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

Author

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

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

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

Published
2024

2. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Published
2023

3. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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4. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages

Published
2022

5. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Patel, Yash, Shin, Jean, Abé, Christoph, Agartz, Ingrid, Alloza, Clara, Alnæs, Dag, Ambrogi, Sonia, Antonucci, Linda A, Arango, Celso, Arolt, Volker, Auzias, Guillaume, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Bandeira, Cibele, Başgöze, Zeynep, Cupertino, Renata Basso, Bau, Claiton HD, Bauer, Jochen, Baumeister, Sarah, Bernardoni, Fabio, Bertolino, Alessandro, Bonnin, Caterina Del Mar, Brandeis, Daniel, Brem, Silvia, Bruggemann, Jason, Bülow, Robin, Bustillo, Juan R, Calderoni, Sara, Calvo, Rosa, Canales-Rodríguez, Erick J, Cannon, Dara M, Carmona, Susanna, Carr, Vaughan J, Catts, Stanley V, Chenji, Sneha, Chew, Qian Hui, Coghill, David, Connolly, Colm G, Conzelmann, Annette, Craven, Alexander R, Crespo-Facorro, Benedicto, Cullen, Kathryn, Dahl, Andreas, Dannlowski, Udo, Davey, Christopher G, Deruelle, Christine, Díaz-Caneja, Covadonga M, Dohm, Katharina, Ehrlich, Stefan, Epstein, Jeffery, Erwin-Grabner, Tracy, Eyler, Lisa T, Fedor, Jennifer, Fitzgerald, Jacqueline, Foran, William, Ford, Judith M, Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice, Furlong, Lisa, Gallagher, Louise, Gao, Bingchen, Gao, Si, Goikolea, Jose M, Gotlib, Ian, Goya-Maldonado, Roberto, Grabe, Hans J, Green, Melissa, Grevet, Eugenio H, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Haavik, Jan, Hahn, Tim, Harrison, Ben J, Heindel, Walter, Henskens, Frans, Heslenfeld, Dirk J, Hilland, Eva, Hoekstra, Pieter J, Hohmann, Sarah, Holz, Nathalie, Howells, Fleur M, Ipser, Jonathan C, Jahanshad, Neda, Jakobi, Babette, Jansen, Andreas, Janssen, Joost, Jonassen, Rune, Kaiser, Anna, Kaleda, Vasiliy, Karantonis, James, King, Joseph A, Kircher, Tilo, Kochunov, Peter, Koopowitz, Sheri-Michelle, Landén, Mikael, Landrø, Nils Inge, and Lawrie, Stephen

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Schizophrenia, Neurosciences, Brain Disorders, Biotechnology, Perinatal Period - Conditions Originating in Perinatal Period, Infant Mortality, Pediatric, Serious Mental Illness, Intellectual and Developmental Disabilities (IDD), Genetics, Mental Health, Preterm, Low Birth Weight and Health of the Newborn, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Reproductive health and childbirth, Good Health and Well Being, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Bipolar Disorder, Cerebral Cortex, Child, Depressive Disorder, Major, Female, Humans, Infant, Newborn, Magnetic Resonance Imaging, Pregnancy, Premature Birth, Cortical growth, Cortical surface area, Mental illness, Neurodevelopment, Neurogenesis, Psychiatric disorders, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundMorphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life.MethodsInterregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed.ResultsAcross the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth.ConclusionsOur findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.

