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2,488 results on '"Walter, Henrik"'

1. Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts

Author

Hemforth, Lisa, Couvy-Duchesne, Baptiste, De Matos, Kevin, Brianceau, Camille, Joulot, Matthieu, Banaschewski, Tobias, Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Papadopoulos, Dimitri, Lemaitre, Herve, Paus, Tomas, Poustka, Luise, Hohmann, Sarah, Holz, Nathalie, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Büchel, Christian, Poline, JB, Itterman, Bernd, Frouin, Vincent, Martin, Alexandre, group, IMAGEN study, Cury, Claire, and Colliot, Olivier

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM/QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the conv5-FC3 network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:016

Published
2024
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3. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Mental health, Neurological, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

4. Predictors of adherence to exercise interventions in people with schizophrenia

Author

Schwaiger, Rebecca, Maurus, Isabel, Lembeck, Moritz, Papazova, Irina, Greska, David, Muenz, Susanne, Sykorova, Eliska, Thieme, Cristina E., Vogel, Bob O., Mohnke, Sebastian, Huppertz, Charlotte, Roeh, Astrid, Keller-Varady, Katriona, Malchow, Berend, Walter, Henrik, Wolfarth, Bernd, Wölwer, Wolfgang, Henkel, Karsten, Hirjak, Dusan, Schmitt, Andrea, Hasan, Alkomiet, Meyer-Lindenberg, Andreas, Falkai, Peter, and Roell, Lukas

Published
2024
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5. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

Author

Huggins, Ashley A, Baird, C Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E, Jahanshad, Neda, Thomopoulos, Sophia I, Veltman, Dick J, Frijling, Jessie L, Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia BJ, Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J, Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L, Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S, Ressler, Kerry J, Jovanovic, Tanja, van Rooij, Sanne JH, Fani, Negar, Hudson, Anna R, Mueller, Sven C, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K, Schmahl, Christian, Herzog, Julia I, Říha, Pavel, Rektor, Ivan, Lebois, Lauren AM, Kaufman, Milissa L, Olson, Elizabeth A, Baker, Justin T, Rosso, Isabelle M, King, Anthony P, Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D, Sponheim, Scott R, Disner, Seth G, Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A, Forster, Gina L, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent A, Fercho, Kelene A, Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S, O’Leary, Erin N, Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J, Grupe, Daniel W, Nitschke, Jack B, Davidson, Richard J, Larson, Christine L, deRoon-Cassini, Terri A, Tomas, Carissa W, Fitzgerald, Jacklynn M, Blackford, Jennifer Urbano, Olatunji, Bunmi O, Kremen, William S, Lyons, Michael J, Franz, Carol E, Gordon, Evan M, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Levy, Ifat, and Harpaz-Rotem, Ilan

Subjects
Clinical and Health Psychology, Psychology, Post-Traumatic Stress Disorder (PTSD), Mental Illness, Behavioral and Social Science, Anxiety Disorders, Mental Health, Brain Disorders, Mind and Body, Neurosciences, Mental health, Humans, Stress Disorders, Post-Traumatic, Cerebellum, Female, Male, Adult, Magnetic Resonance Imaging, Middle Aged, White Matter, Gray Matter, Organ Size, Deep Learning, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR

Published
2024

6. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.

Author

Ge, Ruiyang, Yu, Yuetong, Qi, Yi, Fan, Yu-Nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila, New, Faye, Boomsma, Dorret, Brodaty, Henry, Brouwer, Rachel, Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline, Erk, Susanne, Fisher, Simon, Franke, Barbara, Glahn, David, Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke, Schumann, Gunter, Tamnes, Christian, Walter, Henrik, Wierenga, Lara, Jahanshad, Neda, Thompson, Paul, and Frangou, Sophia

Subjects
Humans, Male, Female, Longevity, Benchmarking, Brain, Models, Statistical, Algorithms
Abstract

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Published
2024

7. Light Cannabis Use and the Adolescent Brain: An 8-years Longitudinal Assessment of Mental Health, Cognition, and Reward Processing

Author

Macedo, Inês, Paiva, Tiago O., Pasion, Rita, Daedelow, Laura, Heinz, Andreas, Magalhães, Ana, Banaschewski, Tobias, Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Holz, Nathalie, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, and Barbosa, Fernando

Published
2024
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8. Regional patterns of human cortex development correlate with underlying neurobiology

Author

Lotter, Leon D., Saberi, Amin, Hansen, Justine Y., Misic, Bratislav, Paquola, Casey, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère, Marie-Laure, Artiges, Eric, Papadopoulos Orfanos, Dimitri, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Nees, Frauke, Banaschewski, Tobias, Eickhoff, Simon B., and Dukart, Juergen

Published
2024
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10. Investigating grey matter volumetric trajectories through the lifespan at the individual level

Author

Shi, Runye, Xiang, Shitong, Jia, Tianye, Robbins, Trevor W., Kang, Jujiao, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Lin, Xiaolei, Sahakian, Barbara J., and Feng, Jianfeng

Published
2024
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11. The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Author

