrison, Alan Anticevic, Martine Hoogman, Francisco X. Castellanos, Bernd Kramer, Neda Jahanshad, Lieuwe de Haan, Dennis van der Meer, John D. West, Alan Breier, Jordan W. Smoller, P. G. P. Rosa, Katharina Wittfeld, Dan J. Stein, Jiyang Jiang, Jilly Naaijen, Christine Lochner, Dorret I. Boomsma, Alessandro Bertolino, Marise W. J. Machielsen, Hilleke E. Hulshoff Pol, Henry Völzke, Christian K. Tamnes, Ingrid Agartz, Georg C. Ziegler, Marieke Klein, Lars Nyberg, Perminder S. Sachdev, Philip Asherson, I.M. Veer, Sean N. Hatton, Núria Bargalló, Annabella Di Giorgio, Henk Temmingh, John A. Joska, Odile A. van den Heuvel, Wei Wen, Eveline A Crone, Kang Sim, Kathryn I. Alpert, Dennis van 't Ent, Jan K. Buitelaar, Joaquim Radua, Julian N. Trollor, B Mazoyer, Chaim Huyser, H. C. Whalley, Irina Lebedeva, Erin W. Dickie, Marcus Vinicus Zanetti, Stefan Borgwardt, Theodore D. Satterthwaite, Daniel H Wolf, Sophia I. Thomopoulos, Giulio Pergola, Luisa Lazaro, Ramona Baur-Streubel, Beathe Haatveit, Yannis Paloyelis, Ian B. Hickie, Jonna Kuntsi, Sophia Frangou, R. Salvador, Geraldo F. Busatto, Margaret J. Wright, Aurora Bonvino, Edith Pomarol-Clotet, Anouk den Braber, Lachlan T. Strike, Phil Lee, Anne Uhlmann, Yuliya N. Yoncheva, Mauricio H. Serpa, Dag Alnæs, Paola Fuentes-Claramonte, Katie L. McMahon, Andrew J. Saykin, Genevieve McPhilemy, Tiffany M. Chaim-Avancini, Sophie Maingault, Barbara Franke, Colm McDonald, Rachel M. Brouwer, Salvador Sarró, Department of Psychology, Education and Child Studies, Biological Psychology, APH - Mental Health, APH - Methodology, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, AMS - Ageing & Vitality, AMS - Sports, APH - Personalized Medicine, Cognitive Psychology, Clinical Neuropsychology, IBBA, Karolinska Schizophrenia Project (KaSP) Consortium, Adult Psychiatry, ANS - Complex Trait Genetics, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, Child Psychiatry, ANS - Cellular & Molecular Mechanisms, ANS - Amsterdam Neuroscience, General Paediatrics, ARD - Amsterdam Reproduction and Development, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Guided Treatment in Optimal Selected Cancer Patients (GUTS), Movement Disorder (MD), Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Neurology, Amsterdam Neuroscience - Neurodegeneration, Anatomy and neurosciences, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Pediatric surgery, and Amsterdam Neuroscience - Brain Imaging
Subjects: Male, Netherlands Twin Register (NTR), SEGMENTATION, Vulnerability, Disease, HM, 0302 clinical medicine, Anàlisi de variància, 130 000 Cognitive Neurology & Memory, diagnostic imaging [Cerebral Cortex], sexual characteristics, Analysis of variance, nuclear magnetic resonance imaging, Cervell, Research Articles, Cerebral Cortex, Sex Characteristics, Radiological and Ultrasound Technology, 05 social sciences, Brain, clinical trial, Brain Structure, Magnetic Resonance Imaging, Early life, Adolescence, medicine.anatomical_structure, Neurology, Cerebral cortex, Healthy individuals, X-CHROMOSOME, anatomy & histology [Cerebral Cortex], Evolution of the brain, Female, Anatomy, Neurovetenskaper, Research Article, Radiology, Nuclear Medicine and Medical Imaging, Neuroinformatics, SEX-DIFFERENCES, diagnostic imaging, brain, Human Development, BF, Neuroimaging, SURFACE-AREA, Evolució del cervell, Regional area, Biology, MULTISAMPLE, 050105 experimental psychology, brain cortex, 03 medical and health sciences, CEREBRAL-CORTEX, Sex differences, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, human, ddc:610, Cortical surface, GENERAL INTELLIGENCE, diagnostic imaging [Brain], METAANALYSIS, biological variation, HUMAN HIPPOCAMPUS, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], physiology [Biological Variation, Population], Neurosciences, Gender, Brain Cortical Thickness, multicenter study, Biological Variation, Population, Diferències entre sexes, physiology, RC0321, Radiologi och bildbehandling, Neurology (clinical), anatomy & histology [Brain], 170 000 Motivational & Cognitive Control, 030217 neurology & neurosurgery, anatomy and histology, meta analysis, physiology [Human Development], Demography
Abstract: Contains fulltext : 248376.pdf (Publisher’s version ) (Open Access) For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
Published: 2022

24. Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder

Author: Martin Alda, Daniel R. Weinberger, Elena de la Serna, Andreas Meyer-Lindenberg, Salvador Sarró, Yoonho Chung, Sonja M C de Zwarte, Ian S. Ramsay, Benson Mwangi, Alessandro Bertolino, Marinka M.G. Koenis, Sophia I. Thomopoulos, Caroline Demro, Joaquim Radua, Ceren Hıdıroglu Ongun, Neeltje E.M. van Haren, Eduard Vieta, Sophia Frangou, Annabella Di Giorgio, Gisela Sugranyes, Scott R. Sponheim, Tomas Hajek, Bernd Kramer, Rhoshel K. Lenroot, Esma M. Simsek, Qiang Chen, Fergus Kane, Miloslav Kopecek, Anja Richter, Jim van Os, Erick J. Canales-Rodríguez, Martin Ingvar, Carrie E. Bearden, Paul M. Thompson, David C. Glahn, Scott C. Fears, Stijn Michielse, Christina M. Hultman, Nefize Yalin, Machteld Marcelis, Giulio Pergola, Aurora Bonvino, Neda Jahanshad, Wiepke Cahn, Josefina Castro-Fornieles, Vina M. Goghari, Manon H.J. Hillegers, Matthew J. Kempton, Ayşegül Özerdem, Fatma Simsek, Ingrid Agartz, Emma L. Hawkins, Sonya Foley, Elvira Bramon, Jair C. Soares, Cheryl A. Olman, Erik G. Jönsson, Oliver Gruber, René S. Kahn, Janice M. Fullerton, Leila Nabulsi, Ole A. Andreassen, Jose Manuel Goikolea, Gaelle E. Doucet, M.C. Eker, Mon Ju Wu, Aaron L. Goldman, Hilleke E. Hulshoff Pol, Stephen M. Lawrie, Venkata S. Mattay, Dara M. Cannon, Caterina del Mar Bonnín, Robin M. Murray, Marco Picchioni, Christopher R.K. Ching, Ali Saffet Gonul, Edith Pomarol-Clotet, Andreas Heinz, Silvia Alonso-Lana, Susanne Erk, Giulia Tronchin, Josselin Houenou, H. C. Whalley, Theo G.M. van Erp, Viktoria Johansson, Dolores Moreno, Henrik Walter, Timothea Toulopoulou, Bronwyn Overs, Aybala Saricicek Aydogan, Rachel M. Brouwer, Philip B. Mitchell, Colm McDonald, Peter R. Schofield, Camille Piguet, Raymond Salvador, Jason Newport, Xavier Caseras, Mar Fatjó-Vilas, Tyrone D. Cannon, Elizabeth E.L. Buimer, Gloria Roberts, Child and Adolescent Psychiatry / Psychology, Toulopoulou, Timothea, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, Neurochirurgie, RS: MHeNs - R3 - Neuroscience, MUMC+: MA Psychiatrie (3), and Ege Üniversitesi
Subjects: relatives, Library science, 050105 experimental psychology, Psykiatri, 03 medical and health sciences, 0302 clinical medicine, YOUNG-ADULTS, THICKNESS, Humans, Cognitive Dysfunction, Family, Genetic Predisposition to Disease, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, PREMORBID IQ, GENOME-WIDE ASSOCIATION, 10. No inequality, Research Articles, METAANALYSIS, National health, bipolar disorder, Psychiatry, education, neuroimaging, Radiological and Ultrasound Technology, HERITABILITY, 4. Education, 05 social sciences, intelligence, Magnetic Resonance Imaging, Educational attainment, 3. Good health, schizophrenia, INDIVIDUALS, DISCORDANT, Neurology, Research council, SCHOOL PERFORMANCE, Educational Status, Christian ministry, Neurology (clinical), Anatomy, 030217 neurology & neurosurgery, Research Article
Abstract: First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d= -0.42,p= 3 x 10(-5)), with weak evidence of IQ reductions among BD-FDRs (d= -0.23,p= .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. in contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1037196, 1063960, 1066177, 510135, 1176716]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 142255]; Departament de Salut de la Generalitat de CatalunyaGeneralitat de Catalunya [SLT002/16/00331]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; Development Service Merit Review Award [I01CX000227]; Dokuz Eylul University Department of Scientific Research [2012.KB.SAG.062]; e:Med program [O1ZX1314B, O1ZX1314G]; Ege University School of Medicine Research Foundation [2009-D-00017]; Fundacio Marato TV3 [091630]; Netherlands Organisation for Health Research and DevelopmentNetherlands Organization for Health Research and Development [10-000-1002]; Generalitat de CatalunyaGeneralitat de Catalunya [2017SGR01271]; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF); Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Ministerstvo Zdravotnictvi Ceske Republiky [NR8786, NT13891]; National Alliance for Research on Schizophrenia and DepressionNARSAD [17319, 20244, 26731]; Swiss National Centre of Competence in Research Robotics [51NF40-185897]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403]; Research Council of NorwayResearch Council of Norway [223273]; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976]; Stanley Medical Research Institute; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [32003B_156914]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Wellcome TrustWellcome Trust [085475/B/08/Z, 085475/Z/08/Z, 064971]; ZonMwNetherlands Organization for Health Research and Development [908-02-123], Australian National Health and Medical Research Council Grants, Grant/Award Numbers: 1037196, 1063960, 1066177, 510135, 1176716; Canadian Institutes of Health Research, Grant/Award Numbers: 103703, 106469, 142255; Departament de Salut de la Generalitat de Catalunya, Grant/Award Number: SLT002/16/00331; Deutsche Forschungsgemeinschaft, Grant/Award Number: 1617; Development Service Merit Review Award, Grant/Award Number: I01CX000227; Dokuz Eylul University Department of Scientific Research Projects Funding, Grant/Award Number: 2012.KB.SAG.062; e:Med program, Grant/Award Numbers: O1ZX1314B, O1ZX1314G; Ege University School of Medicine Research Foundation, Grant/Award Number: 2009-D-00017; Fundacio Marato TV3, Grant/Award Number: 091630; Geestkracht program of the Netherlands Organisation for Health Research and Development, Grant/Award Number: 10-000-1002; Generalitat de Catalunya, Grant/Award Number: 2017SGR01271; German Federal Ministry for Education and Research; Medical Research Council, Grant/Award Number: G0901310; Ministerstvo Zdravotnictvi Ceske Republiky, Grant/Award Numbers: NR8786, NT13891; National Alliance for Research on Schizophrenia and Depression, Grant/Award Numbers: 17319, 20244, 26731; Swiss National Centre of Competence in Research Robotics, Grant/Award Number: 51NF40-185897; National Institute of Mental Health, Grant/Award Numbers: 1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667; National Institute on Aging, Grant/Award Number: T32AG058507; National Institutes of Health, Grant/Award Numbers: P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403; Research Council of Norway, Grant/Award Number: 223273; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III, Grant/Award Numbers: CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976; Stanley Medical Research Institute; Swedish Research Council, Grant/Award Numbers: K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3; Swiss National Science Foundation, Grant/Award Number: 32003B_156914; VIDI, Grant/Award Numbers: 452-11-014, 917-46-370; Wellcome Trust, Grant/Award Numbers: 085475/B/08/Z, 085475/Z/08/Z; Wellcome Trust Research Training Fellowship, Grant/Award Number: 064971; ZonMw, Grant/Award Number: 908-02-123
Published: 2022

