87 results on '"Paul M, Thompson"'
Search Results
2. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children
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Katherine E. Lawrence, Zvart Abaryan, Emily Laltoo, Leanna M. Hernandez, Michael J. Gandal, James T. McCracken, and Paul M. Thompson
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Neurology ,Radiological and Ultrasound Technology ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy - Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
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- 2022
3. Significant heterogeneity in structural asymmetry of the habenula in the human brain: A systematic review and meta‐analysis
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Yilamujiang Abuduaini, Yi Pu, Paul M. Thompson, and Xiang‐Zhen Kong
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Neurology ,Radiological and Ultrasound Technology ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy - Abstract
Understanding the evolutionarily conserved feature of functional laterality in the habenula has been attracting attention due to its potential role in human cognition and neuropsychiatric disorders. Deciphering the structure of the human habenula remains to be challenging, which resulted in inconsistent findings for brain disorders. Here, we present a large-scale meta-analysis of the left–right differences in the habenular volume in the human brain to provide a clearer picture of the habenular asymmetry. We searched PubMed, Web of Science, and Google Scholar for articles that reported volume data of the bilateral habenula in the human brain, and assessed the left–right differences. We also assessed the potential effects of several moderating variables including the mean age of the participants, magnetic field strengths of the scanners and different disorders by using meta-regression and subgroup analysis. In total 52 datasets (N = 1427) were identified and showed significant heterogeneity in the left–right differences and the unilateral volume per se. Moderator analyses suggested that such heterogeneity was mainly due to different MRI scanners and segmentation approaches used. While inversed asymmetry patterns were suggested in patients with depression (leftward) and schizophrenia (rightward), no significant disorder-related differences relative to healthy controls were found in either the left–right asymmetry or the unilateral volume. This study provides useful data for future studies of brain imaging and methodological developments related to precision habenula measurements, and also helps to further understand potential roles of the habenula in various disorders.
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- 2023
4. The additive impact of <scp>cardio‐metabolic</scp> disorders and psychiatric illnesses on accelerated brain aging
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Meghann C. Ryan, L. Elliot Hong, Kathryn S. Hatch, Si Gao, Shuo Chen, Krystl Haerian, Jingtao Wang, Eric L. Goldwaser, Xiaoming Du, Bhim M. Adhikari, Heather Bruce, Stephanie Hare, Mark D. Kvarta, Neda Jahanshad, Thomas E. Nichols, Paul M. Thompson, and Peter Kochunov
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Aging ,Depressive Disorder, Major ,Radiological and Ultrasound Technology ,Mental Disorders ,Brain ,Middle Aged ,Metabolic Diseases ,Neurology ,Hypertension ,Humans ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Aged - Abstract
Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio-metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning "BrainAge" index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD- (N = 964), SMI-/CMD+ (N = 3,765), SMI-/CMD- (N = 8,083). SMI (F = 40.47, p = 2.06 × 10
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- 2022
5. Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder
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Mara Parellada, Kerstin Konrad, Mark A. Bellgrove, Stefan Ehrlich, Thomas Frodl, Ruth O'Gorman Tuura, Evdokia Anagnostou, J. Antoni Ramos-Quiroga, David Coghill, Clyde Francks, Georgii Karkashadze, Damien A. Fair, Francisco X. Castellanos, Oscar Vilarroya, Eugenio H. Grevet, Yash Patel, Eileen Daly, Clodagh M. Murphy, Klaus-Peter Lesch, Kirsten O'Hearn, Claiton H.D. Bau, Jeffery N. Epstein, Daan van Rooij, Liesbeth Reneman, Eileen Oberwelland-Weiss, Timothy J. Silk, Yanli Zhang-James, Christine M. Freitag, Jane McGrath, Tomáš Paus, Jan Haavik, Louise Gallagher, Ting Li, Merel Postema, Celso Arango, Barbara Franke, Sara Calderoni, Jaap Oosterlaan, Andreas Reif, Paulo Mattos, Iva Ilioska, Leanne Tamm, Paul M. Thompson, Joost Janssen, Pieter J. Hoekstra, Neda Jahanshad, Jacqueline Fitzgerald, Odile A. van den Heuvel, Pedro G.P. Rosa, Tobias Banaschewski, Joseph A. King, Katya Rubia, Christine Deruelle, Marieke Klein, Stephen V. Faraone, Philip Shaw, Premika S.W. Boedhoe, Kerstin Jessica Plessen, Luisa Lázaro, Alessandra Retico, Jan K. Buitelaar, Paul Pauli, Guillaume Auzias, Joel T. Nigg, Christine Ecker, Annette Conzelmann, Jonna Kuntsi, Sarah Durston, Beatriz Luna, Silvia Brem, Marlene Behrmann, Jason P. Lerch, Susanne Walitza, Martine Hoogman, Filippo Muratori, Ilan Dinstein, Rosa Calvo, Mario Rodrigues Louzã, Geraldo F. Busatto, Daniel Brandeis, Institut de Neurosciences de la Timone (INT), and Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)
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Neurology ,Autism Spectrum Disorder ,[SDV]Life Sciences [q-bio] ,CHILDREN ,Review Article ,0302 clinical medicine ,Transtorno do espectro autista ,pathology [Brain] ,130 000 Cognitive Neurology & Memory ,GENETIC INFLUENCES ,Neurociències ,Multicenter Studies as Topic ,Review Articles ,Mapeamento encefálico ,ComputingMilieux_MISCELLANEOUS ,LIFE-SPAN ,neuroimaging ,Radiological and Ultrasound Technology ,Consórcios de saúde ,05 social sciences ,ENIGMA ,Brain ,220 Statistical Imaging Neuroscience ,Transtorno do déficit de atenção com hiperatividade ,Autism spectrum disorders ,Cerebral cortex ,Trastorns de l'espectre autista ,3. Good health ,IMAGING FINDINGS ,Escorça cerebral ,cortex ,Autism spectrum disorder ,Cortex ,Trastorns per dèficit d'atenció amb hiperactivitat en els adults ,Anatomy ,Psychology ,diagnostic imaging [Autism Spectrum Disorder] ,Clinical psychology ,medicine.medical_specialty ,DEFICIT HYPERACTIVITY DISORDER ,BRAIN ABNORMALITIES ,ADHD ,ASD ,subcortical volumes ,Brain Structure and Function ,Neuroimaging ,pathology [Autism Spectrum Disorder] ,behavioral disciplines and activities ,050105 experimental psychology ,03 medical and health sciences ,mental disorders ,medicine ,Attention deficit hyperactivity disorder ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,ddc:610 ,diagnostic imaging [Brain] ,METAANALYSIS ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,Neurosciences ,pathology [Attention Deficit Disorder with Hyperactivity] ,medicine.disease ,Mental health ,Subcortical volumes ,Attention Deficit Disorder with Hyperactivity ,Attention deficit disorder with hyperactivity in adults ,Autism ,diagnostic imaging [Attention Deficit Disorder with Hyperactivity] ,Neurology (clinical) ,Working group ,170 000 Motivational & Cognitive Control ,CORTICAL THICKNESS ,030217 neurology & neurosurgery - Abstract
Human brain mapping 43(1), 37-55 (2022). doi:10.1002/hbm.25029 special issue: "Special Issue: The ENIGMA Consortium: the first 10 years / Issue Edited by: P.M. Thompson, N. Jahanshad, L. Schmaal, J.A. Turner, A. Winkler, S.I. Thomopoulos, G.F. Egan, P. Kochunov", Published by Wiley-Liss, New York, NY
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- 2022
6. Cortical thickness across the lifespan
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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
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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)
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- 2022
7. Predicting alcohol dependence from multi-site brain structural measures
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Chiang-Shan R. Li, Sage Hahn, Robert Hester, Martin P. Paulus, Luijten Maartje, Valentina Lorenzetti, Catherine Orr, Kent Hutchinson, Nicholas Allgaier, Falk Kiefer, Henrik Walter, Patricia J. Conrod, Elliot A. Stein, Rajita Sinha, Scott Mackey, Dan J. Stein, Reinout W. Wiers, John J. Foxe, Edythe D. London, Murat Yücel, Tristram A. Lett, Dick J. Veltman, Andreas Heinz, Lianne Schmaal, Hugh Garavan, Janna Cousijn, Ruth J. van Holst, Ozlem Korucuoglu, Zsuzsika Sjoerds, Reza Momenan, Paul M. Thompson, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Ontwikkelingspsychologie (Psychologie, FMG), Adult Psychiatry, ANS - Brain Imaging, ANS - Compulsivity, Impulsivity & Attention, and APH - Mental Health
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Computer science ,alcohol dependence ,02 engineering and technology ,Alcohol use disorder ,Logistic regression ,Machine Learning ,Alcohol Use and Health ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,genetic algorithm ,Multicenter Studies as Topic ,Research Articles ,Cerebral Cortex ,Radiological and Ultrasound Technology ,05 social sciences ,Putamen ,Substance Abuse ,Experimental Psychology ,Magnetic Resonance Imaging ,Regression ,Alcoholism ,machine learning ,Neurology ,Neurological ,020201 artificial intelligence & image processing ,Cognitive Sciences ,addiction ,Anatomy ,Research Article ,Feature selection ,Neuroimaging ,multi‐site ,050105 experimental psychology ,Cross-validation ,03 medical and health sciences ,multi-site ,Genetic algorithm ,medicine ,Humans ,Generalizability theory ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,structural MRI ,Receiver operating characteristic ,business.industry ,Alcohol dependence ,Neurosciences ,Reproducibility of Results ,Pattern recognition ,prediction ,medicine.disease ,Brain Disorders ,Exploratory data analysis ,Test set ,Neurology (clinical) ,Artificial intelligence ,business ,170 000 Motivational & Cognitive Control ,Developmental Psychopathology ,030217 neurology & neurosurgery - 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., To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, we developed classifiers on data collected from multiple sites. Exploratory data analysis revealed that inclusion of case‐ and control‐only sites led to the inadvertent learning of site‐effects. Evolutionary‐based feature selection leveraging leave‐one‐site‐out cross‐validation, to combat unintentional learning, yielded a test‐set area under the receiver operating characteristic curve of 0.768.
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- 2022
8. Greater male than female variability in regional brain structure across the lifespan
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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
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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.
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- 2022
9. Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder
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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
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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
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10. What we learn about bipolar disorder from large-scale neuroimaging
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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.
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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.
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11. Mapping brain asymmetry in health and disease through the ENIGMA consortium
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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
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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.
