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1. Cortical profiles of numerous psychiatric disorders and normal development share a common pattern.

Author

Cao, Z, Cupertino, RB, Ottino-Gonzalez, J, Murphy, A, Pancholi, D, Juliano, A, Chaarani, B, Albaugh, M, Yuan, D, Schwab, N, Stafford, J, Goudriaan, AE, Hutchison, K, Li, C-SR, Luijten, M, Groefsema, M, Momenan, R, Schmaal, L, Sinha, R, van Holst, RJ, Veltman, DJ, Wiers, RW, Porjesz, B, Lett, T, Banaschewski, T, Bokde, ALW, Desrivières, S, Flor, H, Grigis, A, Gowland, P, Heinz, A, Brühl, R, Martinot, J-L, Martinot, M-LP, Artiges, E, Nees, F, Orfanos, DP, Paus, T, Poustka, L, Hohmann, S, Millenet, S, Fröhner, JH, Robinson, L, Smolka, MN, Walter, H, Winterer, J, Schumann, G, Whelan, R, Bhatt, RR, Zhu, A, Conrod, P, Jahanshad, N, Thompson, PM, Mackey, S, Garavan, H, IMAGEN Consortium, ENIGMA Addiction Working Group, Cao, Z, Cupertino, RB, Ottino-Gonzalez, J, Murphy, A, Pancholi, D, Juliano, A, Chaarani, B, Albaugh, M, Yuan, D, Schwab, N, Stafford, J, Goudriaan, AE, Hutchison, K, Li, C-SR, Luijten, M, Groefsema, M, Momenan, R, Schmaal, L, Sinha, R, van Holst, RJ, Veltman, DJ, Wiers, RW, Porjesz, B, Lett, T, Banaschewski, T, Bokde, ALW, Desrivières, S, Flor, H, Grigis, A, Gowland, P, Heinz, A, Brühl, R, Martinot, J-L, Martinot, M-LP, Artiges, E, Nees, F, Orfanos, DP, Paus, T, Poustka, L, Hohmann, S, Millenet, S, Fröhner, JH, Robinson, L, Smolka, MN, Walter, H, Winterer, J, Schumann, G, Whelan, R, Bhatt, RR, Zhu, A, Conrod, P, Jahanshad, N, Thompson, PM, Mackey, S, Garavan, H, IMAGEN Consortium, and ENIGMA Addiction Working Group

Abstract

The neurobiological bases of the association between development and psychopathology remain poorly understood. Here, we identify a shared spatial pattern of cortical thickness (CT) in normative development and several psychiatric and neurological disorders. Principal component analysis (PCA) was applied to CT of 68 regions in the Desikan-Killiany atlas derived from three large-scale datasets comprising a total of 41,075 neurotypical participants. PCA produced a spatially broad first principal component (PC1) that was reproducible across datasets. Then PC1 derived from healthy adult participants was compared to the pattern of CT differences associated with psychiatric and neurological disorders comprising a total of 14,886 cases and 20,962 controls from seven ENIGMA disease-related working groups, normative maturation and aging comprising a total of 17,697 scans from the ABCD Study® and the IMAGEN developmental study, and 17,075 participants from the ENIGMA Lifespan working group, as well as gene expression maps from the Allen Human Brain Atlas. Results revealed substantial spatial correspondences between PC1 and widespread lower CT observed in numerous psychiatric disorders. Moreover, the PC1 pattern was also correlated with the spatial pattern of normative maturation and aging. The transcriptional analysis identified a set of genes including KCNA2, KCNS1 and KCNS2 with expression patterns closely related to the spatial pattern of PC1. The gene category enrichment analysis indicated that the transcriptional correlations of PC1 were enriched to multiple gene ontology categories and were specifically over-represented starting at late childhood, coinciding with the onset of significant cortical maturation and emergence of psychopathology during the prepubertal-to-pubertal transition. Collectively, the present study reports a reproducible latent pattern of CT that captures interregional profiles of cortical changes in both normative brain maturation and a spectrum of ps

Published
2023

2. Effects of urban living environments on mental health in adults.

Author

Xu, Jiayuan, Liu, Nana, Polemiti, E., Garcia-Mondragon, L., Tang, J., Liu, Xiaoxuan, Lett, T., Yu, L., Nöthen, M.M., Feng, J., Yu, C., Marquand, A.F., Schumann, G., Xu, Jiayuan, Liu, Nana, Polemiti, E., Garcia-Mondragon, L., Tang, J., Liu, Xiaoxuan, Lett, T., Yu, L., Nöthen, M.M., Feng, J., Yu, C., Marquand, A.F., and Schumann, G.

