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Start Over Author "Wu, Mon-Ju" Topic depressive disorder, major
9 results on '"Wu, Mon-Ju"'

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

Author

Belov V, Erwin-Grabner T, Aghajani M, Aleman A, Amod AR, Basgoze Z, Benedetti F, Besteher B, Bülow R, Ching CRK, Connolly CG, Cullen K, Davey CG, Dima D, Dols A, Evans JW, Fu CHY, Gonul AS, Gotlib IH, Grabe HJ, Groenewold N, Hamilton JP, Harrison BJ, Ho TC, Mwangi B, Jaworska N, Jahanshad N, Klimes-Dougan B, Koopowitz SM, Lancaster T, Li M, Linden DEJ, MacMaster FP, Mehler DMA, Melloni E, Mueller BA, Ojha A, Oudega ML, Penninx BWJH, Poletti S, Pomarol-Clotet E, Portella MJ, Pozzi E, Reneman L, Sacchet MD, Sämann PG, Schrantee A, Sim K, Soares JC, Stein DJ, Thomopoulos SI, Uyar-Demir A, van der Wee NJA, van der Werff SJA, Völzke H, Whittle S, Wittfeld K, Wright MJ, Wu MJ, Yang TT, Zarate C, Veltman DJ, Schmaal L, Thompson PM, and Goya-Maldonado R

Subjects
Humans, Benchmarking, Brain diagnostic imaging, Neuroimaging methods, Machine Learning, Magnetic Resonance Imaging methods, Depressive Disorder, Major diagnostic imaging, Depressive Disorder, Major psychology
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects., (© 2024. The Author(s).)

Published
2024
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2. The role of educational attainment and brain morphology in major depressive disorder: Findings from the ENIGMA major depressive disorder consortium.

Author

Whittle S, Rakesh D, Schmaal L, Veltman DJ, Thompson PM, Singh A, Gonul AS, Aleman A, Uyar Demir A, Krug A, Mwangi B, Krämer B, Baune BT, Stein DJ, Grotegerd D, Pomarol-Clotet E, Rodríguez-Cano E, Melloni E, Benedetti F, Stein F, Grabe HJ, Völzke H, Gotlib IH, Nenadić I, Soares JC, Repple J, Sim K, Brosch K, Wittfeld K, Berger K, Hermesdorf M, Portella MJ, Sacchet MD, Wu MJ, Opel N, Groenewold NA, Gruber O, Fuentes-Claramonte P, Salvador R, Goya-Maldonado R, Sarró S, Poletti S, Meinert SL, Kircher T, Dannlowski U, and Pozzi E

Subjects
Adult, Brain diagnostic imaging, Educational Status, Frontal Lobe, Humans, Magnetic Resonance Imaging, Depressive Disorder, Major diagnostic imaging
Abstract

Brain structural abnormalities and low educational attainment are consistently associated with major depressive disorder (MDD), yet there has been little research investigating the complex interaction of these factors. Brain structural alterations may represent a vulnerability or differential susceptibility marker, and in the context of low educational attainment, predict MDD. We tested this moderation model in a large multisite sample of 1958 adults with MDD and 2921 controls (aged 18 to 86) from the ENIGMA MDD working group. Using generalized linear mixed models and within-sample split-half replication, we tested whether brain structure interacted with educational attainment to predict MDD status. Analyses revealed that cortical thickness in a number of occipital, parietal, and frontal regions significantly interacted with education to predict MDD. For the majority of regions, models suggested a differential susceptibility effect, whereby thicker cortex was more likely to predict MDD in individuals with low educational attainment, but less likely to predict MDD in individuals with high educational attainment. Findings suggest that greater thickness of brain regions subserving visuomotor and social-cognitive functions confers susceptibility to MDD, dependent on level of educational attainment. Longitudinal work, however, is ultimately needed to establish whether cortical thickness represents a preexisting susceptibility marker. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022
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3. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders : Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group.

Author

Opel N, Thalamuthu A, Milaneschi Y, Grotegerd D, Flint C, Leenings R, Goltermann J, Richter M, Hahn T, Woditsch G, Berger K, Hermesdorf M, McIntosh A, Whalley HC, Harris MA, MacMaster FP, Walter H, Veer IM, Frodl T, Carballedo A, Krug A, Nenadic I, Kircher T, Aleman A, Groenewold NA, Stein DJ, Soares JC, Zunta-Soares GB, Mwangi B, Wu MJ, Walter M, Li M, Harrison BJ, Davey CG, Cullen KR, Klimes-Dougan B, Mueller BA, Sämann PG, Penninx B, Nawijn L, Veltman DJ, Aftanas L, Brak IV, Filimonova EA, Osipov EA, Reneman L, Schrantee A, Grabe HJ, Van der Auwera S, Wittfeld K, Hosten N, Völzke H, Sim K, Gotlib IH, Sacchet MD, Lagopoulos J, Hatton SN, Hickie I, Pozzi E, Thompson PM, Jahanshad N, Schmaal L, Baune BT, and Dannlowski U

Subjects
Aged, Brain diagnostic imaging, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Obesity genetics, Risk Factors, Depressive Disorder, Major genetics
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., (© 2020. The Author(s).)