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- 2022
12. Altered lateralization of the cingulum in deployment‐related traumatic brain injury: An <scp>ENIGMA</scp> military‐relevant brain injury study
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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
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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.
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- 2022
13. The Enhancing NeuroImaging Genetics through Meta‐Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping
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Gary F. Egan, Neda Jahanshad, Sophia I. Thomopoulos, Lianne Schmaal, Paul M. Thompson, Jessica A. Turner, Peter Kochunov, and Anderson M. Winkler
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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.
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- 2021
14. Sex is a defining feature of neuroimaging phenotypes in major brain disorders
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Sophia I. Thomopoulos, Meral A Tubi, Paul M. Thompson, Joanna Bright, Alyssa Wieand, and Lauren E. Salminen
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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.
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- 2021
15. Brain-wide versus genome-wide vulnerability biomarkers for severe mental illnesses
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Peter Kochunov, Yizhou Ma, Kathryn S. Hatch, Si Gao, Neda Jahanshad, Paul M. Thompson, Bhim M. Adhikari, Heather Bruce, Andrew Van der vaart, Eric L. Goldwaser, Aris Sotiras, Mark D. Kvarta, Tianzhou Ma, Shuo Chen, Thomas E. Nichols, and L. Elliot Hong
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Multifactorial Inheritance ,Depressive Disorder, Major ,Neurology ,Radiological and Ultrasound Technology ,Mental Disorders ,Humans ,Brain ,Radiology, Nuclear Medicine and imaging ,Genetic Predisposition to Disease ,Neurology (clinical) ,Anatomy ,Biomarkers ,Genome-Wide Association Study - Abstract
Severe mental illnesses (SMI), including major depressive (MDD), bipolar (BD), and schizophrenia spectrum (SSD) disorders have multifactorial risk factors and capturing their complex etiopathophysiology in an individual remains challenging. Regional vulnerability index (RVI) was used to measure individual's brain-wide similarity to the expected SMI patterns derived from meta-analytical studies. It is analogous to polygenic risk scores (PRS) that measure individual's similarity to genome-wide patterns in SMI. We hypothesized that RVI is an intermediary phenotype between genome and symptoms and is sensitive to both genetic and environmental risks for SMI. UK Biobank sample of N = 17,053/19,265 M/F (age = 64.8 ± 7.4 years) and an independent sample of SSD patients and controls (N = 115/111 M/F, age = 35.2 ± 13.4) were used to test this hypothesis. UKBB participants with MDD had significantly higher RVI-MDD (Cohen's d = 0.20, p = 1 × 10
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- 2022
16. Testing a convolutional neural network‐based hippocampal segmentation method in a stroke population
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Sook-Lei Liew, Paul M. Thompson, Neda Jahanshad, Artemis Zavaliangos-Petropulu, Meral A Tubi, Meredith N. Braskie, Alyssa H. Zhu, and Elizabeth Haddad
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Quality Control ,hippocampus ,Computer science ,Population ,Datasets as Topic ,convolutional neural network ,Neuroimaging ,Hippocampal formation ,Convolutional neural network ,050105 experimental psychology ,lesion ,Correlation ,03 medical and health sciences ,0302 clinical medicine ,Image Processing, Computer-Assisted ,medicine ,Humans ,Dementia ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,education ,image segmentation ,Stroke ,Research Articles ,education.field_of_study ,Radiological and Ultrasound Technology ,business.industry ,05 social sciences ,Pattern recognition ,Image segmentation ,medicine.disease ,Magnetic Resonance Imaging ,stroke ,Neurology ,Neural Networks, Computer ,Neurology (clinical) ,Artificial intelligence ,Anatomy ,business ,030217 neurology & neurosurgery ,Research Article ,MRI - Abstract
As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long‐term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas‐based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network‐based hippocampal segmentation method, does not rely solely on a single atlas‐based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well‐accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy., In this study, we compared three automated hippocampal segmentation methods in a large stroke population in terms of quality control and segmentation accuracy compared to manual segmentations. While all three methods yielded similar volumes, new convolutional neural network based segmentation method Hippodeep had the lowest method‐wise quality control fail rate, suggesting it may be the most robust to post‐stroke anatomical distortions.
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- 2020
17. Estrogen, brain structure, and cognition in postmenopausal women
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Lewis H. Kuller, Oscar L. Lopez, James T. Becker, Paul M. Thompson, H. Michael Gach, Owen Carmichael, William T. Longstreth, Christina P. Boyle, Brandalyn C. Riedel, Kirk I. Erickson, and Cyrus A. Raji
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medicine.drug_class ,medicine.medical_treatment ,Ovariectomy ,Estrogen receptor ,Physiology ,Affect (psychology) ,Hysterectomy ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,Cognition ,medicine ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Longitudinal Studies ,Research Articles ,Aged ,Aged, 80 and over ,Brain volume ,Radiological and Ultrasound Technology ,hormone therapy ,business.industry ,05 social sciences ,Estrogen Replacement Therapy ,Oophorectomy ,Brain ,Estrogens ,Alzheimer's disease ,medicine.disease ,Magnetic Resonance Imaging ,Menopause ,Postmenopause ,Neurology ,Estrogen ,Brain size ,Female ,Neurology (clinical) ,Hormone therapy ,Anatomy ,business ,030217 neurology & neurosurgery ,Research Article - Abstract
Declining estrogen levels before, during, and after menopause can affect memory and risk for Alzheimer's disease. Undesirable side effects of hormone variations emphasize a role for hormone therapy (HT) where possible benefits include a delay in the onset of dementia—yet findings are inconsistent. Effects of HT may be mediated by estrogen receptors found throughout the brain. Effects may also depend on lifestyle factors, timing of use, and genetic risk. We studied the impact of self‐reported HT use on brain volume in 562 elderly women (71–94 years) with mixed cognitive status while adjusting for aforementioned factors. Covariate‐adjusted voxelwise linear regression analyses using a model with 16 predictors showed HT use as positively associated with regional brain volumes, regardless of cognitive status. Examinations of other factors related to menopause, oophorectomy and hysterectomy status independently yielded positive effects on brain volume when added to our model. One interaction term, HTxBMI, out of several examined, revealed significant negative association with overall brain volume, suggesting a greater reduction in brain volume than BMI alone. Our main findings relating HT to regional brain volume were as hypothesized, but some exploratory analyses were not in line with existing hypotheses. Studies suggest lower levels of estrogen resulting from oophorectomy and hysterectomy affect brain volume negatively, and the addition of HT modifies the relation between BMI and brain volume positively. Effects of HT may depend on the age range assessed, motivating studies with a wider age range as well as a randomized design., Declining estrogen levels for women in all stages of menopause can affect memory and risk of Alzheimer's Disease (AD), emphasizing a role for hormone therapy (HT). Effects may differ due to several factors including lifestyle and cardiovascular risk, genetic makeup, and timing and duration of HT. We addressed this hypothesis using self‐reported HT in 562 elderly women with mixed cognitive status, covarying for aforementioned factors. Voxelwise regression showed HT use as correlated with higher brain volume regardless of cognitive status
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- 2020
18. Common and gender‐specific associations with cocaine use on gray matter volume: Data from the ENIGMA addiction working group
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Paul M. Thompson, Rachel A. Rabin, Elliot A. Stein, Chiang-shan Li, Janna Cousijn, Lianne Schmaal, Scott Mackey, Muhammad A. Parvaz, Rita Z. Goldstein, Nelly Alia-Klein, Hugh Garavan, Godfrey D. Pearlson, Rajita Sinha, Dick J. Veltman, Patricia J. Conrod, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and Ontwikkelingspsychologie (Psychologie, FMG)
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Adult ,Male ,hippocampus ,media_common.quotation_subject ,Hippocampus ,cocaine ,Context (language use) ,Craving ,Neuroimaging ,Hippocampal formation ,insula ,050105 experimental psychology ,Cocaine dependence ,Lingual gyrus ,03 medical and health sciences ,Cocaine-Related Disorders ,0302 clinical medicine ,lingual gyrus ,Medicine ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,gray matter volume ,Gray Matter ,Research Articles ,media_common ,Cerebral Cortex ,Sex Characteristics ,Radiological and Ultrasound Technology ,business.industry ,Addiction ,05 social sciences ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,Neurology ,gender differences ,Female ,Neurology (clinical) ,addiction ,Anatomy ,medicine.symptom ,business ,Insula ,030217 neurology & neurosurgery ,Clinical psychology ,Research Article - Abstract
Gray matter volume (GMV) in frontal cortical and limbic regions is susceptible to cocaine‐associated reductions in cocaine‐dependent individuals (CD) and is negatively associated with duration of cocaine use. Gender differences in CD individuals have been reported clinically and in the context of neural responses to cue‐induced craving and stress reactivity. The variability of GMV in select brain areas between men and women (e.g., limbic regions) underscores the importance of exploring interaction effects between gender and cocaine dependence on brain structure. Therefore, voxel‐based morphometry data derived from the ENIGMA Addiction Consortium were used to investigate potential gender differences in GMV in CD individuals compared to matched controls (CTL). T1‐weighted MRI scans and clinical data were pooled from seven sites yielding 420 gender‐ and age‐matched participants: CD men (CDM, n = 140); CD women (CDW, n = 70); control men (CTLM, n = 140); and control women (CTLW, n = 70). Differences in GMV were assessed using a 2 × 2 ANCOVA, and voxelwise whole‐brain linear regressions were conducted to explore relationships between GMV and duration of cocaine use. All analyses were corrected for age, total intracranial volume, and site. Diagnostic differences were predominantly found in frontal regions (CD, Given the variability of gray matter volume (GMV) in select brain areas between men and women, we used voxel‐based morphometry data derived from the ENIGMA Addiction Consortium to investigate potential gender differences in GMV in cocaine‐dependent (CD) individuals compared to matched controls (CTL). Interestingly, gender × diagnosis interactions in the left anterior insula and left lingual gyrus were documented, driven by differences in women (CD
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- 2020
19. Translating <scp>ENIGMA</scp> schizophrenia findings using the regional vulnerability index: Association with cognition, symptoms, and disease trajectory
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Neda Jahanshad, Yimin Cui, Mark D. Kvarta, Heather Bruce, Stephanie M. Hare, Meghann C. Ryan, Eric L. Goldwaser, Kathryn S. Hatch, Theo G.M. van Erp, Baopeng Cao, Bhim M. Adhikari, Yunlong Tan, Xiaoming Du, Shuo Chen, Junchao Huang, Jessica A. Turner, Paul M. Thompson, Shuping Tan, Fengmei Fan, Peter Kochunov, Jinghui Tong, and L. Elliot Hong
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Adult ,medicine.medical_specialty ,Adolescent ,Neuroimaging ,regional vulnerability index ,Affect (psychology) ,050105 experimental psychology ,White matter ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,Humans ,Medicine ,Cognitive Dysfunction ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Association (psychology) ,Research Articles ,Aged ,Cerebral Cortex ,Radiological and Ultrasound Technology ,business.industry ,Working memory ,05 social sciences ,ENIGMA ,Cognition ,gray matter ,Middle Aged ,medicine.disease ,Magnetic Resonance Imaging ,White Matter ,Subcortical gray matter ,schizophrenia ,Diffusion Tensor Imaging ,medicine.anatomical_structure ,Neurology ,Schizophrenia ,Chronic Disease ,Disease Progression ,Cardiology ,Neurology (clinical) ,Anatomy ,business ,030217 neurology & neurosurgery ,Research Article - Abstract
Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta‐Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia‐specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6–20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p, We developed the regional vulnerability index (RVI) to quantify individual similarity to the expected schizophrenia deficits patterns derived from large‐scale meta‐analyses performed by Enhancing Neuro Imaging Genetics Meta‐Analysis (ENIGMA) consortium. RVIs for cortical, subcortical, and white matter measurements and a cross‐modality RVI showed significant association with illness duration, cognitive deficits and symptoms. The similarity to expected disorder patterns captured by RVI may be useful for early diagnosis and as quantitative targets for more effective treatment strategies aiming to alter the formation of neuroanatomical deficits.