Chen, Shi-Dong, You, Jia, Zhang, Wei, Wu, Bang-Sheng, Ge, Yi-Jun, Xiang, Shi-Tong, Du, Jing, Kuo, Kevin, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Lemaitre, Herve, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Baeuchl, Christian, Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Feng, Jian-Feng, Dong, Qiang, Cheng, Wei, and Yu, Jin-Tai

Published
2024
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13. Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods

Author

Yang, Jin, Huggins, Ashley A., Sun, Delin, Baird, C. Lexi, Haswell, Courtney C., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B. J., Nawijn, Laura, Veltman, Dick J., Suarez-Jimenez, Benjamin, Zhu, Xi, Neria, Yuval, Hudson, Anna R., Mueller, Sven C., Baker, Justin T., Lebois, Lauren A. M., Kaufman, Milissa L., Qi, Rongfeng, Lu, Guang Ming, Říha, Pavel, Rektor, Ivan, Dennis, Emily L., Ching, Christopher R. K., Thomopoulos, Sophia I., Salminen, Lauren E., Jahanshad, Neda, Thompson, Paul M., Stein, Dan J., Koopowitz, Sheri M., Ipser, Jonathan C., Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Wang, Li, Zhu, Ye, Li, Gen, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Liberzon, Israel, King, Anthony, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Blackford, Jennifer U., Olatunji, Bunmi O., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Morey, Rajendra A., and Sotiras, Aristeidis

Published
2024
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14. Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

Author

Eitel, Fabian, Schulz, Marc-André, Seiler, Moritz, Walter, Henrik, and Ritter, Kerstin

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Neurons and Cognition
Abstract

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables.

Published
2023
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16. Whole-brain gray matter maturation trajectories associated with autistic traits from adolescence to early adulthood

Author

Gros, Guillaume, Miranda Marcos, Ruben, Latrille, Anthony, Saitovitch, Ana, Gollier-Briant, Fanny, Fossati, Philippe, Schmidt, Liane, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Papadopoulos Orfanos, Dimitri, Poustka, Luise, Hohmann, Sarah, Holz, Nathalie, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Lemaitre, Hervé, and Vulser, Hélène

Published
2024
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17. Differential associations of adolescent versus young adult cannabis initiation with longitudinal brain change and behavior

Author

Albaugh, Matthew D., Owens, Max M., Juliano, Anthony, Ottino-Gonzalez, Jonatan, Cupertino, Renata, Cao, Zhipeng, Mackey, Scott, Lepage, Claude, Rioux, Pierre, Evans, Alan, Banaschewski, Tobias, Bokde, Arun L. W., Conrod, Patricia, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Potter, Alexandra, and Garavan, Hugh

Published
2023
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18. Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder

Author

Zech, Hilmar, Waltmann, Maria, Lee, Ying, Reichert, Markus, Bedder, Rachel L., Rutledge, Robb B., Deeken, Friederike, Wenzel, Julia, Wedemeyer, Friederike, Aguilera, Alvaro, Aslan, Acelya, Bach, Patrick, Bahr, Nadja S., Ebrahimi, Claudia, Fischbach, Pascale C., Ganz, Marvin, Garbusow, Maria, Großkopf, Charlotte M., Heigert, Marie, Hentschel, Angela, Belanger, Matthew, Karl, Damian, Pelz, Patricia, Pinger, Mathieu, Riemerschmid, Carlotta, Rosenthal, Annika, Steffen, Johannes, Strehle, Jens, Weiss, Franziska, Wieder, Gesine, Wieland, Alfred, Zaiser, Judith, Zimmermann, Sina, Liu, Shuyan, Goschke, Thomas, Walter, Henrik, Tost, Heike, Lenz, Bernd, Andoh, Jamila, Ebner-Priemer, Ulrich, Rapp, Michael A., Heinz, Andreas, Dolan, Ray, Smolka, Michael N., and Deserno, Lorenz

Published
2023
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19. Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits

Author

Xue, Kaizhong, Gao, Bo, Chen, Feng, Wang, Meiyun, Cheng, Jingliang, Zhang, Bing, Zhu, Wenzhen, Qiu, Shijun, Geng, Zuojun, Zhang, Xiaochu, Cui, Guangbin, Yu, Yongqiang, Zhang, Quan, Liao, Weihua, Zhang, Hui, Xu, Xiaojun, Han, Tong, Qin, Wen, Liu, Feng, Liang, Meng, Guo, Lining, Xu, Qiang, Xu, Jiayuan, Fu, Jilian, Zhang, Peng, Li, Wei, Shi, Dapeng, Wang, Caihong, Lui, Su, Yan, Zhihan, Zhang, Jing, Li, Jiance, Wang, Dawei, Xian, Junfang, Xu, Kai, Zuo, Xi-Nian, Zhang, Longjiang, Ye, Zhaoxiang, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Lemaitre, Herve, Poustka, Luise, Hohmann, Sarah, Holz, Nathalie, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Shen, Wen, Miao, Yanwei, and Yu, Chunshui

Published
2023
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20. A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Author