Published
2021
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4. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.

Author

Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, Aghajani M, Aleman A, Baune BT, Berger K, Brak I, Filho GB, Carballedo A, Connolly CG, Couvy-Duchesne B, Cullen KR, Dannlowski U, Davey CG, Dima D, Duran FLS, Enneking V, Filimonova E, Frenzel S, Frodl T, Fu CHY, Godlewska BR, Gotlib IH, Grabe HJ, Groenewold NA, Grotegerd D, Gruber O, Hall GB, Harrison BJ, Hatton SN, Hermesdorf M, Hickie IB, Ho TC, Hosten N, Jansen A, Kähler C, Kircher T, Klimes-Dougan B, Krämer B, Krug A, Lagopoulos J, Leenings R, MacMaster FP, MacQueen G, McIntosh A, McLellan Q, McMahon KL, Medland SE, Mueller BA, Mwangi B, Osipov E, Portella MJ, Pozzi E, Reneman L, Repple J, Rosa PGP, Sacchet MD, Sämann PG, Schnell K, Schrantee A, Simulionyte E, Soares JC, Sommer J, Stein DJ, Steinsträter O, Strike LT, Thomopoulos SI, van Tol MJ, Veer IM, Vermeiren RRJM, Walter H, van der Wee NJA, van der Werff SJA, Whalley H, Winter NR, Wittfeld K, Wright MJ, Wu MJ, Völzke H, Yang TT, Zannias V, de Zubicaray GI, Zunta-Soares GB, Abé C, Alda M, Andreassen OA, Bøen E, Bonnin CM, Canales-Rodriguez EJ, Cannon D, Caseras X, Chaim-Avancini TM, Elvsåshagen T, Favre P, Foley SF, Fullerton JM, Goikolea JM, Haarman BCM, Hajek T, Henry C, Houenou J, Howells FM, Ingvar M, Kuplicki R, Lafer B, Landén M, Machado-Vieira R, Malt UF, McDonald C, Mitchell PB, Nabulsi L, Otaduy MCG, Overs BJ, Polosan M, Pomarol-Clotet E, Radua J, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Savitz J, Schene AH, Schofield PR, Serpa MH, Sim K, Soeiro-de-Souza MG, Sutherland AN, Temmingh HS, Timmons GM, Uhlmann A, Vieta E, Wolf DH, Zanetti MV, Jahanshad N, Thompson PM, Veltman DJ, Penninx BWJH, Marquand AF, Cole JH, and Schmaal L

Subjects
Adolescent, Adult, Aged, Aging, Brain diagnostic imaging, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Depressive Disorder, Major
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., (© 2020. The Author(s).)

Published
2021
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5. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.