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- 2020
20. A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium
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Jacqueline Mayoral-van Son, Dana Nguyen, Esther Walton, Vince D. Calhoun, Boris A. Gutman, Pedro G.P. Rosa, Geraldo Busatto Filho, Adrian Preda, Margie Wright, Esther Setién-Suero, Bryon A. Mueller, Fleur M. Howells, Daniel H. Mathalon, Arvin Saremi, Fabrizio Piras, Salvador Sarró, Gianfranco Spalletta, Katie L. McMahon, Judith M. Ford, Lawrence Faziola, Juan R. Bustillo, Fabienne Schönborn-Harrisberger, Alexander J. Huang, Erin W. Dickie, Simon Cervenka, Lei Wang, Shan Cong, Theodore D. Satterthwaite, Anthony A. James, Edith Pomarol-Clotet, Steven G. Potkin, Erick J. Canales-Rodríguez, Kaleda Vg, Dara M. Cannon, Lars T. Westlye, Aiden Corvin, Andrea Weideman, Mauricio H. Serpa, Ole A. Andreassen, Dmitry Isaev, Giuseppe Ducci, Neda Jahanshad, Colm McDonald, Helena Fatouros-Bergman, Theo G.M. van Erp, John G. Csernansky, Dag Alnæs, Kathryn I. Alpert, Laurena Holleran, Li Shen, Dan J. Stein, Peter Kochunov, Raymond Salvador, Artemis Zavaliangos-Petropulu, Nerisa Banaj, Timothy J. Crow, Paola Fuentes-Claramonte, Federica Piras, Jessica A. Turner, Derin Cobia, Christopher R.K. Ching, Derek W. Morris, Paul M. Thompson, Nhat Trung Doan, Diana Tordesillas-Gutiérrez, Benedicto Crespo-Facorro, Alexander Tomyshev, Daniel H. Wolf, Stefan Ehrlich, Ingrid Agartz, Gary Donohoe, Greig I. de Zubicaray, Henk Temmingh, Anne Uhlmann, Stefan Borgwardt, Anjani Ragothaman, Michael Gill, David C. Glahn, Aristotle N. Voineskos, Irina V. Lebedeva, Marcus V. Zanetti, Joaquim Radua, Carl M. Sellgren, Charles Kessler, US Department of Veterans Affairs, Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brasil), Swedish Research Council for Health, Working Life and Welfare, Fundação Amazônia de Amparo a Estudos e Pesquisas, Instituto de Salud Carlos III, National Health and Medical Research Council (Australia), National Institutes of Health (US), National Science Foundation (US), Research Council of Norway, Science Foundation Ireland, and Wellcome Trust
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Thalamus ,Hippocampus ,Neuroimaging ,Amygdala ,03 medical and health sciences ,0302 clinical medicine ,Healthy volunteers ,medicine ,Humans ,Multicenter Studies as Topic ,Radiology, Nuclear Medicine and imaging ,structure ,Research Articles ,Radiological and Ultrasound Technology ,business.industry ,Putamen ,Ventral striatum ,Neurosciences ,1. No poverty ,Experimental Psychology ,subcortical shape ,medicine.disease ,Corpus Striatum ,Brain Disorders ,030227 psychiatry ,3. Good health ,schizophrenia ,Mental Health ,Good Health and Well Being ,medicine.anatomical_structure ,Neurology ,nervous system ,Schizophrenia ,Meta-analysis ,Cognitive Sciences ,Neurology (clinical) ,Anatomy ,business ,Neuroscience ,030217 neurology & neurosurgery ,Research Article - Abstract
Special Issue: The ENIGMA Consortium: the first 10 years., Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia., Center for Integrated Healthcare, U.S. Department of Veterans Affairs, Grant/Award Number: I01 CX000497; Commonwealth Health Research Board, Grant/Award Number: HRA_POR/2011/100; Conselho Nacional de Desenvolvimento Científico e Tecnológico, Grant/Award Numbers: 478466/2009, 480370/2009; Department of Energy, Labor and Economic Growth, Grant/Award Number: DE-FG02-99ER62764; Forskningsrådet om Hälsa, Arbetsliv och Välfärd, Grant/Award Numbers: K2009-62X-15077-06-3, K2012-61X-15077-09-3, 523-2014-3467, 2009-7053, 2013-2838; Fundação Amazônia Paraense de Amparo à Pesquisa, Grant/Award Numbers: 2009/14891-9, 2010/18672-7, 2012/23796-2, 2013/039; Instituto de Salud Carlos III, Grant/Award Numbers: FIS 00/3095, 01/3129, PI020499, PI060507, PI10/001; National Health and Medical Research Council, Grant/Award Numbers: 1009064, 496682; National Institutes of Health, Grant/Award Numbers: 1RC1MH089257, MH 60722, MH019112, MH064045, MH085096, MH098130, MO1 RR025758, P41RR14075, P50 MH071616, R01 DA053028, R01 EB020062, R01 HD050735, R01 MH056584, R01 MH084803, R01 MH116147, R01 MH117601, R01EB005846, R01EB015611, R01MH074797, R21 MH097196, R21MH097196, R37MH43375, S10 OD023696, T32 AG058507, T32 MH073526, TR000153, U01 MH097435, U24 RR021382A, U24 RR021992, U24 RR025736, U24 RR21992, U24RR021992, U54 EB020403, U54EB020403, UL1 TR000153; National Science Foundation, Grant/Award Numbers: 1636893, 1734853; Norges Forskningsråd, Grant/Award Numbers: 213837, 217776, 223273; Science Foundation Ireland, Grant/Award Numbers: 08/IN.1/B1916, 12/IP/1359; Wellcome Trust, Grant/Award Number: 072894/2/03/Z.
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- 2022
21. Age-dependent white matter disruptions after military traumatic brain injury: Multivariate analysis results from ENIGMA brain injury
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Heather C. Bouchard, Delin Sun, Emily L. Dennis, Mary R. Newsome, Seth G. Disner, Jeremy Elman, Annelise Silva, Carmen Velez, Andrei Irimia, Nicholas D. Davenport, Scott R. Sponheim, Carol E. Franz, William S. Kremen, Michael J. Coleman, M. Wright Williams, Elbert Geuze, Inga K. Koerte, Martha E. Shenton, Maheen M. Adamson, Raul Coimbra, Gerald Grant, Lori Shutter, Mark S. George, Ross D. Zafonte, Thomas W. McAllister, Murray B. Stein, Paul M. Thompson, Elisabeth A. Wilde, David F. Tate, Aristeidis Sotiras, and Rajendra A. Morey
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Traumatic ,Physical Injury - Accidents and Adverse Effects ,Traumatic Brain Injury (TBI) ,diffusion MRI ,Stress Disorders, Post-Traumatic ,mTBI ,Clinical Research ,Brain Injuries, Traumatic ,Humans ,Radiology, Nuclear Medicine and imaging ,military ,Traumatic Head and Spine Injury ,Brain Concussion ,Stress Disorders ,Veterans ,Radiological and Ultrasound Technology ,traumatic brain injury ,ENIGMA ,Neurosciences ,nonnegative matrix factorization ,Brain ,Experimental Psychology ,White Matter ,Brain Disorders ,Mental Health ,Military Personnel ,Neurology ,Brain Injuries ,Multivariate Analysis ,Post-Traumatic ,Biomedical Imaging ,Cognitive Sciences ,Neurology (clinical) ,Anatomy - Abstract
Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n= 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q 
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- 2021
22. Cover Image
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Paul M. Thompson, Neda Jahanshad, Lianne Schmaal, Jessica A. Turner, Anderson M. Winkler, Sophia I. Thomopoulos, Gary F. Egan, and Peter Kochunov
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Neurology ,Radiological and Ultrasound Technology ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy - Published
- 2021
23. FreeSurfer-based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts
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Emily K. Clarke-Rubright, Laura K.M. Han, Rajendra A. Morey, Neda Jahanshad, Ramona Leenings, Lianne Schmaal, Paul M. Thompson, Laura S van Velzen, Philipp G. Sämann, Claas Flint, Christopher D. Whelan, Bo Cao, Boris A. Gutman, Dominik Grotegerd, Jean C. Augustinack, Juan Eugenio Iglesias, Udo Dannlowski, and Theo G.M. van Erp
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Quality Control ,Psychosis ,Computer science ,hippocampus ,FreeSurfer ,Context (language use) ,Neuroimaging ,Review Article ,Hippocampal formation ,050105 experimental psychology ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Image Processing, Computer-Assisted ,Humans ,Multicenter Studies as Topic ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Segmentation ,Effects of sleep deprivation on cognitive performance ,quality control ,Review Articles ,hippocampal subregions ,Radiological and Ultrasound Technology ,05 social sciences ,segmentation ,ENIGMA ,Cognition ,medicine.disease ,Magnetic Resonance Imaging ,hippocampal subfields ,Neurology ,nervous system ,Meta-analysis ,Neurology (clinical) ,Anatomy ,Neuroscience ,030217 neurology & neurosurgery ,MRI - Abstract
Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized., Hippocampal subfield analysis is increasingly performed with the availability of automated magnetic resonance imaging segmentation methods. We give a synopsis of the FreeSurfer hippocampal subfield segmentation algorithm, measurement reliability studies and application domains. We discuss how global size and age effects can be modeled and suggest a standardized hippocampal subfield segmentation quality control procedure for improved pipeline harmonization.
