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1. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group

Author: Kelly, S, Jahanshad, N, Zalesky, A, Kochunov, P, Agartz, I, Alloza, C, Andreassen, OA, Arango, C, Banaj, N, Bouix, S, Bousman, CA, Brouwer, RM, Bruggemann, J, Bustillo, J, Cahn, W, Calhoun, V, Cannon, D, Carr, V, Catts, S, Chen, J, Chen, J-X, Chen, X, Chiapponi, C, Cho, Kl K, Ciullo, V, Corvin, AS, Crespo-Facorro, B, Cropley, V, De Rossi, P, Diaz-Caneja, CM, Dickie, EW, Ehrlich, S, Fan, F-M, Faskowitz, J, Fatouros-Bergman, H, Flyckt, L, Ford, JM, Fouche, J-P, Fukunaga, M, Gill, M, Glahn, DC, Gollub, R, Goudzwaard, ED, Guo, H, Gur, RE, Gur, RC, Gurholt, TP, Hashimoto, R, Hatton, SN, Henskens, FA, Hibar, DP, Hickie, IB, Hong, LE, Horacek, J, Howells, FM, Hulshoff Pol, HE, Hyde, CL, Isaev, D, Jablensky, A, Jansen, PR, Janssen, J, Jönsson, EG, Jung, LA, Kahn, RS, Kikinis, Z, Liu, K, Klauser, P, Knöchel, C, Kubicki, M, Lagopoulos, J, Langen, C, Lawrie, S, Lenroot, RK, Lim, KO, Lopez-Jaramillo, C, Lyall, A, Magnotta, V, Mandl, RCW, Mathalon, DH, McCarley, RW, McCarthy-Jones, S, McDonald, C, McEwen, S, McIntosh, A, Melicher, T, Mesholam-Gately, RI, Michie, PT, Mowry, B, Mueller, BA, Newell, DT, O'Donnell, P, Oertel-Knöchel, V, Oestreich, L, Paciga, SA, Pantelis, C, Pasternak, O, Pearlson, G, Pellicano, GR, Pereira, A, and Pineda Zapata, J
Subjects: Neurosciences, Brain Disorders, Mental Health, Serious Mental Illness, Biomedical Imaging, Schizophrenia, Clinical Research, Mental health, Adult, Aged, Aged, 80 and over, Brain, Cohort Studies, Corpus Callosum, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, White Matter, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
Published: 2018

2. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group.

Author: Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Krämer, B, Overs, B, Hartberg, CB, Abé, C, Dima, D, Grotegerd, D, Sprooten, E, Bøen, E, Jimenez, E, Howells, FM, Delvecchio, G, Temmingh, H, Starke, J, Almeida, JRC, Goikolea, JM, Houenou, J, Beard, LM, Rauer, L, Abramovic, L, Bonnin, M, Ponteduro, MF, Keil, M, Rive, MM, Yao, N, Yalin, N, Najt, P, Rosa, PG, Redlich, R, Trost, S, Hagenaars, S, Fears, SC, Alonso-Lana, S, van Erp, TGM, Nickson, T, Chaim-Avancini, TM, Meier, TB, Elvsåshagen, T, Haukvik, UK, Lee, WH, Schene, AH, Lloyd, AJ, Young, AH, Nugent, A, Dale, AM, Pfennig, A, McIntosh, AM, Lafer, B, Baune, BT, Ekman, CJ, Zarate, CA, Bearden, CE, Henry, C, Simhandl, C, McDonald, C, Bourne, C, Stein, DJ, Wolf, DH, Cannon, DM, Glahn, DC, Veltman, DJ, Pomarol-Clotet, E, Vieta, E, Canales-Rodriguez, EJ, Nery, FG, Duran, FLS, Busatto, GF, Roberts, G, Pearlson, GD, Goodwin, GM, Kugel, H, Whalley, HC, Ruhe, HG, Soares, JC, Fullerton, JM, Rybakowski, JK, Savitz, J, Chaim, KT, Fatjó-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landén, M, and Lawrence, NS
Subjects: Brain, Cerebral Cortex, Frontal Lobe, Prefrontal Cortex, Temporal Lobe, Humans, Magnetic Resonance Imaging, Case-Control Studies, Bipolar Disorder, Psychotic Disorders, Age Factors, Sex Factors, Adolescent, Adult, Middle Aged, Female, Male, Young Adult, Neuroimaging, Gray Matter, Clinical Research, Serious Mental Illness, Biomedical Imaging, Brain Disorders, Mental Health, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.
Published: 2018

3. Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium.

Author: Walton, E, Hibar, DP, van Erp, TGM, Potkin, SG, Roiz-Santiañez, R, Crespo-Facorro, B, Suarez-Pinilla, P, van Haren, NEM, de Zwarte, SMC, Kahn, RS, Cahn, W, Doan, NT, Jørgensen, KN, Gurholt, TP, Agartz, I, Andreassen, OA, Westlye, LT, Melle, I, Berg, AO, Morch-Johnsen, L, Færden, A, Flyckt, L, Fatouros-Bergman, H, Karolinska Schizophrenia Project Consortium (KaSP), Jönsson, EG, Hashimoto, R, Yamamori, H, Fukunaga, M, Jahanshad, N, De Rossi, P, Piras, F, Banaj, N, Spalletta, G, Gur, RE, Gur, RC, Wolf, DH, Satterthwaite, TD, Beard, LM, Sommer, IE, Koops, S, Gruber, O, Richter, A, Krämer, B, Kelly, S, Donohoe, G, McDonald, C, Cannon, DM, Corvin, A, Gill, M, Di Giorgio, A, Bertolino, A, Lawrie, S, Nickson, T, Whalley, HC, Neilson, E, Calhoun, VD, Thompson, PM, Turner, JA, and Ehrlich, S
Subjects: Karolinska Schizophrenia Project Consortium, Prefrontal Cortex, Humans, Magnetic Resonance Imaging, Linear Models, Schizophrenia, Psychiatric Status Rating Scales, Schizophrenic Psychology, Internationality, Image Processing, Computer-Assisted, Adult, Female, Male, Functional Laterality, Cortical thickness, ENIGMA, FreeSurfer, MRI, PANSS, SANS, medial orbitofrontal cortex, negative symptoms, schizophrenia, Image Processing, Computer-Assisted, Psychiatry, Psychology, Public Health and Health Services, Neurosciences
Abstract: BackgroundOur understanding of the complex relationship between schizophrenia symptomatology and etiological factors can be improved by studying brain-based correlates of schizophrenia. Research showed that impairments in value processing and executive functioning, which have been associated with prefrontal brain areas [particularly the medial orbitofrontal cortex (MOFC)], are linked to negative symptoms. Here we tested the hypothesis that MOFC thickness is associated with negative symptom severity.MethodsThis study included 1985 individuals with schizophrenia from 17 research groups around the world contributing to the ENIGMA Schizophrenia Working Group. Cortical thickness values were obtained from T1-weighted structural brain scans using FreeSurfer. A meta-analysis across sites was conducted over effect sizes from a model predicting cortical thickness by negative symptom score (harmonized Scale for the Assessment of Negative Symptoms or Positive and Negative Syndrome Scale scores).ResultsMeta-analytical results showed that left, but not right, MOFC thickness was significantly associated with negative symptom severity (β std = -0.075; p = 0.019) after accounting for age, gender, and site. This effect remained significant (p = 0.036) in a model including overall illness severity. Covarying for duration of illness, age of onset, antipsychotic medication or handedness weakened the association of negative symptoms with left MOFC thickness. As part of a secondary analysis including 10 other prefrontal regions further associations in the left lateral orbitofrontal gyrus and pars opercularis emerged.ConclusionsUsing an unusually large cohort and a meta-analytical approach, our findings point towards a link between prefrontal thinning and negative symptom severity in schizophrenia. This finding provides further insight into the relationship between structural brain abnormalities and negative symptoms in schizophrenia.
Published: 2018

4. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group

Author: Schmaal, L, Hibar, DP, Sämann, PG, Hall, GB, Baune, BT, Jahanshad, N, Cheung, JW, van Erp, TGM, Bos, D, Ikram, MA, Vernooij, MW, Niessen, WJ, Tiemeier, H, Hofman, A, Wittfeld, K, Grabe, HJ, Janowitz, D, Bülow, R, Selonke, M, Völzke, H, Grotegerd, D, Dannlowski, U, Arolt, V, Opel, N, Heindel, W, Kugel, H, Hoehn, D, Czisch, M, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Goya-Maldonado, R, Gruber, O, Krämer, B, Hatton, SN, Lagopoulos, J, Hickie, IB, Frodl, T, Carballedo, A, Frey, EM, van Velzen, LS, Penninx, BWJH, van Tol, M-J, van der Wee, NJ, Davey, CG, Harrison, BJ, Mwangi, B, Cao, B, Soares, JC, Veer, IM, Walter, H, Schoepf, D, Zurowski, B, Konrad, C, Schramm, E, Normann, C, Schnell, K, Sacchet, MD, Gotlib, IH, MacQueen, GM, Godlewska, BR, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Hall, J, Sussmann, JE, Li, M, Walter, M, Aftanas, L, Brack, I, Bokhan, NA, Thompson, PM, and Veltman, DJ
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Major Depressive Disorder, Serious Mental Illness, Neurosciences, Biomedical Imaging, Depression, Basic Behavioral and Social Science, Mental Health, Brain Disorders, Clinical Research, Pediatric, Behavioral and Social Science, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Adolescent, Adult, Brain, Cerebral Cortex, Depressive Disorder, Major, Female, Frontal Lobe, Gray Matter, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Neuroimaging, Prefrontal Cortex, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.
Published: 2017

5. Subcortical brain structure and suicidal behaviour in major depressive disorder: a meta-analysis from the ENIGMA-MDD working group.

Author: Rentería, ME, Schmaal, L, Hibar, DP, Couvy-Duchesne, B, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hatton, SN, Lagopoulos, J, Veltman, DJ, van der Wee, N, van Erp, TGM, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Godlewska, BR, Cowen, PJ, Penninx, BWJH, Jahanshad, N, Thompson, PM, Wright, MJ, Martin, NG, Christensen, H, and Hickie, IB
Subjects: Brain, Humans, Magnetic Resonance Imaging, Prevalence, Risk Factors, Suicide, Depressive Disorder, Major, Adult, Aged, Middle Aged, Female, Male, Young Adult, Suicidal Ideation, Neuroimaging, Depressive Disorder, Major, Psychology, Clinical Sciences, Public Health and Health Services
Abstract: The aetiology of suicidal behaviour is complex, and knowledge about its neurobiological mechanisms is limited. Neuroimaging methods provide a noninvasive approach to explore the neural correlates of suicide vulnerability in vivo. The ENIGMA-MDD Working Group is an international collaboration evaluating neuroimaging and clinical data from thousands of individuals collected by research groups from around the world. Here we present analyses in a subset sample (n=3097) for whom suicidality data were available. Prevalence of suicidal symptoms among major depressive disorder (MDD) cases ranged between 29 and 69% across cohorts. We compared mean subcortical grey matter volumes, lateral ventricle volumes and total intracranial volume (ICV) in MDD patients with suicidal symptoms (N=451) vs healthy controls (N=1996) or MDD patients with no suicidal symptoms (N=650). MDD patients reporting suicidal plans or attempts showed a smaller ICV (P=4.12 × 10-3) or a 2.87% smaller volume compared with controls (Cohen's d=-0.284). In addition, we observed a nonsignificant trend in which MDD cases with suicidal symptoms had smaller subcortical volumes and larger ventricular volumes compared with controls. Finally, no significant differences (P=0.28-0.97) were found between MDD patients with and those without suicidal symptoms for any of the brain volume measures. This is by far the largest neuroimaging meta-analysis of suicidal behaviour in MDD to date. Our results did not replicate previous reports of association between subcortical brain structure and suicidality and highlight the need for collecting better-powered imaging samples and using improved suicidality assessment instruments.
Published: 2017

6. Positive symptoms associate with cortical thinning in the superior temporal gyrus via the ENIGMA Schizophrenia consortium