Abstract

01 juni 2023, Item does not contain fulltext, Urban-living individuals are exposed to many environmental factors that may combine and interact to influence mental health. While individual factors of an urban environment have been investigated in isolation, no attempt has been made to model how complex, real-life exposure to living in the city relates to brain and mental health, and how this is moderated by genetic factors. Using the data of 156,075 participants from the UK Biobank, we carried out sparse canonical correlation analyses to investigate the relationships between urban environments and psychiatric symptoms. We found an environmental profile of social deprivation, air pollution, street network and urban land-use density that was positively correlated with an affective symptom group (r = 0.22, P(perm) < 0.001), mediated by brain volume differences consistent with reward processing, and moderated by genes enriched for stress response, including CRHR1, explaining 2.01% of the variance in brain volume differences. Protective factors such as greenness and generous destination accessibility were negatively correlated with an anxiety symptom group (r = 0.10, P(perm) < 0.001), mediated by brain regions necessary for emotion regulation and moderated by EXD3, explaining 1.65% of the variance. The third urban environmental profile was correlated with an emotional instability symptom group (r = 0.03, P(perm) < 0.001). Our findings suggest that different environmental profiles of urban living may influence specific psychiatric symptom groups through distinct neurobiological pathways.

Published
2023

3. Predicting alcohol dependence frommulti-sitebrain structural measures

Author

Hahn, S, Mackey, S, Cousijn, J, Foxe, JJ, Heinz, A, Hester, R, Hutchinson, K, Kiefer, F, Korucuoglu, O, Lett, T, Li, C-SR, London, E, Lorenzetti, V, Maartje, L, Momenan, R, Orr, C, Paulus, M, Schmaal, L, Sinha, R, Sjoerds, Z, Stein, DJ, Stein, E, van Holst, RJ, Veltman, D, Walter, H, Wiers, RW, Yucel, M, Thompson, PM, Conrod, P, Allgaier, N, Garavan, H, Hahn, S, Mackey, S, Cousijn, J, Foxe, JJ, Heinz, A, Hester, R, Hutchinson, K, Kiefer, F, Korucuoglu, O, Lett, T, Li, C-SR, London, E, Lorenzetti, V, Maartje, L, Momenan, R, Orr, C, Paulus, M, Schmaal, L, Sinha, R, Sjoerds, Z, Stein, DJ, Stein, E, van Holst, RJ, Veltman, D, Walter, H, Wiers, RW, Yucel, M, Thompson, PM, Conrod, P, Allgaier, N, and Garavan, H

Abstract

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

Published
2022

7. Functional neuroimaging effects of recently discovered genetic risk loci for schizophrenia and polygenic risk profile in five RDoC subdomains

Author

Erk, S, primary, Mohnke, S, additional, Ripke, S, additional, Lett, T A, additional, Veer, I M, additional, Wackerhagen, C, additional, Grimm, O, additional, Romanczuk-Seiferth, N, additional, Degenhardt, F, additional, Tost, H, additional, Mattheisen, M, additional, Mühleisen, T W, additional, Charlet, K, additional, Skarabis, N, additional, Kiefer, F, additional, Cichon, S, additional, Witt, S H, additional, Nöthen, M M, additional, Rietschel, M, additional, Heinz, A, additional, Meyer-Lindenberg, A, additional, and Walter, H, additional

Published
2017
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8. Brain White Matter Development Is Associated with a Human-Specific Haplotype Increasing the Synthesis of Long Chain Fatty Acids

Author

Peters, B. D., primary, Voineskos, A. N., additional, Szeszko, P. R., additional, Lett, T. A., additional, DeRosse, P., additional, Guha, S., additional, Karlsgodt, K. H., additional, Ikuta, T., additional, Felsky, D., additional, John, M., additional, Rotenberg, D. J., additional, Kennedy, J. L., additional, Lencz, T., additional, and Malhotra, A. K., additional

Published
2014
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10. What we learn about bipolar disorder from large-scale neuroimaging