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- 2020
24. ENIGMA brain injury: Framework, challenges, and opportunities
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Andrew R. Mayer, David F. Tate, Stefania Mondello, Frank G. Hillary, Elisabeth A. Wilde, Kimbra Kenney, Emily L. Dennis, Karen Caeyenberghs, Alexander P. Lin, Brenda Bartnik-Olson, Paul M. Thompson, David Baron, Inga K. Koerte, Alexander Olsen, and Carrie Esopenko
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Traumatic brain injury ,Poison control ,Review Article ,050105 experimental psychology ,03 medical and health sciences ,Social support ,0302 clinical medicine ,Neuroimaging ,Concussion ,Injury prevention ,Brain Injuries, Traumatic ,TBI ,medicine ,Humans ,Multicenter Studies as Topic ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,brain injury ,concussion ,ENIGMA ,neuroimaging ,Review Articles ,Radiological and Ultrasound Technology ,Mechanism (biology) ,business.industry ,05 social sciences ,Human factors and ergonomics ,medicine.disease ,Neurology ,Neurology (clinical) ,Anatomy ,business ,030217 neurology & neurosurgery ,Clinical psychology - Abstract
Traumatic brain injury (TBI) is a major cause of disability worldwide, but the heterogeneous nature of TBI with respect to injury severity and health comorbidities make patient outcome difficult to predict. Injury severity accounts for only some of this variance, and a wide range of preinjury, injury‐related, and postinjury factors may influence outcome, such as sex, socioeconomic status, injury mechanism, and social support. Neuroimaging research in this area has generally been limited by insufficient sample sizes. Additionally, development of reliable biomarkers of mild TBI or repeated subconcussive impacts has been slow, likely due, in part, to subtle effects of injury and the aforementioned variability. The ENIGMA Consortium has established a framework for global collaboration that has resulted in the largest‐ever neuroimaging studies of multiple psychiatric and neurological disorders. Here we describe the organization, recent progress, and future goals of the Brain Injury working group., Graphical Abstract
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- 2020
25. An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration
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Edna Grünblatt, Noam Soreni, Takashi Nakamae, Susanne Walitza, Anthony A. James, Paul D. Arnold, Iliyan Ivanov, Patricia Gruner, Janardhanan C. Narayanaswamy, Dick J. Veltman, Gianfranco Spalletta, Sara Bertolín, H. Blair Simpson, Tomohiro Nakao, Je-Yeon Yun, David Mataix-Cols, Gerd Kvale, Carles Soriano-Mas, Guido van Wingen, Ganesan Venkatasubramanian, Luisa Lázaro, Francesco Benedetti, Chris Vriend, Xiangzhen Kong, Jan C. Beucke, Kate D. Fitzgerald, Martine Hoogman, Y. C.Janardhan Reddy, Kathrin Koch, Daan van Rooij, Jun Soo Kwon, David F. Tolin, Rachel Marsh, Jan K. Buitelaar, Neda Jahanshad, Christopher Pittenger, Stephan F. Taylor, Tomáš Paus, Willem B Bruin, Clyde Francks, Anushree Bose, Chaim Huyser, Christine Lochner, Erika L. Nurmi, Dan J. Stein, Joseph O'Neill, S. Evelyn Stewart, Yash Patel, João Ricardo Sato, Zhen Wang, Irene Bollettini, Lianne Schmaal, Alessandro S. De Nadai, Fabrizio Piras, Yoshiyuki Hirano, Brian P. Brennan, Odile A. van den Heuvel, Pedro Morgado, Sophia I. Thomopoulos, Marcelo Q. Hoexter, Premika S.W. Boedhoe, Paul M. Thompson, van den Heuvel, O. A., Boedhoe, P. S. W., Bertolin, S., Bruin, W. B., Francks, C., Ivanov, I., Jahanshad, N., Kong, X. -Z., Kwon, J. S., O'Neill, J., Paus, T., Patel, Y., Piras, F., Schmaal, L., Soriano-Mas, C., Spalletta, G., van Wingen, G. A., Yun, J. -Y., Vriend, C., Simpson, H. B., van Rooij, D., Hoexter, M. Q., Hoogman, M., Buitelaar, J. K., Arnold, P., Beucke, J. C., Benedetti, F., Bollettini, I., Bose, A., Brennan, B. P., De Nadai, A. S., Fitzgerald, K., Gruner, P., Grunblatt, E., Hirano, Y., Huyser, C., James, A., Koch, K., Kvale, G., Lazaro, L., Lochner, C., Marsh, R., Mataix-Cols, D., Morgado, P., Nakamae, T., Nakao, T., Narayanaswamy, J. C., Nurmi, E., Pittenger, C., Reddy, Y. C. J., Sato, J. R., Soreni, N., Stewart, S. E., Taylor, S. F., Tolin, D., Thomopoulos, S. I., Veltman, D. J., Venkatasubramanian, G., Walitza, S., Wang, Z., Thompson, P. M., and Stein, D. J.
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mega-analysis ,Obsessive-Compulsive Disorder ,Review Article ,Machine Learning ,0302 clinical medicine ,Neurobiology ,Obsessive-compulsive disorder ,Multicenter Studies as Topic ,Disease process ,Cervell ,Review Articles ,Pediatric ,Psychiatry ,Cerebral Cortex ,Collaborative community ,Radiological and Ultrasound Technology ,05 social sciences ,ENIGMA ,Brain ,Experimental Psychology ,Serious Mental Illness ,humanities ,3. Good health ,Mental Health ,Neurology ,Meta-analysis ,Neurological ,Cognitive Sciences ,Mega analysis ,Anatomy ,Psychology ,Neurobiologia ,Clinical psychology ,MRI ,ENIGMA-OCD working group ,Neuroimaging ,behavioral disciplines and activities ,050105 experimental psychology ,Lateralization of brain function ,Power (social and political) ,03 medical and health sciences ,obsessive–compulsive disorder ,Obsessive compulsive ,Clinical Research ,mental disorders ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Psiquiatria ,mega‐analysis ,volume ,Neurosi obsessiva ,Neurosciences ,surface area ,cortical thickness ,Brain Disorders ,meta-analysis ,meta‐analysis ,Neurology (clinical) ,030217 neurology & neurosurgery - Abstract
Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive?compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.
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- 2020
26. Genome‐wide association analysis links multiple psychiatric liability genes to oscillatory brain activity
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Damiaan Denys, Jian Zhang, Paul M. Thompson, Eco J. C. de Geus, Stephen M. Malone, Jacquelyn L. Meyers, Neda Jahanshad, Jouke-Jan Hottenga, Nicholas G. Martin, David B. Chorlian, Bernice Porjesz, Narelle K. Hansell, Sarah E. Medland, Dirk J.A. Smit, William G. Iacono, Margaret J. Wright, Yvonne Y.W. Ho, Catharina E.M. van Beijsterveldt, Scott J. Burwell, Matt McGue, Christopher D. Whelan, Dorret I. Boomsma, Adult Psychiatry, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Brain Imaging, Biological Psychology, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Mental Health, and APH - Methodology
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0301 basic medicine ,Netherlands Twin Register (NTR) ,Male ,Periodicity ,brain expression pathway ,Brain activity and meditation ,Gene Expression ,Genome-wide association study ,Electroencephalography ,Hippocampal formation ,Cohort Studies ,0302 clinical medicine ,electroencephalography (EEG) ,Child ,Research Articles ,Genetics ,0303 health sciences ,Radiological and Ultrasound Technology ,biology ,medicine.diagnostic_test ,Mental Disorders ,Brain ,Middle Aged ,genetic correlation ,endophenotype ,Neurology ,Child, Preschool ,Female ,Anatomy ,Research Article ,Adult ,Adolescent ,Rest ,Single-nucleotide polymorphism ,Quantitative trait locus ,Genome‐Wide Association Study (GWAS) ,Polymorphism, Single Nucleotide ,SNP heritability ,03 medical and health sciences ,Young Adult ,SDG 3 - Good Health and Well-being ,medicine ,SNP ,Humans ,Radiology, Nuclear Medicine and imaging ,GABRA2 ,Genetic Predisposition to Disease ,Bipolar disorder ,030304 developmental biology ,Aged ,Genome-Wide Association Study (GWAS) ,medicine.disease ,030104 developmental biology ,Endophenotype ,Expression quantitative trait loci ,biology.protein ,Neurology (clinical) ,Neuroscience ,Imputation (genetics) ,030217 neurology & neurosurgery ,Genome-Wide Association Study - Abstract
Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but the involvement of specific genetic variants, genes, and brain expression pathways remains elusive. Here, we present a genome-wide association study (GWAS) for the power of oscillations at standard frequencies (~2 Hz delta, ~6 Hz theta, ~10 Hz alpha, and ~20 Hz beta) of the electroencephalogram (EEG), followed up with gene-based and brain-expression analyses based on the extraction of RNA expression quantitative trait loci (eQTL) and imputation of gene expression in brain tissues by machine learning (Metaxcan). Five cohorts with eyes-closed resting EEG and genome-wide single nucleotide polymorphism (SNP) were included with data for 8425 participants. GWAS revealed a significant SNP for alpha oscillation power intronic to protein-coding gene PRKG2. PRKG2 is amongst the genes deleted in the 4q21 microdeletion syndrome which results in speech and mental retardation. GABRA2 -- a known genetic marker for alcohol use disorder and epilepsy -- significantly affected beta power, consistent with the relation between GABAA interneuron activity and beta oscillations, and between beta oscillations and alcohol use disorders. Twenty-four genes in region 3p21.1 -- previously linked to schizophrenia -- reached significance for alpha power, which fits well with observed aberrant oscillatory activity in schizophrenia. SNPs in this region were eQTLs for GLYCTK in hippocampal tissue, and eQTLs for GNL3 and ITIH4 in the frontal cortex. SNP-based imputation of hippocampal GABRA2 expression was significantly associated with beta oscillations and indicated associations of immune genes IL18R1 and IL1RL1 in the Hippocampus and Putamen with alpha oscillation strength. In conclusion, we successfully associated genes and genetic variants with oscillatory brain activity, some of which were previously associated with psychopathology. The results show how psychopathological liability genes affect brain functioning via cortical and subcortical brain expression.