Author

Patel Y, Parker N, Shin J, Howard D, French L, Thomopoulos SI, Pozzi E, Abe Y, Abé C, Anticevic A, Alda M, Aleman A, Alloza C, Alonso-Lana S, Ameis SH, Anagnostou E, McIntosh AA, Arango C, Arnold PD, Asherson P, Assogna F, Auzias G, Ayesa-Arriola R, Bakker G, Banaj N, Banaschewski T, Bandeira CE, Baranov A, Bargalló N, Bau CHD, Baumeister S, Baune BT, Bellgrove MA, Benedetti F, Bertolino A, Boedhoe PSW, Boks M, Bollettini I, Del Mar Bonnin C, Borgers T, Borgwardt S, Brandeis D, Brennan BP, Bruggemann JM, Bülow R, Busatto GF, Calderoni S, Calhoun VD, Calvo R, Canales-Rodríguez EJ, Cannon DM, Carr VJ, Cascella N, Cercignani M, Chaim-Avancini TM, Christakou A, Coghill D, Conzelmann A, Crespo-Facorro B, Cubillo AI, Cullen KR, Cupertino RB, Daly E, Dannlowski U, Davey CG, Denys D, Deruelle C, Di Giorgio A, Dickie EW, Dima D, Dohm K, Ehrlich S, Ely BA, Erwin-Grabner T, Ethofer T, Fair DA, Fallgatter AJ, Faraone SV, Fatjó-Vilas M, Fedor JM, Fitzgerald KD, Ford JM, Frodl T, Fu CHY, Fullerton JM, Gabel MC, Glahn DC, Roberts G, Gogberashvili T, Goikolea JM, Gotlib IH, Goya-Maldonado R, Grabe HJ, Green MJ, Grevet EH, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Guerrero-Pedraza A, Gur RE, Gur RC, Haar S, Haarman BCM, Haavik J, Hahn T, Hajek T, Harrison BJ, Harrison NA, Hartman CA, Whalley HC, Heslenfeld DJ, Hibar DP, Hilland E, Hirano Y, Ho TC, Hoekstra PJ, Hoekstra L, Hohmann S, Hong LE, Höschl C, Høvik MF, Howells FM, Nenadic I, Jalbrzikowski M, James AC, Janssen J, Jaspers-Fayer F, Xu J, Jonassen R, Karkashadze G, King JA, Kircher T, Kirschner M, Koch K, Kochunov P, Kohls G, Konrad K, Krämer B, Krug A, Kuntsi J, Kwon JS, Landén M, Landrø NI, Lazaro L, Lebedeva IS, Leehr EJ, Lera-Miguel S, Lesch KP, Lochner C, Louza MR, Luna B, Lundervold AJ, MacMaster FP, Maglanoc LA, Malpas CB, Portella MJ, Marsh R, Martyn FM, Mataix-Cols D, Mathalon DH, McCarthy H, McDonald C, McPhilemy G, Meinert S, Menchón JM, Minuzzi L, Mitchell PB, Moreno C, Morgado P, Muratori F, Murphy CM, Murphy D, Mwangi B, Nabulsi L, Nakagawa A, Nakamae T, Namazova L, Narayanaswamy J, Jahanshad N, Nguyen DD, Nicolau R, O'Gorman Tuura RL, O'Hearn K, Oosterlaan J, Opel N, Ophoff RA, Oranje B, García de la Foz VO, Overs BJ, Paloyelis Y, Pantelis C, Parellada M, Pauli P, Picó-Pérez M, Picon FA, Piras F, Piras F, Plessen KJ, Pomarol-Clotet E, Preda A, Puig O, Quidé Y, Radua J, Ramos-Quiroga JA, Rasser PE, Rauer L, Reddy J, Redlich R, Reif A, Reneman L, Repple J, Retico A, Richarte V, Richter A, Rosa PGP, Rubia KK, Hashimoto R, Sacchet MD, Salvador R, Santonja J, Sarink K, Sarró S, Satterthwaite TD, Sawa A, Schall U, Schofield PR, Schrantee A, Seitz J, Serpa MH, Setién-Suero E, Shaw P, Shook D, Silk TJ, Sim K, Simon S, Simpson HB, Singh A, Skoch A, Skokauskas N, Soares JC, Soreni N, Soriano-Mas C, Spalletta G, Spaniel F, Lawrie SM, Stern ER, Stewart SE, Takayanagi Y, Temmingh HS, Tolin DF, Tomecek D, Tordesillas-Gutiérrez D, Tosetti M, Uhlmann A, van Amelsvoort T, van der Wee NJA, van der Werff SJA, van Haren NEM, van Wingen GA, Vance A, Vázquez-Bourgon J, Vecchio D, Venkatasubramanian G, Vieta E, Vilarroya O, Vives-Gilabert Y, Voineskos AN, Völzke H, von Polier GG, Walton E, Weickert TW, Weickert CS, Weideman AS, Wittfeld K, Wolf DH, Wu MJ, Yang TT, Yang K, Yoncheva Y, Yun JY, Cheng Y, Zanetti MV, Ziegler GC, Franke B, Hoogman M, Buitelaar JK, van Rooij D, Andreassen OA, Ching CRK, Veltman DJ, Schmaal L, Stein DJ, van den Heuvel OA, Turner JA, van Erp TGM, Pausova Z, Thompson PM, and Paus T

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Attention Deficit Disorder with Hyperactivity diagnostic imaging, Autism Spectrum Disorder diagnostic imaging, Bipolar Disorder diagnostic imaging, Case-Control Studies, Cerebral Cortex cytology, Cerebral Cortex diagnostic imaging, Cerebral Cortex growth & development, Child, Child, Preschool, Cohort Studies, Computational Biology, Depressive Disorder, Major diagnostic imaging, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Obsessive-Compulsive Disorder diagnostic imaging, Principal Component Analysis, Schizophrenia diagnostic imaging, Young Adult, Attention Deficit Disorder with Hyperactivity pathology, Autism Spectrum Disorder pathology, Bipolar Disorder pathology, Cerebral Cortex pathology, Depressive Disorder, Major pathology, Fetal Development physiology, Gene Expression physiology, Human Development physiology, Obsessive-Compulsive Disorder pathology, Schizophrenia pathology
Abstract

Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood., Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia., Design, Setting, and Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244., Main Outcomes and Measures: Interregional profiles of group difference in cortical thickness between cases and controls., Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders., Conclusions and Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.

Published
2021
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6. Hippocampal subfield volumes in children and adolescents with mood disorders.