Sun, Delin, Rakesh, Gopalkumar, Haswell, Courtney C, Logue, Mark, Baird, C Lexi, O'Leary, Erin N, Cotton, Andrew S, Xie, Hong, Tamburrino, Marijo, Chen, Tian, Dennis, Emily L, Jahanshad, Neda, Salminen, Lauren E, Thomopoulos, Sophia I, Rashid, Faisal, Ching, Christopher RK, Koch, Saskia BJ, Frijling, Jessie L, Nawijn, Laura, van Zuiden, Mirjam, Zhu, Xi, Suarez-Jimenez, Benjamin, Sierk, Anika, Walter, Henrik, Manthey, Antje, Stevens, Jennifer S, Fani, Negar, van Rooij, Sanne JH, Stein, Murray, Bomyea, Jessica, Koerte, Inga K, Choi, Kyle, van der Werff, Steven JA, Vermeiren, Robert RJM, Herzog, Julia, Lebois, Lauren AM, Baker, Justin T, Olson, Elizabeth A, Straube, Thomas, Korgaonkar, Mayuresh S, Andrew, Elpiniki, Zhu, Ye, Li, Gen, Ipser, Jonathan, Hudson, Anna R, Peverill, Matthew, Sambrook, Kelly, Gordon, Evan, Baugh, Lee, Forster, Gina, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent, Maron-Katz, Adi, du Plessis, Stefan, Disner, Seth G, Davenport, Nicholas, Grupe, Daniel W, Nitschke, Jack B, deRoon-Cassini, Terri A, Fitzgerald, Jacklynn M, Krystal, John H, Levy, Ifat, Olff, Miranda, Veltman, Dick J, Wang, Li, Neria, Yuval, De Bellis, Michael D, Jovanovic, Tanja, Daniels, Judith K, Shenton, Martha, van de Wee, Nic JA, Schmahl, Christian, Kaufman, Milissa L, Rosso, Isabelle M, Sponheim, Scott R, Hofmann, David Bernd, Bryant, Richard A, Fercho, Kelene A, Stein, Dan J, Mueller, Sven C, Hosseini, Bobak, Phan, K Luan, McLaughlin, Katie A, Davidson, Richard J, Larson, Christine L, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Gomaa, Hassaan, Etkin, Amit, Seedat, Soraya, Harpaz-Rotem, Ilan, Liberzon, Israel, van Erp, Theo GM, Quidé, Yann, Wang, Xin, Thompson, Paul M, and Morey, Rajendra A

Subjects
Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Case-Control Studies, Child, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Stress Disorders, Post-Traumatic, Young Adult, Data Harmonization, Scanner Effects, Site Effects, Cortical Thickness, ComBat, ComBat-GAM, Linear Mixed-Effects Model, General Additive Model, PTSD, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
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

21. Assessing general versus specific liability for externalizing problems in adolescence: Concurrent and prospective prediction of symptoms of conduct disorder, ADHD, and substance use.

Author

Perkins, Emily, Foell, Jens, Drislane, Laura, Brislin, Sarah, Frick, Paul, Yancey, James, Soto, Elia, Ganley, Colleen, Keel, Pamela, Sica, Claudio, Flor, Herta, Nees, Frauke, Banaschewski, Tobias, Bokde, Arun, Desrivières, Sylvane, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Papadopoulos Orfanos, Dimitri, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane, Smolka, Michael, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Patrick, Christopher, and Joyner, Keanan

Subjects
Adolescent, Alcoholism, Attention Deficit Disorder with Hyperactivity, Conduct Disorder, Female, Humans, Male, Prospective Studies, Substance-Related Disorders
Abstract

This study explored the generality versus specificity of two trait-liability factors for externalizing problems-disinhibition and callousness-in the concurrent and prospective prediction of symptoms of conduct disorder, attention-deficit/hyperactivity disorder (ADHD), and substance use (i.e., alcohol use disorder and history of illicit substance use). Disinhibition involves an impulsive, unrestrained cognitive-behavioral style; callousness entails a dispositional lack of social-emotional sensitivity. Participants were European adolescents from the multisite IMAGEN project who completed questionnaires and clinical interviews at ages 14 (N = 1,504, Mage = 14.41, 51.13% female) and 16 (N = 1,407, Mage = 16.46, 51.88% female). Disinhibition was related concurrently and prospectively to greater symptoms of conduct disorder, ADHD, and alcohol use disorder; higher scores on a general externalizing factor; and greater likelihood of having tried an illicit substance. Callousness was selectively related to greater conduct disorder symptoms. These findings indicate disinhibition confers broad liability for externalizing spectrum disorders, perhaps due to its affiliated deficits in executive function. In contrast, callousness appears to represent more specific liability for antagonistic (aggressive/exploitative) forms of externalizing, as exemplified by antisocial behavior. Results support the utility of developmental-ontogenetic and hierarchical-dimensional models of psychopathology and have important implications for early assessment of risk for externalizing problems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022

23. Distinct personality profiles associated with disease risk and diagnostic status in eating disorders

Author

Zhang, Zuo, Robinson, Lauren, Campbell, Iain, Irish, Madeleine, Bobou, Marina, Winterer, Jeanne, Zhang, Yuning, King, Sinead, Vaidya, Nilakshi, Broulidakis, M. John, van Noort, Betteke Maria, Stringaris, Argyris, Banaschewski, Tobias, Bokde, Arun L.W., Brühl, Rüdiger, Fröhner, Juliane H., Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Hohmann, Sarah, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Sinclair, Julia, Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Schmidt, Ulrike, and Desrivières, Sylvane

Published
2024
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25. A stable and replicable neural signature of lifespan adversity in the adult brain

Author

Holz, Nathalie E., Zabihi, Mariam, Kia, Seyed Mostafa, Monninger, Maximillian, Aggensteiner, Pascal-M., Siehl, Sebastian, Floris, Dorothea L., Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Meyer-Lindenberg, Andreas, Brandeis, Daniel, Buitelaar, Jan K., Nees, Frauke, Beckmann, Christian, Banaschewski, Tobias, and Marquand, Andre F.