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- 2018
27. Magnetic resonance spectroscopy of fiber tracts in children with traumatic brain injury: A combined MRS – Diffusion MRI study
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Talin Babikian, Emily L. Dennis, Christopher Babbitt, Christopher C. Giza, Julio E. Villalon-Reina, Jeffrey L. Johnson, Faisal Rashid, Paul M. Thompson, Yan Jin, Alexander Olsen, Robert F. Asarnow, Jeffry R. Alger, and Richard Mink
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Male ,Magnetic Resonance Spectroscopy ,Adolescent ,Traumatic brain injury ,Poison control ,Neuroimaging ,Degeneration (medical) ,Multimodal Imaging ,Article ,Choline ,030218 nuclear medicine & medical imaging ,White matter ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Brain Injuries, Traumatic ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Child ,Aspartic Acid ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,Magnetic resonance imaging ,medicine.disease ,White Matter ,Diffusion Magnetic Resonance Imaging ,medicine.anatomical_structure ,nervous system ,Neurology ,Gliosis ,chemistry ,Anisotropy ,Brain Damage, Chronic ,Female ,Neurology (clinical) ,Anatomy ,medicine.symptom ,Cognition Disorders ,business ,Neuroscience ,030217 neurology & neurosurgery ,Demyelinating Diseases ,Diffusion MRI - Abstract
Traumatic brain injury can cause extensive damage to the white matter (WM) of the brain. These disruptions can be especially damaging in children, whose brains are still maturing. Diffusion magnetic resonance imaging (dMRI) is the most commonly used method to assess WM organization, but it has limited resolution to differentiate causes of WM disruption. Magnetic resonance spectroscopy (MRS) yields spectra showing the levels of neurometabolites that can indicate neuronal/axonal health, inflammation, membrane proliferation/turnover, and other cellular processes that are on-going post-injury. Previous analyses on this dataset revealed a significant division within the msTBI patient group, based on interhemispheric transfer time (IHTT); one subgroup of patients (TBI-normal) showed evidence of recovery over time, while the other showed continuing degeneration (TBI-slow). We combined dMRI with MRS to better understand WM disruptions in children with moderate-severe traumatic brain injury (msTBI). Tracts with poorer WM organization, as shown by lower FA and higher MD and RD, also showed lower N-acetylaspartate (NAA), a marker of neuronal and axonal health and myelination. We did not find lower NAA in tracts with normal WM organization. Choline, a marker of inflammation, membrane turnover, or gliosis, did not show such associations. We further show that multi-modal imaging can improve outcome prediction over a single modality, as well as over earlier cognitive function measures. Our results suggest that demyelination plays an important role in WM disruption post-injury in a subgroup of msTBI children and indicate the utility of multi-modal imaging.
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- 2018
28. Integration of routine QA data into mega‐analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies
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Aristotle N. Voineskos, Meghann C. Ryan, Peter B. Kingsley, Els Fieremans, Peter Kochunov, Neda Jahanshad, Jelle Veraart, Anil K. Malhotra, Dmitry S. Novikov, Jessica A. Turner, Sofia Chavez, Elliot Hong, Paul M. Thompson, Erin W. Dickie, Joseph D. Viviano, and Robert W. Buchanan
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Adult ,Adolescent ,Quality Assurance, Health Care ,Article ,Imaging phantom ,030218 nuclear medicine & medical imaging ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Meta-Analysis as Topic ,Statistics ,Fractional anisotropy ,Covariate ,Humans ,Multicenter Studies as Topic ,Radiology, Nuclear Medicine and imaging ,Mathematics ,Radiological and Ultrasound Technology ,Information Dissemination ,business.industry ,Brain ,Variance (accounting) ,Middle Aged ,Magnetic Resonance Imaging ,Regression ,Diffusion Tensor Imaging ,Neurology ,Principal component analysis ,Schizophrenia ,Regression Analysis ,Neurology (clinical) ,Anatomy ,business ,Quality assurance ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm2 ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.
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- 2017
29. Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group
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Nicholas G. Martin, Matthew S. Panizzon, Karen A. Mather, William S. Kremen, Ian B. Hickie, Anbupalam Thalamuthu, David C. Glahn, Wei Wen, Xue Hua, Narelle K. Hansell, Marinka M.G. Koenis, Lucija Abramovic, Dorret I. Boomsma, Carol E. Franz, Suzanne C. Swagerman, Greig I. de Zubicaray, Rachel M. Brouwer, René S. Kahn, John H. Gilmore, Hugo G. Schnack, Margaret J. Wright, Perminder S. Sachdev, Neda Jahanshad, Hilleke E. Hulshoff Pol, Derrek P. Hibar, Katie L. McMahon, Nitin Gogtay, Paul M. Thompson, and Lachlan T. Strike
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Cerebellum ,Radiological and Ultrasound Technology ,05 social sciences ,Thalamus ,Heritability ,050105 experimental psychology ,03 medical and health sciences ,Lateral ventricles ,0302 clinical medicine ,medicine.anatomical_structure ,Neurology ,Ageing ,Neuroplasticity ,Brain size ,medicine ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,sense organs ,Neurology (clinical) ,Cognitive skill ,Anatomy ,skin and connective tissue diseases ,Psychology ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h2) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure.
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- 2017
30. 3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease
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Hongtu Zhu, Yan Jin, Alzheimer’s Disease Neuroimaging Initiative, Clifford R. Jack, Chao Huang, Madelaine Daianu, Liang Zhan, Robert I. Reid, Paul M. Thompson, and Emily L. Dennis
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Radiological and Ultrasound Technology ,Fornix ,Cognition ,Healthy elderly ,Disease ,030218 nuclear medicine & medical imaging ,White matter ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Neurology ,medicine ,Hum ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Cognitive impairment ,Psychology ,Neuroscience ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion-weighted imaging (DWI) offers a non-invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm-autoMATE (automated Multi-Atlas Tract Extraction); we then extracted multiple DWI-derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method-FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191-1207, 2017. © 2016 Wiley Periodicals, Inc.
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- 2016
31. The joint effect of aging and HIV infection on microstructure of white matter bundles
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Susan Y. Bookheimer, Yan Jin, Phillip Sayegh, Hongtu Zhu, Charles H. Hinkin, Talia M. Nir, Elyse J. Singer, David W. Shattuck, Bianca H. Dang, Neda Jahanshad, Chao Huang, Joseph M. Gullett, Yeun Kim, April D. Thames, Jacob D. Jones, Paul M. Thompson, Taylor Kuhn, and Caroline Chung
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Oncology ,Adult ,Male ,medicine.medical_specialty ,Aging ,AIDS Dementia Complex ,Human immunodeficiency virus (HIV) ,Context (language use) ,HIV Infections ,medicine.disease_cause ,050105 experimental psychology ,White matter ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Internal medicine ,HIV Seronegativity ,Fractional anisotropy ,Medicine ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Research Articles ,Aged ,Radiological and Ultrasound Technology ,business.industry ,05 social sciences ,virus diseases ,Middle Aged ,White Matter ,medicine.anatomical_structure ,Increased risk ,Diffusion Tensor Imaging ,Neurology ,Case-Control Studies ,Disease Progression ,Anisotropy ,Regression Analysis ,Female ,Neurology (clinical) ,Anatomy ,business ,Neurocognitive ,030217 neurology & neurosurgery ,Tractography ,Diffusion MRI - Abstract
Recent evidence suggests the aging process is accelerated by HIV. Degradation of white matter (WM) has been independently associated with HIV and healthy aging. Thus, WM may be vulnerable to joint effects of HIV and aging. Diffusion-weighted imaging (DWI) was conducted with HIV-seropositive (n = 72) and HIV-seronegative (n = 34) adults. DWI data underwent tractography, which was parcellated into 18 WM tracts of interest (TOIs). Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) regression was conducted assessing the joint effect of advanced age and HIV on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) along TOI fibers. In addition to main effects of age and HIV on WM microstructure, the interactive effect of age and HIV was significantly related to lower FA and higher MD, AD, and RD across all TOIs. The location of findings was consistent with the clinical presentation of HIV-associated neurocognitive disorders. While older age is related to poorer WM microstructure, its detrimental effect on WM is stronger among HIV+ relative to HIV- individuals. Loss of WM integrity in the context of advancing age may place HIV+ individuals at increased risk for brain and cognitive compromise.