Author

Christian K. Tamnes, Bartholomeus C M Haarman, Jair C. Soares, Ole A. Andreassen, Viola Oertel, Theodore D. Satterthwaite, G. Tronchin, Michael Stäblein, Bradley J. MacIntosh, Melissa Pauling, Christopher R.K. Ching, Daniel H. Wolf, Dick J. Veltman, Ingrid Agartz, Bernhard T. Baune, Salvador Sarró, Mon-Ju Wu, Scott C Fears, Eduard Vieta, Melissa J. Green, Neeltje E.M. van Haren, Yann Quidé, Erlend Bøen, Yash Patel, Igor Nenadic, Martin Alda, Lisa T. Eyler, Arnaud Pouchon, Danai Dima, Tomáš Paus, Irene Bollettini, Torbjørn Elvsåshagen, Rachel M. Brouwer, Lakshmi N. Yatham, Michael Bauer, Caterina del Mar Bonnín, C. McDonald, Udo Dannlowski, Bronwyn Overs, Edith Pomarol-Clotet, Cristian Vargas Upegui, Oliver Gruber, Henricus G. Ruhé, Márcio Gerhardt Soeiro-de-Souza, Edouard Duchesnay, Hilary P. Blumberg, Tilo Kircher, Miho Ota, Michael Berk, Christoph Abé, Andreas Jansen, Kang Sim, Heather C. Whalley, Derrek P. Hibar, Roel A. Ophoff, Georgios V Thomaidis, Henrik Walter, Sophia Frangou, Michèle Wessa, Dara M. Cannon, Cara M. Altimus, Allison C. Nugent, Rodrigo Machado-Vieira, Orwa Dandash, Marcella Bellani, Unn K. Haukvik, Philip B. Mitchell, Ling-Li Zeng, Christian Knöchel, Jose Manuel Goikolea, Sonja M C de Zwarte, Francesco Benedetti, Sara Poletti, Janice M. Fullerton, Carlos A. Zarate, Aart H. Schene, Dan J. Stein, Chantal Henry, Tristram A. Lett, Mikael Landén, Daniel L Pham, Paolo Brambilla, Silvia Alonso-Lana, Sophia I. Thomopoulos, Carlos López-Jaramillo, Tomas Hajek, Bernd Kramer, G. Delvecchio, Maria M. Rive, Lars T. Westlye, Erick J. Canales-Rodríguez, Victoria L. Ives-Deliperi, Dominik Grotegerd, Beny Lafer, Abraham Nunes, Carrie E. Bearden, Raymond Salvador, Joaquim Radua, Amy C Bilderbeck, Xavier Caseras, Paul M. Thompson, Jorge R. C. Almeida, Pauline Favre, Gloria Roberts, David C. Glahn, Dag Alnæs, Julian A Pineda-Zapata, Tiril P. Gurholt, Mircea Polosan, Josselin Houenou, Fabiano G. Nery, Leila Nabulsi, Mary L. Phillips, Fleur M. Howells, Ana M. Díaz-Zuluaga, Elisa M T Melloni, Ching, C. R. K., Hibar, D. P., Gurholt, T. P., Nunes, A., Thomopoulos, S. I., Abe, C., Agartz, I., Brouwer, R. M., Cannon, D. M., de Zwarte, S. M. C., Eyler, L. T., Favre, P., Hajek, T., Haukvik, U. K., Houenou, J., Landen, M., Lett, T. A., Mcdonald, C., Nabulsi, L., Patel, Y., Pauling, M. E., Paus, T., Radua, J., Soeiro-de-Souza, M. G., Tronchin, G., van Haren, N. E. M., Vieta, E., Walter, H., Zeng, L. -L., Alda, M., Almeida, J., Alnaes, D., Alonso-Lana, S., Altimus, C., Bauer, M., Baune, B. T., Bearden, C. E., Bellani, M., Benedetti, F., Berk, M., Bilderbeck, A. C., Blumberg, H. P., Boen, E., Bollettini, I., del Mar Bonnin, C., Brambilla, P., Canales-Rodriguez, E. J., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Diaz-Zuluaga, A. M., Dima, D., Duchesnay, E., Elvsashagen, T., Fears, S. C., Frangou, S., Fullerton, J. M., Glahn, D. C., Goikolea, J. M., Green, M. J., Grotegerd, D., Gruber, O., Haarman, B. C. M., Henry, C., Howells, F. M., Ives-Deliperi, V., Jansen, A., Kircher, T. T. J., Knochel, C., Kramer, B., Lafer, B., Lopez-Jaramillo, C., Machado-Vieira, R., Macintosh, B. J., Melloni, E. M. T., Mitchell, P. B., Nenadic, I., Nery, F., Nugent, A. C., Oertel, V., Ophoff, R. A., Ota, M., Overs, B. J., Pham, D. L., Phillips, M. L., Pineda-Zapata, J. A., Poletti, S., Polosan, M., Pomarol-Clotet, E., Pouchon, A., Quide, Y., Rive, M. M., Roberts, G., Ruhe, H. G., Salvador, R., Sarro, S., Satterthwaite, T. D., Schene, A. H., Sim, K., Soares, J. C., Stablein, M., Stein, D. J., Tamnes, C. K., Thomaidis, G. V., Upegui, C. V., Veltman, D. J., Wessa, M., Westlye, L. T., Whalley, H. C., Wolf, D. H., Wu, M. -J., Yatham, L. N., Zarate, C. A., Thompson, P. M., and Andreassen, O. A.