Author: Walton, E, Hibar, DP, Erp, TGM, Potkin, SG, Roiz‐Santiañez, R, Crespo‐Facorro, B, Suarez‐Pinilla, P, Van Haren, NEM, Zwarte, SMC, Kahn, RS, Cahn, W, Doan, NT, Jørgensen, KN, Gurholt, TP, Agartz, I, Andreassen, OA, Westlye, LT, Melle, I, Berg, AO, Mørch‐Johnsen, L, Færden, A, Flyckt, L, Fatouros‐Bergman, H, Consortium, Karolinska Schizophrenia Project, Jönsson, EG, Hashimoto, R, Yamamori, H, Fukunaga, M, Preda, A, De Rossi, P, Piras, F, Banaj, N, Ciullo, V, Spalletta, G, Gur, RE, Gur, RC, Wolf, DH, Satterthwaite, TD, Beard, LM, Sommer, IE, Koops, S, Gruber, O, Richter, A, Krämer, B, Kelly, S, Donohoe, G, McDonald, C, Cannon, DM, Corvin, A, Gill, M, Di Giorgio, A, Bertolino, A, Lawrie, S, Nickson, T, Whalley, HC, Neilson, E, Calhoun, VD, Thompson, PM, Turner, JA, and Ehrlich, S
Subjects: Schizophrenia, Mental Health, Brain Disorders, Mental health, Good Health and Well Being, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Prospective Studies, Psychiatric Status Rating Scales, Schizophrenic Psychology, Temporal Lobe, schizophrenia, positive symptoms, superior temporal gyrus, cortical thickness, ENIGMA, FreeSurfer, MRI, scale for the assessment of positive symptoms, positive and negative syndrome scale, Karolinska Schizophrenia Project Consortium, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: ObjectiveBased on the role of the superior temporal gyrus (STG) in auditory processing, language comprehension and self-monitoring, this study aimed to investigate the relationship between STG cortical thickness and positive symptom severity in schizophrenia.MethodThis prospective meta-analysis includes data from 1987 individuals with schizophrenia collected at seventeen centres around the world that contribute to the ENIGMA Schizophrenia Working Group. STG thickness measures were extracted from T1-weighted brain scans using FreeSurfer. The study performed a meta-analysis of effect sizes across sites generated by a model predicting left or right STG thickness with a positive symptom severity score (harmonized SAPS or PANSS-positive scores), while controlling for age, sex and site. Secondary models investigated relationships between antipsychotic medication, duration of illness, overall illness severity, handedness and STG thickness.ResultsPositive symptom severity was negatively related to STG thickness in both hemispheres (left: βstd = -0.052; P = 0.021; right: βstd = -0.073; P = 0.001) when statistically controlling for age, sex and site. This effect remained stable in models including duration of illness, antipsychotic medication or handedness.ConclusionOur findings further underline the important role of the STG in hallmark symptoms in schizophrenia. These findings can assist in advancing insight into symptom-relevant pathophysiological mechanisms in schizophrenia.
Published: 2017

7. Subcortical volumetric abnormalities in bipolar disorder.

Author: Hibar, DP, Westlye, LT, van Erp, TGM, Rasmussen, J, Leonardo, CD, Faskowitz, J, Haukvik, UK, Hartberg, CB, Doan, NT, Agartz, I, Dale, AM, Gruber, O, Krämer, B, Trost, S, Liberg, B, Abé, C, Ekman, CJ, Ingvar, M, Landén, M, Fears, SC, Freimer, NB, Bearden, CE, Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes, Sprooten, E, Glahn, DC, Pearlson, GD, Emsell, L, Kenney, J, Scanlon, C, McDonald, C, Cannon, DM, Almeida, J, Versace, A, Caseras, X, Lawrence, NS, Phillips, ML, Dima, D, Delvecchio, G, Frangou, S, Satterthwaite, TD, Wolf, D, Houenou, J, Henry, C, Malt, UF, Bøen, E, Elvsåshagen, T, Young, AH, Lloyd, AJ, Goodwin, GM, Mackay, CE, Bourne, C, Bilderbeck, A, Abramovic, L, Boks, MP, van Haren, NEM, Ophoff, RA, Kahn, RS, Bauer, M, Pfennig, A, Alda, M, Hajek, T, Mwangi, B, Soares, JC, Nickson, T, Dimitrova, R, Sussmann, JE, Hagenaars, S, Whalley, HC, McIntosh, AM, Thompson, PM, and Andreassen, OA
Subjects: Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes, Brain, Humans, Magnetic Resonance Imaging, Organ Size, Case-Control Studies, Retrospective Studies, Bipolar Disorder, Adult, Middle Aged, Female, Male, Clinical Research, Mental Health, Brain Disorders, Serious Mental Illness, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Mental health, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case-control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen's d=-0.232; P=3.50 × 10-7) and thalamus (d=-0.148; P=4.27 × 10-3) and enlarged lateral ventricles (d=-0.260; P=3.93 × 10-5) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.
Published: 2016

8. Abnormal asymmetries in subcortical brain volume in schizophrenia

Author: Okada, N, Fukunaga, M, Yamashita, F, Koshiyama, D, Yamamori, H, Ohi, K, Yasuda, Y, Fujimoto, M, Watanabe, Y, Yahata, N, Nemoto, K, Hibar, DP, van Erp, TGM, Fujino, H, Isobe, M, Isomura, S, Natsubori, T, Narita, H, Hashimoto, N, Miyata, J, Koike, S, Takahashi, T, Yamasue, H, Matsuo, K, Onitsuka, T, Iidaka, T, Kawasaki, Y, Yoshimura, R, Suzuki, M, Turner, JA, Takeda, M, Thompson, PM, Ozaki, N, Kasai, K, and Hashimoto, R
Subjects: Clinical Research, Mental Health, Brain Disorders, Neurosciences, Schizophrenia, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adult, Amygdala, Basal Ganglia, Brain, Brain Mapping, Cohort Studies, Cross-Sectional Studies, Female, Functional Laterality, Hippocampus, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Psychiatric Status Rating Scales, Putamen, Thalamus, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Subcortical structures, which include the basal ganglia and parts of the limbic system, have key roles in learning, motor control and emotion, but also contribute to higher-order executive functions. Prior studies have reported volumetric alterations in subcortical regions in schizophrenia. Reported results have sometimes been heterogeneous, and few large-scale investigations have been conducted. Moreover, few large-scale studies have assessed asymmetries of subcortical volumes in schizophrenia. Here, as a work completely independent of a study performed by the ENIGMA consortium, we conducted a large-scale multisite study of subcortical volumetric differences between patients with schizophrenia and controls. We also explored the laterality of subcortical regions to identify characteristic similarities and differences between them. T1-weighted images from 1680 healthy individuals and 884 patients with schizophrenia, obtained with 15 imaging protocols at 11 sites, were processed with FreeSurfer. Group differences were calculated for each protocol and meta-analyzed. Compared with controls, patients with schizophrenia demonstrated smaller bilateral hippocampus, amygdala, thalamus and accumbens volumes as well as intracranial volume, but larger bilateral caudate, putamen, pallidum and lateral ventricle volumes. We replicated the rank order of effect sizes for subcortical volumetric changes in schizophrenia reported by the ENIGMA consortium. Further, we revealed leftward asymmetry for thalamus, lateral ventricle, caudate and putamen volumes, and rightward asymmetry for amygdala and hippocampal volumes in both controls and patients with schizophrenia. Also, we demonstrated a schizophrenia-specific leftward asymmetry for pallidum volume. These findings suggest the possibility of aberrant laterality in neural pathways and connectivity patterns related to the pallidum in schizophrenia.
Published: 2016

9. Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

Author: Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Vernooij, MW, Niessen, WJ, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hall, GB, van Velzen, LS, van Tol, M-J, van der Wee, NJ, Veer, IM, Walter, H, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, McIntosh, AM, Whalley, HC, Sussmann, JE, Godlewska, BR, Fischer, FH, Penninx, BWJH, Thompson, PM, and Hibar, DP
Subjects: Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Published: 2016

10. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group.

Author: Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Tiemeier, H, Hofman, A, Niessen, WJ, Vernooij, MW, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Wright, MJ, Hall, GB, MacQueen, GM, Frey, EM, Carballedo, A, van Velzen, LS, van Tol, MJ, van der Wee, NJ, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Sussmann, JE, Godlewska, BR, Cowen, PJ, Fischer, FH, Rose, M, Penninx, BWJH, Thompson, PM, and Hibar, DP
Subjects: Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Case-Control Studies, Depressive Disorder, Major, Adult, Middle Aged, Female, Male, Neuroimaging, major depressive disorder, structural volumes, ENIGMA consortium, meta-analysis, hippocampus, Depressive Disorder, Major, Psychiatry, Medical and Health Sciences, Biological Sciences, Psychology and Cognitive Sciences
Abstract: The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen's d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen's d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen's d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen's d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen's d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.
Published: 2016

11. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium

Author: van Erp, TGM, Hibar, DP, Rasmussen, JM, Glahn, DC, Pearlson, GD, Andreassen, OA, Agartz, I, Westlye, LT, Haukvik, UK, Dale, AM, Melle, I, Hartberg, CB, Gruber, O, Kraemer, B, Zilles, D, Donohoe, G, Kelly, S, McDonald, C, Morris, DW, Cannon, DM, Corvin, A, Machielsen, MWJ, Koenders, L, de Haan, L, Veltman, DJ, Satterthwaite, TD, Wolf, DH, Gur, RC, Gur, RE, Potkin, SG, Mathalon, DH, Mueller, BA, Preda, A, Macciardi, F, Ehrlich, S, Walton, E, Hass, J, Calhoun, VD, Bockholt, HJ, Sponheim, SR, Shoemaker, JM, van Haren, NEM, Pol, HEH, Ophoff, RA, Kahn, RS, Roiz-Santiañez, R, Crespo-Facorro, B, Wang, L, Alpert, KI, Jönsson, EG, Dimitrova, R, Bois, C, Whalley, HC, McIntosh, AM, Lawrie, SM, Hashimoto, R, Thompson, PM, and Turner, JA
Subjects: Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Serious Mental Illness, Brain Disorders, Schizophrenia, Clinical Research, Neurosciences, Mental Health, Biomedical Imaging, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Adult, Brain, Brain Mapping, Case-Control Studies, Female, Functional Laterality, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Prospective Studies, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.
Published: 2016

12. Commonfolate gene variant, MTHFR C677T, is associated with brain structure in two independent cohorts of people with mild cognitive impairment

Author: Weiner, Michael, Rajagopalan, P, Jahanshad, N, Stein, JL, Hua, X, Madsen, SK, Kohannim, O, Hibar, DP, Toga, AW, Jack, CR, and Saykin, AJ
Abstract: A commonly carried C677T polymorphism in a folate-related gene, MTHFR, is associated with higher plasma homocysteine, a well-known mediator of neuronal damage and brain atrophy. As homocysteine promotes brain atrophy, we set out to discover whether people
Published: 2012

13. Multi-Site Meta-Analysis of Morphometry

Author: Jahanshad, N, Faskowitz, J, Roshchupkin, Gennady, Hibar, DP, Gutman, B A, Tustison, NJ, Adams, Hieab, Niessen, Wiro, Vernooij, Meike, Ikram, Arfan, Zwiers, MP, Vasquez, AA, Franke, B, Kroll, JL, Mwangi, B, Soares, JC, Ing, A, Desrivieres, S, Schumann, G, Hansell, NK, de Zubicaray, GI, McMahon, KL, Martin, NG, Wright, MJ, Thompson, PM, Alzheimer's Dis Neuroimaging, I, Epidemiology, Medical Informatics, Radiology & Nuclear Medicine, and Neurology
Subjects: Adult, Male, Databases, Factual, Imaging genetics, Computer science, 0206 medical engineering, Big data, Neuroimaging, Genome-wide association study, Genomics, 02 engineering and technology, computer.software_genre, Polymorphism, Single Nucleotide, Article, 150 000 MR Techniques in Brain Function, Young Adult, All institutes and research themes of the Radboud University Medical Center, Meta-Analysis as Topic, Alzheimer Disease, Genetics, Humans, Aged, Aged, 80 and over, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, Applied Mathematics, Brain, Computational Biology, 220 Statistical Imaging Neuroscience, Middle Aged, Magnetic Resonance Imaging, Data sharing, Meta-analysis, Female, Data mining, business, computer, 020602 bioinformatics, Genome-Wide Association Study, Biotechnology
Abstract: Genome-wide association studies (GWAS) link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses (e.g., tensor-based morphometry; TBM) may help better localize statistical effects. This can lead to $10^{13}$1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.
Published: 2019