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- 2019
32. Functional network connectivity impairments and core cognitive deficits in schizophrenia
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Laura M. Rowland, Neda Jahanshad, Bhim M. Adhikari, Xiaoming Du, Shuo Chen, Peter Kochunov, Paul M. Thompson, Joshua Chiappelli, Vince D. Calhoun, L. Elliot Hong, and Hemalatha Sampath
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Adult ,Male ,Psychosis ,Somatosensory system ,050105 experimental psychology ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Salience (neuroscience) ,medicine ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Cognitive Dysfunction ,Research Articles ,Psychiatric Status Rating Scales ,Brain Mapping ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Working memory ,05 social sciences ,Cognition ,medicine.disease ,Magnetic Resonance Imaging ,Memory, Short-Term ,Neurology ,Psychotic Disorders ,Meta-analysis ,Schizophrenia ,Female ,Schizophrenic Psychology ,Neurology (clinical) ,Anatomy ,Nerve Net ,Functional magnetic resonance imaging ,Psychology ,Neuroscience ,Neurocognitive ,030217 neurology & neurosurgery ,Antipsychotic Agents - Abstract
Cognitive deficits contribute to functional disability in patients with schizophrenia and may be related to altered functional networks that serve cognition. We evaluated the integrity of major functional networks and assessed their role in supporting two cognitive functions affected in schizophrenia: processing speed (PS) and working memory (WM). Resting‐state functional magnetic resonance imaging (rsfMRI) data, N = 261 patients and 327 controls, were aggregated from three independent cohorts and evaluated using Enhancing NeuroImaging Genetics through Meta Analysis rsfMRI analysis pipeline. Meta‐ and mega‐analyses were used to evaluate patient‐control differences in functional connectivity (FC) measures. Canonical correlation analysis was used to study the association between cognitive deficits and FC measures. Patients showed consistent patterns of cognitive and resting‐state FC (rsFC) deficits across three cohorts. Patient‐control differences in rsFC calculated using seed‐based and dual‐regression approaches were consistent (Cohen's d: 0.31 ± 0.09 and 0.29 ± 0.08, p
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- 2019
33. Heterochronicity of white matter development and aging explains regional patient control differences in schizophrenia
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Guohao Zhang, Paul M. Thompson, Habib Ganjgahi, Sinead Kelly, Thomas E. Nichols, Binish Patel, Sara A. Paciga, Patricio O'Donnell, Xiaoming Du, Christian R. Schubert, Laura M. Rowland, L. Elliot Hong, Hemalatha Sampath, Anderson M. Winkler, Zhiyong Xie, Neda Jahanshad, Peter Kochunov, Jian Chen, and Dinesh Shukla
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0301 basic medicine ,Gerontology ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Physiology ,Magnetic resonance imaging ,medicine.disease ,White matter ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,medicine.anatomical_structure ,Neurology ,Schizophrenia ,Fractional anisotropy ,medicine ,Radiology, Nuclear Medicine and imaging ,In patient ,Neurology (clinical) ,Anatomy ,Young adult ,Psychology ,030217 neurology & neurosurgery ,Cohort study ,Diffusion MRI - Abstract
Background Altered brain connectivity is implicated in the development and clinical burden of schizophrenia. Relative to matched controls, schizophrenia patients show (1) a global and regional reduction in the integrity of the brain's white matter (WM), assessed using diffusion tensor imaging (DTI) fractional anisotropy (FA), and (2) accelerated age-related decline in FA values. In the largest mega-analysis to date, we tested if differences in the trajectories of WM tract development influenced patient–control differences in FA. We also assessed if specific tracts showed exacerbated decline with aging. Methods Three cohorts of schizophrenia patients (total n = 177) and controls (total n = 249; age = 18-61 years) were ascertained with three 3T Siemens MRI scanners. Whole-brain and regional FA values were extracted using ENIGMA-DTI protocols. Statistics were evaluated using mega- and meta-analyses to detect effects of diagnosis and age-by-diagnosis interactions. Results In mega-analysis of whole-brain averaged FA, schizophrenia patients had lower FA (P = 10−11) and faster age-related decline in FA (P = 0.02) compared with controls. Tract-specific heterochronicity measures, that is, abnormal rates of adolescent maturation and aging explained approximately 50% of the regional variance effects of diagnosis and age-by-diagnosis interaction in patients. Interactive, three-dimensional visualization of the results is available at www.enigma-viewer.org. Conclusion WM tracts that mature later in life appeared more sensitive to the pathophysiology of schizophrenia and were more susceptible to faster age-related decline in FA values. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
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- 2016
34. Heritability and genetic correlation between the cerebral cortex and associated white matter connections
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Jurgen Fripp, Nicholas G. Martin, Margaret J. Wright, Katie L. McMahon, Kaikai Shen, Paul M. Thompson, Greig I. de Zubicaray, Vincent Dore, Olivier Salvado, and Stephen E. Rose
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Radiological and Ultrasound Technology ,05 social sciences ,Anatomy ,Heritability ,Grey matter ,Genetic correlation ,050105 experimental psychology ,White matter ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Neurology ,Cerebral cortex ,Parietal gyrus ,medicine ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Cortical surface ,Psychology ,Neuroscience ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
The aim of this study is to investigate the genetic influence on the cerebral cortex, based on the analyses of heritability and genetic correlation between grey matter (GM) thickness, derived from structural MR images (sMRI), and associated white matter (WM) connections obtained from diffusion MRI (dMRI). We measured on sMRI the cortical thickness (CT) from a large twin imaging cohort using a surface-based approach (N = 308, average age 22.8 ± 2.3 SD). An ACE model was employed to compute the heritability of CT. WM connections were estimated based on probabilistic tractography using fiber orientation distributions (FOD) from dMRI. We then fitted the ACE model to estimate the heritability of CT and FOD peak measures along WM fiber tracts. The WM fiber tracts where genetic influence was detected were mapped onto the cortical surface. Bivariate genetic modeling was performed to estimate the cross-trait genetic correlation between the CT and the FOD-based connectivity of the tracts associated with the cortical regions. We found some cortical regions displaying heritable and genetically correlated GM thickness and WM connectivity, forming networks under stronger genetic influence. Significant heritability and genetic correlations between the CT and WM connectivity were found in regions including the right postcentral gyrus, left posterior cingulate gyrus, right middle temporal gyri, suggesting common genetic factors influencing both GM and WM. Hum Brain Mapp 37:2331-2347, 2016. © 2016 Wiley Periodicals, Inc.
- Published
- 2016
35. Early developmental gene enhancers affect subcortical volumes in the adult human brain
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Tulio Guadalupe, Simon E. Fisher, Paul M. Thompson, Sonja C. Vernes, Miguel E. Rentería, Martin Becker, Derrek P. Hibar, Barbara Franke, Jason L. Stein, and Clyde Francks
- Subjects
0301 basic medicine ,Genetics ,Radiological and Ultrasound Technology ,Brain morphometry ,Context (language use) ,Genome-wide association study ,Single-nucleotide polymorphism ,Human brain ,Biology ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,medicine.anatomical_structure ,Neurology ,Genetic variation ,Forebrain ,medicine ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Enhancer ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Genome-wide association screens aim to identify common genetic variants contributing to the phenotypic variability of complex traits, such as human height or brain morphology. The identified genetic variants are mostly within noncoding genomic regions and the biology of the genotype-phenotype association typically remains unclear. In this article, we propose a complementary targeted strategy to reveal the genetic underpinnings of variability in subcortical brain volumes, by specifically selecting genomic loci that are experimentally validated forebrain enhancers, active in early embryonic development. We hypothesized that genetic variation within these enhancers may affect the development and ultimately the structure of subcortical brain regions in adults. We tested whether variants in forebrain enhancer regions showed an overall enrichment of association with volumetric variation in subcortical structures of >13,000 healthy adults. We observed significant enrichment of genomic loci that affect the volume of the hippocampus within forebrain enhancers (empirical P = 0.0015), a finding which robustly passed the adjusted threshold for testing of multiple brain phenotypes (cutoff of P
- Published
- 2016
36. Disrupted rich club network in behavioral variant frontotemporal dementia and early‐onset <scp>A</scp> lzheimer's disease
- Author
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Paul M. Thompson, Mario F. Mendez, Elvira E. Jimenez, Madelaine Daianu, Neda Jahanshad, and Adam Mezher
- Subjects
Male ,0301 basic medicine ,Article ,Cohort Studies ,White matter ,03 medical and health sciences ,0302 clinical medicine ,Alzheimer Disease ,Neural Pathways ,Image Processing, Computer-Assisted ,medicine ,Humans ,Dementia ,Radiology, Nuclear Medicine and imaging ,Early-onset Alzheimer's disease ,Age of Onset ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,Brain ,Magnetic resonance imaging ,Cognition ,Human brain ,Middle Aged ,medicine.disease ,White Matter ,Diffusion Magnetic Resonance Imaging ,030104 developmental biology ,medicine.anatomical_structure ,Neurology ,Frontotemporal Dementia ,Connectome ,Female ,Neurology (clinical) ,Anatomy ,Psychology ,Neuroscience ,030217 neurology & neurosurgery ,Frontotemporal dementia - Abstract
In network analysis, the so-called “rich club” describes the core areas of the brain that are more densely interconnected among themselves than expected by chance, and has been identified as a fundamental aspect of the human brain connectome. This is the first in-depth diffusion imaging study to investigate the rich club along with other organizational changes in the brain's anatomical network in behavioral frontotemporal dementia (bvFTD), and a matched cohort with early-onset Alzheimer's disease (EOAD). Our study sheds light on how bvFTD and EOAD affect connectivity of white matter fiber pathways in the brain, revealing differences and commonalities in the connectome among the dementias. To analyze the breakdown in connectivity, we studied three groups: 20 bvFTD, 23 EOAD, and 37 healthy elderly controls. All participants were scanned with diffusion-weighted magnetic resonance imaging (MRI), and based on whole-brain probabilistic tractography and cortical parcellations, we analyzed the rich club of the brain's connectivity network. This revealed distinct patterns of disruption in both forms of dementia. In the connectome, we detected less disruption overall in EOAD than in bvFTD [false discovery rate (FDR) critical Pperm = 5.7 × 10−3, 10,000 permutations], with more involvement of richly interconnected areas of the brain (chi-squared P = 1.4 × 10−4)—predominantly posterior cognitive alterations. In bvFTD, we found a greater spread of disruption including the rich club (FDR critical Pperm = 6 × 10−4), but especially more peripheral alterations (chi-squared P = 6.5 × 10−3), particularly in medial frontal areas of the brain, in line with the known behavioral socioemotional deficits seen in these patients. Hum Brain Mapp, 2015. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
- Published
- 2015
37. Heritability of complex white matter diffusion traits assessed in a population isolate
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Braxton D. Mitchell, Binish Patel, Peter Kochunov, Kevin A. Strauss, George Eskandar, Dinesh Shukla, Mao Fu, Mary Morrissey, Neda Jahanshad, Susan N. Wright, Florian Muellerklein, Katie L. Nugent, Teodor T. Postolache, Alan R. Shuldiner, Xiaoming Du, Paul M. Thompson, and L. Elliot Hong
- Subjects
education.field_of_study ,Radiological and Ultrasound Technology ,05 social sciences ,Population ,Heritability ,Corpus callosum ,050105 experimental psychology ,White matter ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Neurology ,Gyrus ,Fractional anisotropy ,medicine ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Diffusion (business) ,Psychology ,education ,Neuroscience ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Introduction Diffusion weighted imaging (DWI) methods can noninvasively ascertain cerebral microstructure by examining pattern and directions of water diffusion in the brain. We calculated heritability for DWI parameters in cerebral white (WM) and gray matter (GM) to study the genetic contribution to the diffusion signals across tissue boundaries. Methods Using Old Order Amish (OOA) population isolate with large family pedigrees and high environmental homogeneity, we compared the heritability of measures derived from three representative DWI methods targeting the corpus callosum WM and cingulate gyrus GM: diffusion tensor imaging (DTI), the permeability-diffusivity (PD) model, and the neurite orientation dispersion and density imaging (NODDI) model. These successively more complex models represent the diffusion signal modeling using one, two, and three diffusion compartments, respectively. Results We replicated the high heritability of the DTI-based fractional anisotropy (h2 = 0.67) and radial diffusivity (h2 = 0.72) in WM. High heritability in both WM and GM tissues were observed for the permeability-diffusivity index from the PD model (h2 = 0.64 and 0.84), and the neurite density from the NODDI model (h2 = 0.70 and 0.55). The orientation dispersion index from the NODDI model was only significantly heritable in GM (h2 = 0.68). Conclusion DWI measures from multicompartmental models were significantly heritable in WM and GM. DWI can offer valuable phenotypes for genetic research; and genes thus identified may reveal mechanisms contributing to mental and neurological disorders in which diffusion imaging anomalies are consistently found. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
- Published
- 2015
38. Accelerated estimation and permutation inference for ACE modeling
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Elia Formisano, Tian Ge, Xu Chen, Greig I. de Zubicaray, Thomas E. Nichols, Anderson M. Winkler, Gabriëlla A.M. Blokland, Paul M. Thompson, Lachlan T. Strike, Katie L. McMahon, Margaret J. Wright, Audition, RS: FPN MaCSBio, RS: FSE MaCSBio, and RS: FPN CN 2
- Subjects
LIKELIHOOD RATIO TESTS ,Adult ,Male ,Mean squared error ,GENETICS ,Computer science ,Models, Neurological ,Inference ,Bayesian inference ,QUANTITATIVE-TRAIT ,050105 experimental psychology ,03 medical and health sciences ,Permutation ,Young Adult ,0302 clinical medicine ,Resampling ,Linear regression ,LINKAGE ,Twins, Dizygotic ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,heritability inference ,ACE model ,Research Articles ,Radiological and Ultrasound Technology ,05 social sciences ,Linear model ,Brain ,BRAIN STRUCTURE ,Twins, Monozygotic ,Heritability ,Magnetic Resonance Imaging ,Memory, Short-Term ,Neurology ,twin studies ,Linear Models ,HERITABILITY ANALYSIS ,Female ,Gene-Environment Interaction ,Neurology (clinical) ,Anatomy ,BAYESIAN-INFERENCE ,Algorithm ,030217 neurology & neurosurgery ,Research Article ,permutation test - Abstract
There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain‐wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model‐which requires iterative optimisation‐with a (noniterative) linear regression model, by transforming data to squared twin‐pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum‐likelihood‐based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach “Accelerated Permutation Inference for the ACE Model (APACE)” where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset.