25. What we learn about bipolar disorder from large-scale neuroimaging

Author: Christian K. Tamnes, Bartholomeus C M Haarman, Jair C. Soares, Ole A. Andreassen, Viola Oertel, Theodore D. Satterthwaite, G. Tronchin, Michael Stäblein, Bradley J. MacIntosh, Melissa Pauling, Christopher R.K. Ching, Daniel H. Wolf, Dick J. Veltman, Ingrid Agartz, Bernhard T. Baune, Salvador Sarró, Mon-Ju Wu, Scott C Fears, Eduard Vieta, Melissa J. Green, Neeltje E.M. van Haren, Yann Quidé, Erlend Bøen, Yash Patel, Igor Nenadic, Martin Alda, Lisa T. Eyler, Arnaud Pouchon, Danai Dima, Tomáš Paus, Irene Bollettini, Torbjørn Elvsåshagen, Rachel M. Brouwer, Lakshmi N. Yatham, Michael Bauer, Caterina del Mar Bonnín, C. McDonald, Udo Dannlowski, Bronwyn Overs, Edith Pomarol-Clotet, Cristian Vargas Upegui, Oliver Gruber, Henricus G. Ruhé, Márcio Gerhardt Soeiro-de-Souza, Edouard Duchesnay, Hilary P. Blumberg, Tilo Kircher, Miho Ota, Michael Berk, Christoph Abé, Andreas Jansen, Kang Sim, Heather C. Whalley, Derrek P. Hibar, Roel A. Ophoff, Georgios V Thomaidis, Henrik Walter, Sophia Frangou, Michèle Wessa, Dara M. Cannon, Cara M. Altimus, Allison C. Nugent, Rodrigo Machado-Vieira, Orwa Dandash, Marcella Bellani, Unn K. Haukvik, Philip B. Mitchell, Ling-Li Zeng, Christian Knöchel, Jose Manuel Goikolea, Sonja M C de Zwarte, Francesco Benedetti, Sara Poletti, Janice M. Fullerton, Carlos A. Zarate, Aart H. Schene, Dan J. Stein, Chantal Henry, Tristram A. Lett, Mikael Landén, Daniel L Pham, Paolo Brambilla, Silvia Alonso-Lana, Sophia I. Thomopoulos, Carlos López-Jaramillo, Tomas Hajek, Bernd Kramer, G. Delvecchio, Maria M. Rive, Lars T. Westlye, Erick J. Canales-Rodríguez, Victoria L. Ives-Deliperi, Dominik Grotegerd, Beny Lafer, Abraham Nunes, Carrie E. Bearden, Raymond Salvador, Joaquim Radua, Amy C Bilderbeck, Xavier Caseras, Paul M. Thompson, Jorge R. C. Almeida, Pauline Favre, Gloria Roberts, David C. Glahn, Dag Alnæs, Julian A Pineda-Zapata, Tiril P. Gurholt, Mircea Polosan, Josselin Houenou, Fabiano G. Nery, Leila Nabulsi, Mary L. Phillips, Fleur M. Howells, Ana M. Díaz-Zuluaga, Elisa M T Melloni, Ching, C. R. K., Hibar, D. P., Gurholt, T. P., Nunes, A., Thomopoulos, S. I., Abe, C., Agartz, I., Brouwer, R. M., Cannon, D. M., de Zwarte, S. M. C., Eyler, L. T., Favre, P., Hajek, T., Haukvik, U. K., Houenou, J., Landen, M., Lett, T. A., Mcdonald, C., Nabulsi, L., Patel, Y., Pauling, M. E., Paus, T., Radua, J., Soeiro-de-Souza, M. G., Tronchin, G., van Haren, N. E. M., Vieta, E., Walter, H., Zeng, L. -L., Alda, M., Almeida, J., Alnaes, D., Alonso-Lana, S., Altimus, C., Bauer, M., Baune, B. T., Bearden, C. E., Bellani, M., Benedetti, F., Berk, M., Bilderbeck, A. C., Blumberg, H. P., Boen, E., Bollettini, I., del Mar Bonnin, C., Brambilla, P., Canales-Rodriguez, E. J., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Diaz-Zuluaga, A. M., Dima, D., Duchesnay, E., Elvsashagen, T., Fears, S. C., Frangou, S., Fullerton, J. M., Glahn, D. C., Goikolea, J. M., Green, M. J., Grotegerd, D., Gruber, O., Haarman, B. C. M., Henry, C., Howells, F. M., Ives-Deliperi, V., Jansen, A., Kircher, T. T. J., Knochel, C., Kramer, B., Lafer, B., Lopez-Jaramillo, C., Machado-Vieira, R., Macintosh, B. J., Melloni, E. M. T., Mitchell, P. B., Nenadic, I., Nery, F., Nugent, A. C., Oertel, V., Ophoff, R. A., Ota, M., Overs, B. J., Pham, D. L., Phillips, M. L., Pineda-Zapata, J. A., Poletti, S., Polosan, M., Pomarol-Clotet, E., Pouchon, A., Quide, Y., Rive, M. M., Roberts, G., Ruhe, H. G., Salvador, R., Sarro, S., Satterthwaite, T. D., Schene, A. H., Sim, K., Soares, J. C., Stablein, M., Stein, D. J., Tamnes, C. K., Thomaidis, G. V., Upegui, C. V., Veltman, D. J., Wessa, M., Westlye, L. T., Whalley, H. C., Wolf, D. H., Wu, M. -J., Yatham, L. N., Zarate, C. A., Thompson, P. M., and Andreassen, O. A.
Subjects: mega-analysis, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], cortical surface area, Review Article, 0302 clinical medicine, Manic-depressive illness, Multicenter Studies as Topic, Spectrum disorder, Review Articles, bipolar disorder, Cerebral Cortex, Trastorn bipolar, neuroimaging, Radiological and Ultrasound Technology, 05 social sciences, ENIGMA, HUMAN BRAIN, Magnetic Resonance Imaging, psychiatry, 3. Good health, Neurology, Meta-analysis, Scale (social sciences), Anatomy, Psychology, Clinical risk factor, Clinical psychology, MRI, MAJOR PSYCHIATRIC-DISORDERS, Schizoaffective disorder, 050105 experimental psychology, 03 medical and health sciences, Magnetic resonance imaging, Neuroimaging, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, Imatges per ressonància magnètica, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Bipolar disorder, HIPPOCAMPAL VOLUMES, mega‐analysis, GRAY-MATTER VOLUME, SPECTRUM DISORDER, volume, DIABETES-MELLITUS, cortical thickness, COGNITIVE IMPAIRMENT, medicine.disease, Mental illness, meta-analysis, meta‐analysis, RC0321, Neurology (clinical), SCHIZOAFFECTIVE DISORDER, PSYCHOTIC FEATURES, 030217 neurology & neurosurgery
Abstract: MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.
Published: 2022

26. Mapping brain asymmetry in health and disease through the ENIGMA consortium

Author: Lianne Schmaal, Jan K. Buitelaar, Carolien G.F. de Kovel, Neda Jahanshad, Clyde Francks, David C. Glahn, Simon E. Fisher, Dick Schijven, Sarah E. Medland, Merel Postema, Theo G.M. van Erp, Martine Hoogman, Samuel R. Mathias, Jessica A. Turner, Odile A. van den Heuvel, Sophia I. Thomopoulos, Premika S.W. Boedhoe, Paul M. Thompson, Daan van Rooij, Barbara Franke, Tulio Guadalupe, and Xiangzhen Kong
Subjects: mega-analysis, Obsessive-Compulsive Disorder, Autism Spectrum Disorder, Review Article, 0302 clinical medicine, 130 000 Cognitive Neurology & Memory, Brain asymmetry, Multicenter Studies as Topic, Gray Matter, Review Articles, media_common, Cerebral Cortex, Radiological and Ultrasound Technology, brain laterality, 05 social sciences, Experimental Psychology, Human brain, structural imaging, Magnetic Resonance Imaging, medicine.anatomical_structure, Mental Health, Neurology, Autism spectrum disorder, Brain size, Neurological, Major depressive disorder, Cognitive Sciences, Anatomy, Neuroinformatics, media_common.quotation_subject, autism spectrum disorder, Neuroimaging, Biology, Asymmetry, 050105 experimental psychology, 03 medical and health sciences, obsessive–compulsive disorder, Clinical Research, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, mega‐analysis, Depressive Disorder, Major, Depressive Disorder, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], major depressive disorder, Neurosciences, Major, medicine.disease, Brain Disorders, meta-analysis, meta‐analysis, Sample size determination, brain asymmetry, Neurology (clinical), Neuroscience, 170 000 Motivational & Cognitive Control, 030217 neurology & neurosurgery
Abstract: Left–right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Decades of research have suggested that brain asymmetry may be altered in psychiatric disorders. However, findings have been inconsistent and often based on small sample sizes. There are also open questions surrounding which structures are asymmetrical on average in the healthy population, and how variability in brain asymmetry relates to basic biological variables such as age and sex. Over the last 4 years, the ENIGMA‐Laterality Working Group has published six studies of gray matter morphological asymmetry based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. A population‐level mapping of average asymmetry was achieved, including an intriguing fronto‐occipital gradient of cortical thickness asymmetry in healthy brains. ENIGMA's multi‐dataset approach also supported an empirical illustration of reproducibility of hemispheric differences across datasets. Effect sizes were estimated for gray matter asymmetry based on large, international, samples in relation to age, sex, handedness, and brain volume, as well as for three psychiatric disorders: autism spectrum disorder was associated with subtly reduced asymmetry of cortical thickness at regions spread widely over the cortex; pediatric obsessive–compulsive disorder was associated with altered subcortical asymmetry; major depressive disorder was not significantly associated with changes of asymmetry. Ongoing studies are examining brain asymmetry in other disorders. Moreover, a groundwork has been laid for possibly identifying shared genetic contributions to brain asymmetry and disorders., Left–right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Over the last four years, the ENIGMA‐Laterality Working Group has published six studies of grey matter morphological asymmetry in health and disease, based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. Here we review the findings from these six studies.
Published: 2022

27. Volume of subcortical brain regions in social anxiety disorder: Mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group

Author: Nynke A. Groenewold, Janna Marie Bas-Hoogendam, Alyssa R. Amod, Max A. Laansma, Laura S. Van Velzen, Moji Aghajani, Kevin Hilbert, Hyuntaek Oh, Ramiro Salas, Andrea P. Jackowski, Pedro M. Pan, Giovanni A. Salum, James R. Blair, Karina S. Blair, Joy Hirsch, Spiro P. Pantazatos, Franklin R. Schneier, Ardesheer Talati, Karin Roelofs, Inge Volman, Laura Blanco-Hinojo, Narcís Cardoner, Jesus Pujol, Katja Beesdo-Baum, Christopher R. K. Ching, Sophia I. Thomopoulos, Andreas Jansen, Tilo Kircher, Axel Krug, Igor Nenadić, Frederike Stein, Udo Dannlowski, Dominik Grotegerd, Hannah Lemke, Susanne Meinert, Alexandra Winter, Michael Erb, Benjamin Kreifelts, Qiyong Gong, Su Lui, Fei Zhu, Benson Mwangi, Jair C. Soares, Mon-Ju Wu, Ali Bayram, Mesut Canli, Raşit Tükel, P. Michiel Westenberg, Alexandre Heeren, Henk R. Cremers, David Hofmann, Thomas Straube, Alexander G. G. Doruyter, Christine Lochner, Jutta Peterburs, Marie-José Van Tol, Raquel E. Gur, Antonia N. Kaczkurkin, Bart Larsen, Theodore D. Satterthwaite, Courtney A. Filippi, Andrea L. Gold, Anita Harrewijn, André Zugman, Robin Bülow, Hans J. Grabe, Henry Völzke, Katharina Wittfeld, Joscha Böhnlein, Katharina Dohm, Harald Kugel, Elisabeth Schrammen, Peter Zwanzger, Elisabeth J. Leehr, Lisa Sindermann, Tali M. Ball, Gregory A. Fonzo, Martin P. Paulus, Alan Simmons, Murray B. Stein, Heide Klumpp, K. Luan Phan, Tomas Furmark, Kristoffer N. T. Månsson, Amirhossein Manzouri, Suzanne N. Avery, Jennifer Urbano Blackford, Jacqueline A. Clauss, Brandee Feola, Jennifer C. Harper, Chad M. Sylvester, Ulrike Lueken, Dick J. Veltman, Anderson M. Winkler, Neda Jahanshad, Daniel S. Pine, Paul M. Thompson, Dan J. Stein, Nic J. A. Van der Wee, Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Clinical Psychology, UCL - SSH/IPSY - Psychological Sciences Research Institute, and UCL - SSS/IONS/NEUR - Clinical Neuroscience
Subjects: Adult, Adolescent, 230 Affective Neuroscience, Brain, Phobia, Social, Anxiety, Experimental Psychopathology and Treatment, Cellular and Molecular Neuroscience, Psychiatry and Mental health, methods [Magnetic Resonance Imaging], Humans, ddc:610, methods [Neuroimaging], Molecular Biology
Abstract: Contains fulltext : 288062.pdf (Publisher’s version ) (Closed access) There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE
Published: 2023

28. Altered lateralization of the cingulum in deployment‐related traumatic brain injury: An <scp>ENIGMA</scp> military‐relevant brain injury study