Subjects
mega-analysis, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], cortical surface area, Review Article, 0302 clinical medicine, Manic-depressive illness, Multicenter Studies as Topic, Spectrum disorder, Review Articles, bipolar disorder, Cerebral Cortex, Trastorn bipolar, neuroimaging, Radiological and Ultrasound Technology, 05 social sciences, ENIGMA, HUMAN BRAIN, Magnetic Resonance Imaging, psychiatry, 3. Good health, Neurology, Meta-analysis, Scale (social sciences), Anatomy, Psychology, Clinical risk factor, Clinical psychology, MRI, MAJOR PSYCHIATRIC-DISORDERS, Schizoaffective disorder, 050105 experimental psychology, 03 medical and health sciences, Magnetic resonance imaging, Neuroimaging, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, Imatges per ressonància magnètica, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Bipolar disorder, HIPPOCAMPAL VOLUMES, mega‐analysis, GRAY-MATTER VOLUME, SPECTRUM DISORDER, volume, DIABETES-MELLITUS, cortical thickness, COGNITIVE IMPAIRMENT, medicine.disease, Mental illness, meta-analysis, meta‐analysis, RC0321, Neurology (clinical), SCHIZOAFFECTIVE DISORDER, PSYCHOTIC FEATURES, 030217 neurology & neurosurgery
Abstract

MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Published
2022
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11. 10Kin1day: A Bottom-Up Neuroimaging Initiative