Published
2022

7. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Author

Dima, Danai, Modabbernia, Amirhossein, Papachristou, Efstathios, Doucet, Gaelle E, Agartz, Ingrid, Aghajani, Moji, Akudjedu, Theophilus N, Albajes-Eizagirre, Anton, Alnaes, Dag, Alpert, Kathryn I, Andersson, Micael, Andreasen, Nancy C, Andreassen, Ole A, Asherson, Philip, Banaschewski, Tobias, Bargallo, Nuria, Baumeister, Sarah, Baur-Streubel, Ramona, Bertolino, Alessandro, Bonvino, Aurora, Boomsma, Dorret I, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Brodaty, Henry, Brouwer, Rachel M, Buitelaar, Jan K, Busatto, Geraldo F, Buckner, Randy L, Calhoun, Vincent, Canales-Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Castellanos, Francisco X, Cervenka, Simon, Chaim-Avancini, Tiffany M, Ching, Christopher RK, Chubar, Victoria, Clark, Vincent P, Conrod, Patricia, Conzelmann, Annette, Crespo-Facorro, Benedicto, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Dale, Anders M, Davey, Christopher, de Geus, Eco JC, de Haan, Lieuwe, de Zubicaray, Greig I, den Braber, Anouk, Dickie, Erin W, Di Giorgio, Annabella, Doan, Nhat Trung, Dørum, Erlend S, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fatouros-Bergman, Helena, Fisher, Simon E, Fouche, Jean-Paul, Franke, Barbara, Frodl, Thomas, Fuentes-Claramonte, Paola, Glahn, David C, Gotlib, Ian H, Grabe, Hans-Jörgen, Grimm, Oliver, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Gur, Rachel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hartman, Catharine A, Hatton, Sean N, Heinz, Andreas, Heslenfeld, Dirk J, Hibar, Derrek P, Hickie, Ian B, Ho, Beng-Choon, Hoekstra, Pieter J, Hohmann, Sarah, Holmes, Avram J, Hoogman, Martine, Hosten, Norbert, Howells, Fleur M, Hulshoff Pol, Hilleke E, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Jernigan, Terry L, Jiang, Jiyang, Jönsson, Erik G, Joska, John A, Kahn, Rene, and Kalnin, Andrew

Subjects
Karolinska Schizophrenia Project, Amygdala, Hippocampus, Thalamus, Corpus Striatum, Humans, Human Development, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Child, Preschool, Female, Male, Young Adult, Neuroimaging, ENIGMA, brain morphometry, longitudinal trajectories, multisite, Neurosciences, Aging, Aetiology, 2.1 Biological and endogenous factors, Mental health, Cognitive Sciences, Experimental Psychology
Abstract

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Published
2022

8. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years

Author

Frangou, Sophia, Modabbernia, Amirhossein, Williams, Steven CR, Papachristou, Efstathios, Doucet, Gaelle E, Agartz, Ingrid, Aghajani, Moji, Akudjedu, Theophilus N, Albajes‐Eizagirre, Anton, Alnæs, Dag, Alpert, Kathryn I, Andersson, Micael, Andreasen, Nancy C, Andreassen, Ole A, Asherson, Philip, Banaschewski, Tobias, Bargallo, Nuria, Baumeister, Sarah, Baur‐Streubel, Ramona, Bertolino, Alessandro, Bonvino, Aurora, Boomsma, Dorret I, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Brodaty, Henry, Brouwer, Rachel M, Buitelaar, Jan K, Busatto, Geraldo F, Buckner, Randy L, Calhoun, Vincent, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Castellanos, Francisco X, Cervenka, Simon, Chaim‐Avancini, Tiffany M, Ching, Christopher RK, Chubar, Victoria, Clark, Vincent P, Conrod, Patricia, Conzelmann, Annette, Crespo‐Facorro, Benedicto, Crivello, Fabrice, Crone, Eveline A, Dale, Anders M, Dannlowski, Udo, Davey, Christopher, Geus, Eco JC, Haan, Lieuwe, Zubicaray, Greig I, Braber, Anouk, Dickie, Erin W, Di Giorgio, Annabella, Doan, Nhat Trung, Dørum, Erlend S, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fatouros‐Bergman, Helena, Fisher, Simon E, Fouche, Jean‐Paul, Franke, Barbara, Frodl, Thomas, Fuentes‐Claramonte, Paola, Glahn, David C, Gotlib, Ian H, Grabe, Hans‐Jörgen, Grimm, Oliver, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Gur, Rachel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hartman, Catharine A, Hatton, Sean N, Heinz, Andreas, Heslenfeld, Dirk J, Hibar, Derrek P, Hickie, Ian B, Ho, Beng‐Choon, Hoekstra, Pieter J, Hohmann, Sarah, Holmes, Avram J, Hoogman, Martine, Hosten, Norbert, Howells, Fleur M, Pol, Hilleke E Hulshoff, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Jernigan, Terry L, Jiang, Jiyang, Jönsson, Erik G, Joska, John A, and Kahn, Rene