Author: Emily L Dennis, Mary R Newsome, Hannah M Lindsey, Maheen M Adams, Tara A Austin, Seth G Disner, Blessen C Eapen, Carrie Esopenko, Carol E Franz, Elbert Geuze, Courtney Haswell, Sidney R Hinds, Cooper B Hodges, Andrei Irimia, Kimbra Kenney, Inga K Koerte, William S Kremen, Harvey S Levin, Rajendra A Morey, John Ollinger, Jared A Rowland, Randall S Scheibel, Martha E Shenton, Danielle R Sullivan, Leah D Talbert, Sophia I Thomopoulos, Maya Troyanskaya, William C Walker, Xin Wang, Ashley L Ware, J Kent Werner, Wright Williams, Paul M Thompson, David F Tate, and Elisabeth A Wilde
Subjects: Adult, Traumatic, Physical Injury - Accidents and Adverse Effects, 6.6 Psychological and behavioural, Neuropsychological Tests, Traumatic Brain Injury (TBI), Humans, Radiology, Nuclear Medicine and imaging, military, Traumatic Head and Spine Injury, Radiological and Ultrasound Technology, traumatic brain injury, Neurosciences, Brain, Evaluation of treatments and therapeutic interventions, Experimental Psychology, White Matter, Brain Disorders, Mental Health, Neurology, DTI, Brain Injuries, Neurological, Biomedical Imaging, Cognitive Sciences, Neurology (clinical), Anatomy
Abstract: Traumatic brain injury (TBI), a significant concern in military populations, is associated with alterations in brain structure and function, cognition, as well as physical and psychological dysfunction. Diffusion magnetic resonance imaging (dMRI) is particularly sensitive to changes in brain structure following TBI, as alterations in white matter (WM) microstructure are common. However, dMRI studies in mild TBI (mTBI) are conflicting, likely due to relatively small samples, sample heterogeneity (demographics, pre- and comorbidities) and injury characteristics (mechanism; chronicity). Furthermore, few studies account for brain asymmetry, which may impact cognitive functions subserved by WM tracts. Examining brain asymmetry in large samples may increase sensitivity to detect heterogeneous areas of subtle WM alteration in mTBI.Through the Enhancing Neuroimaging and Genetics through Meta-analysis (ENIGMA) Military-Relevant Brain Injury working group, we conducted a mega-analysis of neuroimaging and clinical data from 16 cohorts of Active Duty Service Members and Veterans (n=2,598; 2,321 males/277 females; age 19-85 years). 1,080 reported a deployment-related TBI, 480 had a history of only non-military-related TBI, 823 reported no history of TBI, and 215 did not differentiate between military and non-military TBI. dMRI data were processed in a harmonized manner along with harmonized demographic, injury, psychiatric, and cognitive measures. Hemispheric asymmetry of fractional anisotropy (FA, a common proxy for myelin organization) was calculated for 19 WM tracts and compared between those with and without TBI history.FA in the cingulum showed greater asymmetry in individuals with a history of deployment-related TBI; this effect was driven by greater left lateralization in the group with TBI. There was a trend towards lower FA of the right cingulum in the TBI group. These results remained significant after accounting for potentially confounding variables including posttraumatic stress disorder, depression, and handedness and were driven primarily by individuals who had sustained their worst TBI before age 40. We further found that alterations in the cingulum were associated with slower processing speed and poorer set shifting.The results indicate an enhancement of the previously reported natural left laterality, possibly due to vulnerability of the non-dominant hemisphere or compensatory mechanisms in the dominant hemisphere. The cingulum is one of the last WM tracts to mature, reaching peak FA around 42 years old. This effect was primarily detected in individuals whose worst injury occurred before age 40, suggesting that the protracted development of the cingulum may lead to increased vulnerability to insults, such as TBI.
Published: 2022

29. The Enhancing NeuroImaging Genetics through Meta‐Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping

Author: Gary F. Egan, Neda Jahanshad, Sophia I. Thomopoulos, Lianne Schmaal, Paul M. Thompson, Jessica A. Turner, Peter Kochunov, and Anderson M. Winkler
Subjects: international research, Review Article, Electroencephalography, Brain mapping, neuroscience, Neuroimaging, big data, medicine, GWAS, Radiology, Nuclear Medicine and imaging, genetics, Review Articles, reproducibility, Depression (differential diagnoses), Cognitive science, neuroimaging, Radiological and Ultrasound Technology, medicine.diagnostic_test, Resting state fMRI, ENIGMA, Magnetoencephalography, Human brain, medicine.disease, medicine.anatomical_structure, Neurology, Schizophrenia, DTI, Neurology (clinical), Anatomy, Psychology, MRI
Abstract: This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large‐scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task‐based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well‐powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large‐scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke., This Special Issue of Human Brain Mapping is dedicated to a 10‐year anniversary of the international Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA) Consortium. It reports updates from abroad range of international neuroimaging projects that pool data from over 45 countries to answer fundamental questions in neuroscience. Here, we summarize the 31 papers in this Special Issue from across the ENIGMA Consortium.
Published: 2021

30. Brain-Based Classification of Youth with Anxiety Disorders: an ENIGMA-ANXIETY Transdiagnostic Examination using Machine Learning

Author: Willem B. Bruin, Paul Zhutovsky, Guido van Wingen, Janna Marie Bas-Hoogendam, Nynke A. Groenewold, Kevin Hilbert, Anderson M. Winkler, André Zugman, Federica Agosta, Fredrik Åhs, Carmen Andreescu, Chase Antonacci, Takeshi Asami, Michal Assaf, Jacques Barber, Jochen Bauer, Shreya Bavdekar, Katja Beesdo-Baum, Francesco Benedetti, Rachel Bernstein, Johannes Björkstrand, Robert Blair, Karina S. Blair, Laura Blanco-Hinojo, Joscha Böhnlein, Paolo Brambilla, Rodrigo Bressan, Fabian Breuer, Marta Cano, Elisa Canu, Elise M Cardinale, Narcís Cardoner, Camilla Cividini, Henk Cremers, Udo Dannlowski, Gretchen J. Diefenbach, Katharina Domschke, Alexander Doruyter, Thomas Dresler, Angelika Erhardt, Massimo Filippi, Gregory Fonzo, Gabrielle Felice Freitag, Tomas Furmark, Tian Ge, Andrew J. Gerber, Savannah Gosnell, Hans J. Grabe, Dominik Grotegerd, Ruben C. Gur, Raquel E. Gur, Alfons O. Hamm, Laura K. M. Han, Jennifer Harper, Anita Harrewijn, Alexandre Heeren, David Hoffman, Andrea P. Jackowski, Neda Jahanshad, Laura Jett, Antonia N. Kaczkurkin, Parmis Khosravi, Ellen Kingsley, Tilo Kircher, Milutin Kostić, Bart Larsen, Sang-Hyuk Lee, Elisabeth Leehr, Ellen Leibenluft, Christine Lochner, Su Lui, Eleonora Maggioni, Gisele Gus Manfro, Kristoffer Månsson, Claire Marino, Frances Meeten, Barbara Milrod, Ana Munjiza, Benson Irungu, Michael Myers, Susanne Neufang, Jared Nielsen, Patricia Ohrmann, Cristina Ottaviani, Martin P Paulus, Michael T. Perino, K Luan Phan, Sara Poletti, Daniel Porta-Casteràs, Jesus Pujol, Andrea Reinecke, Grace Ringlein, Pavel Rjabtsenkov, Karin Roelofs, Ramiro Salas, Giovanni Salum, Theodore D. Satterthwaite, Elisabeth Schrammen, Lisa Sindermann, Jordan Smoller, Jair Soares, Rudolf Stark, Frederike Stein, thomas straube, Benjamin Straube, Jeffrey Strawn, Benjamin Suarez-Jimenez, Chad M. Sylvester, Ardesheer Talati, Sophia I Thomopoulos, Raşit Tükel, Helena van Nieuwenhuizen, Katy E. Werwath, Katharina Wittfeld, Barry Wright, Mon-Ju Wu, Yunbo Yang, Anna Zilverstand, Peter Zwanzger, Jennifer Blackford, Suzanne Avery, Jacqueline Clauss, Ulrike Lueken, Paul Thompson, Daniel Pine, Dan J. Stein, Nic van der Wee, Dick Veltman, and Moji Aghajani
Abstract: Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.
Published: 2022

31. Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3

Author: Elizabeth Haddad, Fabrizio Pizzagalli, Alyssa H. Zhu, Ravi R. Bhatt, Tasfiya Islam, Iyad Ba Gari, Daniel Dixon, Sophia I. Thomopoulos, Paul M. Thompson, and Neda Jahanshad
Subjects: neuroimaging, reliability, Neurology, Radiological and Ultrasound Technology, Radiology, Nuclear Medicine and imaging, Neurology (clinical), surface area, Anatomy, version compatibility, thickness, MRI
Abstract: Automatic neuroimaging segmentation and parcellation tools provide convenient and systematic methods for extracting numerous features from brain MRI scans, and are becoming standard practice for large-scale coordinated studies. One such tool, FreeSurfer, provides an easy-to-use pipeline to extract metrics describing cortical and subcortical morphometry. Over the past two decades, there have been over 25 stable releases of FreeSurfer, and different versions are used across published works. Despite this, the reliability and compatibility of metrics derived from the most recent major version releases have yet to be assessed empirically. Here, we use test-retest data from three public brain MRI datasets to assess within-version reliability and between-version compatibility across 42 regional outputs from three versions of FreeSurfer: the latest, v7.1, and two previous stable releases - v5.3, and v6.0. We find v7.1 was less compatible with older versions for measuring cortical thickness. In particular, the thickness of the cingulate gyrus had low compatibility (intraclass correlation coefficient (ICC) between 0.37 and 0.61) between versions. Temporal and frontal poles, and the medial orbitofrontal surface area metrics, also showed low to moderate compatibility with v7.1. While our work compares all three versions, our sub-comparisons between the older versions (v5.3 and v6.0) replicates earlier findings of low compatibility of pallidum and putamen volumes. Low between-version compatibility was not always indicative of low within-version reliability – all versions showed good to excellent reliability across most regional measures (ICC>0.8). Age associations, quality control metrics, and Dice coefficients in an independent sample of 106 individual scans, processed with all three versions of FreeSurfer, revealed differences in results of downstream statistical analysis. As neuroimaging studies adopt more recently released software, we provide researchers with a reference to highlight the regions and metrics that may yield findings inconsistent with published works using older FreeSurfer software. An interactive viewer for the results is provided at http://data.brainescience.org/Freesurfer_Reliability/
Published: 2022

32. Progressive Spinal Cord Degeneration in Friedreich's Ataxia: Results from ENIGMA-Ataxia

Author: Thiago J.R. Rezende, Isaac M. Adanyeguh, Filippo Arrigoni, Benjamin Bender, Fernando Cendes, Louise A. Corben, Andreas Deistung, Martin Delatycki, Imis Dogan, Gary F. Egan, Sophia L. Göricke, Nellie Georgiou‐Karistianis, Pierre‐Gilles Henry, Diane Hutter, Neda Jahanshad, James M. Joers, Christophe Lenglet, Tobias Lindig, Alberto R.M. Martinez, Andrea Martinuzzi, Gabriella Paparella, Denis Peruzzo, Kathrin Reetz, Sandro Romanzetti, Ludger Schöls, Jörg B. Schulz, Matthis Synofzik, Sophia I. Thomopoulos, Paul M. Thompson, Dagmar Timmann, Ian H. Harding, and Marcondes C. França
Subjects: Movement Disorders, pathology [Friedreich Ataxia], Pyramidal Tracts, Medizin, Friedreich's ataxia, spinal cord, complications [Friedreich Ataxia], methods [Magnetic Resonance Imaging], Neurology, Humans, Ataxia, ddc:610, Neurology (clinical), ENIGMA-ataxia, MRI, SCT
Abstract: Movement disorders 38(1), 45-56 (2023). doi:10.1002/mds.29261, Published by Wiley, New York, NY
Published: 2022

33. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Author: Willem B. Bruin, Yoshinari Abe, Pino Alonso, Alan Anticevic, Srinivas Balachander, Nuria Bargallo, Marcelo Camargo Batistuzzo, Francesco Benedetti, Sara Bertolín, Silvia Brem, Federico Calesella, beatriz couto, Damiaan Denys, Marco A.N. Echevarria, Goi Khia Eng, Sónia Ferreira, Jamie Feusner, Rachael Grazioplene, Patricia Gruner, Joyce Y. Guo, Kristen Hagen, Bjarne Hansen, Yoshiyuki Hirano, Marcelo Queiroz Hoexter, Neda Jahanshad, Fern Jaspers-Fayer, Selina Kasprzak, Minah Kim, kathrin koch, Yoo Bin Kwak, Jun Soo Kwon, Luisa Lazaro, Chiang-Shan R. Li, Christine Lochner, Rachel Marsh, Ignacio Martínez-Zalacaín, Jose M. Menchon, Pedro Silva Moreira, Pedro Morgado, Akiko Nakagawa, Tomohiro Nakao, Janardhanan C. Narayanaswamy, Erika L. Nurmi, Jose C. Pariente Zorrilla, John Piacentini, Maria Picó-Pérez, Federica Piras, Fabrizio Piras, Christopher Pittenger, Janardhan Y.C. Reddy, Daniela Rodriguez-Manrique, Yuki Sakai, Eiji Shimizu, Venkataram Shivakumar, Blair H. Simpson, Carles Soriano-Mas, Nuno Sousa, Gianfranco Spalletta, Emily R. Stern, S. Evelyn Stewart, Philip R. Szeszko, Jinsong Tang, Sophia I Thomopoulos, Anders Lillevik Thorsen, Yoshida Tokiko, Hirofumi Tomiyama, Benedetta Vai, Ilya Veer, Venkatasubramanian G, Nora C. Vetter, Chris Vriend, Susanne Walitza, Lea Waller, Zhen Wang, Anri Watanabe, Nicole Wolff, Je-Yeon Yun, Qing Zhao, Wieke A. van Leeuwen, Hein J.F. van Marle, Laurens A. van de Mortel, Anouk van der Straten, Ysbrand van der Werf, Paul Thompson, Dan J. Stein, Odile A. van den Heuvel, and Guido van Wingen
Abstract: Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1,024 OCD patients and 1,028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen’s d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen’s d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC=0.702) than unmedicated (AUC=0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.
Published: 2022

34. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author: Yash Patel, Jean Shin, Christoph Abé, Ingrid Agartz, Clara Alloza, Dag Alnæs, Sonia Ambrogi, Linda A. Antonucci, Celso Arango, Volker Arolt, Guillaume Auzias, Rosa Ayesa-Arriola, Nerisa Banaj, Tobias Banaschewski, Cibele Bandeira, Zeynep Başgöze, Renata Basso Cupertino, Claiton H.D. Bau, Jochen Bauer, Sarah Baumeister, Fabio Bernardoni, Alessandro Bertolino, Caterina del Mar Bonnin, Daniel Brandeis, Silvia Brem, Jason Bruggemann, Robin Bülow, Juan R. Bustillo, Sara Calderoni, Rosa Calvo, Erick J. Canales-Rodríguez, Dara M. Cannon, Susanna Carmona, Vaughan J. Carr, Stanley V. Catts, Sneha Chenji, Qian Hui Chew, David Coghill, Colm G. Connolly, Annette Conzelmann, Alexander R. Craven, Benedicto Crespo-Facorro, Kathryn Cullen, Andreas Dahl, Udo Dannlowski, Christopher G. Davey, Christine Deruelle, Covadonga M. Díaz-Caneja, Katharina Dohm, Stefan Ehrlich, Jeffery Epstein, Tracy Erwin-Grabner, Lisa T. Eyler, Jennifer Fedor, Jacqueline Fitzgerald, William Foran, Judith M. Ford, Lydia Fortea, Paola Fuentes-Claramonte, Janice Fullerton, Lisa Furlong, Louise Gallagher, Bingchen Gao, Si Gao, Jose M. Goikolea, Ian Gotlib, Roberto Goya-Maldonado, Hans J. Grabe, Melissa Green, Eugenio H. Grevet, Nynke A. Groenewold, Dominik Grotegerd, Oliver Gruber, Jan Haavik, Tim Hahn, Ben J. Harrison, Walter Heindel, Frans Henskens, Dirk J. Heslenfeld, Eva Hilland, Pieter J. Hoekstra, Sarah Hohmann, Nathalie Holz, Fleur M. Howells, Jonathan C. Ipser, Neda Jahanshad, Babette Jakobi, Andreas Jansen, Joost Janssen, Rune Jonassen, Anna Kaiser, Vasiliy Kaleda, James Karantonis, Joseph A. King, Tilo Kircher, Peter Kochunov, Sheri-Michelle Koopowitz, Mikael Landén, Nils Inge Landrø, Stephen Lawrie, Irina Lebedeva, Beatriz Luna, Astri J. Lundervold, Frank P. MacMaster, Luigi A. Maglanoc, Daniel H. Mathalon, Colm McDonald, Andrew McIntosh, Susanne Meinert, Patricia T. Michie, Philip Mitchell, Ana Moreno-Alcázar, Bryan Mowry, Filippo Muratori, Leila Nabulsi, Igor Nenadić, Ruth O'Gorman Tuura, Jaap Oosterlaan, Bronwyn Overs, Christos Pantelis, Mara Parellada, Jose C. Pariente, Paul Pauli, Giulio Pergola, Francesco Maria Piarulli, Felipe Picon, Fabrizio Piras, Edith Pomarol-Clotet, Clara Pretus, Yann Quidé, Joaquim Radua, J. Antoni Ramos-Quiroga, Paul E. Rasser, Andreas Reif, Alessandra Retico, Gloria Roberts, Susan Rossell, Diego Luiz Rovaris, Katya Rubia, Matthew D. Sacchet, Josep Salavert, Raymond Salvador, Salvador Sarró, Akira Sawa, Ulrich Schall, Rodney Scott, Pierluigi Selvaggi, Tim Silk, Kang Sim, Antonin Skoch, Gianfranco Spalletta, Filip Spaniel, Dan J. Stein, Olaf Steinsträter, Aleks Stolicyn, Yoichiro Takayanagi, Leanne Tamm, Maria Tavares, Alexander Teumer, Katharina Thiel, Sophia I. Thomopoulos, David Tomecek, Alexander S. Tomyshev, Diana Tordesillas-Gutiérrez, Michela Tosetti, Anne Uhlmann, Tamsyn Van Rheenen, Javier Vazquez-Bourgón, Meike W. Vernooij, Eduard Vieta, Oscar Vilarroya, Cynthia Weickert, Thomas Weickert, Lars T. Westlye, Heather Whalley, David Willinger, Alexandra Winter, Katharina Wittfeld, Tony T. Yang, Yuliya Yoncheva, Jendé L. Zijlmans, Martine Hoogman, Barbara Franke, Daan van Rooij, Jan Buitelaar, Christopher R.K. Ching, Ole A. Andreassen, Elena Pozzi, Dick Veltman, Lianne Schmaal, Theo G.M. van Erp, Jessica Turner, F. Xavier Castellanos, Zdenka Pausova, Paul Thompson, Tomas Paus, Pediatric surgery, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, General Paediatrics, ARD - Amsterdam Reproduction and Development, Paediatrics, Radiology & Nuclear Medicine, Epidemiology, Clinical Cognitive Neuropsychiatry Research Program (CCNP), German Research Foundation, University of Münster, National Health and Medical Research Council (Australia), University of Cape Town, National Research Foundation (South Africa), Swinburne Universit, Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L. Cooper Foundation, Society of Mental Health Research, Swedish Research Council, Swedish Foundation for Strategic Research, Swedish Brain Foundation, Health Research Board (Ireland), Russian Foundation for Basic Research, University of Zurich, Pratt Foundation, Ramsay Health Care, Viertel Charitable Foundation, Schizophrenia Research Institute, European Commission, Australian Research Council, Instituto de Salud Carlos III, National Institute for Health and Care Research (US), National Institute for Health Research (UK), Ministry of Health of the Czech Republic, Bill & Melinda Gates Foundation, South African Medical Research Council, Carnegie Corporation of New York, Wellcome Trust, Medical Research Council (UK), Medical Research Scotland, Netherlands Organization for Scientific Research, Ambrogi, Sonia, Banaschewski, Tobias, Bandeira, Cibele Edom, Cupertino, Renata, Calderoni, Sara, Cannon, Dara, Carr, Vaughan, Chew, Qian Hui, Coghill, David, Cullen, Kathryn, Dahl, Andreas, Epstein, Jeffery, Foran, William, Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice M., Furlong, Lisa, Gallagher, Louise, Gao, Si, Gotlib, Ian, Haavik, Jan, Henskens, Frans, Hilland, Eva, Hoekstra, Pieter J, Howells, Fleur M, Ipser, Jonathan, Jørgensen, Jes Kristian, Karantonis, James A., Lawrie, Stephen, Research Institute of the Hospital for Sick Children and University of Toronto, University of Toronto, Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Canada, KG Jebsen Centre for Psychosis Research, University of Oslo (UiO)-Institute of Clinical Medicine-Oslo University Hospital [Oslo], Universidad Complutense de Madrid = Complutense University of Madrid [Madrid] (UCM), Mental Health Sciences Unit, University College of London [London] (UCL), Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Clinical and Behavioral Neurology - Neuroscienze e riabilitazione, IRCCS Fondazione Santa Lucia [Roma], Heidelberg University, Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Universität Heidelberg [Heidelberg] = Heidelberg University, Clinical Neuropsychology, IBBA, APH - Mental Health, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Hospital Sant Joan de Déu [Barcelona], and Centro de Investigación Biomédica en Red Salud Mental [Madrid] (CIBER-SAM)
Subjects: Bipolar Disorder, Autism Spectrum Disorder, [SDV]Life Sciences [q-bio], Neurogenesis, pathology [Premature Birth], Neurodevelopment, Cortical surface area, pathology [Autism Spectrum Disorder], Cortical growth, methods [Magnetic Resonance Imaging], SDG 3 - Good Health and Well-being, Pregnancy, 130 000 Cognitive Neurology & Memory, Humans, ddc:610, Child, Biological Psychiatry, Cerebral Cortex, pathology [Depressive Disorder, Major], Depressive Disorder, Major, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Infant, Newborn, Magnetic Resonance Imaging, Mental illness, Attention Deficit Disorder with Hyperactivity, Premature Birth, genetics [Autism Spectrum Disorder], Female, Psychiatric disorders, 170 000 Motivational & Cognitive Control
Abstract: [Background]: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., [Methods]: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., [Results]: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., [Conclusions]: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., This work was supported by the German Research Foundation (DFG Grant Nos. HA7070/2-2, HA7070/3, HA7070/4 [to TH]) and IZKF of the medical faculty of Münster (Grants No. Dan3/012/17 [to UD] and MzH 3/020/20 [to TH]), and NHMRC projects (Grant No. 1064643 [to BJH] and 1024570 [to CGD]). The CIAM study was supported by the University of Cape Town Research Committee, South African National Research Foundation, and the South African Medical Research Council (principal investigator [PI], Fleur M. Howells) and Grant No. R01MH117601 (to NJ). This work was funded by the German Research Foundation (Grant Nos. FOR2107 JA 1890/7-1 and FOR2107 JA 1890/7-2 [to AJ]) and Swinburne University scholarship/Australian Postgraduate Award (to JK). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to JK). This work was funded by the German Research Foundation (Grant Nos. FOR2107 KI588/14-1 and FOR2107 KI588/14-2 [to TK]). The St. Göran study was supported by grants from the Swedish Research Council (Grant No. 2018-02653 [to ML]), the Swedish foundation for Strategic Research (Grant No. KF10-0039 [to ML]), the Swedish Brain foundation (Grant No. FO2020-0261 [to ML]), and the Swedish Government under the LUA/ALF agreement (Grant Nos. ALF 20170019 and ALFGBG-716801 [to ML]). This work was supported by RFBR (Grant No. 20-013-00748 [to IL, AST]) and funded by the Health Research Board (Grant No. HRA_POR/2011/100 [to CM]). The Australian Schizophrenia Research Bank (ASRB) was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This work was supported by Deutsche Forschungsgemeinschaft (Grant Nos. DFG NE 2254/2-1, NE 2254/3-1, NE2254/4-1 [to IN]), the University Research Priority Program “Integrative Human Physiology” at the University of Zurich (to ROT), an NHMRC Senior Principal Research Fellowship (Grant No. 1105825 [to CPa]), an NHMRC L3 Investigator Grant (Grant No. 1196508 [to CPa]), and NHMRC Program Grant (Grant No. 1150083 [to CPa]). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (Agreement No. 798181 [to GP]). ASRB was supported by the NHMRC (Enabling Grant, ID 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. The Imaging Genetics in Psychosis study was funded by Project Grants from the NHMRC (Grant Nos. APP630471 and APP1081603 [to YQ]) and the Macquarie University’s Australian Research Council (ARC) Centre of Excellence in Cognition and its Disorders (Grant No. CE110001021 [to YQ]). This work was supported by the Spanish Ministry of Science, Innovation and Universities/Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI11/01766 and CPII19/00009 [to JR]), co-financed by European Regional Development Fund funds from the European Commission (“A Way of Making Europe”). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. SR was supported by an NHMRC Senior Fellowship (Grant No. GNT1154651). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to SR). This work was supported by NIHR; MRC (to KR), NIH (Grant Nos. MH-094268, MH-105660, and MH-107730 [to ASS]). The Neuroimaging of the Children's Attention Project cohort was funded by NHMRC, Australia (Grant No. 1065895). Earlier funding for the cohort as also provided by NHMRC (Grant No. 1008522) and a grant from the Collier Foundation. The ACPU cohort was funded by NHMRC, Australia (Project Grant Nos. 384419 and 569533 [to TS]). This work was supported by research grants from the National Healthcare Group, Singapore (Grant Nos. SIG/05004, SIG/05028, and SIG /1103), and the Singapore Bioimaging Consortium (RP C009/2006 [to KS]), and the Ministry of Health, Czech Republic - conceptual development of research organization (“Institute for Clinical and Experimental Medicine - IKEM, IN 00023001” [to ASk]). This study was supported by the Italian Ministry of Health (Grant No. RC/17-18-19-20-21/A [to GS]) and Ministry of Health of the Czech Republic (Grant No. NU20-04-00393 [to FS]). The Drakenstein Child Health Study (DCHS) cohort is funded by the Bill and Melinda Gates Foundation (Grant No. OPP 1017641) and the South African Medical Research Council. This DCHS contribution was made possible in part by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author (DJS). STRADL study was supported and funded by the Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (Grant No. 104036/Z/14/Z), and the Medical Research Council Mental Health Pathfinder Award “Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders” (MRC, Grant No. MC_PC_17209). Scottish Bipolar Family Study (SBFS) was supported by National Health Service Research Scotland, through the Scottish Mental Health Research Network (www.smhrn.org.uk), who provided assistance with subject recruitment and assessments. SBFS was conducted at the Brain Research Imaging Centre (http://www.bric.ed.ac.uk), which is supported by SINAPSE (Scottish Imaging Network, a Platform for Scientific Excellence, http://www.sinapse.ac.uk). Processing of the datasets used the resources provided by the Edinburgh Compute and Data Facility (http://www.ecdf.ed.ac.uk/) (ASt). TVR was supported by an NHMRC Early Career Fellowship (Grant No. GNT1088785). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to TVR). EV was supported by the Spanish Ministry of Science and Innovation (PI18/00805) integrated into the Plan Nacional de I+D+I and co-financed by the ISCIII-Subdirección General de Evaluación and the FEDER; the Instituto de Salud Carlos III; the CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental); by the CERCA Programme/Generalitat de Catalunya and the Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement (Grant No. 2017SGR1355). This study was also supported by the Departament de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca i Innovació en Salut (PERIS) 2016-2020 (Grant No. SLT006/17/00345) and the European Union Horizon 2020 research and innovation program (EU.3.1.1. Understanding health, wellbeing and disease: Grant Nos. 754907 and EU.3.1.3 [to EB]; Treating and managing disease: Grant No. 945151 [to EV]). This study was supported by the National Center for Complementary and Integrative Health (Grant Nos. R21AT009173 and R61AT009864 [to TTY]); by the National Center for Advancing Translational Sciences (CTSI), National Institutes of Health, through UCSF-CTSI (Grant No. UL1TR001872 [to TTY]); by the American Foundation for Suicide Prevention (Grant No. SRG-1-141-18 [to TTY]); by UCSF Research Evaluation and Allocation Committee (REAC) and J. Jacobson Fund (to TTY); by the NIMH (Grant No. R01MH085734 [to TTY]); and by the Brain and Behavior Research Foundation (formerly NARSAD) (to TTY). This work was supported by a personal Veni grant to MH from the Netherlands Organization for Scientific Research (NWO, Grant No. 91619115 [to MH]) and European Community’s Horizon 2020 Programme (H2020/2014 – 2020) (Grant Agreements Nos. 667302 [CoCA], 728018 [Eat2beNICE], and 847879 [PRIME] [to BF]). JBu has been supported by the EU-AIMS (European Autism Interventions) and AIMS-2-TRIALS programmes, which receive support from Innovative Medicines Initiative Joint Undertaking Grant Nos. 115300 and 777394, the resources of which are composed of financial contributions from the European Union’s FP7 and Horizon 2020 Programmes, and from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions, and AUTISM SPEAKS, Autistica and SFARI; and by the Horizon 2020–supported programme CANDY (Grant No. 847818 [to JBu]). This work is supported by Grant No. NIA T32AG058507 and NIH Grant No. U54EB020403 from the Big Data to Knowledge (BD2K) Program (to CRKC). ENIGMA MDD work is supported by NIH (Grant Nos. U54 EB020403 [to PT], R01 MH116147 [to PT], and R01 MH117601 [to NJ and LS]). LS was supported by an NHMRC Career Development Fellowship (Grant No. 1140764). This work was supported by the National Center for Research Resources at the NIH (Grant Nos. NIH 1 U24 RR021992 [Function Biomedical Informatics Research Network], NIH 1 U24 RR025736-01 [Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org]). TGMvE is supported by ENIGMA’s NIH BD2K initiative (Grant No. U54 EB020403), ENIGMA Sex Differences (Grant No. R01MH116147), and ENIGMA-COINSTAC: Advanced Worldwide Transdiagnostic Analysis of Valence System Brain Circuits (Grant No. R01MH121246). This work was supported by the NIH (Grant No. R01MH121246 [to JT, Calhoun, and TGMvE]). This work was supported in part by NIH (Grant No. U54 EB020403 [to PT]).
Published: 2022

35. Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization

Author: Sophia I. Thomopoulos, Hosung Kim, Yaqiong Chai, Alyssa H. Zhu, Mengting Liu, Piyush Maiti, and Neda Jahanshad
Subjects: business.industry, Computer science, Pooling, Harmonization, Variation (game tree), Machine learning, computer.software_genre, Imaging phantom, Article, Image (mathematics), Neuroimaging, A priori and a posteriori, Artificial intelligence, business, computer, Generative grammar
Abstract: Large data initiatives and high-powered brain imaging analyses require the pooling of MR images acquired across multiple scanners, often using different protocols. Prospective cross-site harmonization often involves the use of a phantom or traveling subjects. However, as more datasets are becoming publicly available, there is a growing need for retrospective harmonization, pooling data from sites not originally coordinated together. Several retrospective harmonization techniques have shown promise in removing cross-site image variation. However, most unsupervised methods cannot distinguish between image-acquisition based variability and cross-site population variability, so they require that datasets contain subjects or patient groups with similar clinical or demographic information. To overcome this limitation, we consider cross-site MRI image harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a reference image directly, without knowing their site/scanner labelsa priori. We trained our model using data from five large-scale multi-site datasets with varied demographics. Results demonstrated that our styleencoding model can harmonize MR images, and match intensity profiles, successfully, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. Moreover, we further demonstrated that if we included diverse enough images into the training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising novel tool for ongoing collaborative studies.
Published: 2022

36. Sex is a defining feature of neuroimaging phenotypes in major brain disorders

Author: Sophia I. Thomopoulos, Meral A Tubi, Paul M. Thompson, Joanna Bright, Alyssa Wieand, and Lauren E. Salminen
Subjects: medicine.medical_specialty, Neurology, Population, Review Article, 050105 experimental psychology, diffusion MRI, 03 medical and health sciences, 0302 clinical medicine, male, Neuroimaging, gender, medicine, Humans, sex, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, education, structural MRI, Brain Diseases, Sex Characteristics, education.field_of_study, neuroimaging, Human Connectome Project, Radiological and Ultrasound Technology, business.industry, Mental Disorders, Clinical study design, 05 social sciences, ENIGMA, Brain, Magnetic Resonance Imaging, Phenotype, Biobank, psychiatry, female, Normative, Neurology (clinical), Anatomy, business, 030217 neurology & neurosurgery, Clinical psychology
Abstract: Sex is a biological variable that contributes to individual variability in brain structure and behavior. Neuroimaging studies of population‐based samples have identified normative differences in brain structure between males and females, many of which are exacerbated in psychiatric and neurological conditions. Still, sex differences in MRI outcomes are understudied, particularly in clinical samples with known sex differences in disease risk, prevalence, and expression of clinical symptoms. Here we review the existing literature on sex differences in adult brain structure in normative samples and in 14 distinct psychiatric and neurological disorders. We discuss commonalities and sources of variance in study designs, analysis procedures, disease subtype effects, and the impact of these factors on MRI interpretation. Lastly, we identify key problems in the neuroimaging literature on sex differences and offer potential recommendations to address current barriers and optimize rigor and reproducibility. In particular, we emphasize the importance of large‐scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta‐Analyses consortium, the UK Biobank, Human Connectome Project, and others to provide unprecedented power to evaluate sex‐specific phenotypes in major brain diseases., Here, we review the human neuroimaging literature examining sex effects on adult brain structure using structural and diffusion MRI. We discuss normative sex differences based on population‐based studies as well as sex differences in 14 major brain diseases. Finally, we identify key barriers to advancing the science on neuroimaging sex effects and offer recommendations to mitigate these challenges, particularly through large‐scale neuroimaging.
Published: 2021

37. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author: Thomas Frodl, Eduard Vieta, Sean N. Hatton, Sophia I. Thomopoulos, Jens Sommer, Fábio L.S. Duran, Pauline Favre, Cynthia H.Y. Fu, Tony T. Yang, Knut Schnell, Tilo Kircher, Gloria Roberts, Lyubomir I. Aftanas, Norbert Hosten, Elena Pozzi, Henrik Walter, Pedro G.P. Rosa, Oliver Gruber, Colm G. Connolly, Klaus Berger, Jonathan Repple, Geraldo Busatto Filho, Tomas Hajek, Bernd Kramer, Salvador Sarró, Ulrik Fredrik Malt, Richard Dinga, Danai Dima, Mikael Landén, Laura K.M. Han, Jonathan Savitz, Peter R. Schofield, Axel Krug, Maria M. Rive, Caterina del Mar Bonnín, Edith Pomarol-Clotet, Olaf Steinsträter, Chantal Henry, Udo Dannlowski, Henricus G. Ruhé, Mauricio H. Serpa, Quinn McLellan, Benson Mwangi, Philipp G. Saemann, Christopher G. Davey, Marie-José van Tol, Mircea Polosan, Torbjørn Elvsåshagen, Nynke A. Groenewold, Marcus V. Zanetti, Claas Kähler, Jair C. Soares, Steven J.A. van der Werff, Kathryn R. Cullen, Lachlan T. Strike, Ilya M. Veer, Beata R. Godlewska, Giovana Zunta-Soares, Xavier Caseras, Janice M. Fullerton, Bronwyn Overs, Tiffany M. Chaim-Avancini, Lisa T. Eyler, Theodore D. Satterthwaite, Martin Ingvar, Ramona Leenings, Angela Carballedo, Brenda W.J.H. Penninx, Ian B. Hickie, James H. Cole, Elena Filimonova, Márcio Gerhardt Soeiro-de-Souza, Rayus Kuplicki, Leila Nabulsi, Ben J. Harrison, Aart H. Schene, Ivan V. Brak, Nic J.A. van der Wee, Hans J. Grabe, Katharina Wittfeld, Anouk Schrantee, Matthew D. Sacchet, Margaret J. Wright, Dan J. Stein, Erlend Bøen, Heather C. Whalley, Egle Simulionyte, Fleur M. Howells, Tim Hahn, Lianne Schmaal, Garrett M. Timmons, Bartholomeus C M Haarman, Kang Sim, Andrew M. McIntosh, Moji Aghajani, Jim Lagopoulos, Anne Uhlmann, Rodrigo Machado-Vieira, Jose Manuel Goikolea, Mon-Ju Wu, Christopher R.K. Ching, Dara M. Cannon, Liesbeth Reneman, Andreas Jansen, Josselin Houenou, Ian H. Gotlib, Bonnie Klimes-Dougan, Raymond Salvador, Maria J. Portella, Ole A. Andreassen, Greig I. de Zubicaray, Robert Vermeiren, Bryon A. Mueller, Nils R. Winter, Dick J. Veltman, Neda Jahanshad, Stefan Frenzel, Philip B. Mitchell, Colm McDonald, Henry Völzke, Daniel H. Wolf, Katie L. McMahon, Evgeny Osipov, Marco Hermesdorf, Tiffany C. Ho, Bernhard T. Baune, Paul M. Thompson, Glenda MacQueen, Andre F. Marquand, Maria Concepcion Garcia Otaduy, Vasileios Zannias, Christoph Abé, Ashley N. Sutherland, Sarah E. Medland, Beny Lafer, Erick J. Canales-Rodríguez, Geoffrey B. Hall, Martin Alda, Henk Temmingh, Sonya Foley, Verena Enneking, Frank P. MacMaster, Dominik Grotegerd, Joaquim Radua, Baptiste Couvy-Duchesne, André Aleman, Radiology and Nuclear Medicine, ANS - Brain Imaging, ANS - Compulsivity, Impulsivity & Attention, APH - Personalized Medicine, APH - Mental Health, Ontwikkelingspsychologie (Psychologie, FMG), Clinical Cognitive Neuropsychiatry Research Program (CCNP), Clinical Neuropsychology, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Radiology and nuclear medicine, Pediatric surgery, Amsterdam Reproduction & Development (AR&D), Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Complex Trait Genetics, and APH - Digital Health
Subjects: Adult, Male, medicine.medical_specialty, Aging, Adolescent, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], BF, Health outcomes, Article, Cellular and Molecular Neuroscience, 03 medical and health sciences, Lateral ventricles, Young Adult, 0302 clinical medicine, Atrophy, Internal medicine, medicine, Humans, ddc:610, Longitudinal Studies, Molecular Biology, diagnostic imaging [Brain], Brain aging, Depression (differential diagnoses), 030304 developmental biology, Aged, 0303 health sciences, Depressive Disorder, Major, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, Depression, 220 Statistical Imaging Neuroscience, Brain, Chronological age, Middle Aged, medicine.disease, Magnetic Resonance Imaging, 030227 psychiatry, Psychiatry and Mental health, Increased risk, RC0321, Major depressive disorder, Female, Age of onset, business, 030217 neurology & neurosurgery, Neuroscience
Abstract: BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset.MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain predicted age difference (brain-PAD).FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen’s d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004).InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates.FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.
Published: 2021

38. Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis

Author: Kelene A. Fercho, Steven M. Nelson, Thomas Straube, Nic J.A. van der Wee, Gina L. Forster, Jack B. Nitschke, Jessie L. Frijling, Mirjam van Zuiden, Steven E. Bruce, Faisal Rashid, Emily K. Clarke-Rubright, Gen Li, Kyle Choi, Antje Manthey, Tian Chen, Richard A. Bryant, Elbert Geuze, Neda Jahanshad, Mark W. Logue, Matthew Peverill, Andrew S. Cotton, David Hofmann, Seth G. Disner, Jessica Bomyea, Daniel W. Grupe, Elizabeth A. Olson, Emily L. Dennis, Chadi G. Abdallah, Jeffrey S. Simons, Robert Vermeiren, Israel Liberzon, Jacklynn M. Fitzgerald, Jennifer S. Stevens, Kerry J. Ressler, Theo G.M. van Erp, Ilan Harpaz-Rotem, Sven C. Mueller, Lauren A.M. Lebois, Jonathan C Ipser, Benjamin Suarez-Jimenez, Katie A. McLaughlin, Raluca M. Simons, Tim Varkevisser, Hong Xie, Michael Hollifield, Negar Fani, Yuval Neria, Hassaan Gomaa, Vincent A. Magnotta, Henrik Walter, Anthony P. King, Anika Sierk, Tanja Jovanovic, Judith K. Daniels, Ifat Levy, Isabelle M. Rosso, Li Wang, Ye Zhu, Kelly A. Sambrook, Murray B. Stein, Paul M. Thompson, Bobak Hosseini, K. Luan Phan, Nicholas D. Davenport, Christine L. Larson, Terri A. deRoon-Cassini, Saskia B. J. Koch, Richard J. Davidson, Xin Wang, Geoffrey J May, Anna R. Hudson, Marijo Tamburrino, Christian Schmahl, Steven J.A. van der Werff, Elpiniki Andrew, Sophia I. Thomopoulos, Martha E. Shenton, Scott R. Sponheim, Miranda Olff, Julia Herzog, Dick J. Veltman, Inga K. Koerte, Michael D. DeBellis, Mayuresh S. Korgaonkar, Lauren E. Salminen, Xi Zhu, Lee A. Baugh, Laura Nawijn, Brian M. O’Leary, Milissa L. Kaufman, John H. Krystal, Rajendra A. Morey, John Wall, Sanne J.H. van Rooij, Courtney C. Haswell, Dan J. Stein, Evan M. Gordon, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Pediatric surgery, APH - Mental Health, Amsterdam Reproduction & Development (AR&D), APH - Personalized Medicine, APH - Global Health, Clinical Psychology and Experimental Psychopathology, Adult Psychiatry, ANS - Amsterdam Neuroscience, and ANS - Mood, Anxiety, Psychosis, Stress & Sleep
Subjects: 0301 basic medicine, Sensory processing, medicine.medical_treatment, Medical and Health Sciences, behavioral disciplines and activities, Cortical volume, Article, Stress Disorders, Post-Traumatic, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Clinical Research, Behavioral and Social Science, mental disorders, medicine, Humans, Prefrontal cortex, Molecular Biology, Depression (differential diagnoses), Stress Disorders, Cerebral Cortex, Psychiatry, business.industry, Psychology and Cognitive Sciences, Neurosciences, Genomics, Voxel-based morphometry, Biological Sciences, Post-Traumatic Stress Disorder (PTSD), medicine.disease, Magnetic Resonance Imaging, Temporal Lobe, Brain Disorders, Psychiatry and Mental health, Posttraumatic stress, Mental Health, 030104 developmental biology, medicine.anatomical_structure, Cerebral cortex, Post-Traumatic, Major depressive disorder, business, 030217 neurology & neurosurgery, Clinical psychology
Abstract: Studies of posttraumatic stress disorder (PTSD) report volume abnormalities in multiple regions of the cerebral cortex. However, findings for many regions, particularly regions outside commonly studied emotion-related prefrontal, insular, and limbic regions, are inconsistent and tentative. Also, few studies address the possibility that PTSD abnormalities may be confounded by comorbid depression. A mega-analysis investigating all cortical regions in a large sample of PTSD and control subjects can potentially provide new insight into these issues. Given this perspective, our group aggregated regional volumes data of 68 cortical regions across both hemispheres from 1379 PTSD patients to 2192 controls without PTSD after data were processed by 32 international laboratories using ENIGMA standardized procedures. We examined whether regional cortical volumes were different in PTSD vs. controls, were associated with posttraumatic stress symptom (PTSS) severity, or were affected by comorbid depression. Volumes of left and right lateral orbitofrontal gyri (LOFG), left superior temporal gyrus, and right insular, lingual and superior parietal gyri were significantly smaller, on average, in PTSD patients than controls (standardized coefficients = −0.111 to −0.068, FDR corrected P values < 0.039) and were significantly negatively correlated with PTSS severity. After adjusting for depression symptoms, the PTSD findings in left and right LOFG remained significant. These findings indicate that cortical volumes in PTSD patients are smaller in prefrontal regulatory regions, as well as in broader emotion and sensory processing cortical regions.
Published: 2020

39. Longitudinal structural brain changes in bipolar disorder: A multicenter neuroimaging study of 1232 individuals by the ENIGMA Bipolar Disorder Working Group

Author: Theophilus N. Akudjedu, Frederike Stein, Edith Pomarol-Clotet, Lars T. Westlye, Ulrik Fredrik Malt, Erlend Bøen, Fabian Breuer, Chao Suo, Tina Meller, Tim Hahn, Francesco Benedetti, Jose Manuel Goikolea, Silvia Alonso-Lana, Adam George White, Dag Alnæs, Julia-Katharina Pfarr, Beathe Haatveit, Sara Poletti, Kai Ringwald, Nathalia Zak, Benny Liberg, Kelvin Sarink, Giulia Tronchin, Yann Chye, Janice M. Fullerton, Orwa Dandash, Igor Nenadic, Caterina del Mar Bonnín, Elisa M T Melloni, Udo Dannlowski, Michael Berk, Dominik Grotegerd, Christopher R.K. Ching, Lukas Fisch, Torbjørn Elvsåshagen, Andreas Dahl, Martin Alda, Francesco Panicalli, Ingrid Agartz, Martin Ingvar, Bronwyn Overs, Joaquim Radua, Katharina Brosch, Alexander V. Lebedev, Kang Sim, Tilo Kircher, Leila Nabulsi, Dara M. Cannon, Erick J. Canales-Rodríguez, Paul M. Thompson, Nils Opel, Jonathan Repple, R. Salvador, Katharina Dohm, Philip B. Mitchell, Colm McDonald, Salvador Sarró, Rachel M. Brouwer, Ole A. Andreassen, Tomas Hajek, Mikael Landén, Simon Schmitt, Sophia I. Thomopoulos, Elena Rodriguez-Cano, Eduard Vieta, Ingrid Melle, Rhoshel K. Lenroot, Lakshmi N. Yatham, Sean R. McWhinney, Gloria Roberts, Christoph Abé, Walter Heindel, Abe, C., Ching, C. R. K., Liberg, B., Lebedev, A. V., Agartz, I., Akudjedu, T. N., Alda, M., Alnaes, D., Alonso-Lana, S., Benedetti, F., Berk, M., Boen, E., Bonnin, C. D. M., Breuer, F., Brosch, K., Brouwer, R. M., Canales-Rodriguez, E. J., Cannon, D. M., Chye, Y., Dahl, A., Dandash, O., Dannlowski, U., Dohm, K., Elvsashagen, T., Fisch, L., Fullerton, J. M., Goikolea, J. M., Grotegerd, D., Haatveit, B., Hahn, T., Hajek, T., Heindel, W., Ingvar, M., Sim, K., Kircher, T. T. J., Lenroot, R. K., Malt, U. F., Mcdonald, C., Mcwhinney, S. R., Melle, I., Meller, T., Melloni, E. M. T., Mitchell, P. B., Nabulsi, L., Nenadic, I., Opel, N., Overs, B. J., Panicalli, F., Pfarr, J. -K., Poletti, S., Pomarol-Clotet, E., Radua, J., Repple, J., Ringwald, K. G., Roberts, G., Rodriguez-Cano, E., Salvador, R., Sarink, K., Sarro, S., Schmitt, S., Stein, F., Suo, C., Thomopoulos, S. I., Tronchin, G., Vieta, E., Westlye, L. T., White, A. G., Yatham, L. N., Zak, N., Thompson, P. M., Andreassen, O. A., Landen, M., Complex Trait Genetics, and Amsterdam Neuroscience - Complex Trait Genetics
Subjects: Adult, Male, Longitudinal study, medicine.medical_specialty, Bipolar disorder, Neuroimaging, volume changes, surface-based analysis, Young Adult, gray-matter, Cortex (anatomy), Internal medicine, medicine, Humans, Multicenter Studies as Topic, Biological Psychiatry, mri, human cerebral-cortex, Psychiatry, medicine.diagnostic_test, business.industry, ENIGMA, Brain, Magnetic resonance imaging, Cerebral Cortical Thinning, Middle Aged, cortical thickness, medicine.disease, Magnetic Resonance Imaging, Neuroprogression, Mania, genetic influences, medicine.anatomical_structure, Mood, Meta-analysis, Cardiology, lithium treatment, Female, medicine.symptom, i disorder, business, metaanalysis
Abstract: Background: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD. Methods: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables. Results: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18 < d < 0.22). More (hypo)manic episodes were associated with faster cortical thinning, primarily in the prefrontal cortex. Conclusions: In the hitherto largest longitudinal MRI study on BD, we did not detect accelerated cortical thinning but noted faster ventricular enlargements in BD. However, abnormal frontocortical thinning was observed in association with frequent manic episodes. Our study yields insights into disease progression in BD and highlights the importance of mania prevention in BD treatment.
Published: 2022

40. Challenges and opportunities for neuroimaging in young patients with traumatic brain injury: a coordinated effort towards advancing discovery from the ENIGMA pediatric moderate/severe TBI group

Author: Christopher C. Giza, Harvey S. Levin, Karen Caeyenberghs, Marsh Königs, Paul M. Thompson, Jeffrey E. Max, Naomi J. Goodrich-Hunsaker, Matthew S Spruiell, Abigail Livny, Mary R. Newsome, Nicholas P Ryan, Brenda Bartnik-Olson, Robert F. Asarnow, Hannah M. Lindsey, Anthony Figaji, Sophia I. Thomopoulos, Talin Babikian, Elisabeth A. Wilde, Keith Owen Yeates, Christopher G. Watson, David F. Tate, Brandon A. Zielinski, Erin D. Bigler, Ashley L. Ware, Kristen R. Hoskinson, Tricia L. Merkley, Anne L. Wheeler, Emily L. Dennis, Stacy J. Suskauer, Cooper B. Hodges, Alexander Olsen, and General Paediatrics
Subjects: Traumatic, Gerontology, Pediatrics, Medical and Health Sciences, Behavioral Neuroscience, 0302 clinical medicine, Brain Injuries, Traumatic, Child, Cause of death, Neuroradiology, Pediatric, education.field_of_study, traumatic brain injury, 05 social sciences, ENIGMA, Neuropsychology, Experimental Psychology, Injuries and accidents, Childhood Injury, Magnetic Resonance Imaging, Psychiatry and Mental health, Neurology, Biomedical Imaging, Adult, Pediatric Research Initiative, medicine.medical_specialty, Physical Injury - Accidents and Adverse Effects, Adolescent, Moderate-severe TBI, Traumatic brain injury, Cognitive Neuroscience, Population, Developing country, Neuroimaging, Context (language use), Traumatic Brain Injury (TBI), Article, 050105 experimental psychology, Unintentional Childhood Injury, 03 medical and health sciences, Cellular and Molecular Neuroscience, Clinical Research, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, education, traumatic brain injury (TBI), Traumatic Head and Spine Injury, business.industry, Psychology and Cognitive Sciences, Neurosciences, medicine.disease, Brain Disorders, Brain Injuries, Neurology (clinical), business, Biomarkers, 030217 neurology & neurosurgery
Abstract: Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population, and specific developmental issues require a unique context since findings from adult research do not necessarily directly translate to children. Findings in pediatric cohorts tend to lag behind those in adult samples. This may be due, in part, both to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate/Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis of neuroimaging data. In this paper, we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. With the paucity of research studies examining neuroimaging biomarkers in pediatric patients with TBI and the challenges of recruiting large numbers of participants, collaborating to improve statistical power and to address technical challenges like lesions will significantly advance the field. We conclude with recommendations for future research in this field of study.
Published: 2020