Author

Martijn P. van den Heuvel, Lianne H. Scholtens, Hannelore K. van der Burgh, Federica Agosta, Clara Alloza, Celso Arango, Bonnie Auyeung, Simon Baron-Cohen, Silvia Basaia, Manon J. N. L. Benders, Frauke Beyer, Linda Booij, Kees P. J. Braun, Geraldo Busatto Filho, Wiepke Cahn, Dara M. Cannon, Tiffany M. Chaim-Avancini, Sandra S. M. Chan, Eric Y. H. Chen, Benedicto Crespo-Facorro, Eveline A. Crone, Udo Dannlowski, Sonja M. C. de Zwarte, Bruno Dietsche, Gary Donohoe, Stefan Du Plessis, Sarah Durston, Covadonga M. Díaz-Caneja, Ana M. Díaz-Zuluaga, Robin Emsley, Massimo Filippi, Thomas Frodl, Martin Gorges, Beata Graff, Dominik Grotegerd, Dariusz Gąsecki, Julie M. Hall, Laurena Holleran, Rosemary Holt, Helene J. Hopman, Andreas Jansen, Joost Janssen, Krzysztof Jodzio, Lutz Jäncke, Vasiliy G. Kaleda, Jan Kassubek, Shahrzad Kharabian Masouleh, Tilo Kircher, Martijn G. J. C. Koevoets, Vladimir S. Kostic, Axel Krug, Stephen M. Lawrie, Irina S. Lebedeva, Edwin H. M. Lee, Tristram A. Lett, Simon J. G. Lewis, Franziskus Liem, Michael V. Lombardo, Carlos Lopez-Jaramillo, Daniel S. Margulies, Sebastian Markett, Paulo Marques, Ignacio Martínez-Zalacaín, Colm McDonald, Andrew M. McIntosh, Genevieve McPhilemy, Susanne L. Meinert, José M. Menchón, Christian Montag, Pedro S. Moreira, Pedro Morgado, David O. Mothersill, Susan Mérillat, Hans-Peter Müller, Leila Nabulsi, Pablo Najt, Krzysztof Narkiewicz, Patrycja Naumczyk, Bob Oranje, Victor Ortiz-Garcia de la Foz, Jiska S. Peper, Julian A. Pineda, Paul E. Rasser, Ronny Redlich, Jonathan Repple, Martin Reuter, Pedro G. P. Rosa, Amber N. V. Ruigrok, Agnieszka Sabisz, Ulrich Schall, Soraya Seedat, Mauricio H. Serpa, Stavros Skouras, Carles Soriano-Mas, Nuno Sousa, Edyta Szurowska, Alexander S. Tomyshev, Diana Tordesillas-Gutierrez, Sofie L. Valk, Leonard H. van den Berg, Theo G. M. van Erp, Neeltje E. M. van Haren, Judith M. C. van Leeuwen, Arno Villringer, Christiaan H. Vinkers, Christian Vollmar, Lea Waller, Henrik Walter, Heather C. Whalley, Marta Witkowska, A. Veronica Witte, Marcus V. Zanetti, Rui Zhang, Siemon C. de Lange, University Medical Center [Utrecht], Center for Nanotechnology Innovation, @NEST (CNI), National Enterprise for nanoScience and nanoTechnology (NEST), Scuola Normale Superiore di Pisa (SNS)-Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP)-Istituto Italiano di Tecnologia (IIT)-Consiglio Nazionale delle Ricerche [Pisa] (CNR PISA)-Scuola Normale Superiore di Pisa (SNS)-Scuola Universitaria Superiore Sant'Anna [Pisa] (SSSUP)-Istituto Italiano di Tecnologia (IIT)-Consiglio Nazionale delle Ricerche [Pisa] (CNR PISA), Psychiatry Department, Adolescent Unit, Hospital General Universitario Gregorio Marañón, Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, University of Edinburgh, University of Cambridge [UK] (CAM), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Department of Psychiatry, Icahn School of Medicine at Mount Sinai [New York] (MSSM), National University of Ireland [Galway] (NUI Galway), Centro de Investigación Biomédica en Red Salud Mental [Madrid] (CIBER-SAM), Trinity College Dublin-St. James's Hospital, University Hospital San Raffaele, Psychiatry and Psychotherapy, Universität Zürich [Zürich] = University of Zurich (UZH), Department of Neurology [Ulm], Universität Ulm - Ulm University [Ulm, Allemagne], Max-Planck-Institut für Mathematik in den Naturwissenschaften (MPI-MiS), Max-Planck-Gesellschaft, Dept. of Psychiatry, University of Marburg, Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Department of Psychology, Laboratory of Neurogenetics, sans affiliation, Division of Psychiatry, University of Edinburgh-Royal Edinburgh Hospital, Centro de Quimica Estrutural (CQE), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), Humboldt-Universität zu Berlin, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS), Instituto Superior Técnico, Universidade Técnica de Lisboa, Schizophrenia Research Institute [Sydney], Magnetic Resonance Imaging, Universidade do Minho, Metacohorts Consortium, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Department of Psychiatry and Human Behavior [Irvine], University of California [Irvine] (UCI), University of California-University of California, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Berlin School of Mind and Brain [Berlin], Department of Chemistry, Centre for Molecular Simulation, University of Calgary, Child and Adolescent Psychiatry / Psychology, Utrecht University, Wellcome Trust, Medical Research Council (UK), Canadian Institutes of Health Research, European Research Council, European Commission, German Research Foundation, Science Foundation Ireland, Russian Foundation for Basic Research, Fundação para a Ciência e a Tecnologia (Portugal), Instituto de Salud Carlos III, National Institutes of Health (US), Van Den Heuvel, M. P., Scholtens, L. H., Van Der Burgh, H. K., Agosta, F., Alloza, C., Arango, C., Auyeung, B., Baron-Cohen, S., Basaia, S., Benders, M. J. N. L., Beyer, F., Booij, L., Braun, K. P. J., Filho, G. B., Cahn, W., Cannon, D. M., Chaim-Avancini, T. M., Chan, S. S. M., Chen, E. Y. H., Crespo-Facorro, B., Crone, E. A., Dannlowski, U., De Zwarte, S. M. C., Dietsche, B., Donohoe, G., Plessis, S. D., Durston, S., Diaz-Caneja, C. M., Diaz-Zuluaga, A. M., Emsley, R., Filippi, M., Frodl, T., Gorges, M., Graff, B., Grotegerd, D., Gasecki, D., Hall, J. M., Holleran, L., Holt, R., Hopman, H. J., Jansen, A., Janssen, J., Jodzio, K., Jancke, L., Kaleda, V. G., Kassubek, J., Masouleh, S. K., Kircher, T., Koevoets, M. G. J. C., Kostic, V. S., Krug, A., Lawrie, S. M., Lebedeva, I. S., Lee, E. H. M., Lett, T. A., Lewis, S. J. G., Liem, F., Lombardo, M. V., Lopez-Jaramillo, C., Margulies, D. S., Markett, S., Marques, P., Martinez-Zalacain, I., Mcdonald, C., Mcintosh, A. M., Mcphilemy, G., Meinert, S. L., Menchon, J. M., Montag, C., Moreira, P. S., Morgado, P., Mothersill, D. O., Merillat, S., Muller, H. -P., Nabulsi, L., Najt, P., Narkiewicz, K., Naumczyk, P., Oranje, B., De la Foz, V. O. -G., Peper, J. S., Pineda, J. A., Rasser, P. E., Redlich, R., Repple, J., Reuter, M., Rosa, P. G. P., Ruigrok, A. N. V., Sabisz, A., Schall, U., Seedat, S., Serpa, M. H., Skouras, S., Soriano-Mas, C., Sousa, N., Szurowska, E., Tomyshev, A. S., Tordesillas-Gutierrez, D., Valk, S. L., Van Den Berg, L. H., Van Erp, T. G. M., Van Haren, N. E. M., Van Leeuwen, J. M. C., Villringer, A., Vinkers, C. H., Vollmar, C., Waller, L., Walter, H., Whalley, H. C., Witkowska, M., Witte, A. V., Zanetti, M. V., Zhang, R., De Lange, S. C., Baron-Cohen, Simon [0000-0001-9217-2544], Ruigrok, Amber [0000-0001-7711-8056], and Apollo - University of Cambridge Repository