Published
2023
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27. Autistic traits and alcohol use in adolescents within the general population

Author

Pijnenburg, Lisa J., Kaplun, Anais, de Haan, Lieuwe, Janecka, Magdalena, Smith, Lauren, Reichenberg, Abraham, Banaschewski, Tobias, Bokde, Arun L. W., Quinlan, Erin Burke, Desrivières, Sylvane, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, and Velthorst, Eva

Published
2023
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32. The relationship between negative life events and cortical structural connectivity in adolescents

Author

Sibilia, Francesca, Jost-Mousseau, Coline, Banaschewski, Tobias, Barker, Gareth J., Büchel, Christian, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, and Bokde, Arun L.W.

Published
2024
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35. Association between vmPFC gray matter volume and smoking initiation in adolescents

Author

Xiang, Shitong, Jia, Tianye, Xie, Chao, Cheng, Wei, Chaarani, Bader, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Büchel, Christian, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Gowland, Penny A., Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Garavan, Hugh, Schumann, Gunter, Sahakian, Barbara J., Robbins, Trevor W., and Feng, Jianfeng

Published
2023
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37. Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

Author

Gallo, Selene, El-Gazzar, Ahmed, Zhutovsky, Paul, Thomas, Rajat M., Javaheripour, Nooshin, Li, Meng, Bartova, Lucie, Bathula, Deepti, Dannlowski, Udo, Davey, Christopher, Frodl, Thomas, Gotlib, Ian, Grimm, Simone, Grotegerd, Dominik, Hahn, Tim, Hamilton, Paul J., Harrison, Ben J., Jansen, Andreas, Kircher, Tilo, Meyer, Bernhard, Nenadić, Igor, Olbrich, Sebastian, Paul, Elisabeth, Pezawas, Lukas, Sacchet, Matthew D., Sämann, Philipp, Wagner, Gerd, Walter, Henrik, Walter, Martin, and van Wingen, Guido

Published
2023
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38. Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability

Author

de Matos, Kevin, Cury, Claire, Chougar, Lydia, Strike, Lachlan T., Rolland, Thibault, Riche, Maximilien, Hemforth, Lisa, Martin, Alexandre, Banaschewski, Tobias, Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Papadopoulos Orfanos, Dimitri, Lemaitre, Herve, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Frouin, Vincent, Bach Cuadra, Meritxell, Colliot, Olivier, and Couvy-Duchesne, Baptiste

Published
2023
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39. A shared neural basis underlying psychiatric comorbidity

Author

Xie, Chao, Xiang, Shitong, Shen, Chun, Peng, Xuerui, Kang, Jujiao, Li, Yuzhu, Cheng, Wei, He, Shiqi, Banaschewski, Tobias, Barker, Gareth J., Bokde, Arun L. W., Bromberg, Uli, Büchel, Christian, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Ittermann, Bernd, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Sahakian, Barbara J., Robbins, Trevor W., Schumann, Gunter, Jia, Tianye, and Feng, Jianfeng

Published
2023
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41. Adolescents’ pain-related ontogeny shares a neural basis with adults’ chronic pain in basothalamo-cortical organization

Author

Heukamp, Nils Jannik, Banaschewski, Tobias, Bokde, Arun L.W., Desrivières, Sylvane, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Kandić, Mina, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Papadopoulos Orfanos, Dimitri, Lemaitre, Herve, Löffler, Martin, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H., Smolka, Michael N., Usai, Katrin, Vaidya, Nilakshi, Walter, Henrik, Whelan, Robert, Schumann, Gunter, Flor, Herta, and Nees, Frauke

Published
2024
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42. Intelligence, educational attainment, and brain structure in those at familial high‐risk for schizophrenia or bipolar disorder