- Published
- 2018
39. Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline
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Bhim M. Adhikari, Dinesh Shukla, Paul M. Thompson, Peter Kochunov, Robert W. Cox, Els Fieremans, Jelle Veraart, Dmitry S. Novikov, L. Elliot Hong, Peter T. Fox, Neda Jahanshad, Thomas E. Nichols, Richard C. Reynolds, John Blangero, and David C. Glahn
- Subjects
Adult ,Male ,Heredity ,Twins ,Pedigree chart ,Biology ,050105 experimental psychology ,Article ,Cohort Studies ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Neuroimaging genetics ,Mexican Americans ,Connectome ,Humans ,0501 psychology and cognitive sciences ,Radiology, Nuclear Medicine and imaging ,Family ,Cerebral Cortex ,Human Connectome Project ,Radiological and Ultrasound Technology ,Resting state fMRI ,Functional connectivity ,05 social sciences ,Heritability ,Middle Aged ,Magnetic Resonance Imaging ,Phenotype ,Neurology ,Cohort ,Female ,Neurology (clinical) ,Anatomy ,Nerve Net ,Cartography ,030217 neurology & neurosurgery ,Gene Discovery - Abstract
BACKGROUND: We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the GOBS (Genetics of Brain Structure) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. METHODS: The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age=47.9±13.2 years) from 29 extended pedigrees (average family size=9 people; range 5–32). The GOBS rsfMRI data was collected using a 7.5-minute acquisition sequence (spatial resolution=1.72×1.72×3 mm(3)). The HCP cohort consisted of 518 twins and family members (240M/278F; average age=28.7± 3.7 years). rsfMRI data was collected using 28.8-minute sequence (spatial resolution=2×2×2 mm(3)). We used the single-modality ENIGMA rsfMRI preprocessing pipeline to estimate heritability values for measures from eight major functional networks, using (1) seed-based connectivity and (2) dual regression approaches. RESULTS: We observed significant heritability (h(2)=0.2–0.4, p
- Published
- 2017
40. Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network
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Talia M. Nir, Paul M. Thompson, Michael W. Weiner, Matt A. Bernstein, Madelaine Daianu, Neda Jahanshad, and Clifford R. Jack
- Subjects
Radiological and Ultrasound Technology ,Core network ,medicine.disease ,Network topology ,Neurology ,Neuroimaging ,medicine ,Connectome ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Alzheimer's disease ,Cognitive decline ,Psychology ,Neuroscience ,Clustering coefficient ,Tractography - Abstract
Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative-50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the "rich club" - a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.
- Published
- 2015
41. Reproducibility of brain-cognition relationships using three cortical surface-based protocols: An exhaustive analysis based on cortical thickness
- Author
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Anand A. Joshi, Julio E. Villalon-Reina, Kenia Martínez, Miguel Burgaleta, Francisco J. Román, Shantanu H. Joshi, Sarah K. Madsen, Manuel Desco, Eugenio Marinetto, Paul M. Thompson, Sherif Karama, Roberto Colom, and Joost Janssen
- Subjects
Reproducibility ,Radiological and Ultrasound Technology ,Working memory ,Cognition ,Spatial intelligence ,Replicate ,Developmental psychology ,Neurology ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Cortical surface ,Effects of sleep deprivation on cognitive performance ,Cognitive skill ,Anatomy ,Psychology ,Cognitive psychology - Abstract
People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T1-weighted images were processed using three different surface-based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT-cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT-cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples. Hum Brain Mapp 36:3227–3245, 2015. © 2015 Wiley Periodicals, Inc.
- Published
- 2015
42. Abnormal hippocampal morphology in dissociative identity disorder and post-traumatic stress disorder correlates with childhood trauma and dissociative symptoms
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Sarah K. Madsen, Arthur W. Toga, Sima Chalavi, Priya Rajagopalan, Dick J. Veltman, Ellert R. S. Nijenhuis, Paola Dazzan, Antje A. T. S. Reinders, Paul M. Thompson, Mechteld E. Giesen, Nel Draijer, James H. Cole, Carmine M. Pariante, and Eline M. Vissia
- Subjects
medicine.medical_specialty ,Radiological and Ultrasound Technology ,medicine.drug_class ,Subiculum ,Traumatic stress ,Hippocampal formation ,Dissociative ,medicine.disease ,Structural magnetic resonance imaging ,Dissociative identity disorder ,Neurology ,Internal medicine ,medicine ,Etiology ,Cardiology ,Hippocampal volume ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Psychology ,Psychiatry - Abstract
Smaller hippocampal volume has been reported in individuals with post-traumatic stress disorder (PTSD) and dissociative identity disorder (DID), but the regional specificity of hippocampal volume reductions and the association with severity of dissociative symptoms and/or childhood traumatization are still unclear. Brain structural magnetic resonance imaging scans were analyzed for 33 outpatients (17 with DID and 16 with PTSD only) and 28 healthy controls (HC), all matched for age, sex, and education. DID patients met criteria for PTSD (PTSD-DID). Hippocampal global and subfield volumes and shape measurements were extracted. We found that global hippocampal volume was significantly smaller in all 33 patients (left: 6.75%; right: 8.33%) compared with HC. PTSD-DID (left: 10.19%; right: 11.37%) and PTSD-only with a history of childhood traumatization (left: 7.11%; right: 7.31%) had significantly smaller global hippocampal volume relative to HC. PTSD-DID had abnormal shape and significantly smaller volume in the CA2-3, CA4-DG and (pre)subiculum compared with HC. In the patient groups, smaller global and subfield hippocampal volumes significantly correlated with higher severity of childhood traumatization and dissociative symptoms. These findings support a childhood trauma-related etiology for abnormal hippocampal morphology in both PTSD and DID and can further the understanding of neurobiological mechanisms involved in these disorders.
- Published
- 2014
43. Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: AnN= 725 surface‐based Alzheimer's disease neuroimaging initiative study
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Yalin Wang, Paul M. Thompson, Richard J. Caselli, Boris A. Gutman, Jie Shi, Natasha Lepore, and Leslie C. Baxter
- Subjects
Apolipoprotein E ,Pathology ,medicine.medical_specialty ,Radiological and Ultrasound Technology ,Imaging biomarker ,Hippocampus ,Hippocampal formation ,medicine.disease ,3. Good health ,Neurology ,Neuroimaging ,medicine ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Allele ,Alzheimer's disease ,Psychology ,Alzheimer's Disease Neuroimaging Initiative - Abstract
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database—the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High‐order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor‐based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T 2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD. Hum Brain Mapp 35:3903–3918, 2014. © 2014 Wiley Periodicals, Inc.
- Published
- 2014
44. Cognitive and behavioral correlates of caudate subregion shape variation in fragile X syndrome
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Eve-Marie Quintin, Ryan Kelley, Mira M. Raman, Paul M. Thompson, Allan L. Reiss, and Daniel X. Peng
- Subjects
congenital, hereditary, and neonatal diseases and abnormalities ,Radiological and Ultrasound Technology ,Intelligence quotient ,Behavioral assessment ,Caudate nucleus ,Cognition ,medicine.disease ,Imaging data ,Fragile X syndrome ,Neurology ,medicine ,Radiology, Nuclear Medicine and imaging ,Psychological testing ,Neurology (clinical) ,Anatomy ,Abnormality ,Psychology ,Neuroscience - Abstract
Individuals with fragile X syndrome (FXS) exhibit frontal lobe-associated cognitive and behavioral deficits, including impaired general cognitive abilities, perseverative behaviors, and social difficulties. Neural signals related to these functions are communicated through frontostriatal circuits, which connect with distinct regions of the caudate nucleus (CN). Enlargement of the CN is the most robust and reproduced neuroanatomical abnormality in FXS, but very little is known on how this affects behavioral/cognitive outcomes in this condition. Here, we investigated topography within focal regions of the CN associated with prefrontal circuitry and its link with aberrant behavior and intellect in FXS. Imaging data were acquired from 48 individuals with FXS, 28 IQ-matched controls without FXS (IQ-CTL), and 36 typically developing controls (TD-CTL). Of the total participant count, cognitive and behavioral assessment data were obtained from 44 individuals with FXS and 27 participants in the IQ-CTL group. CN volume and topography were compared between groups. Correlations were performed between CN topography and cognitive as well as behavioral measures within FXS and IQ-CTL groups. As expected, the FXS group had larger CN compared with both IQ-CTL and TD-CTL groups. Correlations between focal CN topography and frontal lobe-associated cognitive and behavioral deficits in the FXS group supported the hypothesis that CN enlargement is related to abnormal orbitofrontal-caudate and dorsolateral-caudate circuitry in FXS. These findings deepen our understanding of neuroanatomical mechanisms underlying cognitive-behavioral problems in FXS and hold promise for informing future behavioral and psychopharmacological interventions targeting specific neural pathways.