Subjects
Computer science, diffusion weighted MRI, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], Network, Brain mapping, lcsh:RC346-429, HUMAN CONNECTOME, Diffusion, 0302 clinical medicine, Medicine and Health Sciences, yttria mould coating, Cervell, Anàlisi, ComputingMilieux_MISCELLANEOUS, Brain network, 0303 health sciences, Event (computing), Brain, Human Connectome, Top-down and bottom-up design, 3. Good health, Neurology, investment casting, Perspective, Connectome, Difusió, PROJECT, MRI, Connectome analysis, AZ91D-1 wt% CaO, brain, Clinical Neurology, 03 medical and health sciences, SDG 17 - Partnerships for the Goals, Neuroimaging, Journal Article, ddc:610, Diffusion weighted MRI, lcsh:Neurology. Diseases of the nervous system, 030304 developmental biology, Connectome analysi, Science & Technology, Assaying, [SCCO.NEUR]Cognitive science/Neuroscience, mould–metal interaction, Biology and Life Sciences, Data science, Clinical neurology, network, Neurology (clinical), HUMAN CEREBRAL-CORTEX, 030217 neurology & neurosurgery
Abstract

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain. Ongoing grand-scale projects like the European Human Brain Project (1), the US Brain Initiative (2), the Human Connectome Project (3), the Chinese Brainnetome (4) and exciting world-wide neuroimaging collaborations such as ENIGMA (5) herald the new era of big neuroscience. In conjunction with these major undertakings, there is an emerging trend for bottom-up initiatives, starting with small-scale projects built upon existing collaborations and infrastructures. As described by Mainen et al. (6), these initiatives are centralized around self-organized groups of researchers working on the same challenges and sharing interests and specialized expertise. These projects could scale and open up to a larger audience and other disciplines over time, eventually lining up and merging their findings with other programs to make the bigger picture., The 10Kin1day workshop was generously sponsored by the Neuroscience and Cognition program Utrecht (NCU) of the Utrecht University (https://www.uu.nl/en/research/ neuroscience-and-cognition-utrecht), the ENIGMA consortium (http://enigma.ini.usc.edu), and personal grants: MvdH: NWOVIDI (452-16-015), MQ Fellowship; SB-C: the Wellcome Trust; Medical Research Council UK; NIHR CLAHRC for Cambridgeshire and Peterborough Foundation National Health Services Trust; Autism Research Trust; LB: New Investigator Award, Canadian Institutes of Health Research; Dara Cannon: Health Research Board (HRB), Ireland (grant code HRA-POR2013-324); SC: Research Grant Council (Hong Kong)-GRF 14101714; Eveline Crone: ERC-2010-StG-263234; UD: DFG, grant FOR2107 DA1151/5-1, DA1151/5-2, SFB-TRR58, Project C09, IZKF, grant Dan3/012/17; SD: MRC-RFA-UFSP-012013 (Shared Roots MRC Flagship grant); TF: Marie Curie Programme, International Training Programme, r’Birth; DG: National Science Centre (UMO-2011/02/A/NZ5/00329); BG: National Science Centre (UMO-2011/02/A/NZ5/00329); JH: Western Sydney University Postgraduate Research Award; LH: Science Foundation Ireland, ERC; HH: Research Grant Council (Hong Kong)-GRF 14101714; LJ: Velux Stiftung, grant 369 & UZH University Research Priority Program Dynamics of Healthy Aging; AJ: DFG, grant FOR2107 JA 1890/7-1; KJ: National Science Centre (UMO-2013/09/N/HS6/02634); VK: The Russian Foundation for Basic Research (grant code 15-06-05758A); TK: DFG, grant FOR2107 KI 588/14-1, DFG, grant FOR2107 KI 588/15-1; AK: DFG, grant FOR2107 KO 4291/4-1, DFG, grant FOR2107 KO 4291/3-1; IL: The Russian Foundation for Basic Research (grant code 15-06-05758A); EL: Health and Medical Research Fund - 11121271; SiL: NHMRC-ARC Dementia Fellowship 1110414, NHMRC Dementia Research Team Grant 1095127, NHMRC Project Grant 1062319; CL-J: 537-2011, 2014849; AM: Wellcome Trust Strategic Award (104036/Z/14/Z), MRC Grant MC_PC_17209; CM: Heisenberg-Grant, German Research Foundation, DFG MO 2363/3-2; PM: Foundation for Science and Technology, Portugal - PDE/BDE/113601/2015; KN: National Science Centre (UMO-2011/02/A/NZ5/00329); PN: National Science Centre (UMO-2013/09/N/HS6/02634); JiP: NWO-Veni 451-10-007; PaR: PER and US would like to thank the Schizophrenia Research Institute and the Chief-Investigators of the Australian Schizophrenia Research Bank V. Carr, U. Schall, R. Scott, A. Jablensky, B. Mowry, P. Michie, S. Catts, F. Henskens, and C. Pantelis; AS: National Science Centre (UMO-2011/02/A/NZ5/00329); SS: European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 707730; CS-M: Carlos III Health Institute (PI13/01958), Carlos III Health Institute (PI16/00889), Carlos III Health Institute (CPII16/00048); ES: National Science Centre (UMO-2011/02/A/NZ5/00329); AT: The Russian Foundation for Basic Research (grant code 1506-05758A); DT-G: PI14/00918, PI14/00639; Leonardo Tozzi: Marie Curie Programme, International Training Programme, r’Birth; SV: IMPRS Neurocom stipend; TvE: National Center for Research Resources at the National Institutes of Health (grant numbers: NIH 1 U24 RR021992 (Function Biomedical Informatics Research Network), NIH 1 U24 RR025736-01 (Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org) and the NIH Big Data to Knowledge (BD2K) award (U54 EB020403 to Paul Thompson). NvH: NWO-VIDI (452-11-014); MW: National Science Centre (UMO-2011/02/A/NZ5/00329); Veronica O’Keane: Meath Foundation; AV and AW: CRC Obesity Mechanism (SFB 1052) Project A1 funded by DFG. The funding sources had no role in the study design, data collection, analysis, and interpretation of the data.

Published
2019
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12. Diurnal oscillations of MRI metrics in the brains of male participants.