Author

Tannous J, Amaral-Silva H, Cao B, Wu MJ, Zunta-Soares GB, Kazimi I, Zeni C, Mwangi B, and Soares JC

Subjects
Adolescent, Bipolar Disorder diagnostic imaging, Child, Depressive Disorder, Major diagnostic imaging, Female, Hippocampus diagnostic imaging, Humans, Magnetic Resonance Imaging, Male, Bipolar Disorder pathology, Depressive Disorder, Major pathology, Hippocampus pathology, Neuroimaging methods
Abstract

The hippocampus has been implicated in various mood disorders, with global volume deficits consistently found in patient populations. The hippocampus, however, consists of anatomically distinct subfields, and examination of specific subfield differences may elucidate the possible molecular mechanisms behind psychiatric pathologies. Indeed, adult studies have reported smaller hippocampal subfield volumes in regions within the cornu ammonis (CA1 and CA4), dentate gyrus (DG), and hippocampal tails in both patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD) compared to healthy controls. Subfield differences in pediatric patients with mood disorders, on the other hand, have not been extensively investigated. In the current study, magnetic resonance imaging scans were acquired for 141 children and adolescents between the ages of eight and eighteen (57 with BD, 30 with MDD, and 54 healthy controls). An automated segmentation method was then used to assess differences in hippocampal subfield volumes. Children and adolescents with BD were found to have significantly smaller volumes in the right CA1, CA4, and right subiculum, as well as the bilateral granule cell layer (GCL), molecular layer (ML), and hippocampal tails. The volume of the right subiculum in BD patients was also found to be negatively correlated with illness duration. Overall, the findings from this cross-sectional study provide evidence for specific hippocampal subfield volume differences in children and adolescents with BD compared to healthy controls and suggest progressive reductions with increased illness duration., (Copyright © 2018. Published by Elsevier Ltd.)

Published
2018
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7. Lifespan Gyrification Trajectories of Human Brain in Healthy Individuals and Patients with Major Psychiatric Disorders.

Author

Cao B, Mwangi B, Passos IC, Wu MJ, Keser Z, Zunta-Soares GB, Xu D, Hasan KM, and Soares JC

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Demography, Female, Humans, Male, Middle Aged, Young Adult, Bipolar Disorder pathology, Brain pathology, Depressive Disorder, Major pathology, Longevity, Schizophrenia pathology
Abstract

Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex. How the brain cortical gyrification changes from childhood to old age in healthy human subjects is still unclear. Additionally, studies have shown regional gyrification alterations in patients with major psychiatric disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, whether the lifespan trajectory of gyrification over the brain is altered in patients diagnosed with major psychiatric disorders is still unknown. In this study, we investigated the trajectories of gyrification in three independent cohorts based on structural brain images of 881 subjects from age 4 to 83. We discovered that the trajectory of gyrification during normal development and aging was not linear and could be modeled with a logarithmic function. We also found that the gyrification trajectories of patients with MDD, BD and SCZ were deviated from the healthy one during adulthood, indicating altered aging in the brain of these patients.

Published
2017
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8. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