Subjects
Behavioral and Social Science, Neurosciences, Aging, Adolescent, Adult, Aged, Aged, 80 and over, Cerebral Cortex, Child, Child, Preschool, Cross-Sectional Studies, Female, Human Development, Humans, Male, Middle Aged, Neuroimaging, Young Adult, aging, cortical thickness, development, trajectories, Karolinska Schizophrenia Project, Cognitive Sciences, Experimental Psychology
Abstract

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.

Published
2022

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

Author

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

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

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

Published
2021

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

Author

Ge, Ruiyang, primary, Yu, Yuetong, additional, Qi, Yi Xuan, additional, Fan, Yu-nan, additional, Chen, Shiyu, additional, Gao, Chuntong, additional, Haas, Shalaila S, additional, New, Faye, additional, Boomsma, Dorret I, additional, Brodaty, Henry, additional, Brouwer, Rachel M, additional, Buckner, Randy, additional, Caseras, Xavier, additional, Crivello, Fabrice, additional, Crone, Eveline A, additional, Erk, Susanne, additional, Fisher, Simon E, additional, Franke, Barbara, additional, Glahn, David C, additional, Dannlowski, Udo, additional, Grotegerd, Dominik, additional, Gruber, Oliver, additional, Hulshoff Pol, Hilleke E, additional, Schumann, Gunter, additional, Tamnes, Christian K, additional, Walter, Henrik, additional, Wierenga, Lara M, additional, Jahanshad, Neda, additional, Thompson, Paul M, additional, Frangou, Sophia, additional, Agartz, Ingrid, additional, Asherson, Philip, additional, Ayesa-Arriola, Rosa, additional, Banaj, Nerisa, additional, Banaschewski, Tobias, additional, Baumeister, Sarah, additional, Bertolino, Alessandro, additional, Borgwardt, Stefan, additional, Bourque, Josiane, additional, Brandeis, Daniel, additional, Breier, Alan, additional, Buitelaar, Jan K, additional, Cannon, Dara M, additional, Cervenka, Simon, additional, Conrod, Patricia J, additional, Crespo-Facorro, Benedicto, additional, Davey, Christopher G, additional, de Haan, Lieuwe, additional, de Zubicaray, Greig I, additional, Di Giorgio, Annabella, additional, Frodl, Thomas, additional, Gruner, Patricia, additional, Gur, Raquel E, additional, Gur, Ruben C, additional, Harrison, Ben J, additional, Hatton, Sean N, additional, Hickie, Ian, additional, Howells, Fleur M, additional, Huyser, Chaim, additional, Jernigan, Terry L, additional, Jiang, Jiyang, additional, Joska, John A, additional, Kahn, René S, additional, Kalnin, Andrew J, additional, Kochan, Nicole A, additional, Koops, Sanne, additional, Kuntsi, Jonna, additional, Lagopoulos, Jim, additional, Lazaro, Luisa, additional, Lebedeva, Irina S, additional, Lochner, Christine, additional, Martin, Nicholas G, additional, Mazoyer, Bernard, additional, McDonald, Brenna C, additional, McDonald, Colm, additional, McMahon, Katie L, additional, Medland, Sarah, additional, Modabbernia, Amirhossein, additional, Mwangi, Benson, additional, Nakao, Tomohiro, additional, Nyberg, Lars, additional, Piras, Fabrizio, additional, Portella, Maria J, additional, Qiu, Jiang, additional, Roffman, Joshua L, additional, Sachdev, Perminder S, additional, Sanford, Nicole, additional, Satterthwaite, Theodore D, additional, Saykin, Andrew J, additional, Sellgren, Carl M, additional, Sim, Kang, additional, Smoller, Jordan W, additional, Soares, Jair C, additional, Sommer, Iris E, additional, Spalletta, Gianfranco, additional, Stein, Dan J, additional, Thomopoulos, Sophia I, additional, Tomyshev, Alexander S, additional, Tordesillas-Gutiérrez, Diana, additional, Trollor, Julian N, additional, van 't Ent, Dennis, additional, van den Heuvel, Odile A, additional, van Erp, Theo GM, additional, van Haren, Neeltje EM, additional, Vecchio, Daniela, additional, Veltman, Dick J, additional, Wang, Yang, additional, Weber, Bernd, additional, Wei, Dongtao, additional, Wen, Wei, additional, Westlye, Lars T, additional, Williams, Steven CR, additional, Wright, Margaret J, additional, Wu, Mon-Ju, additional, and Yu, Kevin, additional