Author

Zwarte, Sonja MC, Brouwer, Rachel M, Agartz, Ingrid, Alda, Martin, Alonso‐Lana, Silvia, Bearden, Carrie E, Bertolino, Alessandro, Bonvino, Aurora, Bramon, Elvira, Buimer, Elizabeth EL, Cahn, Wiepke, Canales‐Rodríguez, Erick J, Cannon, Dara M, Cannon, Tyrone D, Caseras, Xavier, Castro‐Fornieles, Josefina, Chen, Qiang, Chung, Yoonho, De la Serna, Elena, Bonnin, Caterina Mar, Demro, Caroline, Di Giorgio, Annabella, Doucet, Gaelle E, Eker, Mehmet Cagdas, Erk, Susanne, Fatjó‐Vilas, Mar, Fears, Scott C, Foley, Sonya F, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goghari, Vina M, Goikolea, Jose M, Goldman, Aaron L, Gonul, Ali Saffet, Gruber, Oliver, Hajek, Tomas, Hawkins, Emma L, Heinz, Andreas, Ongun, Ceren Hidiroglu, Hillegers, Manon HJ, Houenou, Josselin, Pol, Hilleke E Hulshoff, Hultman, Christina M, Ingvar, Martin, Johansson, Viktoria, Jönsson, Erik G, Kane, Fergus, Kempton, Matthew J, Koenis, Marinka MG, Kopecek, Miloslav, Krämer, Bernd, Lawrie, Stephen M, Lenroot, Rhoshel K, Marcelis, Machteld, Mattay, Venkata S, McDonald, Colm, Meyer‐Lindenberg, Andreas, Michielse, Stijn, Mitchell, Philip B, Moreno, Dolores, Murray, Robin M, Mwangi, Benson, Nabulsi, Leila, Newport, Jason, Olman, Cheryl A, Os, Jim, Overs, Bronwyn J, Ozerdem, Aysegul, Pergola, Giulio, Picchioni, Marco M, Piguet, Camille, Pomarol‐Clotet, Edith, Radua, Joaquim, Ramsay, Ian S, Richter, Anja, Roberts, Gloria, Salvador, Raymond, Aydogan, Aybala Saricicek, Sarró, Salvador, Schofield, Peter R, Simsek, Esma M, Simsek, Fatma, Soares, Jair C, Sponheim, Scott R, Sugranyes, Gisela, Toulopoulou, Timothea, Tronchin, Giulia, Vieta, Eduard, Walter, Henrik, Weinberger, Daniel R, Whalley, Heather C, Wu, Mon‐Ju, Yalin, Nefize, Andreassen, Ole A, Ching, Christopher RK, Thomopoulos, Sophia I, Erp, Theo GM, Jahanshad, Neda, and Thompson, Paul M

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Bipolar Disorder, Clinical Research, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, 2.1 Biological and endogenous factors, Mental health, Cognitive Dysfunction, Educational Status, Family, Genetic Predisposition to Disease, Humans, Intelligence, Magnetic Resonance Imaging, Neuroimaging, bipolar disorder, education, intelligence, neuroimaging, relatives, schizophrenia, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
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 × 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.

Published
2022

43. Predicting alcohol dependence from multi‐site brain structural measures

Author

Hahn, Sage, Mackey, Scott, Cousijn, Janna, Foxe, John J, Heinz, Andreas, Hester, Robert, Hutchinson, Kent, Kiefer, Falk, Korucuoglu, Ozlem, Lett, Tristram, Li, Chiang‐Shan R, London, Edythe, Lorenzetti, Valentina, Maartje, Luijten, Momenan, Reza, Orr, Catherine, Paulus, Martin, Schmaal, Lianne, Sinha, Rajita, Sjoerds, Zsuzsika, Stein, Dan J, Stein, Elliot, Holst, Ruth J, Veltman, Dick, Walter, Henrik, Wiers, Reinout W, Yucel, Murat, Thompson, Paul M, Conrod, Patricia, Allgaier, Nicholas, and Garavan, Hugh

Subjects
Biological Psychology, Psychology, Brain Disorders, Neurosciences, Substance Misuse, Alcoholism, Alcohol Use and Health, Neurological, Good Health and Well Being, Alcoholism, Cerebral Cortex, Humans, Machine Learning, Magnetic Resonance Imaging, Multicenter Studies as Topic, Neuroimaging, Putamen, Reproducibility of Results, addiction, alcohol dependence, genetic algorithm, machine learning, multi-site, prediction, structural MRI, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.