41. In vivo white matter microstructure in adolescents with early-onset psychosis: a multi-site mega-analysis via the ENIGMA Consortium

Author: Claudia Barth, Sinead Kelly, Stener Nerland, Neda Jahanshad, Clara Alloza, Sonia Ambrogi, Ole A Andreassen, Dimitrios Andreou, Celso Arango, Inmaculada Baeza, Nerisa Banaj, Carrie E Bearden, Michael Berk, Hannes Bohman, Josefina Castro-Fornieles, Yann Chye, Benedicto Crespo-Facorro, Elena de la Serna, Covadonga M Díaz-Caneja, Tiril P Gurholt, Catherine E Hegarty, Anthony James, Joost Janssen, Cecilie Johannessen, Erik G Jönsson, Katherine H Karlsgodt, Peter Kochunov, Noemi G Lois, Mathias Lundberg, Anne M Myhre, Saül Pascual-Diaz, Fabrizio Piras, Runar E Smelror, Gianfranco Spalletta, Therese S Stokkan, Gisela Sugranyes, Chao Suo, Sophia I Thomopoulos, Diana Tordesillas-Gutiérrez, Daniela Vecchio, Kirsten Wedervang-Resell, Laura A Wortinger, Paul M. Thompson, and Ingrid Agartz
Abstract: BackgroundEmerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset MethodsOur sample included 321 adolescents with EOP (mean age: 16.3 ± 1.4 years, 46.4% females) and 265 adolescent CTR (mean age: 16.0 ± 1.7 years, 57.7% females) pooled from nine sites. All sites extracted mean fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) for 25 white matter regions of interest per participant. ComBat harmonization was performed for all DTI measures to adjust for scanner differences. Multiple linear regression models were fitted to investigate case-control differences and associations with clinical variables in regional DTI measures.ResultsWe found widespread lower FA in EOP compared to CTR, with the largest effect sizes in the superior longitudinal fasciculus (Cohen’s d = 0.37), posterior corona radiata (d = 0.32), and superior fronto□occipital fasciculus (d = 0.31). We also found widespread higher RD and more localized higher MD and AD. We detected significant effects of diagnostic subgroup, sex, and duration of illness, but not medication status.ConclusionUsing the largest EOP DTI sample to date, our findings suggest a profile of widespread white matter microstructure alterations in adolescents with EOP, most prominently in male patients with early-onset schizophrenia and patients with a shorter duration of illness.
Published: 2022

42. Molecular and connectomic vulnerability shape cross-disorder cortical abnormalities

Author: Justine Y. Hansen, Golia Shafiei, Jacob W. Vogel, Kelly Smart, Carrie E. Bearden, Martine Hoogman, Barbara Franke, Daan van Rooij, Jan Buitelaar, Carrie R. McDonald, Sanjay M. Sisodiya, Lianne Schmaal, Dick J. Veltman, Odile A. van den Heuvel, Dan J. Stein, Theo G. M. van Erp, Christopher R. K. Ching, Ole A. Andreassen, Tomas Hajek, Nils Opel, Gemma Modinos, André Aleman, Ysbrand van der Werf, Neda Jahanshad, Sophia I. Thomopoulos, Paul M. Thompson, Richard E. Carson, Alain Dagher, and Bratislav Misic
Abstract: Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21 000 patients and N = 26 000 controls, collected using a harmonized processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find that regional molecular vulnerability and macroscale brain network architecture interact to drive the spatial patterning of cortical abnormalities in multiple disorders. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, medial temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local biological attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
Published: 2022

43. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Author: Constantinos Constantinides, Laura KM Han, Clara Alloza, Linda Antonucci, Celso Arango, Rosa Ayesa-Arriola, Nerisa Banaj, Alessandro Bertolino, Stefan Borgwardt, Jason Bruggemann, Juan Bustillo, Oleg Bykhovski, Vaughan Carr, Stanley Catts, Young-Chul Chung, Benedicto Crespo-Facorro, Covadonga M Díaz-Caneja, Gary Donohoe, Stefan Du Plessis, Jesse Edmond, Stefan Ehrlich, Robin Emsley, Lisa T Eyler, Paola Fuentes-Claramonte, Foivos Georgiadis, Melissa Green, Amalia Guerrero-Pedraza, Minji Ha, Tim Hahn, Frans A Henskens, Laurena Holleran, Stephanie Homan, Philipp Homan, Neda Jahanshad, Joost Janssen, Ellen Ji, Stefan Kaiser, Vasily Kaleda, Minah Kim, Woo-Sung Kim, Matthias Kirschner, Peter Kochunov, Yoo Bin Kwak, Jun Soo Kwon, Irina Lebedeva, Jingyu Liu, Patricia Mitchie, Stijn Michielse, David Mothersill, Bryan Mowry, Víctor Ortiz-García de la Foz, Christos Pantelis, Giulio Pergola, Fabrizio Piras, Edith Pomarol-Clotet, Adrian Preda, Yann Quidé, Paul E Rasser, Kelly Rootes-Murdy, Raymond Salvador, Marina Sangiuliano, Salvador Sarró, Ulrich Schall, André Schmidt, Rodney J Scott, Pierluigi Selvaggi, Kang Sim, Antonin Skoch, Gianfranco Spalletta, Filip Spaniel, Sophia I. Thomopoulos, David Tomecek, Alexander S Tomyshev, Diana Tordesillas-Gutiérrez, Therese van Amelsvoort, Javier Vázquez-Bourgon, Daniela Vecchio, Aristotle Voineskos, Cynthia S Weickert, Thomas Weickert, Paul M Thompson, Lianne Schmaal, Theo GM van Erp, Jessica Turner, James H Cole, Danai Dima, and Esther Walton
Abstract: Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.64 years (95% CI: 3.01, 4.26; I2 = 55.28%) compared to controls, after adjusting for age and sex (Cohen’s d = 0.50). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
Published: 2022

44. In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics throughMeta-AnalysisBipolar Disorder Working Group

Author: Sophia I. Thomopoulos, Julian A Pineda-Zapata, Ronny Redlich, Bartholomeus C M Haarman, Mathew A. Harris, Orwa Dandash, Ulrik Fredrik Malt, Lauren E. Salminen, Michael Stäblein, Theo G.M. van Erp, Gloria Roberts, Michael Berk, Jochen Bauer, Edith Pomarol-Clotet, Carlos López-Jaramillo, Dominik Grotegerd, Tomas Hajek, Paul M. Thompson, Philipp G. Sämann, Francesco Benedetti, Tilo Kircher, Brian Hallahan, Jonathan Repple, Lena Waltemate, Maria M. Rive, Heather C. Whalley, Caterina del Mar Bonnín, Oliver Gruber, Igor Nenadic, Udo Dannlowski, Henricus G. Ruhé, Raymond Salvador, Bronwyn Overs, Torbjørn Elvsåshagen, Márcio Gerhardt Soeiro-de-Souza, Ana M. Díaz-Zuluaga, Katharina Förster, Jose Manuel Goikolea, Emma L. Hawkins, Vera Lonning, Silvia Alonso-Lana, Dan J. Stein, Theophilus N. Akudjedu, Elisa M T Melloni, Dag Alnæs, Nils Opel, Martin Alda, Rayus Kuplicki, Erlend Bøen, Salvador Sarró, Unn K. Haukvik, Philip B. Mitchell, Kang Sim, Lisa Rauer, Ole A. Andreassen, Colm McDonald, Eduard Vieta, Erick J. Canales-Rodríguez, Axel Krug, Viola Oertel, Frederike Stein, Xavier Caseras, Christopher R.K. Ching, Lucio Oldani, Dara M. Cannon, Andrew M. McIntosh, Kjetil Nordbø Jørgensen, Ingrid Melle, Rhoshel K. Lenroot, Lars T. Westlye, Giuseppe Delvecchio, Dick J. Veltman, Mar Fatjó-Vilas, Trine Vik Lagerberg, Leila Nabulsi, Henk Temmingh, Carina Hülsmann, Francesco Bettella, Paolo Brambilla, Dennis van der Meer, Sonya Foley, Tiril P. Gurholt, Fleur M. Howells, Joaquim Radua, Thomas M. Lancaster, Christian K. Tamnes, Maria Cg Otaduy, Jonathan Savitz, Stener Nerland, Genevieve McPhilemy, Janice M. Fullerton, Aart H. Schene, Neda Jahanshad, Ingrid Agartz, Bernhard T. Baune, Beathe Haatveit, Bernd Krämer, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, Haukvik, U. K., Gurholt, T. P., Nerland, S., Elvsashagen, T., Akudjedu, T. N., Alda, M., Alnaes, D., Alonso-Lana, S., Bauer, J., Baune, B. T., Benedetti, F., Berk, M., Bettella, F., Boen, E., Bonnin, C. M., Brambilla, P., Canales-Rodriguez, E. J., Cannon, D. M., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Diaz-Zuluaga, A. M., van Erp, T. G. M., Fatjo-Vilas, M., Foley, S. F., Forster, K., Fullerton, J. M., Goikolea, J. M., Grotegerd, D., Gruber, O., Haarman, B. C. M., Haatveit, B., Hajek, T., Hallahan, B., Harris, M., Hawkins, E. L., Howells, F. M., Hulsmann, C., Jahanshad, N., Jorgensen, K. N., Kircher, T., Kramer, B., Krug, A., Kuplicki, R., Lagerberg, T. V., Lancaster, T. M., Lenroot, R. K., Lonning, V., Lopez-Jaramillo, C., Malt, U. F., Mcdonald, C., Mcintosh, A. M., Mcphilemy, G., van der Meer, D., Melle, I., Melloni, E. M. T., Mitchell, P. B., Nabulsi, L., Nenadic, I., Oertel, V., Oldani, L., Opel, N., Otaduy, M. C. G., Overs, B. J., Pineda-Zapata, J. A., Pomarol-Clotet, E., Radua, J., Rauer, L., Redlich, R., Repple, J., Rive, M. M., Roberts, G., Ruhe, H. G., Salminen, L. E., Salvador, R., Sarro, S., Savitz, J., Schene, A. H., Sim, K., Soeiro-de-Souza, M. G., Stablein, M., Stein, D. J., Stein, F., Tamnes, C. K., Temmingh, H. S., Thomopoulos, S. I., Veltman, D. J., Vieta, E., Waltemate, L., Westlye, L. T., Whalley, H. C., Samann, P. G., Thompson, P. M., Ching, C. R. K., Andreassen, O. A., Agartz, I., Clinical Cognitive Neuropsychiatry Research Program (CCNP), Psychiatry, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and Adult Psychiatry
Subjects: structural brain MRI, Bipolar Disorder, HALOPERIDOL, hippocampus, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], SEGMENTATION, Hippocampus, Hippocampal formation, 0302 clinical medicine, SCHIZOPHRENIA, Manic-depressive illness, psychosis, BRAIN, Research Articles, Trastorn bipolar, Radiological and Ultrasound Technology, 05 social sciences, Subiculum, ATLAS, Magnetic Resonance Imaging, Liti, 3. Good health, medicine.anatomical_structure, Neurology, Schizophrenia, lithium, Anatomy, Hippocampus (Brain), Research Article, MRI, INTERNEURONS, Psychosis, Hipocamp (Cervell), Neuroimaging, Amygdala, 050105 experimental psychology, CELL-PROLIFERATION, 03 medical and health sciences, Genetics, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Bipolar disorder, large‐scale, LITHIUM-TREATED PATIENTS, business.industry, Dentate gyrus, medicine.disease, nervous system, DENTATE GYRUS, large-scale, bipolar disorder subtype, Neurology (clinical), business, Neuroscience, 030217 neurology & neurosurgery
Abstract: The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder.In the largest study of hippocampal subfields in bipolar disorder to date, from 23 sites worldwide, we report widespread reductions in nine of 12 subfields.The lack of differences between lithium users and healthy controls supports a possible protective role of lithium in bipolar disorder.
Published: 2022