Published
2024
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21. Revisiting deficits in threat and safety appraisal in obsessive‐compulsive disorder

Author

Hearne, Luke J., primary, Breakspear, Michael, additional, Harrison, Ben J., additional, Hall, Caitlin V., additional, Savage, Hannah S., additional, Robinson, Conor, additional, Sonkusare, Saurabh, additional, Savage, Emma, additional, Nott, Zoie, additional, Marcus, Leo, additional, Naze, Sebastien, additional, Burgher, Bjorn, additional, Zalesky, Andrew, additional, and Cocchi, Luca, additional

Published
2023
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22. Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium

Author

Javaheripour, Nooshin, Li, Meng, Chand, Tara, Krug, Axel, Kircher, Tilo, Dannlowski, Udo, Nenadić, Igor, Hamilton, J. Paul, Sacchet, Matthew D., Gotlib, Ian H., Walter, Henrik, Frodl, Thomas, Grimm, Simone, Harrison, Ben J., Wolf, Christian Robert, Olbrich, Sebastian, van Wingen, Guido, Pezawas, Lukas, Parker, Gordon, Hyett, Matthew P., Sämann, Philipp G., Hahn, Tim, Steinsträter, Olaf, Jansen, Andreas, Yuksel, Dilara, Kämpe, Robin, Davey, Christopher G., Meyer, Bernhard, Bartova, Lucie, Croy, Ilona, Walter, Martin, and Wagner, Gerd

Published
2021
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24. Acute Effects of Modafinil on Brain Resting State Networks in Young Healthy Subjects

Author

Esposito, Roberto, Cilli, Franco, Pieramico, Valentina, Ferretti, Antonio, Macchia, Antonella, Tommasi, Marco, Saggino, Aristide, Ciavardelli, Domenico, Manna, Antonietta, Navarra, Riccardo, Cieri, Filippo, Stuppia, Liborio, Tartaro, Armando, Sensi, Stefano L, and Harrison, Ben J

Subjects
Working-Memory, Diffusion Tensor, Functional Connectivity, Dopamine Transporter, Progressive Matrices, Inhibitory Control, Prefrontal Cortex, Parietal Cortex, Default Mode, Long-Term
Published
2013

26. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Author

Schmaal, Lianne, Pozzi, Elena, C. Ho, Tiffany, van Velzen, Laura S., Veer, Ilya M., Opel, Nils, Van Someren, Eus J. W., Han, Laura K. M., Aftanas, Lybomir, Aleman, André, Baune, Bernhard T., Berger, Klaus, Blanken, Tessa F., Capitão, Liliana, Couvy-Duchesne, Baptiste, R. Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher, Erwin-Grabner, Tracy, Evans, Jennifer, Frodl, Thomas, Fu, Cynthia H. Y., Godlewska, Beata, Gotlib, Ian H., Goya-Maldonado, Roberto, Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Gruber, Oliver, Gutman, Boris A., Hall, Geoffrey B., Harrison, Ben J., Hatton, Sean N., Hermesdorf, Marco, Hickie, Ian B., Hilland, Eva, Irungu, Benson, Jonassen, Rune, Kelly, Sinead, Kircher, Tilo, Klimes-Dougan, Bonnie, Krug, Axel, Landrø, Nils Inge, Lagopoulos, Jim, Leerssen, Jeanne, Li, Meng, Linden, David E. J., MacMaster, Frank P., M. McIntosh, Andrew, Mehler, David M. A., Nenadić, Igor, Penninx, Brenda W. J. H., Portella, Maria J., Reneman, Liesbeth, Rentería, Miguel E., Sacchet, Matthew D., G. Sämann, Philipp, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Tozzi, Leonardo, van Der Wee, Nic J. A., van Tol, Marie-José, Vermeiren, Robert, Vives-Gilabert, Yolanda, Walter, Henrik, Walter, Martin, Whalley, Heather C., Wittfeld, Katharina, Whittle, Sarah, Wright, Margaret J., Yang, Tony T., Zarate, Jr, Carlos, Thomopoulos, Sophia I., Jahanshad, Neda, Thompson, Paul M., and Veltman, Dick J.

Published
2020
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31. Functional connectivity signatures of major depressive disorder: Machine learning analysis of two multicenter neuroimaging studies

Author

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

Subjects
Psychiatry, Cellular and Molecular Neuroscience, Psychiatry and Mental health, Cognitive artificial intelligence, Molecular Biology, Psykiatri
Abstract

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies. Funding Agencies|Netherlands Organization for Scientific Research (NWO) [628.011.023]; Philips Research; ZonMW (Vidi) [016.156.318]; Region Ostergoetland; Phyllis and Jerome Lyle Rappaport Foundation; BIAL Foundation; Brain and Behavior Research Foundation; Center for Depression, Anxiety, and Stress Research at McLean Hospital; Ad Astra Chandaria Foundation [DA1151/5-2, SFB-TRR58, Dan3/012/17]; German Research Foundation (DFG); Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Munster [1024570]; European Commission [KLI 597-827]; Australian National Health and Medical Research Council of Australia (NHMRC) [F3514-B1, GR 4510/2-1]; Austrian Science Fund (FWF) [WA1539/4-1]; Science Foundation Ireland (SFI); [FOR2107 DA1151/5-1]; [H2020-634541]; [1064643]; [KLI-148-B00]

Published
2023

34. Anomalous basal ganglia connectivity and obsessive-compulsive behaviour in patients with Prader Willi syndrome

Author

Pujol, Jesus, Blanco-Hinojo, Laura, Esteba-Castillo, Susanna, Caixas, Assumpta, Harrison, Ben J., Bueno, Marta, Deus, Joan, Rigla, Mercedes, Macia, Didac, Llorente-Onaindia, Jone, and Novell-Alsina, Ramon

Subjects
Basal ganglia -- Health aspects, Obsessive-compulsive disorder -- Physiological aspects, Prader-Willi syndrome -- Physiological aspects, Health, Psychology and mental health
Abstract