14. 1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans

Author: Sonderby, IE, van der Meer, D, Moreau, C, Kaufmann, T, Walters, GB, Ellegaard, M, Abdellaoui, A, Ames, D, Amunts, K, Andersson, M, Armstrong, NJ, Bernard, M, Blackburn, NB, Blangero, J, Boomsma, DI, Brodaty, H, Brouwer, RM, Buelow, R, Boen, R, Cahn, W, Calhoun, VD, Caspers, S, Ching, CRK, Cichon, S, Ciufolini, S, Crespo-Facorro, B, Curran, JE, Dale, AM, Dalvie, S, Dazzan, P, de Geus, EJC, de Zubicaray, GI, de Zwarte, SMC, Desrivieres, S, Doherty, JL, Donohoe, G, Draganski, B, Ehrlich, S, Eising, E, Espeseth, T, Fejgin, K, Fisher, SE, Fladby, T, Frei, O, Frouin, V, Fukunaga, M, Gareau, T, Ge, T, Glahn, DC, Grabe, HJ, Groenewold, NA, Gustafsson, O, Haavik, J, Haberg, AK, Hall, J, Hashimoto, R, Hehir-Kwa, JY, Hibar, DP, Hillegers, MHJ, Hoffmann, P, Holleran, L, Holmes, AJ, Homuth, G, Hottenga, J-J, Hulshoff Pol, HE, Ikeda, M, Jahanshad, N, Jockwitz, C, Johansson, S, Joensson, EG, Jorgensen, NR, Kikuchi, M, Knowles, EEM, Kumar, K, Le Hellard, S, Leu, C, Linden, DEJ, Liu, J, Lundervold, A, Lundervold, AJ, Maillard, AM, Martin, NG, Martin-Brevet, S, Mather, KA, Mathias, SR, McMahon, KL, McRae, AF, Medland, SE, Meyer-Lindenberg, A, Moberget, T, Modenato, C, Sanchez, JM, Morris, DW, Muehleisen, TW, Murray, RM, Nielsen, J, Nordvik, JE, Nyberg, L, Loohuis, LMO, Ophoff, RA, Owen, MJ, Paus, T, Pausova, Z, Peralta, JM, Pike, GB, Prieto, C, Quinlan, EB, Reinbold, CS, Marques, TR, Rucker, JJH, Sachdev, PS, Sando, SB, Schofield, PR, Schork, AJ, Schumann, G, Shin, J, Shumskaya, E, Silva, AI, Sisodiya, SM, Steen, VM, Stein, DJ, Strike, LT, Suzuki, IK, Tamnes, CK, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Uhlmann, A, Ulfarsson, MO, van 't Ent, D, van den Bree, MBM, Vanderhaeghen, P, Vassos, E, Wen, W, Wittfeld, K, Wright, MJ, Agartz, I, Djurovic, S, Westlye, LT, Stefansson, H, Stefansson, K, Jacquemont, S, Thompson, PM, Andreassen, OA, Sonderby, IE, van der Meer, D, Moreau, C, Kaufmann, T, Walters, GB, Ellegaard, M, Abdellaoui, A, Ames, D, Amunts, K, Andersson, M, Armstrong, NJ, Bernard, M, Blackburn, NB, Blangero, J, Boomsma, DI, Brodaty, H, Brouwer, RM, Buelow, R, Boen, R, Cahn, W, Calhoun, VD, Caspers, S, Ching, CRK, Cichon, S, Ciufolini, S, Crespo-Facorro, B, Curran, JE, Dale, AM, Dalvie, S, Dazzan, P, de Geus, EJC, de Zubicaray, GI, de Zwarte, SMC, Desrivieres, S, Doherty, JL, Donohoe, G, Draganski, B, Ehrlich, S, Eising, E, Espeseth, T, Fejgin, K, Fisher, SE, Fladby, T, Frei, O, Frouin, V, Fukunaga, M, Gareau, T, Ge, T, Glahn, DC, Grabe, HJ, Groenewold, NA, Gustafsson, O, Haavik, J, Haberg, AK, Hall, J, Hashimoto, R, Hehir-Kwa, JY, Hibar, DP, Hillegers, MHJ, Hoffmann, P, Holleran, L, Holmes, AJ, Homuth, G, Hottenga, J-J, Hulshoff Pol, HE, Ikeda, M, Jahanshad, N, Jockwitz, C, Johansson, S, Joensson, EG, Jorgensen, NR, Kikuchi, M, Knowles, EEM, Kumar, K, Le Hellard, S, Leu, C, Linden, DEJ, Liu, J, Lundervold, A, Lundervold, AJ, Maillard, AM, Martin, NG, Martin-Brevet, S, Mather, KA, Mathias, SR, McMahon, KL, McRae, AF, Medland, SE, Meyer-Lindenberg, A, Moberget, T, Modenato, C, Sanchez, JM, Morris, DW, Muehleisen, TW, Murray, RM, Nielsen, J, Nordvik, JE, Nyberg, L, Loohuis, LMO, Ophoff, RA, Owen, MJ, Paus, T, Pausova, Z, Peralta, JM, Pike, GB, Prieto, C, Quinlan, EB, Reinbold, CS, Marques, TR, Rucker, JJH, Sachdev, PS, Sando, SB, Schofield, PR, Schork, AJ, Schumann, G, Shin, J, Shumskaya, E, Silva, AI, Sisodiya, SM, Steen, VM, Stein, DJ, Strike, LT, Suzuki, IK, Tamnes, CK, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Uhlmann, A, Ulfarsson, MO, van 't Ent, D, van den Bree, MBM, Vanderhaeghen, P, Vassos, E, Wen, W, Wittfeld, K, Wright, MJ, Agartz, I, Djurovic, S, Westlye, LT, Stefansson, H, Stefansson, K, Jacquemont, S, Thompson, PM, and Andreassen, OA
Abstract: Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.
Published: 2021

15. A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies

Author: Altmann, A, Ryten, M, Di Nunzio, M, Ravizza, T, Tolomeo, D, Reynolds, RH, Somani, A, Bacigaluppi, M, Iori, V, Micotti, E, Di Sapia, R, Cerovic, M, Palma, E, Ruffolo, G, Botia, JA, Absil, J, Alhusaini, S, Alvim, MKM, Auvinen, P, Bargallo, N, Bartolini, E, Bender, B, Bergo, FPG, Bernardes, T, Bernasconi, A, Bernasconi, N, Bernhardt, BC, Blackmon, K, Braga, B, Caligiuri, ME, Calvo, A, Carlson, C, Carr, SJA, Cavalleri, GL, Cendes, F, Chen, J, Chen, S, Cherubini, A, Concha, L, David, P, Delanty, N, Depondt, C, Devinsky, O, Doherty, CP, Domin, M, Focke, NK, Foley, S, Franca, W, Gambardella, A, Guerrini, R, Hamandi, K, Hibar, DP, Isaev, D, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kowalczyk, MA, Kuzniecky, R, Kwan, P, Labate, A, Langner, S, Lenge, M, Liu, M, Martin, P, Mascalchi, M, Meletti, S, Morita-Sherman, ME, O'Brien, TJ, Pariente, JC, Richardson, MP, Rodriguez-Cruces, R, Rummel, C, Saavalainen, T, Semmelroch, MK, Severino, M, Striano, P, Thesen, T, Thomas, RH, Tondelli, M, Tortora, D, Vaudano, AE, Vivash, L, Podewils, F, Wagner, J, Weber, B, Wiest, R, Yasuda, CL, Zhang, G, Zhang, J, Leu, C, Avbersek, A, Thom, M, Whelan, CD, Thompson, P, McDonald, CR, Vezzani, A, Sisodiya, SM, Altmann, A, Ryten, M, Di Nunzio, M, Ravizza, T, Tolomeo, D, Reynolds, RH, Somani, A, Bacigaluppi, M, Iori, V, Micotti, E, Di Sapia, R, Cerovic, M, Palma, E, Ruffolo, G, Botia, JA, Absil, J, Alhusaini, S, Alvim, MKM, Auvinen, P, Bargallo, N, Bartolini, E, Bender, B, Bergo, FPG, Bernardes, T, Bernasconi, A, Bernasconi, N, Bernhardt, BC, Blackmon, K, Braga, B, Caligiuri, ME, Calvo, A, Carlson, C, Carr, SJA, Cavalleri, GL, Cendes, F, Chen, J, Chen, S, Cherubini, A, Concha, L, David, P, Delanty, N, Depondt, C, Devinsky, O, Doherty, CP, Domin, M, Focke, NK, Foley, S, Franca, W, Gambardella, A, Guerrini, R, Hamandi, K, Hibar, DP, Isaev, D, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kowalczyk, MA, Kuzniecky, R, Kwan, P, Labate, A, Langner, S, Lenge, M, Liu, M, Martin, P, Mascalchi, M, Meletti, S, Morita-Sherman, ME, O'Brien, TJ, Pariente, JC, Richardson, MP, Rodriguez-Cruces, R, Rummel, C, Saavalainen, T, Semmelroch, MK, Severino, M, Striano, P, Thesen, T, Thomas, RH, Tondelli, M, Tortora, D, Vaudano, AE, Vivash, L, Podewils, F, Wagner, J, Weber, B, Wiest, R, Yasuda, CL, Zhang, G, Zhang, J, Leu, C, Avbersek, A, Thom, M, Whelan, CD, Thompson, P, McDonald, CR, Vezzani, A, and Sisodiya, SM
Abstract: AIMS: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis. METHODS: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy. RESULTS: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia. CONCLUSIONS: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.
Published: 2021

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

Author: Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Frangou, S, Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Frangou, S
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: 2021

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

Author: Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Dima, D, Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Dima, D
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: 2021

18. 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, Abe, 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, Bargallo, 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, Bulow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodriguez, 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, Fatjo-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, Hoschl, C, Hovik, 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, Kramer, B, Krug, A, Kuntsi, J, Kwon, JS, Landen, M, Landro, NI, Lazaro, L, Lebedeva, IS, Leehr, EJ, Lera-Miguel, S, Lesch, K-P, 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, Menchon, 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, Garcia de la Foz, VO, Overs, BJ, Paloyelis, Y, Pantelis, C, Parellada, M, Pauli, P, Pico-Perez, M, Picon, FA, Piras, F, Plessen, KJ, Pomarol-Clotet, E, Preda, A, Puig, O, Quide, 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, Sarro, S, Satterthwaite, TD, Sawa, A, Schall, U, Schofield, PR, Schrantee, A, Seitz, J, Serpa, MH, Setien-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-Gutierrez, D, Tosetti, M, Uhlmann, A, van Amelsvoort, T, van der Wee, NJA, van der Werff, SJA, van Haren, NEM, van Wingen, GA, Vance, A, Vazquez-Bourgon, J, Vecchio, D, Venkatasubramanian, G, Vieta, E, Vilarroya, O, Vives-Gilabert, Y, Voineskos, AN, Volzke, H, von Polier, GG, Walton, E, Weickert, TW, Weickert, CS, Weideman, AS, Wittfeld, K, Wolf, DH, Wu, M-J, Yang, TT, Yang, K, Yoncheva, Y, Yun, J-Y, 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, Paus, T, Patel, Y, Parker, N, Shin, J, Howard, D, French, L, Thomopoulos, SI, Pozzi, E, Abe, Y, Abe, 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, Bargallo, 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, Bulow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodriguez, 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, Fatjo-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, Hoschl, C, Hovik, 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, Kramer, B, Krug, A, Kuntsi, J, Kwon, JS, Landen, M, Landro, NI, Lazaro, L, Lebedeva, IS, Leehr, EJ, Lera-Miguel, S, Lesch, K-P, 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, Menchon, 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, Garcia de la Foz, VO, Overs, BJ, Paloyelis, Y, Pantelis, C, Parellada, M, Pauli, P, Pico-Perez, M, Picon, FA, Piras, F, Plessen, KJ, Pomarol-Clotet, E, Preda, A, Puig, O, Quide, 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, Sarro, S, Satterthwaite, TD, Sawa, A, Schall, U, Schofield, PR, Schrantee, A, Seitz, J, Serpa, MH, Setien-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-Gutierrez, D, Tosetti, M, Uhlmann, A, van Amelsvoort, T, van der Wee, NJA, van der Werff, SJA, van Haren, NEM, van Wingen, GA, Vance, A, Vazquez-Bourgon, J, Vecchio, D, Venkatasubramanian, G, Vieta, E, Vilarroya, O, Vives-Gilabert, Y, Voineskos, AN, Volzke, H, von Polier, GG, Walton, E, Weickert, TW, Weickert, CS, Weideman, AS, Wittfeld, K, Wolf, DH, Wu, M-J, Yang, TT, Yang, K, Yoncheva, Y, Yun, J-Y, 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
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
Published: 2021

19. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults

Author: Hofer, E, Roshchupkin, GV, Adams, HHH, Knol, MJ, Lin, H, Li, S, Zare, H, Ahmad, S, Armstrong, NJ, Satizabal, CL, Bernard, M, Bis, JC, Gillespie, NA, Luciano, M, Mishra, A, Scholz, M, Teumer, A, Xia, R, Jian, X, Mosley, TH, Saba, Y, Pirpamer, L, Seiler, S, Becker, JT, Carmichael, O, Rotter, JI, Psaty, BM, Lopez, OL, Amin, N, van der Lee, SJ, Yang, Q, Himali, JJ, Maillard, P, Beiser, AS, Decarli, C, Karama, S, Lewis, L, Harris, M, Bastin, ME, Deary, IJ, Witte, AV, Beyer, F, Loeffler, M, Mather, KA, Schofield, PR, Thalamuthu, A, Kwok, JB, Wright, MJ, Ames, D, Trollor, J, Jiang, J, Brodaty, H, Wen, W, Vernooij, MW, Hofman, A, Uitterlinden, AG, Niessen, WJ, Wittfeld, K, Bülow, R, Völker, U, Pausova, Z, Pike, GB, Maingault, S, Crivello, F, Tzourio, C, Amouyel, P, Mazoyer, B, Neale, MC, Franz, CE, Lyons, MJ, Panizzon, MS, Andreassen, OA, Dale, AM, Logue, M, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Stein, JL, Thompson, PM, Medland, SE, Sachdev, PS, Kremen, WS, Wardlaw, JM, Villringer, A, van Duijn, CM, Grabe, HJ, Longstreth, WT, Fornage, M, Paus, T, Debette, S, Ikram, MA, Schmidt, H, Schmidt, R, Seshadri, S, Ching, CRK, Hofer, E, Roshchupkin, GV, Adams, HHH, Knol, MJ, Lin, H, Li, S, Zare, H, Ahmad, S, Armstrong, NJ, Satizabal, CL, Bernard, M, Bis, JC, Gillespie, NA, Luciano, M, Mishra, A, Scholz, M, Teumer, A, Xia, R, Jian, X, Mosley, TH, Saba, Y, Pirpamer, L, Seiler, S, Becker, JT, Carmichael, O, Rotter, JI, Psaty, BM, Lopez, OL, Amin, N, van der Lee, SJ, Yang, Q, Himali, JJ, Maillard, P, Beiser, AS, Decarli, C, Karama, S, Lewis, L, Harris, M, Bastin, ME, Deary, IJ, Witte, AV, Beyer, F, Loeffler, M, Mather, KA, Schofield, PR, Thalamuthu, A, Kwok, JB, Wright, MJ, Ames, D, Trollor, J, Jiang, J, Brodaty, H, Wen, W, Vernooij, MW, Hofman, A, Uitterlinden, AG, Niessen, WJ, Wittfeld, K, Bülow, R, Völker, U, Pausova, Z, Pike, GB, Maingault, S, Crivello, F, Tzourio, C, Amouyel, P, Mazoyer, B, Neale, MC, Franz, CE, Lyons, MJ, Panizzon, MS, Andreassen, OA, Dale, AM, Logue, M, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Stein, JL, Thompson, PM, Medland, SE, Sachdev, PS, Kremen, WS, Wardlaw, JM, Villringer, A, van Duijn, CM, Grabe, HJ, Longstreth, WT, Fornage, M, Paus, T, Debette, S, Ikram, MA, Schmidt, H, Schmidt, R, Seshadri, S, and Ching, CRK
Abstract: Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
Published: 2020

20. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Author: Nunes, A, Schnack, HG, Ching, CRK, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Bøen, E, Bonnin, CDM, Busatto, GF, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Chaim-Avancini, TM, Dannlowski, U, Díaz-Zuluaga, AM, Dietsche, B, Doan, NT, Duchesnay, E, Elvsåshagen, T, Emden, D, Eyler, LT, Fatjó-Vilas, M, Favre, P, Foley, SF, Fullerton, JM, Glahn, DC, Goikolea, JM, Grotegerd, D, Hahn, T, Henry, C, Hibar, DP, Houenou, J, Howells, FM, Jahanshad, N, Kaufmann, T, Kenney, J, Kircher, TTJ, Krug, A, Lagerberg, TV, Lenroot, RK, López-Jaramillo, C, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Mwangi, B, Nabulsi, L, Opel, N, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Redlich, R, Roberts, G, Rosa, PG, Salvador, R, Satterthwaite, TD, Soares, JC, Stein, DJ, Temmingh, HS, Trappenberg, T, Uhlmann, A, van Haren, NEM, Vieta, E, Westlye, LT, Wolf, DH, Yüksel, D, Zanetti, MV, Andreassen, OA, Thompson, PM, Hajek, T, Nunes, A, Schnack, HG, Ching, CRK, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Bøen, E, Bonnin, CDM, Busatto, GF, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Chaim-Avancini, TM, Dannlowski, U, Díaz-Zuluaga, AM, Dietsche, B, Doan, NT, Duchesnay, E, Elvsåshagen, T, Emden, D, Eyler, LT, Fatjó-Vilas, M, Favre, P, Foley, SF, Fullerton, JM, Glahn, DC, Goikolea, JM, Grotegerd, D, Hahn, T, Henry, C, Hibar, DP, Houenou, J, Howells, FM, Jahanshad, N, Kaufmann, T, Kenney, J, Kircher, TTJ, Krug, A, Lagerberg, TV, Lenroot, RK, López-Jaramillo, C, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Mwangi, B, Nabulsi, L, Opel, N, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Redlich, R, Roberts, G, Rosa, PG, Salvador, R, Satterthwaite, TD, Soares, JC, Stein, DJ, Temmingh, HS, Trappenberg, T, Uhlmann, A, van Haren, NEM, Vieta, E, Westlye, LT, Wolf, DH, Yüksel, D, Zanetti, MV, Andreassen, OA, Thompson, PM, and Hajek, T
Abstract: Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
Published: 2020

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

Author: Ching, CRK, Hibar, DP, Gurholt, TP, Nunes, A, Thomopoulos, SI, Abé, C, Agartz, I, Brouwer, RM, Cannon, DM, de Zwarte, SMC, Eyler, LT, Favre, P, Hajek, T, Haukvik, UK, Houenou, J, Landén, M, Lett, TA, McDonald, C, Nabulsi, L, Patel, Y, Pauling, ME, Paus, T, Radua, J, Soeiro-de-Souza, MG, Tronchin, G, van Haren, NEM, Vieta, E, Walter, H, Zeng, LL, Alda, M, Almeida, J, Alnæs, D, Alonso-Lana, S, Altimus, C, Bauer, M, Baune, BT, Bearden, CE, Bellani, M, Benedetti, F, Berk, M, Bilderbeck, AC, Blumberg, HP, Bøen, E, Bollettini, I, del Mar Bonnin, C, Brambilla, P, Canales-Rodríguez, EJ, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, Dima, D, Duchesnay, É, Elvsåshagen, T, Fears, SC, Frangou, S, Fullerton, JM, Glahn, DC, Goikolea, JM, Green, MJ, Grotegerd, D, Gruber, O, Haarman, BCM, Henry, C, Howells, FM, Ives-Deliperi, V, Jansen, A, Kircher, TTJ, Knöchel, C, Kramer, B, Lafer, B, López-Jaramillo, C, Machado-Vieira, R, MacIntosh, BJ, Melloni, EMT, Mitchell, PB, Nenadic, I, Nery, F, Nugent, AC, Oertel, V, Ophoff, RA, Ota, M, Overs, BJ, Pham, DL, Phillips, ML, Pineda-Zapata, JA, Poletti, S, Polosan, M, Pomarol-Clotet, E, Pouchon, A, Quidé, Y, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarró, S, Satterthwaite, TD, Schene, AH, Sim, K, Ching, CRK, Hibar, DP, Gurholt, TP, Nunes, A, Thomopoulos, SI, Abé, C, Agartz, I, Brouwer, RM, Cannon, DM, de Zwarte, SMC, Eyler, LT, Favre, P, Hajek, T, Haukvik, UK, Houenou, J, Landén, M, Lett, TA, McDonald, C, Nabulsi, L, Patel, Y, Pauling, ME, Paus, T, Radua, J, Soeiro-de-Souza, MG, Tronchin, G, van Haren, NEM, Vieta, E, Walter, H, Zeng, LL, Alda, M, Almeida, J, Alnæs, D, Alonso-Lana, S, Altimus, C, Bauer, M, Baune, BT, Bearden, CE, Bellani, M, Benedetti, F, Berk, M, Bilderbeck, AC, Blumberg, HP, Bøen, E, Bollettini, I, del Mar Bonnin, C, Brambilla, P, Canales-Rodríguez, EJ, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, Dima, D, Duchesnay, É, Elvsåshagen, T, Fears, SC, Frangou, S, Fullerton, JM, Glahn, DC, Goikolea, JM, Green, MJ, Grotegerd, D, Gruber, O, Haarman, BCM, Henry, C, Howells, FM, Ives-Deliperi, V, Jansen, A, Kircher, TTJ, Knöchel, C, Kramer, B, Lafer, B, López-Jaramillo, C, Machado-Vieira, R, MacIntosh, BJ, Melloni, EMT, Mitchell, PB, Nenadic, I, Nery, F, Nugent, AC, Oertel, V, Ophoff, RA, Ota, M, Overs, BJ, Pham, DL, Phillips, ML, Pineda-Zapata, JA, Poletti, S, Polosan, M, Pomarol-Clotet, E, Pouchon, A, Quidé, Y, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarró, S, Satterthwaite, TD, Schene, AH, and Sim, K
Abstract: MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.
Published: 2020

22. The genetic architecture of the human cerebral cortex

Author: Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnæs, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bülow, R, Bürger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Doan, NT, Dohm, K, Ehrlich, S, Engelbrecht, HR, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, JJ, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Krämer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, Milaneschi, Y, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnæs, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bülow, R, Bürger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Doan, NT, Dohm, K, Ehrlich, S, Engelbrecht, HR, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, JJ, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Krämer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, and Milaneschi, Y
Abstract: The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
Published: 2020

23. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

Author: Thompson, PM, Jahanshad, N, Ching, CRK, Salminen, LE, Thomopoulos, SI, Bright, J, Baune, BT, Bertolin, S, Bralten, J, Bruin, WB, Buelow, R, Chen, J, Chye, Y, Dannlowski, U, de Kovel, CGF, Donohoe, G, Eyler, LT, Faraone, SV, Favre, P, Filippi, CA, Frodl, T, Garijo, D, Gil, Y, Grabe, HJ, Grasby, KL, Hajek, T, Han, LKM, Hatton, SN, Hilbert, K, Ho, TC, Holleran, L, Homuth, G, Hosten, N, Houenou, J, Ivanov, I, Jia, T, Kelly, S, Klein, M, Kwon, JS, Laansma, MA, Leerssen, J, Lueken, U, Nunes, A, Neill, JO, Opel, N, Piras, F, Postema, MC, Pozzi, E, Shatokhina, N, Soriano-Mas, C, Spalletta, G, Sun, D, Teumer, A, Tilot, AK, Tozzi, L, van der Merwe, C, Van Someren, EJW, van Wingen, GA, Voelzke, H, Walton, E, Wang, L, Winkler, AM, Wittfeld, K, Wright, MJ, Yun, J-Y, Zhang, G, Zhang-James, Y, Adhikari, BM, Agartz, I, Aghajani, M, Aleman, A, Althoff, RR, Altmann, A, Andreassen, OA, Baron, DA, Bartnik-Olson, BL, Bas-Hoogendam, J, Baskin-Sommers, AR, Bearden, CE, Berner, LA, Boedhoe, PSW, Brouwer, RM, Buitelaar, JK, Caeyenberghs, K, Cecil, CAM, Cohen, RA, Cole, JH, Conrod, PJ, De Brito, SA, de Zwarte, SMC, Dennis, EL, Desrivieres, S, Dima, D, Ehrlich, S, Esopenko, C, Fairchild, G, Fisher, SE, Fouche, J-P, Francks, C, Frangou, S, Franke, B, Garavan, HP, Glahn, DC, Groenewold, NA, Gurholt, TP, Gutman, BA, Hahn, T, Harding, IH, Hernaus, D, Hibar, DP, Hillary, FG, Hoogman, M, Pol, HE, Jalbrzikowski, M, Karkashadze, GA, Klapwijk, ET, Knickmeyer, RC, Kochunov, P, Koerte, IK, Kong, X-Z, Liew, S-L, Lin, AP, Logue, MW, Luders, E, Macciardi, F, Mackey, S, Mayer, AR, McDonald, CR, McMahon, AB, Medland, SE, Modinos, G, Morey, RA, Mueller, SC, Mukherjee, P, Namazova-Baranova, L, Nir, TM, Olsen, A, Paschou, P, Pine, DS, Pizzagalli, F, Renteria, ME, Rohrer, JD, Saemann, PG, Schmaal, L, Schumann, G, Shiroishi, MS, Sisodiya, SM, Smit, DJA, Sonderby, IE, Stein, DJ, Stein, JL, Tahmasian, M, Tate, DF, Turner, JA, van den Heuvel, OA, van der Wee, NJA, van der Werf, YD, van Erp, TGM, van Haren, NEM, van Rooij, D, van Velzen, LS, Veer, IM, Veltman, DJ, Villalon-Reina, JE, Walter, H, Whelan, CD, Wilde, EA, Zarei, M, Zelman, V, Thompson, PM, Jahanshad, N, Ching, CRK, Salminen, LE, Thomopoulos, SI, Bright, J, Baune, BT, Bertolin, S, Bralten, J, Bruin, WB, Buelow, R, Chen, J, Chye, Y, Dannlowski, U, de Kovel, CGF, Donohoe, G, Eyler, LT, Faraone, SV, Favre, P, Filippi, CA, Frodl, T, Garijo, D, Gil, Y, Grabe, HJ, Grasby, KL, Hajek, T, Han, LKM, Hatton, SN, Hilbert, K, Ho, TC, Holleran, L, Homuth, G, Hosten, N, Houenou, J, Ivanov, I, Jia, T, Kelly, S, Klein, M, Kwon, JS, Laansma, MA, Leerssen, J, Lueken, U, Nunes, A, Neill, JO, Opel, N, Piras, F, Postema, MC, Pozzi, E, Shatokhina, N, Soriano-Mas, C, Spalletta, G, Sun, D, Teumer, A, Tilot, AK, Tozzi, L, van der Merwe, C, Van Someren, EJW, van Wingen, GA, Voelzke, H, Walton, E, Wang, L, Winkler, AM, Wittfeld, K, Wright, MJ, Yun, J-Y, Zhang, G, Zhang-James, Y, Adhikari, BM, Agartz, I, Aghajani, M, Aleman, A, Althoff, RR, Altmann, A, Andreassen, OA, Baron, DA, Bartnik-Olson, BL, Bas-Hoogendam, J, Baskin-Sommers, AR, Bearden, CE, Berner, LA, Boedhoe, PSW, Brouwer, RM, Buitelaar, JK, Caeyenberghs, K, Cecil, CAM, Cohen, RA, Cole, JH, Conrod, PJ, De Brito, SA, de Zwarte, SMC, Dennis, EL, Desrivieres, S, Dima, D, Ehrlich, S, Esopenko, C, Fairchild, G, Fisher, SE, Fouche, J-P, Francks, C, Frangou, S, Franke, B, Garavan, HP, Glahn, DC, Groenewold, NA, Gurholt, TP, Gutman, BA, Hahn, T, Harding, IH, Hernaus, D, Hibar, DP, Hillary, FG, Hoogman, M, Pol, HE, Jalbrzikowski, M, Karkashadze, GA, Klapwijk, ET, Knickmeyer, RC, Kochunov, P, Koerte, IK, Kong, X-Z, Liew, S-L, Lin, AP, Logue, MW, Luders, E, Macciardi, F, Mackey, S, Mayer, AR, McDonald, CR, McMahon, AB, Medland, SE, Modinos, G, Morey, RA, Mueller, SC, Mukherjee, P, Namazova-Baranova, L, Nir, TM, Olsen, A, Paschou, P, Pine, DS, Pizzagalli, F, Renteria, ME, Rohrer, JD, Saemann, PG, Schmaal, L, Schumann, G, Shiroishi, MS, Sisodiya, SM, Smit, DJA, Sonderby, IE, Stein, DJ, Stein, JL, Tahmasian, M, Tate, DF, Turner, JA, van den Heuvel, OA, van der Wee, NJA, van der Werf, YD, van Erp, TGM, van Haren, NEM, van Rooij, D, van Velzen, LS, Veer, IM, Veltman, DJ, Villalon-Reina, JE, Walter, H, Whelan, CD, Wilde, EA, Zarei, M, and Zelman, V
Abstract: This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
Published: 2020

24. Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort

Author: Tozzi, L, Garczarek, L, Janowitz, D, Stein, DJ, Wittfeld, K, Dobrowolny, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Buelow, R, Voelzke, H, Klinger-Koenig, J, Schnell, K, Schoepf, D, Grotegerd, D, Opel, N, Dannlowski, U, Kugel, H, Schramm, E, Konrad, C, Kircher, T, Jueksel, DI, Nenadic, I, Krug, A, Hahn, T, Steinstraeter, O, Redlich, R, Zaremba, D, Zurowski, B, Fu, CHY, Dima, D, Cole, J, Grabe, HJ, Connolly, CG, Yang, TT, Ho, TC, Lewinn, KZ, Li, M, Groenewold, NA, Salminen, LE, Walter, M, Simmons, AN, van Erp, TGM, Jahanshad, N, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Hibar, DP, Thompson, PM, Veltman, DJ, Schmaal, L, Frodl, T, Tozzi, L, Garczarek, L, Janowitz, D, Stein, DJ, Wittfeld, K, Dobrowolny, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Buelow, R, Voelzke, H, Klinger-Koenig, J, Schnell, K, Schoepf, D, Grotegerd, D, Opel, N, Dannlowski, U, Kugel, H, Schramm, E, Konrad, C, Kircher, T, Jueksel, DI, Nenadic, I, Krug, A, Hahn, T, Steinstraeter, O, Redlich, R, Zaremba, D, Zurowski, B, Fu, CHY, Dima, D, Cole, J, Grabe, HJ, Connolly, CG, Yang, TT, Ho, TC, Lewinn, KZ, Li, M, Groenewold, NA, Salminen, LE, Walter, M, Simmons, AN, van Erp, TGM, Jahanshad, N, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Hibar, DP, Thompson, PM, Veltman, DJ, Schmaal, L, and Frodl, T
Abstract: BACKGROUND: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age. METHODS: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer. RESULTS: CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions. CONCLUSIONS: Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.
Published: 2020

25. What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from theENIGMABipolar Disorder Working Group

Author: Ching, CRK, Hibar, DP, Gurholt, TP, Nunes, A, Thomopoulos, SI, Abe, C, Agartz, I, Brouwer, RM, Cannon, DM, de Zwarte, SMC, Eyler, LT, Favre, P, Hajek, T, Haukvik, UK, Houenou, J, Landen, M, Lett, TA, McDonald, C, Nabulsi, L, Patel, Y, Pauling, ME, Paus, T, Radua, J, Soeiro-de-Souza, MG, Tronchin, G, van Haren, NEM, Vieta, E, Walter, H, Zeng, L-L, Alda, M, Almeida, J, Alnaes, D, Alonso-Lana, S, Altimus, C, Bauer, M, Baune, BT, Bearden, CE, Bellani, M, Benedetti, F, Berk, M, Bilderbeck, AC, Blumberg, HP, Boen, E, Bollettini, I, del Mar Bonnin, C, Brambilla, P, Canales-Rodriguez, EJ, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Dima, D, Duchesnay, E, Elvsashagen, T, Fears, SC, Frangou, S, Fullerton, JM, Glahn, DC, Goikolea, JM, Green, MJ, Grotegerd, D, Gruber, O, Haarman, BCM, Henry, C, Howells, FM, Ives-Deliperi, V, Jansen, A, Kircher, TTJ, Knoechel, C, Kramer, B, Lafer, B, Lopez-Jaramillo, C, Machado-Vieira, R, MacIntosh, BJ, Melloni, EMT, Mitchell, PB, Nenadic, I, Nery, F, Nugent, AC, Oertel, V, Ophoff, RA, Ota, M, Overs, BJ, Pham, DL, Phillips, ML, Pineda-Zapata, JA, Poletti, S, Polosan, M, Pomarol-Clotet, E, Pouchon, A, Quide, Y, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Schene, AH, Sim, K, Soares, JC, Staeblein, M, Stein, DJ, Tamnes, CK, Thomaidis, GV, Upegui, CV, Veltman, DJ, Wessa, M, Westlye, LT, Whalley, HC, Wolf, DH, Wu, M-J, Yatham, LN, Zarate, CA, Thompson, PM, Andreassen, OA, Ching, CRK, Hibar, DP, Gurholt, TP, Nunes, A, Thomopoulos, SI, Abe, C, Agartz, I, Brouwer, RM, Cannon, DM, de Zwarte, SMC, Eyler, LT, Favre, P, Hajek, T, Haukvik, UK, Houenou, J, Landen, M, Lett, TA, McDonald, C, Nabulsi, L, Patel, Y, Pauling, ME, Paus, T, Radua, J, Soeiro-de-Souza, MG, Tronchin, G, van Haren, NEM, Vieta, E, Walter, H, Zeng, L-L, Alda, M, Almeida, J, Alnaes, D, Alonso-Lana, S, Altimus, C, Bauer, M, Baune, BT, Bearden, CE, Bellani, M, Benedetti, F, Berk, M, Bilderbeck, AC, Blumberg, HP, Boen, E, Bollettini, I, del Mar Bonnin, C, Brambilla, P, Canales-Rodriguez, EJ, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Dima, D, Duchesnay, E, Elvsashagen, T, Fears, SC, Frangou, S, Fullerton, JM, Glahn, DC, Goikolea, JM, Green, MJ, Grotegerd, D, Gruber, O, Haarman, BCM, Henry, C, Howells, FM, Ives-Deliperi, V, Jansen, A, Kircher, TTJ, Knoechel, C, Kramer, B, Lafer, B, Lopez-Jaramillo, C, Machado-Vieira, R, MacIntosh, BJ, Melloni, EMT, Mitchell, PB, Nenadic, I, Nery, F, Nugent, AC, Oertel, V, Ophoff, RA, Ota, M, Overs, BJ, Pham, DL, Phillips, ML, Pineda-Zapata, JA, Poletti, S, Polosan, M, Pomarol-Clotet, E, Pouchon, A, Quide, Y, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Schene, AH, Sim, K, Soares, JC, Staeblein, M, Stein, DJ, Tamnes, CK, Thomaidis, GV, Upegui, CV, Veltman, DJ, Wessa, M, Westlye, LT, Whalley, HC, Wolf, DH, Wu, M-J, Yatham, LN, Zarate, CA, Thompson, PM, and Andreassen, OA
Abstract: MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.
Published: 2020

26. Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

Author: van Erp, TGM, Walton, E, Hibar, DP, Schmaal, L, Jiang, W, Glahn, DC, Pearlson, GD, Yao, N, Fukunaga, M, Hashimoto, R, Okada, N, Yamamori, H, Clark, VP, Mueller, BA, de Zwarte, SMC, Ophoff, RA, van Haren, NEM, Andreassen, OA, Gurholt, TP, Gruber, O, Kraemer, B, Richter, A, Calhoun, VD, Crespo-Facorro, B, Roiz-Santiañez, R, Tordesillas-Gutiérrez, D, Loughland, C, Catts, S, Fullerton, JM, Green, MJ, Henskens, F, Jablensky, A, Mowry, BJ, Pantelis, C, Quidé, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Shannon Weickert, C, Weickert, TW, Hong, E, Kochunov, P, Gur, RE, Gur, RC, Ford, JM, Macciardi, F, Mathalon, DH, Potkin, SG, Preda, A, Fan, F, Ehrlich, S, King, MD, De Haan, L, Veltman, DJ, Assogna, F, Banaj, N, de Rossi, P, Iorio, M, Piras, F, Spalletta, G, Pomarol-Clotet, E, Kelly, S, Ciufolini, S, Radua, J, Murray, R, Marques, TR, Simmons, A, Borgwardt, S, Schönborn-Harrisberger, F, Riecher-Rössler, A, Smieskova, R, Alpert, KI, Bertolino, A, Bonvino, A, Di Giorgio, A, Neilson, E, Mayer, AR, Yun, JY, Cannon, DM, Lebedeva, I, Tomyshev, AS, Akhadov, T, Kaleda, V, Fatouros-Bergman, H, Flyckt, L, Farde, L, Cervenka, S, Agartz, I, Collste, K, Victorsson, P, Engberg, G, Erhardt, S, Schwieler, L, Malmqvist, A, Hedberg, M, Orhan, F, van Erp, TGM, Walton, E, Hibar, DP, Schmaal, L, Jiang, W, Glahn, DC, Pearlson, GD, Yao, N, Fukunaga, M, Hashimoto, R, Okada, N, Yamamori, H, Clark, VP, Mueller, BA, de Zwarte, SMC, Ophoff, RA, van Haren, NEM, Andreassen, OA, Gurholt, TP, Gruber, O, Kraemer, B, Richter, A, Calhoun, VD, Crespo-Facorro, B, Roiz-Santiañez, R, Tordesillas-Gutiérrez, D, Loughland, C, Catts, S, Fullerton, JM, Green, MJ, Henskens, F, Jablensky, A, Mowry, BJ, Pantelis, C, Quidé, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Shannon Weickert, C, Weickert, TW, Hong, E, Kochunov, P, Gur, RE, Gur, RC, Ford, JM, Macciardi, F, Mathalon, DH, Potkin, SG, Preda, A, Fan, F, Ehrlich, S, King, MD, De Haan, L, Veltman, DJ, Assogna, F, Banaj, N, de Rossi, P, Iorio, M, Piras, F, Spalletta, G, Pomarol-Clotet, E, Kelly, S, Ciufolini, S, Radua, J, Murray, R, Marques, TR, Simmons, A, Borgwardt, S, Schönborn-Harrisberger, F, Riecher-Rössler, A, Smieskova, R, Alpert, KI, Bertolino, A, Bonvino, A, Di Giorgio, A, Neilson, E, Mayer, AR, Yun, JY, Cannon, DM, Lebedeva, I, Tomyshev, AS, Akhadov, T, Kaleda, V, Fatouros-Bergman, H, Flyckt, L, Farde, L, Cervenka, S, Agartz, I, Collste, K, Victorsson, P, Engberg, G, Erhardt, S, Schwieler, L, Malmqvist, A, Hedberg, M, and Orhan, F
Published: 2019

27. A genome-wide association study identifies genetic loci associated with specific lobar brain volumes.

Author: van der Lee, SJ, Knol, MJ, Chauhan, G, Satizabal, CL, Smith, AV, Hofer, E, Bis, JC, Hibar, DP, Hilal, S, van den Akker, EB, Arfanakis, K, Bernard, M, Yanek, LR, Amin, N, Crivello, F, Cheung, JW, Harris, TB, Saba, Y, Lopez, OL, Li, S, van der Grond, J, Yu, L, Paus, T, Roshchupkin, GV, Amouyel, P, Jahanshad, N, Taylor, KD, Yang, Q, Mathias, RA, Boehringer, S, Mazoyer, B, Rice, K, Cheng, CY, Maillard, P, van Heemst, D, Wong, TY, Niessen, WJ, Beiser, AS, Beekman, M, Zhao, W, Nyquist, PA, Chen, C, Launer, LJ, Psaty, BM, Ikram, MK, Vernooij, MW, Schmidt, H, Pausova, Z, Becker, DM, De Jager, PL, Thompson, PM, van Duijn, CM, Bennett, DA, Slagboom, PE, Schmidt, R, Longstreth, WT, Ikram, MA, Seshadri, S, Debette, S, Gudnason, V, Adams, HHH, DeCarli, C, van der Lee, SJ, Knol, MJ, Chauhan, G, Satizabal, CL, Smith, AV, Hofer, E, Bis, JC, Hibar, DP, Hilal, S, van den Akker, EB, Arfanakis, K, Bernard, M, Yanek, LR, Amin, N, Crivello, F, Cheung, JW, Harris, TB, Saba, Y, Lopez, OL, Li, S, van der Grond, J, Yu, L, Paus, T, Roshchupkin, GV, Amouyel, P, Jahanshad, N, Taylor, KD, Yang, Q, Mathias, RA, Boehringer, S, Mazoyer, B, Rice, K, Cheng, CY, Maillard, P, van Heemst, D, Wong, TY, Niessen, WJ, Beiser, AS, Beekman, M, Zhao, W, Nyquist, PA, Chen, C, Launer, LJ, Psaty, BM, Ikram, MK, Vernooij, MW, Schmidt, H, Pausova, Z, Becker, DM, De Jager, PL, Thompson, PM, van Duijn, CM, Bennett, DA, Slagboom, PE, Schmidt, R, Longstreth, WT, Ikram, MA, Seshadri, S, Debette, S, Gudnason, V, Adams, HHH, and DeCarli, C
Abstract: Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications (DAAM1 and THBS3), or close to genes causing Mendelian brain-related diseases (ZIC4 and FGFRL1). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.
Published: 2019

28. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Author: Boedhoe, PSW, Heymans, MW, Schmaal, L, Abe, Y, Alonso, P, Ameis, SH, Anticevic, A, Arnold, PD, Batistuzzo, MC, Benedetti, F, Beucke, JC, Bollettini, I, Bose, A, Brem, S, Calvo, A, Calvo, R, Cheng, Y, Cho, KLK, Ciullo, V, Dallaspezia, S, Denys, D, Feusner, JD, Fitzgerald, KD, Fouches, J-P, Fridgeirsson, EA, Gruner, P, Henna, GL, Hibar, DP, Hoexter, MQ, Hu, H, Huyser, C, Jahanshad, N, James, A, Kathmann, N, Kaufmann, C, Koch, K, Kwon, JS, Lazaro, L, Lochner, C, Marsh, R, Martinez-Zalacain, I, Mataix-Cols, D, Menchon, JM, Minuzzi, L, Morer, A, Nakamae, T, Nakao, T, Narayanaswamy, JC, Nishida, S, Nurmi, EL, O'Neill, J, Piacentini, J, Piras, F, Reddy, YCJ, Reess, TJ, Sakai, Y, Sato, JP, Simpson, HB, Soreni, N, Soriano-Mas, C, Spalletta, G, Stevens, MC, Szeszkos, PP, Tolin, DF, van Wingen, GA, Venkatasubramanian, G, Walitza, S, Wang, Z, Yun, J-Y, Thompson, PM, Stein, DJ, van den Heuvel, OA, Twisk, JWR, Boedhoe, PSW, Heymans, MW, Schmaal, L, Abe, Y, Alonso, P, Ameis, SH, Anticevic, A, Arnold, PD, Batistuzzo, MC, Benedetti, F, Beucke, JC, Bollettini, I, Bose, A, Brem, S, Calvo, A, Calvo, R, Cheng, Y, Cho, KLK, Ciullo, V, Dallaspezia, S, Denys, D, Feusner, JD, Fitzgerald, KD, Fouches, J-P, Fridgeirsson, EA, Gruner, P, Henna, GL, Hibar, DP, Hoexter, MQ, Hu, H, Huyser, C, Jahanshad, N, James, A, Kathmann, N, Kaufmann, C, Koch, K, Kwon, JS, Lazaro, L, Lochner, C, Marsh, R, Martinez-Zalacain, I, Mataix-Cols, D, Menchon, JM, Minuzzi, L, Morer, A, Nakamae, T, Nakao, T, Narayanaswamy, JC, Nishida, S, Nurmi, EL, O'Neill, J, Piacentini, J, Piras, F, Reddy, YCJ, Reess, TJ, Sakai, Y, Sato, JP, Simpson, HB, Soreni, N, Soriano-Mas, C, Spalletta, G, Stevens, MC, Szeszkos, PP, Tolin, DF, van Wingen, GA, Venkatasubramanian, G, Walitza, S, Wang, Z, Yun, J-Y, Thompson, PM, Stein, DJ, van den Heuvel, OA, and Twisk, JWR
Abstract: Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.
Published: 2019

29. Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium

Author: van Erp, TGM, Walton, E, Hibar, DP, Schmaal, L, Jiang, W, Glahn, DC, Pearlson, GD, Yao, N, Fukunaga, M, Hashimoto, R, Okada, N, Yamamori, H, Bustillo, JR, Clark, VP, Agartz, I, Mueller, BA, Cahn, W, de Zwarte, SMC, Hulshoff Pol, HE, Kahn, RS, Ophoff, RA, van Haren, NEM, Andreassen, OA, Dale, AM, Doan, NT, Gurholt, TP, Hartberg, CB, Haukvik, UK, Jørgensen, KN, Lagerberg, TV, Melle, I, Westlye, LT, Gruber, O, Kraemer, B, Richter, A, Zilles, D, Calhoun, VD, Crespo-Facorro, B, Roiz-Santiañez, R, Tordesillas-Gutiérrez, D, Loughland, C, Carr, VJ, Catts, S, Cropley, VL, Fullerton, JM, Green, MJ, Henskens, FA, Jablensky, A, Lenroot, RK, Mowry, BJ, Michie, PT, Pantelis, C, Quidé, Y, Schall, U, and Scott, RJ
Abstract: © 2018 Society of Biological Psychiatry Background: The profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group. Methods: The study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11–78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10–87 years; 53% male) assessed with standardized methods at 39 centers worldwide. Results: Compared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = −0.530/−0.516) and smaller surface area (left/right hemisphere: Cohen's d = −0.251/−0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset. Conclusions: The findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.
Published: 2018

30. Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study

Author: Whelan, Cd, Altmann, A, Botía, Ja, Jahanshad, N, Hibar, Dp, Absil, J, Alhusaini, S, Alvim, Mkm, Auvinen, P, Bartolini, E, Bergo, Fpg, Bernardes, T, Blackmon, K, Braga, B, Caligiuri, Me, Calvo, A, Carr, Sj, Chen, J, Chen, S, Cherubini, A, David, P, Domin, M, Foley, S, França, W, Haaker, G, Isaev, D, Keller, Ss, Kotikalapudi, R, Kowalczyk, Ma, Kuzniecky, R, Langner, S, Lenge, M, Leyden, Km, Liu, M, Loi, Rq, Martin, P, Mascalchi, M, Morita, Me, Pariente, Jc, Rodríguez-Cruces, R, Rummel, C, Saavalainen, T, Semmelroch, Mk, Severino, M, Thomas, Rh, Tondelli, M, Tortora, D, Vaudano, AE39, Vivash, L40, von Podewils F42, Wagner, J43, Weber, B43, Yao, Y46, Yasuda, CL7, Zhang, G47, Bargalló, N15, Bender, B26, Bernasconi, N, Bernasconi, A, Bernhardt, Bc, Blümcke, I, Carlson, C, Cavalleri, Gl, Cendes, F, Concha, L, Delanty, N, Depondt, C, Devinsky, O, Doherty, CP51, Focke, Nk, Gambardella, A, Guerrini, R, Hamandi, K, Jackson, Gd, Kälviäinen, R, Kochunov, P, Kwan, P, Labate, A, McDonald, Cr, Meletti, S, O'Brien, Tj, Ourselin, S, Richardson MP Striano, P, Thesen, T, Wiest, R, Zhang, J, Vezzani, A, Ryten, M, Thompson, Pm, and Sisodiya, Sm.
Subjects: MRI, epilepsy, precentral gyrus, thalamus
Published: 2018

31. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis

Author: Hoogman, M, Bralten, J, Hibar, DP, Mennes, M, Zwiers, MP, Schweren, LSJ, van Hulzen, KJE, Medland, SE, Shumskaya, E, Jahanshad, N, Zeeuw, PD, Szekely, E, Sudre, G, Wolfers, T, Onnink, AMH, Dammers, JT, Mostert, JC, Vives-Gilabert, Y, Kohls, G, Oberwelland, E, Seitz, J, Schulte-Rüther, M, Ambrosino, S, Doyle, AE, Høvik, MF, Dramsdahl, M, Tamm, L, van Erp, TGM, Dale, A, Schork, A, Conzelmann, A, Zierhut, K, Baur, R, McCarthy, H, Yoncheva, YN, Cubillo, A, Chantiluke, K, Mehta, MA, Paloyelis, Y, Hohmann, S, Baumeister, S, Bramati, I, Mattos, P, Tovar-Moll, F, Douglas, P, Banaschewski, T, Brandeis, D, Kuntsi, J, Asherson, P, Rubia, K, Kelly, C, Martino, AD, Milham, MP, Castellanos, FX, Frodl, T, Zentis, M, Lesch, KP, Reif, A, Pauli, P, Jernigan, TL, Haavik, J, Plessen, KJ, Lundervold, AJ, Hugdahl, K, Seidman, LJ, Biederman, J, Rommelse, N, and Heslenfeld, DJ
Abstract: © 2017 Elsevier Ltd Background Neuroimaging studies have shown structural alterations in several brain regions in children and adults with attention deficit hyperactivity disorder (ADHD). Through the formation of the international ENIGMA ADHD Working Group, we aimed to address weaknesses of previous imaging studies and meta-analyses, namely inadequate sample size and methodological heterogeneity. We aimed to investigate whether there are structural differences in children and adults with ADHD compared with those without this diagnosis. Methods In this cross-sectional mega-analysis, we used the data from the international ENIGMA Working Group collaboration, which in the present analysis was frozen at Feb 8, 2015. Individual sites analysed structural T1-weighted MRI brain scans with harmonised protocols of individuals with ADHD compared with those who do not have this diagnosis. Our primary outcome was to assess case-control differences in subcortical structures and intracranial volume through pooling of all individual data from all cohorts in this collaboration. For this analysis, p values were significant at the false discovery rate corrected threshold of p=0·0156. Findings Our sample comprised 1713 participants with ADHD and 1529 controls from 23 sites with a median age of 14 years (range 4–63 years). The volumes of the accumbens (Cohen's d=−0·15), amygdala (d=−0·19), caudate (d=−0·11), hippocampus (d=−0·11), putamen (d=−0·14), and intracranial volume (d=−0·10) were smaller in individuals with ADHD compared with controls in the mega-analysis. There was no difference in volume size in the pallidum (p=0·95) and thalamus (p=0·39) between people with ADHD and controls. Exploratory lifespan modelling suggested a delay of maturation and a delay of degeneration, as effect sizes were highest in most subgroups of children (21 years): in the accumbens (Cohen's d=−0·19 vs −0·10), amygdala (d=−0·18 vs −0·14), caudate (d=−0·13 vs −0·07), hippocampus (d=−0·12 vs −0·06), putamen (d=−0·18 vs −0·08), and intracranial volume (d=−0·14 vs 0·01). There was no difference between children and adults for the pallidum (p=0·79) or thalamus (p=0·89). Case-control differences in adults were non-significant (all p>0·03). Psychostimulant medication use (all p>0·15) or symptom scores (all p>0·02) did not influence results, nor did the presence of comorbid psychiatric disorders (all p>0·5). Interpretation With the largest dataset to date, we add new knowledge about bilateral amygdala, accumbens, and hippocampus reductions in ADHD. We extend the brain maturation delay theory for ADHD to include subcortical structures and refute medication effects on brain volume suggested by earlier meta-analyses. Lifespan analyses suggest that, in the absence of well powered longitudinal studies, the ENIGMA cross-sectional sample across six decades of ages provides a means to generate hypotheses about lifespan trajectories in brain phenotypes. Funding National Institutes of Health.
Published: 2017

32. Novel genetic loci associated with hippocampal volume

Author: Hibar, DP, Adams, HHH, Jahanshad, N, Chauhan, G, Stein, JL, Hofer, E, Renteria, ME, Bis, JC, Arias-Vasquez, A, Ikram, MK, Desrivières, S, Vernooij, MW, Abramovic, L, Alhusaini, S, Amin, N, Andersson, M, Arfanakis, K, Aribisala, BS, Armstrong, NJ, Athanasiu, L, Axelsson, T, Beecham, AH, Beiser, A, Bernard, M, Blanton, SH, Bohlken, MM, Boks, MP, Bralten, J, Brickman, AM, Carmichael, O, Chakravarty, MM, Chen, Q, Ching, CRK, Chouraki, V, Cuellar-Partida, G, Crivello, F, Den Braber, A, Doan, NT, Ehrlich, S, Giddaluru, S, Goldman, AL, Gottesman, RF, Grimm, O, Griswold, ME, Guadalupe, T, Gutman, BA, Hass, J, Haukvik, UK, Hoehn, D, Holmes, AJ, Hoogman, M, Janowitz, D, Jia, T, Jørgensen, KN, Karbalai, N, Kasperaviciute, D, Kim, S, Klein, M, Kraemer, B, and Lee, PH
Abstract: © The Author(s) 2017. The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
Published: 2017