Published
2022

44. What we learn about bipolar disorder from large‐scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group

Author

Ching, Christopher RK, Hibar, Derrek P, Gurholt, Tiril P, Nunes, Abraham, Thomopoulos, Sophia I, Abé, Christoph, Agartz, Ingrid, Brouwer, Rachel M, Cannon, Dara M, Zwarte, Sonja MC, Eyler, Lisa T, Favre, Pauline, Hajek, Tomas, Haukvik, Unn K, Houenou, Josselin, Landén, Mikael, Lett, Tristram A, McDonald, Colm, Nabulsi, Leila, Patel, Yash, Pauling, Melissa E, Paus, Tomas, Radua, Joaquim, Soeiro‐de‐Souza, Marcio G, Tronchin, Giulia, Haren, Neeltje EM, Vieta, Eduard, Walter, Henrik, Zeng, Ling‐Li, Alda, Martin, Almeida, Jorge, Alnæs, Dag, Alonso‐Lana, Silvia, Altimus, Cara, Bauer, Michael, Baune, Bernhard T, Bearden, Carrie E, Bellani, Marcella, Benedetti, Francesco, Berk, Michael, Bilderbeck, Amy C, Blumberg, Hilary P, Bøen, Erlend, Bollettini, Irene, Bonnin, Caterina Mar, Brambilla, Paolo, Canales‐Rodríguez, Erick J, Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Díaz‐Zuluaga, Ana M, Dima, Danai, Duchesnay, Édouard, Elvsåshagen, Torbjørn, Fears, Scott C, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goikolea, Jose M, Green, Melissa J, Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus CM, Henry, Chantal, Howells, Fleur M, Ives‐Deliperi, Victoria, Jansen, Andreas, Kircher, Tilo TJ, Knöchel, Christian, Kramer, Bernd, Lafer, Beny, López‐Jaramillo, Carlos, Machado‐Vieira, Rodrigo, MacIntosh, Bradley J, Melloni, Elisa MT, Mitchell, Philip B, Nenadic, Igor, Nery, Fabiano, Nugent, Allison C, Oertel, Viola, Ophoff, Roel A, Ota, Miho, Overs, Bronwyn J, Pham, Daniel L, Phillips, Mary L, Pineda‐Zapata, Julian A, Poletti, Sara, Polosan, Mircea, Pomarol‐Clotet, Edith, Pouchon, Arnaud, Quidé, Yann, Rive, Maria M, Roberts, Gloria, Ruhe, Henricus G, Salvador, Raymond, Sarró, Salvador, Satterthwaite, Theodore D, Schene, Aart H, and Sim, Kang

Subjects
Serious Mental Illness, Brain Disorders, Biomedical Imaging, Bipolar Disorder, Clinical Research, Behavioral and Social Science, Mental Health, Neurosciences, Mental health, Neurological, Good Health and Well Being, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Meta-Analysis as Topic, Multicenter Studies as Topic, Neuroimaging, bipolar disorder, cortical surface area, cortical thickness, ENIGMA, mega-analysis, meta-analysis, MRI, neuroimaging, psychiatry, volume, ENIGMA Bipolar Disorder Working Group, Cognitive Sciences, Experimental Psychology
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.

Published
2022

46. Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker.

Author

Qi, Shile, Schumann, Gunter, Bustillo, Juan, Turner, Jessica A, Jiang, Rongtao, Zhi, Dongmei, Fu, Zening, Mayer, Andrew R, Vergara, Victor M, Silva, Rogers F, Iraji, Armin, Chen, Jiayu, Damaraju, Eswar, Ma, Xiaohong, Yang, Xiao, Stevens, Michael, Mathalon, Daniel H, Ford, Judith M, Voyvodic, James, Mueller, Bryon A, Belger, Aysenil, Potkin, Steven G, Preda, Adrian, Zhuo, Chuanjun, Xu, Yong, Chu, Congying, Banaschewski, Tobias, Barker, Gareth J, Bokde, Arun LW, Quinlan, Erin Burke, Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H, Smolka, Michael N, Walter, Henrik, Whelan, Robert, Calhoun, Vince D, Sui, Jing, and IMAGEN Consortium

Subjects
IMAGEN Consortium, Humans, Magnetic Resonance Imaging, Reward, Attention Deficit Disorder with Hyperactivity, Depressive Disorder, Major, Adolescent, Adult, Young Adult, Biomarkers, ADHD, Attention-deficit/hyperactivity disorder, MDD, Major depressive disorders, Novelty seeking, Reward processing, Schizophrenia, Substance use, Prevention, Serious Mental Illness, Pediatric, Mental Health, Neurosciences, Brain Disorders, Substance Misuse, Behavioral and Social Science, Depression, Alcoholism, Alcohol Use and Health, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Mental health, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundDysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues.MethodsA subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n ≈1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels.ResultsMultimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%.ConclusionsAdolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS-associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging biomarker to predict disease risks or severity.

Published
2021

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

Author

Han, Laura KM, Dinga, Richard, Hahn, Tim, Ching, Christopher RK, Eyler, Lisa T, Aftanas, Lyubomir, Aghajani, Moji, Aleman, André, Baune, Bernhard T, Berger, Klaus, Brak, Ivan, Filho, Geraldo Busatto, Carballedo, Angela, Connolly, Colm G, Couvy-Duchesne, Baptiste, Cullen, Kathryn R, Dannlowski, Udo, Davey, Christopher G, Dima, Danai, Duran, Fabio LS, Enneking, Verena, Filimonova, Elena, Frenzel, Stefan, Frodl, Thomas, Fu, Cynthia HY, Godlewska, Beata R, Gotlib, Ian H, Grabe, Hans J, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Hall, Geoffrey B, Harrison, Ben J, Hatton, Sean N, Hermesdorf, Marco, Hickie, Ian B, Ho, Tiffany C, Hosten, Norbert, Jansen, Andreas, Kähler, Claas, Kircher, Tilo, Klimes-Dougan, Bonnie, Krämer, Bernd, Krug, Axel, Lagopoulos, Jim, Leenings, Ramona, MacMaster, Frank P, MacQueen, Glenda, McIntosh, Andrew, McLellan, Quinn, McMahon, Katie L, Medland, Sarah E, Mueller, Bryon A, Mwangi, Benson, Osipov, Evgeny, Portella, Maria J, Pozzi, Elena, Reneman, Liesbeth, Repple, Jonathan, Rosa, Pedro GP, Sacchet, Matthew D, Sämann, Philipp G, Schnell, Knut, Schrantee, Anouk, Simulionyte, Egle, Soares, Jair C, Sommer, Jens, Stein, Dan J, Steinsträter, Olaf, Strike, Lachlan T, Thomopoulos, Sophia I, van Tol, Marie-José, Veer, Ilya M, Vermeiren, Robert RJM, Walter, Henrik, van der Wee, Nic JA, van der Werff, Steven JA, Whalley, Heather, Winter, Nils R, Wittfeld, Katharina, Wright, Margaret J, Wu, Mon-Ju, Völzke, Henry, Yang, Tony T, Zannias, Vasileios, de Zubicaray, Greig I, Zunta-Soares, Giovana B, Abé, Christoph, Alda, Martin, Andreassen, Ole A, Bøen, Erlend, Bonnin, Caterina M, Canales-Rodriguez, Erick J, Cannon, Dara, Caseras, Xavier, Chaim-Avancini, Tiffany M, Elvsåshagen, Torbjørn, Favre, Pauline, Foley, Sonya F, and Fullerton, Janice M