Background: Prader Willi syndrome is a genetic disorder with a behavioural expression characterized by the presence of obsessivecompulsive phenomena ranging from elaborate obsessive eating behaviour to repetitive skin picking. Obsessive-compulsive disorder (OCD) has been recently associated with abnormal functional coupling between the frontal cortex and basal ganglia. We have tested the potential association of functional connectivity anomalies in basal ganglia circuits with obsessive-compulsive behaviour in patients with Prader Willi syndrome. Methods: We analyzed resting-state functional MRI in adult patients and healthy controls. Whole-brain functional connectivity maps were generated for the dorsal and ventral aspects of the caudate nucleus and putamen. A selected obsessive-compulsive behaviour assessment included typical OCD compulsions, self picking and obsessive eating behaviour. Results: We included 24 adults with Prader Willi syndrome and 29 controls in our study. Patients with Prader Willi syndrome showed abnormal functional connectivity between the prefrontal cortex and basal ganglia and within subcortical structures that correlated with the presence and severity of obsessive-compulsive behaviours. In addition, abnormally heightened functional connectivity was identified in the primary sensorimotor cortex-putamen loop, which was strongly associated with self picking. Finally, obsessive eating behaviour correlated with abnormal functional connectivity both within the basal ganglia loops and between the striatum and the hypothalamus and the amygdala. Limitations: Limitations of the study include the difficulty in evaluating the nature of content of obsessions in patients with Prader Willi Syndrome and the risk of excessive head motion artifact on brain imaging. Conclusion: Patients with Prader Willi syndrome showed broad functional connectivity anomalies combining prefrontal loop alterations characteristic of OCD with 1) enhanced coupling in the primary sensorimotor loop that correlated with the most impulsive aspects of the behaviour and 2) reduced coupling of the ventral striatum with limbic structures for basic internal homeostasis that correlated with the obsession to eat., Introduction Prader Willi syndrome is a genetic disorder with an estimated prevalence of 1 in 10 000-30 000 caused by chromosome 15 long arm anomalies. The syndrome combines intellectual disability [...]

Published
2016
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36. Functional dysconnectivity of corticostriatal circuitry and differential response to methylphenidate in youth with attention-deficit/hyperactivity disorder

Author

Hong, Soon-Beom, Harrison, Ben J., Fornito, Alex, Sohn, Chul-Ho, Song, In-Chan, and Kim, Jae-Won

Subjects
Methylphenidate -- Dosage and administration, Methylphenidate hydrochloride -- Dosage and administration, Attention-deficit hyperactivity disorder -- Drug therapy, Drug interactions -- Research, Pharmaceutical research, Health, Psychology and mental health
Abstract

Background: Brain frontostriatal circuits have been implicated in the pathophysiology of attention- deficit/hyperactivity disorder (ADHD). However, effects of methylphenidate on circuit-level functional connectivity are as yet unclear. The aim of the present study was to comprehensively investigate the functional connectivity of major striatal subregions in children with ADHD, including subanalyses directed at mapping cognitive and treatment response characteristics. Methods: Using a comprehensive seeding strategy, we examined resting-state functional connectivity of dorsal and ventral subdivisions of the caudate nucleus and putamen in children and adolescents with ADHD and in age- and sex-matched healthy controls. Results: We enrolled 83 patients with ADHD and 22 controls in our study. Patients showed significantly reduced dorsal caudate functional connectivity with the superior and middle prefrontal cortices as well as reduced dorsal putamen connectivity with the parahippocampal cortex. These connectivity measures were correlated in opposite directions in patients and controls with attentional performance, as assessed using the Continuous Performance Test. Patients showing a good response to methylphenidate had significantly reduced ventral caudate/nucleus accumbens connectivity with the inferior frontal cortices compared with poor responders. Limitations: Possible confounding effects of age-related functional connectivity change were not excluded owing to the wide age range of participants. Conclusion: We observed a region- specific effect of methylphenidate on resting-state functional connectivity, suggesting the pretreatment level of ventral frontostriatal functional connectivity as a possible methylphenidate response biomarker of ADHD., Introduction Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurobehavioural disorder in children and adolescents, which frequently persists into adulthood and manifests with symptoms of inattention and/or hyperactivity and impulsivity. (1) [...]

Published
2015
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38. Disruption of brain white matter microstructure in women with anorexia nervosa

Author

Via, Esther, Zalesky, Andrew, Sanchez, Isabel, Forcano, Laura, Harrison, Ben J., Pujol, Jesus, Fernandez-Aranda, Fernando, Menchon, Jose Manuel, Soriano-Mas, Carles, Cardoner, Narcis, and Fornito, Alex

Subjects
Brain -- Health aspects, Women -- Health aspects, Anorexia nervosa -- Physiological aspects, Health, Psychology and mental health
Abstract