Author

Committee, Writing, Disorder, Autism Spectrum, French, Leon, Grevet, Eugenio H, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Guerrero-Pedraza, Amalia, Gur, Raquel E, Gur, Ruben C, Haar, Shlomi, Haarman, Bartholomeus C M, Thomopoulos, Sophia I, Haavik, Jan, Hahn, Tim, Hajek, Tomas, Harrison, Benjamin J, Harrison, Neil A, Hartman, Catharina A, Whalley, Heather C, Heslenfeld, Dirk J, Hibar, Derrek P, Hilland, Eva, Pozzi, Elena, Hirano, Yoshiyuki, Ho, Tiffany C, Hoekstra, Pieter J, Hoekstra, Liesbeth, Hohmann, Sarah, Hong, L. E., Höschl, Cyril, Høvik, Marie F, Howells, Fleur M, Nenadic, Igor, Abe, Yoshinari, Jalbrzikowski, Maria, James, Anthony C, Janssen, Joost, Jaspers-Fayer, Fern, Xu, Jian, Jonassen, Rune, Karkashadze, Georgii, King, Joseph A, Kircher, Tilo, Kirschner, Matthias, Abé, Christoph, Koch, Kathrin, Kochunov, Peter, Kohls, Gregor, Konrad, Kerstin, Krämer, Bernd, Krug, Axel, Kuntsi, Jonna, Kwon, Jun Soo, Landén, Mikael, Landrø, Nils I, Anticevic, Alan, Lazaro, Luisa, Lebedeva, Irina S, Leehr, Elisabeth J, Lera-Miguel, Sara, Lesch, Klaus-Peter, Lochner, Christine, Louza, Mario R, Luna, Beatriz, Lundervold, Astri J, MacMaster, Frank P, Alda, Martin, Maglanoc, Luigi A, Malpas, Charles B, Portella, Maria J, Marsh, Rachel, Martyn, Fiona M, Mataix-Cols, David, Mathalon, Daniel H, McCarthy, Hazel, McDonald, Colm, McPhilemy, Genevieve, Aleman, Andre, Meinert, Susanne, Menchón, José M, Minuzzi, Luciano, Mitchell, Philip B, Moreno, Carmen, Morgado, Pedro, Muratori, Filippo, Murphy, Clodagh M, Murphy, Declan, Mwangi, Benson, Alloza, Clara, Nabulsi, Leila, Nakagawa, Akiko, Nakamae, Takashi, Namazova, Leyla, Narayanaswamy, Janardhanan, Jahanshad, Neda, Nguyen, Danai D, Nicolau, Rosa, O'Gorman Tuura, Ruth L, O'Hearn, Kirsten, Alonso-Lana, Silvia, Oosterlaan, Jaap, Opel, Nils, Ophoff, Roel A, Oranje, Bob, García de la Foz, Victor Ortiz, Overs, Bronwyn J, Paloyelis, Yannis, Pantelis, Christos, Parellada, Mara, Pauli, Paul, Disorder, Bipolar, Ameis, Stephanie H, Picó-Pérez, Maria, Picon, Felipe A, Piras, Fabrizio, Piras, Federica, Plessen, Kerstin J, Pomarol-Clotet, Edith, Preda, Adrian, Puig, Olga, Quidé, Yann, Radua, Joaquim, Anagnostou, Evdokia, Ramos-Quiroga, J Antoni, Rasser, Paul E, Rauer, Lisa, Reddy, Janardhan, Redlich, Ronny, Reif, Andreas, Reneman, Liesbeth, Repple, Jonathan, Retico, Alessandra, Richarte, Vanesa, McIntosh, Andrew A, Richter, Anja, Rosa, Pedro G P, Rubia, Katya K, Hashimoto, Ryota, Sacchet, Matthew D, Salvador, Raymond, Santonja, Javier, Sarink, Kelvin, Sarró, Salvador, Satterthwaite, Theodore D, Arango, Celso, Sawa, Akira, Schall, Ulrich, Schofield, Peter R, Schrantee, Anouk, Seitz, Jochen, Serpa, Mauricio H, Setién-Suero, Esther, Shaw, Philip, Shook, Devon, Silk, Tim J, Arnold, Paul D, Sim, Kang, Simon, Schmitt, Simpson, Helen Blair, Singh, Aditya, Skoch, Antonin, Skokauskas, Norbert, Soares, Jair C, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Asherson, Philip, Spaniel, Filip, Lawrie, Stephen M, Stern, Emily R, Stewart, S Evelyn, Takayanagi, Yoichiro, Temmingh, Henk S, Tolin, David F, Tomecek, David, Tordesillas-Gutiérrez, Diana, Tosetti, Michela, Assogna, Francesca, Uhlmann, Anne, van Amelsvoort, Therese, van der Wee, Nic J A, van der Werff, Steven J A, van Haren, Neeltje E M, van Wingen, Guido A, Vance, Alasdair, Vázquez-Bourgon, Javier, Vecchio, Daniela, Venkatasubramanian, Ganesan, Auzias, Guillaume, Vieta, Eduard, Vilarroya, Oscar, Vives-Gilabert, Yolanda, Voineskos, Aristotle N, Völzke, Henry, von Polier, Georg G, Walton, Esther, Weickert, Thomas W, Weickert, Cynthia Shannon, Weideman, Andrea S, Ayesa-Arriola, Rosa, Wittfeld, Katharina, Wolf, Daniel H, Wu, Mon-Ju, Yang, T. T., Yang, Sikun, Yoncheva, Yuliya, Yun, Je-Yeon, Cheng, Yuqi, Zanetti, Marcus V, Ziegler, Georg C, Bakker, Geor, Franke, Barbara, Hoogman, Martine, Buitelaar, Jan K, van Rooij, Daan, Andreassen, Ole A, Ching, Christopher R K, Veltman, Dick J, Schmaal, Lianne, Stein, Dan J, van den Heuvel, Odile A, Disorder, Major Depressive, Banaj, Nerisa, Turner, Jessica A, van Erp, Theo G M, Pausova, Zdenka, Thompson, Paul M, Paus, Tomáš, Attention-Deficit/Hyperactivity Disorder, Banaschewski, Tobias, Bandeira, Cibele E, Baranov, Alexandr, Bargalló, Núria, Bau, Claiton H D, Baumeister, Sarah, Baune, Bernhard T, Bellgrove, Mark A, Benedetti, Francesco, Disorder, Obsessive-Compulsive, Bertolino, Alessandro, Boedhoe, Premika S W, Boks, Marco, Bollettini, Irene, Del Mar Bonnin, Caterina, Borgers, Tiana, Borgwardt, Stefan, Brandeis, Daniel, Brennan, Brian P, Bruggemann, Jason M, Groups, Schizophrenia ENIGMA Working, Bülow, Robin, Busatto, Geraldo F, Calderoni, Sara, Calhoun, Vince D, Calvo, Rosa, Canales-Rodríguez, Erick J, Cannon, Dara M, Carr, Vaughan J, Cascella, Nicola, Cercignani, Mara, Patel, Yash, Chaim-Avancini, Tiffany M, Christakou, Anastasia, Coghill, David, Conzelmann, Annette, Crespo-Facorro, Benedicto, Cubillo, Ana I, Cullen, Kathryn R, Cupertino, Renata B, Daly, Eileen, Dannlowski, Udo, Parker, Nadine, Davey, Christopher G, Denys, Damiaan, Deruelle, Christine, Di Giorgio, Annabella, Dickie, Erin W, Dima, Danai, Dohm, Katharina, Ehrlich, Stefan, Ely, Benjamin A, Erwin-Grabner, Tracy, Shin, Jean, Ethofer, Thomas, Fair, Damien A, Fallgatter, Andreas, Faraone, Stephen V, Fatjó-Vilas, Mar, Fedor, Jennifer M, Fitzgerald, Kate