Subjects
Depression, Aging, Brain Disorders, Biomedical Imaging, Serious Mental Illness, Mental Health, Major Depressive Disorder, Behavioral and Social Science, Clinical Research, Neurosciences, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Adolescent, Adult, Aged, Brain, Depressive Disorder, Major, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 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). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities 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 clinical value of these brain-PAD estimates.

Published
2021

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

Author

Wang, Xin, Xie, Hong, Chen, Tian, Cotton, Andrew S, Salminen, Lauren E, Logue, Mark W, Clarke-Rubright, Emily K, Wall, John, Dennis, Emily L, O’Leary, Brian M, Abdallah, Chadi G, Andrew, Elpiniki, Baugh, Lee A, Bomyea, Jessica, Bruce, Steven E, Bryant, Richard, Choi, Kyle, Daniels, Judith K, Davenport, Nicholas D, Davidson, Richard J, DeBellis, Michael, deRoon-Cassini, Terri, Disner, Seth G, Fani, Negar, Fercho, Kelene A, Fitzgerald, Jacklynn, Forster, Gina L, Frijling, Jessie L, Geuze, Elbert, Gomaa, Hassaan, Gordon, Evan M, Grupe, Dan, Harpaz-Rotem, Ilan, Haswell, Courtney C, Herzog, Julia I, Hofmann, David, Hollifield, Michael, Hosseini, Bobak, Hudson, Anna R, Ipser, Jonathan, Jahanshad, Neda, Jovanovic, Tanja, Kaufman, Milissa L, King, Anthony P, Koch, Saskia BJ, Koerte, Inga K, Korgaonkar, Mayuresh S, Krystal, John H, Larson, Christine, Lebois, Lauren AM, Levy, Ifat, Li, Gen, Magnotta, Vincent A, Manthey, Antje, May, Geoffrey, McLaughlin, Katie A, Mueller, Sven C, Nawijn, Laura, Nelson, Steven M, Neria, Yuval, Nitschke, Jack B, Olff, Miranda, Olson, Elizabeth A, Peverill, Matthew, Phan, K Luan, Rashid, Faisal M, Ressler, Kerry, Rosso, Isabelle M, Sambrook, Kelly, Schmahl, Christian, Shenton, Martha E, Sierk, Anika, Simons, Jeffrey S, Simons, Raluca M, Sponheim, Scott R, Stein, Murray B, Stein, Dan J, Stevens, Jennifer S, Straube, Thomas, Suarez-Jimenez, Benjamin, Tamburrino, Marijo, Thomopoulos, Sophia I, van der Wee, Nic JA, van der Werff, Steven JA, van Erp, Theo GM, van Rooij, Sanne JH, van Zuiden, Mirjam, Varkevisser, Tim, Veltman, Dick J, Vermeiren, Robert RJM, Walter, Henrik, Wang, Li, Zhu, Ye, Zhu, Xi, Thompson, Paul M, Morey, Rajendra A, and Liberzon, Israel

Subjects
Clinical Research, Behavioral and Social Science, Mental Health, Post-Traumatic Stress Disorder (PTSD), Neurosciences, Brain Disorders, Mental health, Cerebral Cortex, Genomics, Humans, Magnetic Resonance Imaging, Stress Disorders, Post-Traumatic, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
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

Published
2021

49. Altered white matter microstructural organization in posttraumatic stress disorder across 3047 adults: results from the PGC-ENIGMA PTSD consortium