Background: The etiology of anorexia nervosa is still unknown. Multiple and distributed brain regions have been implicated in its pathophysiology, implying a dysfunction of connected neural circuits. Despite these findings, the role of white matter in anorexia nervosa has been rarely assessed. In this study, we used diffusion tensor imaging (DTI) to characterize alterations of white matter microstructure in a clinically homogeneous sample of patients with anorexia nervosa. Methods: Women with anorexia nervosa (restricting subtype) and healthy controls underwent brain DTI. We used tract-based spatial statistics to compare fractional anisotropy (FA) and mean diffusivity (MD) maps between the groups. Furthermore, axial (AD) and radial diffusivity (RD) measures were extracted from regions showing group differences in either FA or MD. Results: We enrolled 19 women with anorexia nervosa and 19 healthy controls in our study. Patients with anorexia nervosa showed significant FA decreases in the parietal part of the left superior longitudinal fasciculus (SLF; [p.sub.FWE] < 0.05), with increased MD and RD but no differences in AD. Patients with anorexia nervosa also showed significantly increased MD in the fornix ([p.sub.FWE] < 0.05), accompanied by decreased FA and increased RD and AD. Limitations: Limitations include our modest sample size and cross-sectional design. Conclusion: Our findings support the presence of white matter pathology in patients with anorexia nervosa. Alterations in the SLF and fornix might be relevant to key symptoms of anorexia nervosa, such as body image distortion or impairments in body-energy-balance and reward processes. The differences found in both areas replicate those found in previous DTI studies and support a role for white matter pathology of specific neural circuits in individuals with anorexia nervosa., Introduction Anorexia nervosa is an eating disorder characterized by disturbing preoccupations about body self-image, weight and dieting. Patients with anorexia nervosa engage in intense food restrictions that, in some cases, [...]

Published
2014
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40. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders

Author

Opel, Nils, Thalamuthu, Anbupalam, Milaneschi, Yuri, Grotegerd, Dominik, Flint, Claas, Leenings, Ramona, Goltermann, Janik, Richter, Maike, Hahn, Tim, Woditsch, Georg, Berger, Klaus, Hermesdorf, Marco, McIntosh, Andrew, Whalley, Heather C., Harris, Mathew A., MacMaster, Frank P., Walter, Henrik, Veer, Ilya M., Frodl, Thomas, Carballedo, Angela, Krug, Axel, Nenadic, Igor, Kircher, Tilo, Aleman, Andre, Groenewold, Nynke A., Stein, Dan J., Soares, Jair C., Zunta-Soares, Giovana B., Mwangi, Benson, Wu, Mon-Ju, Walter, Martin, Li, Meng, Harrison, Ben J., Davey, Christopher G., Cullen, Kathryn R., Klimes-Dougan, Bonnie, Mueller, Bryon A., Sämann, Philipp G., Penninx, Brenda, Nawijn, Laura, Veltman, Dick J., Aftanas, Lyubomir, Brak, Ivan V., Filimonova, Elena A., Osipov, Evgeniy A., Reneman, Liesbeth, Schrantee, Anouk, Grabe, Hans J., Van der Auwera, Sandra, Wittfeld, Katharina, Hosten, Norbert, Völzke, Henry, Sim, Kang, Gotlib, Ian H., Sacchet, Matthew D., Lagopoulos, Jim, Hatton, Sean N., Hickie, Ian, Pozzi, Elena, Thompson, Paul M., Jahanshad, Neda, Schmaal, Lianne, Baune, Bernhard T., and Dannlowski, Udo

Subjects
Depression, Diagnostic markers, Article
Abstract

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

Published
2020

49. A neural mediator of human anxiety sensitivity

Author

Harrison, Ben J., Fullana, Miquel A., Soriano-Mas, Carles, Via, Esther, Pujol, Jesus, Martínez-Zalacaín, Ignacio, Tinoco-Gonzalez, Daniella, Davey, Christopher G., López-Solà, Marina, Sola, Victor Pérez, Menchón, José M., and Cardoner, Narcís

Published
2015
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