45. Brain Structure in Acutely Underweight and Partially Weight-Restored Individuals With Anorexia Nervosa : A Coordinated Analysis by the ENIGMA Eating Disorders Working Group

Author: Esther Walton, Fabio Bernardoni, Victoria-Luise Batury, Klaas Bahnsen, Sara Larivière, Giovanni Abbate-Daga, Susana Andres-Perpiña, Lasse Bang, Amanda Bischoff-Grethe, Samantha J. Brooks, Iain C. Campbell, Giammarco Cascino, Josefina Castro-Fornieles, Enrico Collantoni, Federico D’Agata, Brigitte Dahmen, Unna N. Danner, Angela Favaro, Jamie D. Feusner, Guido K.W. Frank, Hans-Christoph Friederich, John L. Graner, Beate Herpertz-Dahlmann, Andreas Hess, Stefanie Horndasch, Allan S. Kaplan, Lisa-Katrin Kaufmann, Walter H. Kaye, Sahib S. Khalsa, Kevin S. LaBar, Luca Lavagnino, Luisa Lazaro, Renzo Manara, Amy E. Miles, Gabriella F. Milos, Alessio Maria Monteleone, Palmiero Monteleone, Benson Mwangi, Owen O’Daly, Jose Pariente, Julie Roesch, Ulrike H. Schmidt, Jochen Seitz, Megan E. Shott, Joe J. Simon, Paul A.M. Smeets, Christian K. Tamnes, Elena Tenconi, Sophia I. Thomopoulos, Annemarie A. van Elburg, Aristotle N. Voineskos, Georg G. von Polier, Christina E. Wierenga, Nancy L. Zucker, Neda Jahanshad, Joseph A. King, Paul M. Thompson, Laura A. Berner, Stefan Ehrlich, Walton, Esther, Bernardoni, Fabio, Batury, Victoria-Luise, Bahnsen, Klaa, Larivière, Sara, Abbate-Daga, Giovanni, Andres-Perpiña, Susana, Bang, Lasse, Bischoff-Grethe, Amanda, Brooks, Samantha J, Campbell, Iain C, Cascino, Giammarco, Castro-Fornieles, Josefina, Collantoni, Enrico, D'Agata, Federico, Dahmen, Brigitte, Danner, Unna N, Favaro, Angela, Feusner, Jamie D, Frank, Guido K W, Friederich, Hans-Christoph, Graner, John L, Herpertz-Dahlmann, Beate, Hess, Andrea, Horndasch, Stefanie, Kaplan, Allan S, Kaufmann, Lisa-Katrin, Kaye, Walter H, Khalsa, Sahib S, Labar, Kevin S, Lavagnino, Luca, Lazaro, Luisa, Manara, Renzo, Miles, Amy E, Milos, Gabriella F, Monteleone, Alessio Maria, Monteleone, Palmiero, Mwangi, Benson, O'Daly, Owen, Pariente, Jose, Roesch, Julie, Schmidt, Ulrike H, Seitz, Jochen, Shott, Megan E, Simon, Joe J, Smeets, Paul A M, Tamnes, Christian K, Tenconi, Elena, Thomopoulos, Sophia I, van Elburg, Annemarie A, Voineskos, Aristotle N, von Polier, Georg G, Wierenga, Christina E, Zucker, Nancy L, Jahanshad, Neda, King, Joseph A, Thompson, Paul M, Berner, Laura A, and Ehrlich, Stefan
Subjects: Anorexia Nervosa, Surface area, Cortical thickness, BMI, Thinness, MRI, Subcortical volume, Humans, Multicenter Studies as Topic, Prospective Studies, ddc:610, SDG 2 - Zero Hunger, Sensory Science and Eating Behaviour, Biological Psychiatry, VLAG, Cortical thickne, Cerebral Cortex, Thinne, Brain, Magnetic Resonance Imaging, Prospective Studie, Sensoriek en eetgedrag, Female, Human
Abstract: Background: The pattern of structural brain abnormalities in anorexia nervosa (AN) is still not well understood. While several studies report substantial deficits in gray matter volume and cortical thickness in acutely underweight patients, others find no differences, or even increases in patients compared with healthy control subjects. Recent weight regain before scanning may explain some of this heterogeneity. To clarify the extent, magnitude, and dependencies of gray matter changes in AN, we conducted a prospective, coordinated meta-analysis of multicenter neuroimaging data. Methods: We analyzed T1-weighted structural magnetic resonance imaging scans assessed with standardized methods from 685 female patients with AN and 963 female healthy control subjects across 22 sites worldwide. In addition to a case-control comparison, we conducted a 3-group analysis comparing healthy control subjects with acutely underweight AN patients (n = 466) and partially weight-restored patients in treatment (n = 251). Results: In AN, reductions in cortical thickness, subcortical volumes, and, to a lesser extent, cortical surface area were sizable (Cohen's d up to 0.95), widespread, and colocalized with hub regions. Highlighting the effects of undernutrition, these deficits were associated with lower body mass index in the AN sample and were less pronounced in partially weight-restored patients. Conclusions: The effect sizes observed for cortical thickness deficits in acute AN are the largest of any psychiatric disorder investigated in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium to date. These results confirm the importance of considering weight loss and renutrition in biomedical research on AN and underscore the importance of treatment engagement to prevent potentially long-lasting structural brain changes in this population.
Published: 2022

46. Cover Image

Author: Paul M. Thompson, Neda Jahanshad, Lianne Schmaal, Jessica A. Turner, Anderson M. Winkler, Sophia I. Thomopoulos, Gary F. Egan, and Peter Kochunov
Subjects: Neurology, Radiological and Ultrasound Technology, Radiology, Nuclear Medicine and imaging, Neurology (clinical), Anatomy
Published: 2021

47. 3D convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI

Author: Philip A. Cook, Daniel Weintraub, Conor Owens-Walton, Greg Ver Steeg, José Luis Ambite, Dimitris Stripelis, Sophia I. Thomopoulos, Corey T. McMillan, Nikhil J. Dhinagar, and Paul M. Thompson
Subjects: Parkinson's disease, business.industry, Computer science, Deep learning, Inference, Pattern recognition, Overfitting, medicine.disease, Convolutional neural network, Random forest, Neuroimaging, Test set, medicine, Artificial intelligence, business
Abstract: Parkinson9s disease (PD) and Alzheimer9s disease (AD) are progressive neurodegenerative disorders that affect millions of people worldwide. In this work, we propose a deep learning approach to classify these diseases based on 3D T1-weighted brain MRI. We analyzed several datasets including the Parkinson9s Progression Markers Initiative (PPMI), an independent dataset from the University of Pennsylvania School of Medicine (UPenn), the Alzheimer9s Disease Neuroimaging Initiative (ADNI), and the Open Access Series of Imaging Studies (OASIS) dataset. PPMI and ADNI were partitioned to train (70%), validate (20%), and test (10%) a 3D convolutional neural network (CNN) for PD and AD classification. The UPenn and OASIS datasets were used as independent test sets to evaluate the model performance during inference. We also implemented a random forest classifier as a baseline model by extracting key radiomics features from the same T1-weighted MRI scans. The proposed 3D CNN model was trained from scratch for the classification tasks. For AD classification, the 3D CNN model achieved an ROC-AUC of 0.878 on the ADNI test set and an average ROC-AUC of 0.789 on the OASIS dataset. For PD classification, the proposed 3D CNN model achieved an ROC-AUC of 0.667 on the PPMI test set and an average ROC-AUC of 0.743 on the UPenn dataset. We also found that model performance was largely maintained when using only 25% of the training dataset. The 3D CNN outperformed the random forest classifier for both the PD and AD tasks. The 3D CNN also generalized better on unseen MRI data from different imaging centers. Our results show that the proposed 3D CNN model was less prone to overfitting for AD than for PD classification. This approach shows promise for screening of PD and AD patients using only T1-weighted brain MRI, which is relatively widely available. This model with additional validation could also be used to help differentiate between challenging cases of AD and PD when they present with similarly subtle motor and non-motor symptoms.
Published: 2021

48. Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches

Author: Artemis Zavaliangos-Petropulu, Hong Zheng, Neda Jahanshad, Julio E. Villalon-Reina, Talia M. Nir, Piyush Maiti, Sophia I. Thomopoulos, Paul M. Thompson, and Elnaz Nourollahimoghadam
Subjects: medicine.medical_specialty, medicine.diagnostic_test, business.industry, Clinical Dementia Rating, Magnetic resonance imaging, Normal aging, medicine.disease, White matter, medicine.anatomical_structure, Physical medicine and rehabilitation, Neuroimaging, medicine, Dementia, Alzheimer's disease, business, Diffusion MRI
Abstract: Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to microstructural changes in the brain that occur with normal aging and Alzheimer’s disease (AD). There is much interest in which dMRI measures are most strongly correlated with clinical measures of AD severity, such as the clinical dementia rating (CDR), and biological processes that may be disrupted in AD, such as brain amyloid load measured using PET. Of these processes, some can be targeted using novel drugs. Since 2016, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has collected dMRI data from three scanner manufacturers across 58 sites using 7 different protocols that vary in angular resolution, scan duration, and in the number and distribution of diffusion-weighted gradients. Here, we assessed dMRI data from 730 of those individuals (447 cognitively normal controls, 214 with mild cognitive impairment, 69 with dementia; age: 74.1±7.9 years; 381 female/349 male). To harmonize data from different protocols, we applied ComBat, ComBat-GAM, and CovBat to dMRI metrics from 28 white matter regions of interest. We ranked all dMRI metrics in order of the strength of clinically relevant associations, and assessed how this depended on the harmonization methods employed. dMRI metrics were associated with age and clinical impairment, but also with amyloid positivity. All harmonization methods gave comparable results while enabling data integration across multiple scanners and protocols.
Published: 2021

49. Multilevel neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

Author: Lianne Schmaal, Paul M. Thompson, Sara Larivière, van Rooij D, Kebets, van Erp Tgm, Jan K. Buitelaar, Ching Crk, Ole A. Andreassen, Barbara Franke, Jessica A. Turner, Casey Paquola, Dick J. Veltman, Alain Dagher, Bo-yong Park, Sofie L. Valk, Alan C. Evans, Martine Hoogman, Matthias Kirschner, Hettwer, Dan J. Stein, Sophia I. Thomopoulos, van den Heuvel O, and Boris C. Bernhardt
Subjects: medicine.medical_specialty, medicine.disease, Paralimbic cortex, chemistry.chemical_compound, medicine.anatomical_structure, chemistry, Cytoarchitecture, Schizophrenia, Autism spectrum disorder, Dopamine, medicine, Connectome, Bipolar disorder, Psychiatry, Psychology, Neurotransmitter, medicine.drug
Abstract: It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we assessed i) shared dimensions of alterations in cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression, obsessive-compulsive disorder, bipolar disorder, schizophrenia) and ii) carried out a multiscale neural contextualization, by cross-referencing shared anomalies against cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we computed a shared disease dimension on cortical morphology using principal component analysis that described a sensory-fugal pattern with paralimbic regions showing the most consistent abnormalities across conditions. The shared disease dimension was closely related to cortical gradients of microstructure and intrinsic connectivity, as well as neurotransmitter systems, specifically serotonin and dopamine. Our findings embed the shared effects of major psychiatric conditions on brain structure in multiple scales of brain organization and may provide novel insights into neural mechanisms into transdiagnostic vulnerability.
Published: 2021

50. FiberNeat: Unsupervised White Matter Tract Filtering

Author: Bramsh Qamar Chandio, Leila Nabulsi, Conor Owens-Walton, Eleftherios Garyfallidis, Tamoghna Chattopadhyay, Sophia I. Thomopoulos, Paul M. Thompson, and Julio E Villalon Reina
Subjects: DBSCAN, Computer science, business.industry, Nonlinear dimensionality reduction, Pattern recognition, White matter, Set (abstract data type), ComputingMethodologies_PATTERNRECOGNITION, medicine.anatomical_structure, Outlier, medicine, Streamlines, streaklines, and pathlines, Artificial intelligence, business, Cluster analysis, Diffusion MRI
Abstract: Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines. Such tractograms can have false positive and anatomically implausible streamlines. To obtain anatomically relevant streamlines and tracts, supervised and unsupervised methods can be used for tractogram clustering and tract extraction. Here we propose FiberNeat, an unsupervised white matter tract filtering method. FiberNeat takes an input set of streamlines that could either be unlabeled clusters or labeled tracts. Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, t-SNE and UMAP, to find spurious and outlier streamlines. In addition, outlier streamline clusters are detected using DBSCAN and then removed from the data in streamline space. We performed quantitative comparisons with expertly delineated tracts. We ran FiberNeat on 131 participants’ data from the ADNI3 dataset. We show that applying FiberNeat as a filtering step after bundle segmentation improves the quality of extracted tracts and helps improve tractometry.
Published: 2021

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