Author

Dennis, Emily L, Disner, Seth G, Fani, Negar, Salminen, Lauren E, Logue, Mark, Clarke, Emily K, Haswell, Courtney C, Averill, Christopher L, Baugh, Lee A, Bomyea, Jessica, Bruce, Steven E, Cha, Jiook, Choi, Kyle, Davenport, Nicholas D, Densmore, Maria, du Plessis, Stefan, Forster, Gina L, Frijling, Jessie L, Gonenc, Atilla, Gruber, Staci, Grupe, Daniel W, Guenette, Jeffrey P, Hayes, Jasmeet, Hofmann, David, Ipser, Jonathan, Jovanovic, Tanja, Kelly, Sinead, Kennis, Mitzy, Kinzel, Philipp, Koch, Saskia BJ, Koerte, Inga, Koopowitz, Sheri, Korgaonkar, Mayuresh, Krystal, John, Lebois, Lauren AM, Li, Gen, Magnotta, Vincent A, Manthey, Antje, May, Geoff J, Menefee, Deleene S, Nawijn, Laura, Nelson, Steven M, Neufeld, Richard WJ, Nitschke, Jack B, O’Doherty, Daniel, Peverill, Matthew, Ressler, Kerry J, Roos, Annerine, Sheridan, Margaret A, Sierk, Anika, Simmons, Alan, Simons, Raluca M, Simons, Jeffrey S, Stevens, Jennifer, Suarez-Jimenez, Benjamin, Sullivan, Danielle R, Théberge, Jean, Tran, Jana K, van den Heuvel, Leigh, van der Werff, Steven JA, van Rooij, Sanne JH, van Zuiden, Mirjam, Velez, Carmen, Verfaellie, Mieke, Vermeiren, Robert RJM, Wade, Benjamin SC, Wager, Tor, Walter, Henrik, Winternitz, Sherry, Wolff, Jonathan, York, Gerald, Zhu, Ye, Zhu, Xi, Abdallah, Chadi G, Bryant, Richard, Daniels, Judith K, Davidson, Richard J, Fercho, Kelene A, Franz, Carol, Geuze, Elbert, Gordon, Evan M, Kaufman, Milissa L, Kremen, William S, Lagopoulos, Jim, Lanius, Ruth A, Lyons, Michael J, McCauley, Stephen R, McGlinchey, Regina, McLaughlin, Katie A, Milberg, William, Neria, Yuval, Olff, Miranda, Seedat, Soraya, Shenton, Martha, Sponheim, Scott R, Stein, Dan J, Stein, Murray B, Straube, Thomas, Tate, David F, and van der Wee, Nic JA

Subjects
Biological Psychology, Psychology, Neurosciences, Mental Health, Brain Disorders, Biomedical Imaging, Post-Traumatic Stress Disorder (PTSD), Clinical Research, Anxiety Disorders, Mental health, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Anisotropy, Brain, Diffusion Tensor Imaging, Female, Humans, Male, Middle Aged, Stress Disorders, Post-Traumatic, White Matter, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1426 individuals with PTSD and 1621 controls (2174 males/873 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen's d = -0.11, p = 0.0055). The tapetum connects the left and right hippocampus, for which structure and function have been consistently implicated in PTSD. Results were consistent even after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Published
2021

50. Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group

Author

Jia, Tianye, Chu, Congying, Liu, Yun, van Dongen, Jenny, Papastergios, Evangelos, Armstrong, Nicola J, Bastin, Mark E, Carrillo-Roa, Tania, den Braber, Anouk, Harris, Mathew, Jansen, Rick, Liu, Jingyu, Luciano, Michelle, Ori, Anil PS, Roiz Santiañez, Roberto, Ruggeri, Barbara, Sarkisyan, Daniil, Shin, Jean, Sungeun, Kim, Tordesillas Gutiérrez, Diana, van’t Ent, Dennis, Ames, David, Artiges, Eric, Bakalkin, Georgy, Banaschewski, Tobias, Bokde, Arun LW, Brodaty, Henry, Bromberg, Uli, Brouwer, Rachel, Büchel, Christian, Burke Quinlan, Erin, Cahn, Wiepke, de Zubicaray, Greig I, Ehrlich, Stefan, Ekström, Tomas J, Flor, Herta, Fröhner, Juliane H, Frouin, Vincent, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Hoare, Jacqueline, Ittermann, Bernd, Jahanshad, Neda, Jiang, Jiyang, Kwok, John B, Martin, Nicholas G, Martinot, Jean-Luc, Mather, Karen A, McMahon, Katie L, McRae, Allan F, Nees, Frauke, Papadopoulos Orfanos, Dimitri, Paus, Tomáš, Poustka, Luise, Sämann, Philipp G, Schofield, Peter R, Smolka, Michael N, Stein, Dan J, Strike, Lachlan T, Teeuw, Jalmar, Thalamuthu, Anbupalam, Trollor, Julian, Walter, Henrik, Wardlaw, Joanna M, Wen, Wei, Whelan, Robert, Apostolova, Liana G, Binder, Elisabeth B, Boomsma, Dorret I, Calhoun, Vince, Crespo-Facorro, Benedicto, Deary, Ian J, Hulshoff Pol, Hilleke, Ophoff, Roel A, Pausova, Zdenka, Sachdev, Perminder S, Saykin, Andrew, Wright, Margaret J, Thompson, Paul M, Schumann, Gunter, and Desrivières, Sylvane

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Mental Health, Neurosciences, Genetics, Human Genome, Diabetes, Neurodegenerative, 2.1 Biological and endogenous factors, Aetiology, Neurological, Mental health, CpG Islands, DNA Methylation, Epigenesis, Genetic, Epigenome, Genome-Wide Association Study, Humans, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)-three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

Published
2021

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