33. S02. Pre-Implantation Genetic Diagnosis (PGD) in Ireland - from validation to introduction of a clinical service

Author: Morrison, PJ, Campbell, E, Kennedy, F, Russell, A, Smithson, WH, Parsons, L, Liggan, B, Irwin, B, Delanty, N, Hunt, SJ, Craig, J, Morrow, J, Dineen, T, Zhang, X, Flanagan, J, Kovacs, A, Mihart, R, O'Callaghan, J, Culligan, J, Daly, N, Waterstone, J, Magee, AC, Stewart, FJ, Dabir, TA, McConachie, M, McCoubrey, A, McConnell, VPM, Stack, D, O'Meara, E, Phelan, S, McDonagh, N, Kelly, L, Sciot, R, Debiec-Rychter, M, Morris, T, Cochrane, D, Sorensen, P, O'Sullivan, MJ, O'Byrne, JJ, Sweeney, M, Donnelly, D, Lambert, D, Beattie, D, Gervin, C, Graham, CA, Barton, DE, Lynch, SA, Whelan, CD, Hibar, DP, Stein, JL, Speed, D, Sisodiya, S, Ohnson, M, Goldstein, D, Medland, SE, Ranke, B, Thompson, PM, Cavalleri, G, Coleman, C, Quinn, EM, Ryan, AW, Anney, RJL, Trimble, V, Morris, DW, Donohoe, G, Conroy, J, Trynka, G, Wijmenga, C, Ennis, S, McManus, R, O'Halloran, ET, Magalhaes, TR, Cole, A, Cox, S, Jeong, C, Witonsky, D, Robbins, P, Montgomery, H, Ota, M, Hanaoka, M, Droma, Y, Beall, CM, Rienzo, A Di, Casey, J, McGettigan, P, Crushell, E, Hughes, J, Smyth, LJ, Kilner, JK, Benson, KA, Maxwell, AP, McKnight, AJ, Donnelly, DE, Jeffers, L, Hampton, S, Baillie, N, Cooke, S, O'Connell, SM, McDonald, A, O'Toole, N, Bradfield, A, Bradley, M, Hattersley, A, Ellard, S, Proks, P, Mattis, KK, Ashcroft, F, O'Riordan, SMP, Coyle, D, McDermott, M, O'Sullivan, M, Roche, E, Quinn, F, Cody, D, MacMahon, JM, Morrissey, R, Green, A, Thompson, AR, Kulkarni, A, Marks, KJ, Snape, K, Taylor, R, Bradley, L, Ramachandrappa, S, Pinto, CF, Dabir, T, Logan, P, Liew, S., Znaczko, A, Ryan, H., McDevitt, T, Higgins, M, Crowley, A, Rogers, M, Geoghegan, S, Shorto, J, Ramsden, S, O'Riordan, MP, Moore, M, Murphy, M, Irvine, A, Znaczko, Anna, Wilson, A, Stewart, F, Cather, MH, Young, IS, Nicholls, DP, O'Kane, M, Sharpe, P, Hanna, E, Hart, PJ, Savage, N, Humphreys, MW, Shaw-Smith, C, Osio, D, Collinson, MN, McKee, S, McNerlan, S, McGorrian, C, Galvin, J, O'Byrne, J, Stewart, S, Heggarty, SV, Hegarty, SP, McConnell, V, Turner, J, Ward, A, Kelly, R, Joyce, C, ó hIcí, B, Meaney, K, Gibson, L, Kelly, PM, Costigan, C, Gul, R, Byrne, S, Hughes, L, Ozaki, M, O'Sullivan, F, Parle-McDermott, A, Heavin, SB, McCormack, M, Slattery, L, Walley, N, Avbersek, A, Novy, J, Sinha, S, S, Alarts, N, Legros, B, Radtke, R., Sisodiya, Depondt, C, Cavalleri, GL, Connolly, S, Heron, EA, Irvine, MAG, Hughes, AE, Darlow, JM, Darlay, R, Hunziker, M, Kutasy, B, Green, AJ, Cordell, H, Puri, P, Chand, S, McCaughan, JA, Shabir, S, Chan, W, Kilner, J, Borrows, R, Douglas, AP, O'Neill, T, Shepherd, C, Hardy, R, Kenny, Molloy, B, Freeley, M, Quinn, E, McGinn, R, Long, A, Gahan, JM, Connolly, E, Byrne, MM, Gray, SG, Murphy, RT, Gui, H, Heinzen, E, Goldstein, D B, Petrovski, S, O'Brien, TJ, Cherny, S, Sham, PC, Baum, L, Duffy, S, Catherwood, N, McVeigh, TP, Sweeney, KJ, Miller, N, Kerin, MJ, and Weidhaas, JB
Subjects: Poster Presentations, Abstracts, Spoken Papers
Published: 2014

34. Novel genetic loci underlying human intracranial volume identified through genome-wide association

Author: Adams, HHH, Hibar, DP, Chouraki, V, Stein, JL, Nyquist, PA, Rentería, ME, Trompet, S, Arias-Vasquez, A, Seshadri, S, Desrivières, S, Beecham, AH, Jahanshad, N, Wittfeld, K, Van Der Lee, SJ, Abramovic, L, Alhusaini, S, Amin, N, Andersson, M, Arfanakis, K, Aribisala, BS, Armstrong, NJ, Athanasiu, L, Axelsson, T, Beiser, A, Bernard, M, Bis, JC, Blanken, LME, Blanton, SH, Bohlken, MM, Boks, MP, Bralten, J, Brickman, AM, Carmichael, O, Chakravarty, MM, Chauhan, G, Chen, Q, Ching, CRK, Cuellar-Partida, G, Braber, AD, Doan, NT, Ehrlich, S, Filippi, I, Ge, T, Giddaluru, S, Goldman, AL, Gottesman, RF, Greven, CU, Grimm, O, Griswold, ME, Guadalupe, T, Hass, J, Haukvik, UK, Hilal, S, Hofer, E, Hoehn, D, Holmes, AJ, Hoogman, M, Janowitz, D, and Jia, T
Abstract: © 2016 Nature America, Inc., part of Springer Nature. All rights reserved. Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (genetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N combined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.
Published: 2016

35. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

Author: Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, Francks, C, Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, and Francks, C
Abstract: The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.
Published: 2017

36. Childhood adversity impacts on brain subcortical structures relevant to depression

Author: Frodl, T, Janowitz, D, Schmaal, L, Tozzi, L, Dobrowolny, H, Stein, DJ, Veltman, DJ, Wittfeld, K, van Erp, TGM, Jahanshad, N, Block, A, Hegenscheid, K, Voelzke, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Frey, EM, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Schnell, K, Grotegerd, D, Arolt, V, Kugel, H, Schramm, E, Konrad, C, Zurowski, B, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Thompson, PM, Hibar, DP, Dannlowski, U, Grabe, HJ, Frodl, T, Janowitz, D, Schmaal, L, Tozzi, L, Dobrowolny, H, Stein, DJ, Veltman, DJ, Wittfeld, K, van Erp, TGM, Jahanshad, N, Block, A, Hegenscheid, K, Voelzke, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Frey, EM, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Schnell, K, Grotegerd, D, Arolt, V, Kugel, H, Schramm, E, Konrad, C, Zurowski, B, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Thompson, PM, Hibar, DP, Dannlowski, U, and Grabe, HJ
Abstract: Childhood adversity plays an important role for development of major depressive disorder (MDD). There are differences in subcortical brain structures between patients with MDD and healthy controls, but the specific impact of childhood adversity on such structures in MDD remains unclear. Thus, aim of the present study was to investigate whether childhood adversity is associated with subcortical volumes and how it interacts with a diagnosis of MDD and sex. Within the ENIGMA-MDD network, nine university partner sites, which assessed childhood adversity and magnetic resonance imaging in patients with MDD and controls, took part in the current joint mega-analysis. In this largest effort world-wide to identify subcortical brain structure differences related to childhood adversity, 3036 participants were analyzed for subcortical brain volumes using FreeSurfer. A significant interaction was evident between childhood adversity, MDD diagnosis, sex, and region. Increased exposure to childhood adversity was associated with smaller caudate volumes in females independent of MDD. All subcategories of childhood adversity were negatively associated with caudate volumes in females - in particular emotional neglect and physical neglect (independently from age, ICV, imaging site and MDD diagnosis). There was no interaction effect between childhood adversity and MDD diagnosis on subcortical brain volumes. Childhood adversity is one of the contributors to brain structural abnormalities. It is associated with subcortical brain abnormalities that are relevant to psychiatric disorders such as depression.
Published: 2017

37. ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide

Author: Thompson, PM, Andreassen, OA, Arias-Vasquez, A, Bearden, CE, Boedhoe, PS, Brouwer, RM, Buckner, RL, Buitelaar, JK, Bulayeva, KB, Cannon, DM, Cohen, RA, Conrod, PJ, Dale, AM, Deary, IJ, Dennis, EL, de Reus, MA, Desrivieres, S, Dima, D, Donohoe, G, Fisher, SE, Fouche, J-P, Francks, C, Frangou, S, Franke, B, Ganjgahi, H, Garavan, H, Glahn, DC, Grabe, HJ, Guadalupe, T, Gutman, BA, Hashimoto, R, Hibar, DP, Holland, D, Hoogman, M, Pol, HEH, Hosten, N, Jahanshad, N, Kelly, S, Kochunov, P, Kremen, WS, Lee, PH, Mackey, S, Martin, NG, Mazoyer, B, McDonald, C, Medland, SE, Morey, RA, Nichols, TE, Paus, T, Pausova, Z, Schmaal, L, Schumann, G, Shen, L, Sisodiya, SM, Smit, DJA, Smoller, JW, Stein, DJ, Stein, JL, Toro, R, Turner, JA, van den Heuvel, MP, van den Heuvel, OL, van Erp, TGM, van Rooij, D, Veltman, DJ, Walter, H, Wang, Y, Wardlaw, JM, Whelan, CD, Wright, MJ, Ye, J, Thompson, PM, Andreassen, OA, Arias-Vasquez, A, Bearden, CE, Boedhoe, PS, Brouwer, RM, Buckner, RL, Buitelaar, JK, Bulayeva, KB, Cannon, DM, Cohen, RA, Conrod, PJ, Dale, AM, Deary, IJ, Dennis, EL, de Reus, MA, Desrivieres, S, Dima, D, Donohoe, G, Fisher, SE, Fouche, J-P, Francks, C, Frangou, S, Franke, B, Ganjgahi, H, Garavan, H, Glahn, DC, Grabe, HJ, Guadalupe, T, Gutman, BA, Hashimoto, R, Hibar, DP, Holland, D, Hoogman, M, Pol, HEH, Hosten, N, Jahanshad, N, Kelly, S, Kochunov, P, Kremen, WS, Lee, PH, Mackey, S, Martin, NG, Mazoyer, B, McDonald, C, Medland, SE, Morey, RA, Nichols, TE, Paus, T, Pausova, Z, Schmaal, L, Schumann, G, Shen, L, Sisodiya, SM, Smit, DJA, Smoller, JW, Stein, DJ, Stein, JL, Toro, R, Turner, JA, van den Heuvel, MP, van den Heuvel, OL, van Erp, TGM, van Rooij, D, Veltman, DJ, Walter, H, Wang, Y, Wardlaw, JM, Whelan, CD, Wright, MJ, and Ye, J
Abstract: In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.
Published: 2017

38. Erratum: Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium (Molecular Psychiatry (2015) DOI:10.1038/mp.2015.63)

Author: Van Erp, TGM, Hibar, DP, Rasmussen, JM, Glahn, DC, Pearlson, GD, Andreassen, OA, Agartz, I, Westlye, LT, Haukvik, UK, Dale, AM, Melle, I, Hartberg, CB, Gruber, O, Kraemer, B, Zilles, D, Donohoe, G, Kelly, S, Mcdonald, C, Morris, DW, Cannon, DM, Corvin, A, Machielsen, MWJ, Koenders, L, De Haan, L, Veltman, DJ, Satterthwaite, TD, Wolf, DH, Gur, RC, Gur, RE, Potkin, SG, Mathalon, DH, Mueller, BA, Preda, A, Macciardi, F, Ehrlich, S, Walton, E, Hass, J, Calhoun, VD, Bockholt, HJ, Sponheim, SR, Shoemaker, JM, Van Haren, NEM, Pol, HEH, Ophoff, RA, Kahn, RS, Roiz-Santiañez, R, Crespo-Facorro, B, Wang, L, Alpert, KI, Jönsson, EG, Dimitrova, R, Bois, C, Whalley, HC, Mcintosh, AM, Lawrie, SM, Hashimoto, R, Thompson, PM, and Turner, JA
Published: 2016

39. Size matters; but so does what you do with it! Response

Author: Schmaal, L, Veltman, DJ, van Erp, TGM, Saemann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Vernooij, MW, Niessen, WJ, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Kraemer, B, Gruber, O, Couvy-Duchesne, B, Renteria, ME, Strike, LT, Wright, MJ, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hall, GB, van Velzen, LS, van Tol, M-J, van der Wee, NJ, Veer, IM, Walter, H, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, McIntosh, AM, Whalley, HC, Sussmann, JE, Godlewska, BR, Fischer, FH, Penninx, BWJH, Thompson, PM, Hibar, DP, Disorder, E-MD, Epidemiology, Radiology & Nuclear Medicine, Medical Informatics, and Neurology
Published: 2016