D, Ford, Judith M, Frodl, Thomas, Fu, Cynthia H Y, Howard, Derek, Fullerton, Janice M, Gabel, Matt C, Glahn, David C, Roberts, Gloria, Gogberashvili, Tinatin, Goikolea, Jose M, Gotlib, Ian H, Goya-Maldonado, Roberto, Grabe, Hans, Green, Melissa J, Patel, Y., Parker, N., Shin, J., Howard, D., French, L., Thomopoulos, S. I., Pozzi, E., Abe, Y., Abe, C., Anticevic, A., Alda, M., Aleman, A., Alloza, C., Alonso-Lana, S., Ameis, S. H., Anagnostou, E., Mcintosh, A. A., Arango, C., Arnold, P. D., Asherson, P., Assogna, F., Auzias, G., Ayesa-Arriola, R., Bakker, G., Banaj, N., Banaschewski, T., Bandeira, C. E., Baranov, A., Bargallo, N., Bau, C. H. D., Baumeister, S., Baune, B. T., Bellgrove, M. A., Benedetti, F., Bertolino, A., Boedhoe, P. S. W., Boks, M., Bollettini, I., Del Mar Bonnin, C., Borgers, T., Borgwardt, S., Brandeis, D., Brennan, B. P., Bruggemann, J. M., Bulow, R., Busatto, G. F., Calderoni, S., Calhoun, V. D., Calvo, R., Canales-Rodriguez, E. J., Cannon, D. M., Carr, V. J., Cascella, N., Cercignani, M., Chaim-Avancini, T. M., Christakou, A., Coghill, D., Conzelmann, A., Crespo-Facorro, B., Cubillo, A. I., Cullen, K. R., Cupertino, R. B., Daly, E., Dannlowski, U., Davey, C. G., Denys, D., Deruelle, C., Di Giorgio, A., Dickie, E. W., Dima, D., Dohm, K., Ehrlich, S., Ely, B. A., Erwin-Grabner, T., Ethofer, T., Fair, D. A., Fallgatter, A. J., Faraone, S. V., Fatjo-Vilas, M., Fedor, J. M., Fitzgerald, K. D., Ford, J. M., Frodl, T., Fu, C. H. Y., Fullerton, J. M., Gabel, M. C., Glahn, D. C., Roberts, G., Gogberashvili, T., Goikolea, J. M., Gotlib, I. H., Goya-Maldonado, R., Grabe, H. J., Green, M. J., Grevet, E. H., Groenewold, N. A., Grotegerd, D., Gruber, O., Gruner, P., Guerrero-Pedraza, A., Gur, R. E., Gur, R. C., Haar, S., Haarman, B. C. M., Haavik, J., Hahn, T., Hajek, T., Harrison, B. J., Harrison, N. A., Hartman, C. A., Whalley, H. C., Heslenfeld, D. J., Hibar, D. P., Hilland, E., Hirano, Y., Ho, T. C., Hoekstra, P. J., Hoekstra, L., Hohmann, S., Hong, L. E., Hoschl, C., Hovik, M. F., Howells, F. M., Nenadic, I., Jalbrzikowski, M., James, A. C., Janssen, J., Jaspers-Fayer, F., Xu, J., Jonassen, R., Karkashadze, G., King, J. A., Kircher, T., Kirschner, M., Koch, K., Kochunov, P., Kohls, G., Konrad, K., Kramer, B., Krug, A., Kuntsi, J., Kwon, J. S., Landen, M., Landro, N. I., Lazaro, L., Lebedeva, I. S., Leehr, E. J., Lera-Miguel, S., Lesch, K. -P., Lochner, C., Louza, M. R., Luna, B., Lundervold, A. J., Macmaster, F. P., Maglanoc, L. A., Malpas, C. B., Portella, M. J., Marsh, R., Martyn, F. M., Mataix-Cols, D., Mathalon, D. H., Mccarthy, H., Mcdonald, C., Mcphilemy, G., Meinert, S., Menchon, J. M., Minuzzi, L., Mitchell, P. B., Moreno, C., Morgado, P., Muratori, F., Murphy, C. M., Murphy, D., Mwangi, B., Nabulsi, L., Nakagawa, A., Nakamae, T., Namazova, L., Narayanaswamy, J., Jahanshad, N., Nguyen, D. D., Nicolau, R., O'Gorman Tuura, R. L., O'Hearn, K., Oosterlaan, J., Opel, N., Ophoff, R. A., Oranje, B., Garcia De La Foz, V. O., Overs, B. J., Paloyelis, Y., Pantelis, C., Parellada, M., Pauli, P., Pico-Perez, M., Picon, F. A., Piras, F., Plessen, K. J., Pomarol-Clotet, E., Preda, A., Puig, O., Quide, Y., Radua, J., Ramos-Quiroga, J. A., Rasser, P. E., Rauer, L., Reddy, J., Redlich, R., Reif, A., Reneman, L., Repple, J., Retico, A., Richarte, V., Richter, A., Rosa, P. G. P., Rubia, K. K., Hashimoto, R., Sacchet, M. D., Salvador, R., Santonja, J., Sarink, K., Sarro, S., Satterthwaite, T. D., Sawa, A., Schall, U., Schofield, P. R., Schrantee, A., Seitz, J., Serpa, M. H., Setien-Suero, E., Shaw, P., Shook, D., Silk, T. J., Sim, K., Simon, S., Simpson, H. B., Singh, A., Skoch, A., Skokauskas, N., Soares, J. C., Soreni, N., Soriano-Mas, C., Spalletta, G., Spaniel, F., Lawrie, S. M., Stern, E. R., Stewart, S. E., Takayanagi, Y., Temmingh, H. S., Tolin, D. F., Tomecek, D., Tordesillas-Gutierrez, D., Tosetti, M., Uhlmann, A., Van Amelsvoort, T., Van Der Wee, N. J. A., Van Der Werff, S. J. A., Van Haren, N. E. M., Van Wingen, G. A., Vance, A., Vazquez-Bourgon, J., Vecchio, D., Venkatasubramanian, G., Vieta, E., Vilarroya, O., Vives-Gilabert, Y., Voineskos, A. N., Volzke, H., Von Polier, G. G., Walton, E., Weickert, T. W., Weickert, C. S., Weideman, A. S., Wittfeld, K., Wolf, D. H., Wu, M. -J., Yang, T. T., Yang, K., Yoncheva, Y., Yun, J. -Y., Cheng, Y., Zanetti, M. V., Ziegler, G. C., Franke, B., Hoogman, M., Buitelaar, J. K., Van Rooij, D., Andreassen, O. A., Ching, C. R. K., Veltman, D. J., Schmaal, L., Stein, D. J., Van Den Heuvel, O. A., Turner, J. A., Van Erp, T. G. M., Pausova, Z., Thompson, P. M., Paus, T., Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Pediatric surgery, Radiology and nuclear medicine, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Neurodegeneration, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Adult Psychiatry, ANS - Compulsivity, Impulsivity & Attention, General Paediatrics, ARD - Amsterdam Reproduction and Development, Radiology and Nuclear Medicine, APH - Personalized Medicine, ANS - Brain Imaging, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, University of Zurich, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Clinical Neuropsychology, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Child and Adolescent Psychiatry / Psychology, IBBA, and Cognitive Psychology