- Published
- 2013
45. Reliability of neuroanatomical measurements in a multisite longitudinal study of youth at risk for psychosis
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Maolin Qiu, Kristin S. Cadenhead, Carrie E. Bearden, Doreen M. Olvet, Frank Sun, Tyrone D. Cannon, Jean Addington, Scott W. Woods, Thomas H. McGlashan, Sarah Jacobson McEwen, George He, Arthur W. Toga, Ming T. Tsuang, Aron Jacobson, Diana O. Perkins, Heline Mirzakhanian, Richard Frayne, Lei Zhou, Xiaoping Hu, Theo G.M. van Erp, Larry J. Seidman, R. Todd Constable, Aysenil Belger, Barbara A. Cornblatt, Paul M. Thompson, Xenophon Papademetris, Daniel H. Mathalon, Heidi W. Thermenos, and Elaine F. Walker
- Subjects
Longitudinal study ,Radiological and Ultrasound Technology ,Voxel-based morphometry ,computer.software_genre ,Brain mapping ,Imaging phantom ,Prodrome ,Neurology ,Neuroimaging ,Voxel ,Radiology, Nuclear Medicine and imaging ,Segmentation ,Neurology (clinical) ,Anatomy ,Psychology ,computer ,Neuroscience ,Cartography - Abstract
Multisite longitudinal neuroimaging designs are used to identify differential brain structural change associated with onset or progression of disease. The reliability of neuroanatomical measurements over time and across sites is a crucial aspect of power in such studies. Prior work has found that while within-site reliabilities of neuroanatomical measurements are excellent, between-site reliability is generally more modest. Factors that may increase between-site reliability include standardization of scanner platform and sequence parameters and correction for between-scanner variations in gradient nonlinearities. Factors that may improve both between- and within-site reliability include use of registration algorithms that account for individual differences in cortical patterning and shape. In this study 8 healthy volunteers were scanned twice on successive days at 8 sites participating in the North American Prodrome Longitudinal Study (NAPLS). All sites employed 3 Tesla scanners and standardized acquisition parameters. Site accounted for 2 to 30% of the total variance in neuroanatomical measurements. However, site-related variations were trivial (
- Published
- 2013
46. Development of insula connectivity between ages 12 and 30 revealed by high angular resolution diffusion imaging
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Emily L. Dennis, Nicholas G. Martin, Arthur W. Toga, Ian B. Hickie, Margaret J. Wright, Paul M. Thompson, Katie L. McMahon, Neda Jahanshad, and Greig I. de Zubicaray
- Subjects
Temporal cortex ,Radiological and Ultrasound Technology ,Parietal lobe ,Posterior parietal cortex ,behavioral disciplines and activities ,Temporal lobe ,nervous system ,Neurology ,Frontal lobe ,Neuroimaging ,mental disorders ,behavior and behavior mechanisms ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Psychology ,Neuroscience ,Insula ,psychological phenomena and processes ,Tractography - Abstract
The insula, hidden deep within the Sylvian fissures, has proven difficult to study from a connectivity perspective. Most of our current information on the anatomical connectivity of the insula comes from studies of nonhuman primates and post mortem human dissections. To date, only two neuroimaging studies have successfully examined the connectivity of the insula. Here we examine how the connectivity of the insula develops between ages 12 and 30, in 307 young adolescent and adult subjects scanned with 4-Tesla high angular resolution diffusion imaging (HARDI). The density of fiber connections between the insula and the frontal and parietal cortex decreased with age, but the connection density between the insula and the temporal cortex generally increased with age. This trajectory is in line with well-known patterns of cortical development in these regions. In addition, males and females showed different developmental trajectories for the connection between the left insula and the left precentral gyrus. The insula plays many different roles, some of them affected in neuropsychiatric disorders; this information on the insula's connectivity may help efforts to elucidate mechanisms of brain disorders in which it is implicated.
- Published
- 2013
47. Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation
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Jamie D. Feusner, Alex D. Leow, Olusola Ajilore, Lori L. Altshuler, Johnson GadElkarim, Dan Schonfeld, Liang Zhan, Anand Kumar, and Paul M. Thompson
- Subjects
Theoretical computer science ,Binary tree ,Radiological and Ultrasound Technology ,Community structure ,medicine.disease ,Brain mapping ,Developmental psychology ,Neurology ,Path length ,medicine ,Hum ,Connectome ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Bipolar disorder ,Anatomy ,Psychology ,Default mode network - Abstract
In this article, we present path length associated community estimation (PLACE), a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, ΨPL, which measures the difference between intercommunity versus intracommunity path lengths. We compared community structures in human healthy brain networks generated using these two metrics and argued that ΨPL may have theoretical advantages. PLACE consists of the following: (1) extracting community structure using top-down hierarchical binary trees, where a branch at each bifurcation denotes a collection of nodes that form a community at that level, (2) constructing and assessing mean group community structure, and (3) detecting node-level changes in community between groups. We applied PLACE and investigated the structural brain networks obtained from a sample of 25 euthymic bipolar I subjects versus 25 gender- and age-matched healthy controls. Results showed community structural differences in posterior default mode network regions, with the bipolar group exhibiting left-right decoupling. Hum Brain Mapp 35:2253–2264, 2014. © 2013 Wiley Periodicals, Inc.
- Published
- 2013
48. A commonly carried genetic variant in the delta opioid receptor gene,OPRD1,is associated with smaller regional brain volumes: Replication in elderly and young populations
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Nicholas G. Martin, Clifford R. Jack, Paul M. Thompson, Greig I. de Zubicaray, Grant W. Montgomery, Derrek P. Hibar, Michael Weiner, Narelle K. Hansell, Arthur W. Toga, Margaret J. Wright, Neda Jahanshad, Elizabeth R. Sowell, Katie L. McMahon, Marina Barysheva, Florence F. Roussotte, and Omid Kohannim
- Subjects
Radiological and Ultrasound Technology ,Addiction ,media_common.quotation_subject ,Disease ,Bioinformatics ,medicine.disease ,δ-opioid receptor ,Neurology ,Opioid ,Neuroimaging ,medicine ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Allele ,Alzheimer's disease ,Receptor ,Psychology ,Neuroscience ,media_common ,medicine.drug - Abstract
Delta opioid receptors are implicated in a variety of psychiatric and neurological disorders. These receptors play a key role in the reinforcing properties of drugs of abuse, and polymorphisms in OPRD1 (the gene encoding delta opioid receptors) are associated with drug addiction. Delta opioid receptors are also involved in protecting neurons against hypoxic and ischemic stress. Here, we first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer's Disease Neuroimaging Initiative. We hypothesized that common variants in OPRD1 would be associated with differences in brain structure, particularly in regions relevant to addictive and neurodegenerative disorders. One very common variant (rs678849) predicted differences in regional brain volumes. We replicated the association of this single-nucleotide polymorphism with regional tissue volumes in a large sample of young participants in the Queensland Twin Imaging study. Although the same allele was associated with reduced volumes in both cohorts, the brain regions affected differed between the two samples. In healthy elderly, exploratory analyses suggested that the genotype associated with reduced brain volumes in both cohorts may also predict cerebrospinal fluid levels of neurodegenerative biomarkers, but this requires confirmation. If opiate receptor genetic variants are related to individual differences in brain structure, genotyping of these variants may be helpful when designing clinical trials targeting delta opioid receptors to treat neurological disorders.
- Published
- 2013
49. Mapping white matter integrity in elderly people with HIV
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Paul M. Thompson, Edgar Busovaca, Lauren A. Wendelken, Krista Nicolas, Talia M. Nir, Neda Jahanshad, and Victor Valcour
- Subjects
Cart ,Pathology ,medicine.medical_specialty ,Radiological and Ultrasound Technology ,Neuropsychology ,Human immunodeficiency virus (HIV) ,Physiology ,Corpus callosum ,medicine.disease ,medicine.disease_cause ,White matter ,Atrophy ,medicine.anatomical_structure ,Neurology ,Fractional anisotropy ,medicine ,Radiology, Nuclear Medicine and imaging ,Neurology (clinical) ,Anatomy ,Psychology ,Diffusion MRI - Abstract
People with HIV are living longer as combination antiretroviral therapy (cART) becomes more widely available. However, even when plasma viral load is reduced to untraceable levels, chronic HIV infection is associated with neurological deficits and brain atrophy beyond that of normal aging. HIV is often marked by cortical and subcortical atrophy, but the integrity of the brain's white matter (WM) pathways also progressively declines. Few studies focus on older cohorts where normal aging may be compounded with HIV infection to influence deficit patterns. In this relatively large diffusion tensor imaging (DTI) study, we investigated abnormalities in WM fiber integrity in 56 HIV+ adults with access to cART (mean age: 63.9 ± 3.7 years), compared to 31 matched healthy controls (65.4 ± 2.2 years). Statistical 3D maps revealed the independent effects of HIV diagnosis and age on fractional anisotropy (FA) and diffusivity, but we did not find any evidence for an age by diagnosis interaction in our current sample. Compared to healthy controls, HIV patients showed pervasive FA decreases and diffusivity increases throughout WM. We also assessed neuropsychological (NP) summary z-score associations. In both patients and controls, fiber integrity measures were associated with NP summary scores. The greatest differences were detected in the corpus callosum and in the projection fibers of the corona radiata. These deficits are consistent with published NP deficits and cortical atrophy patterns in elderly people with HIV.
- Published
- 2013
50. 3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease
- Author
-
Yan, Jin, Chao, Huang, Madelaine, Daianu, Liang, Zhan, Emily L, Dennis, Robert I, Reid, Clifford R, Jack, Hongtu, Zhu, and Paul M, Thompson
- Subjects
Male ,functional statistical analysis ,Neuropsychological Tests ,Alzheimer's disease ,Magnetic Resonance Imaging ,Nerve Fibers, Myelinated ,White Matter ,Diffusion Tensor Imaging ,Imaging, Three-Dimensional ,Alzheimer Disease ,Leukoencephalopathies ,diffusion‐weighted MRI ,Humans ,Female ,Research Articles ,Research Article ,tract‐specific analysis - Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.
- Published
- 2016
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