Author

Carlucci M, Lett T, Chavez S, Malinowski A, Lobaugh NJ, and Petronis A

Subjects
Adult, Humans, Male, Brain diagnostic imaging, Magnetic Resonance Imaging, Neuroimaging, Circadian Rhythm physiology, Bipolar Disorder
Abstract

Regulation of biological processes according to a 24-hr rhythm is essential for the normal functioning of an organism. Temporal variation in brain MRI data has often been attributed to circadian or diurnal oscillations; however, it is not clear if such oscillations exist. Here, we provide evidence that diurnal oscillations indeed govern multiple MRI metrics. We recorded cerebral blood flow, diffusion-tensor metrics, T1 relaxation, and cortical structural features every three hours over a 24-hr period in each of 16 adult male controls and eight adult male participants with bipolar disorder. Diurnal oscillations are detected in numerous MRI metrics at the whole-brain level, and regionally. Rhythmicity parameters in the participants with bipolar disorder are similar to the controls for most metrics, except for a larger phase variation in cerebral blood flow. The ubiquitous nature of diurnal oscillations has broad implications for neuroimaging studies and furthers our understanding of the dynamic nature of the human brain., (© 2023. Crown.)

Published
2023
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13. Effects of urban living environments on mental health in adults.

Author

Xu J, Liu N, Polemiti E, Garcia-Mondragon L, Tang J, Liu X, Lett T, Yu L, Nöthen MM, Feng J, Yu C, Marquand A, and Schumann G

Subjects
Humans, Adult, Anxiety epidemiology, Mood Disorders, Cities, Mental Health, Air Pollution adverse effects
Abstract

Urban-living individuals are exposed to many environmental factors that may combine and interact to influence mental health. While individual factors of an urban environment have been investigated in isolation, no attempt has been made to model how complex, real-life exposure to living in the city relates to brain and mental health, and how this is moderated by genetic factors. Using the data of 156,075 participants from the UK Biobank, we carried out sparse canonical correlation analyses to investigate the relationships between urban environments and psychiatric symptoms. We found an environmental profile of social deprivation, air pollution, street network and urban land-use density that was positively correlated with an affective symptom group (r = 0.22, P perm  < 0.001), mediated by brain volume differences consistent with reward processing, and moderated by genes enriched for stress response, including CRHR1, explaining 2.01% of the variance in brain volume differences. Protective factors such as greenness and generous destination accessibility were negatively correlated with an anxiety symptom group (r = 0.10, P perm  < 0.001), mediated by brain regions necessary for emotion regulation and moderated by EXD3, explaining 1.65% of the variance. The third urban environmental profile was correlated with an emotional instability symptom group (r = 0.03, P perm  < 0.001). Our findings suggest that different environmental profiles of urban living may influence specific psychiatric symptom groups through distinct neurobiological pathways., (© 2023. The Author(s).)

Published
2023
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14. Predicting alcohol dependence from multi-site brain structural measures.

Author

Hahn S, Mackey S, Cousijn J, Foxe JJ, Heinz A, Hester R, Hutchinson K, Kiefer F, Korucuoglu O, Lett T, Li CR, London E, Lorenzetti V, Maartje L, Momenan R, Orr C, Paulus M, Schmaal L, Sinha R, Sjoerds Z, Stein DJ, Stein E, van Holst RJ, Veltman D, Walter H, Wiers RW, Yucel M, Thompson PM, Conrod P, Allgaier N, and Garavan H

Subjects
Cerebral Cortex pathology, Humans, Putamen pathology, Reproducibility of Results, Alcoholism diagnostic imaging, Cerebral Cortex diagnostic imaging, Machine Learning, Magnetic Resonance Imaging methods, Magnetic Resonance Imaging standards, Multicenter Studies as Topic methods, Multicenter Studies as Topic standards, Neuroimaging methods, Neuroimaging standards, Putamen diagnostic imaging
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., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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15. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain.

Author

Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, and Ritter P

Abstract

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Stefanovski, Meier, Pai, Triebkorn, Lett, Martin, Bülau, Hofmann-Apitius, Solodkin, McIntosh and Ritter.)

Published
2021
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16. A multimodal neuroimaging classifier for alcohol dependence.

Author

Guggenmos M, Schmack K, Veer IM, Lett T, Sekutowicz M, Sebold M, Garbusow M, Sommer C, Wittchen HU, Zimmermann US, Smolka MN, Walter H, Heinz A, and Sterzer P

Subjects
Adult, Aged, Alcoholism classification, Case-Control Studies, Female, Humans, Male, Middle Aged, Social Class, Young Adult, Alcoholism diagnosis, Brain diagnostic imaging, Magnetic Resonance Imaging
Abstract

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.

Published
2020
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