Subjects
Male, Obsessive-Compulsive Disorder, Bipolar Disorder, Autism Spectrum Disorder, Autism, [SDV]Life Sciences [q-bio], Gene Expression, cytology [Cerebral Cortex], Cohort Studies, Fetal Development, physiology [Gene Expression], 2738 Psychiatry and Mental Health, 0302 clinical medicine, diagnostic imaging [Cerebral Cortex], SCHIZOPHRENIA, BRAIN, Child, Obsessive-compulsive disorder (OCD), Original Investigation, Aged, 80 and over, Cerebral Cortex, 0303 health sciences, pathology [Depressive Disorder, Major], Principal Component Analysis, Adolescent psychiatry, 10058 Department of Child and Adolescent Psychiatry, Middle Aged, diagnostic imaging [Obsessive-Compulsive Disorder], REGIONS, Magnetic Resonance Imaging, 3. Good health, FALSE DISCOVERY RATE, Psychiatry and Mental health, Autism spectrum disorder, Schizophrenia, growth & development [Cerebral Cortex], Child, Preschool, Major depressive disorder, diagnostic imaging [Schizophrenia], Esquizofrènia, Female, Psiquiatria infantil, Psiquiatria de l'adolescència, diagnostic imaging [Autism Spectrum Disorder], Adult, medicine.medical_specialty, Adolescent, Human Development, 610 Medicine & health, diagnostic imaging [Bipolar Disorder], pathology [Autism Spectrum Disorder], diagnostic imaging [Depressive Disorder, Major], 03 medical and health sciences, Young Adult, All institutes and research themes of the Radboud University Medical Center, Neuroimaging, SDG 3 - Good Health and Well-being, CEREBRAL-CORTEX, Child psychiatry, medicine, Attention deficit hyperactivity disorder, Humans, Bipolar disorder, ddc:610, Psychiatry, pathology [Schizophrenia], 030304 developmental biology, Aged, Depressive Disorder, Major, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, DENDRITE, Computational Biology, Correction, pathology [Attention Deficit Disorder with Hyperactivity], physiology [Fetal Development], medicine.disease, PATHOLOGY, pathology [Bipolar Disorder], pathology [Obsessive-Compulsive Disorder], 10036 Medical Clinic, Attention Deficit Disorder with Hyperactivity, 10054 Clinic for Psychiatry, Psychotherapy, and Psychosomatics, Case-Control Studies, DENSITY, ORIGINS, HIPPOCAMPUS, diagnostic imaging [Attention Deficit Disorder with Hyperactivity], pathology [Cerebral Cortex], Autisme, business, Neuroscience, 030217 neurology & neurosurgery, physiology [Human Development]
Abstract

[Importance] Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood., [Objective] To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia., [Design, Setting, and Participants] Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244., [Main Outcomes and Measures] Interregional profiles of group difference in cortical thickness between cases and controls., [Results] A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders., [Conclusions and Relevance] In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.

Published
2021

9. The role of educational attainment and brain morphology in major depressive disorder: Findings from the ENIGMA major depressive disorder consortium

Author

Sarah Whittle, Divyangana Rakesh, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson, Aditya Singh, Ali Saffet Gonul, Andre Aleman, Aslıhan Uyar Demir, Axel Krug, Benson Mwangi, Bernd Krämer, Bernhard T. Baune, Dan J. Stein, Dominik Grotegerd, Edith Pomarol-Clotet, Elena Rodríguez-Cano, Elisa Melloni, Francesco Benedetti, Frederike Stein, Hans J. Grabe, Henry Völzke, Ian H. Gotlib, Igor Nenadić, Jair C. Soares, Jonathan Repple, Kang Sim, Katharina Brosch, Katharina Wittfeld, Klaus Berger, Marco Hermesdorf, Maria J. Portella, Matthew D. Sacchet, Mon-Ju Wu, Nils Opel, Nynke A. Groenewold, Oliver Gruber, Paola Fuentes-Claramonte, Raymond Salvador, Roberto Goya-Maldonado, Salvador Sarró, Sara Poletti, Susanne L. Meinert, Tilo Kircher, Udo Dannlowski, Elena Pozzi, Whittle, Sarah, Rakesh, Divyangana, Schmaal, Lianne, Veltman, Dick J., Thompson, Paul M., Singh, Aditya, Gonul, Ali Saffet, Aleman, Andre, Uyar Demir, Aslıhan, Krug, Axel, Mwangi, Benson, Krämer, Bernd, Baune, Bernhard T., Stein, Dan J., Grotegerd, Dominik, Pomarol-Clotet, Edith, Rodríguez-Cano, Elena, Melloni, Elisa, Benedetti, Francesco, Stein, Frederike, Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Nenadić, Igor, Soares, Jair C., Repple, Jonathan, Sim, Kang, Brosch, Katharina, Wittfeld, Katharina, Berger, Klau, Hermesdorf, Marco, Portella, Maria J., Sacchet, Matthew D., Wu, Mon-Ju, Opel, Nil, Groenewold, Nynke A., Gruber, Oliver, Fuentes-Claramonte, Paola, Salvador, Raymond, Goya-Maldonado, Roberto, Sarró, Salvador, Poletti, Sara, Meinert, Susanne L., Kircher, Tilo, Dannlowski, Udo, and Pozzi, Elena

Subjects
Adult, Depressive Disorder, Major, Brain, Educational Status, Humans, Magnetic Resonance Imaging, Frontal Lobe
Abstract

Brain structural abnormalities and low educational attainment are consistently associated with major depressive disorder (MDD), yet there has been little research investigating the complex interaction of these factors. Brain structural alterations may represent a vulnerability or differential susceptibility marker, and in the context of low educational attainment, predict MDD. We tested this moderation model in a large multisite sample of 1958 adults with MDD and 2921 controls (aged 18 to 86) from the ENIGMA MDD working group. Using generalized linear mixed models and within-sample split-half replication, we tested whether brain structure interacted with educational attainment to predict MDD status. Analyses revealed that cortical thickness in a number of occipital, parietal, and frontal regions significantly interacted with education to predict MDD. For the majority of regions, models suggested a differential susceptibility effect, whereby thicker cortex was more likely to predict MDD in individuals with low educational attainment, but less likely to predict MDD in individuals with high educational attainment. Findings suggest that greater thickness of brain regions subserving visuomotor and social-cognitive functions confers susceptibility to MDD, dependent on level of educational attainment. Longitudinal work, however, is ultimately needed to establish whether cortical thickness represents a preexisting susceptibility marker. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022

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