40. Common genetic variants influence human subcortical brain structures

Author: Hibar, DP, Stein, JL, Renteria, ME, Arias-Vasquez, A, Desrivières, S, Jahanshad, N, Toro, R, Wittfeld, K, Abramovic, L, Andersson, M, Aribisala, BS, Armstrong, NJ, Bernard, M, Bohlken, MM, Boks, MP, Bralten, J, Brown, AA, Mallar Chakravarty, M, Chen, Q, Ching, CRK, Cuellar-Partida, G, Den Braber, A, Giddaluru, S, Goldman, AL, Grimm, O, Guadalupe, T, Hass, J, Woldehawariat, G, Holmes, AJ, Hoogman, M, Janowitz, D, Jia, T, Kim, S, Klein, M, Kraemer, B, Lee, PH, Olde Loohuis, LM, Luciano, M, MacAre, C, Mather, KA, Mattheisen, M, Milaneschi, Y, Nho, K, Papmeyer, M, Ramasamy, A, Risacher, SL, Roiz-Santiañez, R, Rose, EJ, Salami, A, Sämann, PG, Schmaal, L, Schork, AJ, Shin, J, Strike, LT, Teumer, A, Van Donkelaar, MMJ, Van Eijk, KR, Walters, RK, Westlye, LT, and Whelan, CD
Abstract: ©2015 Macmillan Publishers Limited. All rights reserved. The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08×10 -33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.
Published: 2015

41. The effect of increased genetic risk for Alzheimer's disease on hippocampal and amygdala volume

Author: Lupton, MK, Strike, L, Hansell, NK, Wen, W, Mather, KA, Armstrong, NJ, Thalamuthu, A, McMahon, KL, de Zubicaray, GI, Assareh, AA, Simmons, A, Proitsi, P, Powell, JF, Montgomery, GW, Hibar, DP, Westman, E, Tsolaki, M, Kloszewska, I, Soininen, H, Mecocci, P, Velas, B, Lovestone, S, Brodaty, H, Ames, D, Trollor, JN, Martin, NG, Thompson, PM, Sachdev, PS, Wright, MJ, Lupton, MK, Strike, L, Hansell, NK, Wen, W, Mather, KA, Armstrong, NJ, Thalamuthu, A, McMahon, KL, de Zubicaray, GI, Assareh, AA, Simmons, A, Proitsi, P, Powell, JF, Montgomery, GW, Hibar, DP, Westman, E, Tsolaki, M, Kloszewska, I, Soininen, H, Mecocci, P, Velas, B, Lovestone, S, Brodaty, H, Ames, D, Trollor, JN, Martin, NG, Thompson, PM, Sachdev, PS, and Wright, MJ
Abstract: Reduction in hippocampal and amygdala volume measured via structural magnetic resonance imaging is an early marker of Alzheimer's disease (AD). Whether genetic risk factors for AD exert an effect on these subcortical structures independent of clinical status has not been fully investigated. We examine whether increased genetic risk for AD influences hippocampal and amygdala volumes in case-control and population cohorts at different ages, in 1674 older (aged >53 years; 17% AD, 39% mild cognitive impairment [MCI]) and 467 young (16-30 years) adults. An AD polygenic risk score combining common risk variants excluding apolipoprotein E (APOE), and a single nucleotide polymorphism in TREM2, were both associated with reduced hippocampal volume in healthy older adults and those with MCI. APOE ε4 was associated with hippocampal and amygdala volume in those with AD and MCI but was not associated in healthy older adults. No associations were found in young adults. Genetic risk for AD affects the hippocampus before the clinical symptoms of AD, reflecting a neurodegenerative effect before clinical manifestations in older adults.
Published: 2016

42. Genetic influences on schizophrenia and subcortical brain volumes: Large-scale proof of concept

Author: Franke, B, van Hulzen, KJE, Arias-Vasquez, A, Bralten, J, Hoogman, M, Klein, M, van Donkelaar, MMJ, Hakobjan, MMH, Heister, AJGAM, Makkinje, RRR, Naber, MAM, van der Marel, SSL, Mostert, JC, Brunner, HG, van Bokhoven, H, Zwiers, MP, Buitelaar, JK, Fernández, G, Fisher, SE, Francks, C, Stein, JL, Hibar, DP, Thompson, PM, Ripke, S, Anttila, V, Neale, BM, Farh, KH, Bulik-Sullivan, B, Huang, H, Fromer, M, Goldstein, JI, Daly, MJ, Walters, RK, Smoller, JW, Lee, P, Belliveau, RA, Bergen, SE, Bevilacqua, E, Chambert, KD, Genovese, G, O'Dushlaine, C, Scolnick, EM, McCarroll, SA, Moran, JL, Palotie, A, Petryshen, TL, Erk, S, Heinz, A, Mohnke, S, Romanczuk-Seiferth, N, Walter, H, DeLisi, LE, McCarley, RW, Mesholam-Gately, RI, Seidman, LJ, Nichols, TE, Neale, MC, McIntosh, AM, Papmeyer, M, Sprooten, E, Lawrie, SM, Sussmann, JE, McMahon, FJ, Yao, Y, Meyer-Lindenberg, A, Schwarz, E, Grimm, O, Mattheisen, M, Agerbo, E, Demontis, D, Hansen, T, Mors, O, Olsen, L, Rasmussen, HB, Børglum, AD, Mortensen, PB, Werge, T, Andreassen, OA, Brown, AA, Athanasiu, L, Hartberg, CB, Haukvik, U, Melle, I, Gruber, O, Kraemer, B, Keil, M, Sachdev, PS, Mather, KA, Thalamuthu, A, Armstrong, NJ, Assareh, AA, Brodaty, H, Reppermund, S, Wen, W, Roiz-Santiañez, R, Perez-Iglesias, R, Saykin, AJ, Kim, S, Nho, K, Risacher, SL, Franke, B, van Hulzen, KJE, Arias-Vasquez, A, Bralten, J, Hoogman, M, Klein, M, van Donkelaar, MMJ, Hakobjan, MMH, Heister, AJGAM, Makkinje, RRR, Naber, MAM, van der Marel, SSL, Mostert, JC, Brunner, HG, van Bokhoven, H, Zwiers, MP, Buitelaar, JK, Fernández, G, Fisher, SE, Francks, C, Stein, JL, Hibar, DP, Thompson, PM, Ripke, S, Anttila, V, Neale, BM, Farh, KH, Bulik-Sullivan, B, Huang, H, Fromer, M, Goldstein, JI, Daly, MJ, Walters, RK, Smoller, JW, Lee, P, Belliveau, RA, Bergen, SE, Bevilacqua, E, Chambert, KD, Genovese, G, O'Dushlaine, C, Scolnick, EM, McCarroll, SA, Moran, JL, Palotie, A, Petryshen, TL, Erk, S, Heinz, A, Mohnke, S, Romanczuk-Seiferth, N, Walter, H, DeLisi, LE, McCarley, RW, Mesholam-Gately, RI, Seidman, LJ, Nichols, TE, Neale, MC, McIntosh, AM, Papmeyer, M, Sprooten, E, Lawrie, SM, Sussmann, JE, McMahon, FJ, Yao, Y, Meyer-Lindenberg, A, Schwarz, E, Grimm, O, Mattheisen, M, Agerbo, E, Demontis, D, Hansen, T, Mors, O, Olsen, L, Rasmussen, HB, Børglum, AD, Mortensen, PB, Werge, T, Andreassen, OA, Brown, AA, Athanasiu, L, Hartberg, CB, Haukvik, U, Melle, I, Gruber, O, Kraemer, B, Keil, M, Sachdev, PS, Mather, KA, Thalamuthu, A, Armstrong, NJ, Assareh, AA, Brodaty, H, Reppermund, S, Wen, W, Roiz-Santiañez, R, Perez-Iglesias, R, Saykin, AJ, Kim, S, Nho, K, and Risacher, SL
Abstract: Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.
Published: 2016

43. Heritability and reliability of automatically segmented human hippocampal formation subregions

Author: Whelan, CD, Hibar, DP, van Velzen, LS, Zannas, AS, Carrillo-Roa, T, McMahon, K, Prasad, G, Kelly, S, Faskowitz, J, deZubiracay, G, Iglesias, JE, van Erp, TGM, Frodl, T, Martin, NG, Wright, MJ, Jahanshad, N, Schmaal, L, Saemann, PG, Thompson, PM, Whelan, CD, Hibar, DP, van Velzen, LS, Zannas, AS, Carrillo-Roa, T, McMahon, K, Prasad, G, Kelly, S, Faskowitz, J, deZubiracay, G, Iglesias, JE, van Erp, TGM, Frodl, T, Martin, NG, Wright, MJ, Jahanshad, N, Schmaal, L, Saemann, PG, and Thompson, PM
Abstract: The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57
Published: 2016

44. The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data

Author: Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, Ø, Binder, EB, Blangero, J, Bockholt, HJ, Bøen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, and Djurovic, S
Subjects: endocrine system
Abstract: The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way. © 2014 The Author(s).
Published: 2014

45. Allelic differences between Europeans and Chinese for CREB1 SNPs and their implications in gene expression regulation, hippocampal structure and function, and bipolar disorder susceptibility

Author: Li, M, Luo, X-J, Rietschel, M, Lewis, CM, Mattheisen, M, Müller-Myhsok, B, Jamain, S, Leboyer, M, Landén, M, Thompson, PM, Cichon, S, Nöthen, MM, Schulze, TG, Sullivan, PF, Bergen, SE, Donohoe, G, Morris, DW, Hargreaves, A, Gill, M, Corvin, A, Hultman, C, Toga, AW, Shi, L, Lin, Q, Shi, H, Gan, L, Meyer-Lindenberg, A, Czamara, D, Henry, C, Etain, B, Bis, JC, Ikram, MA, Fornage, M, Debette, S, Launer, LJ, Seshadri, S, Erk, S, Walter, H, Heinz, A, Bellivier, F, Stein, JL, Medland, SE, Arias Vasquez, A, Hibar, DP, Franke, B, Martin, NG, Wright, MJ, Su, B, Li, M, Luo, X-J, Rietschel, M, Lewis, CM, Mattheisen, M, Müller-Myhsok, B, Jamain, S, Leboyer, M, Landén, M, Thompson, PM, Cichon, S, Nöthen, MM, Schulze, TG, Sullivan, PF, Bergen, SE, Donohoe, G, Morris, DW, Hargreaves, A, Gill, M, Corvin, A, Hultman, C, Toga, AW, Shi, L, Lin, Q, Shi, H, Gan, L, Meyer-Lindenberg, A, Czamara, D, Henry, C, Etain, B, Bis, JC, Ikram, MA, Fornage, M, Debette, S, Launer, LJ, Seshadri, S, Erk, S, Walter, H, Heinz, A, Bellivier, F, Stein, JL, Medland, SE, Arias Vasquez, A, Hibar, DP, Franke, B, Martin, NG, Wright, MJ, and Su, B
Published: 2014

46. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.

Author: García-Marín LM, Campos AI, Diaz-Torres S, Rabinowitz JA, Ceja Z, Mitchell BL, Grasby KL, Thorp JG, Agartz I, Alhusaini S, Ames D, Amouyel P, Andreassen OA, Arfanakis K, Arias-Vasquez A, Armstrong NJ, Athanasiu L, Bastin ME, Beiser AS, Bennett DA, Bis JC, Boks MPM, Boomsma DI, Brodaty H, Brouwer RM, Buitelaar JK, Burkhardt R, Cahn W, Calhoun VD, Carmichael OT, Chakravarty M, Chen Q, Ching CRK, Cichon S, Crespo-Facorro B, Crivello F, Dale AM, Smith GD, de Geus EJC, De Jager PL, de Zubicaray GI, Debette S, DeCarli C, Depondt C, Desrivières S, Djurovic S, Ehrlich S, Erk S, Espeseth T, Fernández G, Filippi I, Fisher SE, Fleischman DA, Fletcher E, Fornage M, Forstner AJ, Francks C, Franke B, Ge T, Goldman AL, Grabe HJ, Green RC, Grimm O, Groenewold NA, Gruber O, Gudnason V, Håberg AK, Haukvik UK, Heinz A, Hibar DP, Hilal S, Himali JJ, Ho BC, Hoehn DF, Hoekstra PJ, Hofer E, Hoffmann W, Holmes AJ, Homuth G, Hosten N, Ikram MK, Ipser JC, Jack CR Jr, Jahanshad N, Jönsson EG, Kahn RS, Kanai R, Klein M, Knol MJ, Launer LJ, Lawrie SM, Hellard SL, Lee PH, Lemaître H, Li S, Liewald DCM, Lin H, Longstreth WT Jr, Lopez OL, Luciano M, Maillard P, Marquand AF, Martin NG, Martinot JL, Mather KA, Mattay VS, McMahon KL, Mecocci P, Melle I, Meyer-Lindenberg A, Mirza-Schreiber N, Milaneschi Y, Mosley TH, Mühleisen TW, Müller-Myhsok B, Maniega SM, Nauck M, Nho K, Niessen WJ, Nöthen MM, Nyquist PA, Oosterlaan J, Pandolfo M, Paus T, Pausova Z, Penninx BWJH, Pike GB, Psaty BM, Pütz B, Reppermund S, Rietschel MD, Risacher SL, Romanczuk-Seiferth N, Romero-Garcia R, Roshchupkin GV, Rotter JI, Sachdev PS, Sämann PG, Saremi A, Sargurupremraj M, Saykin AJ, Schmaal L, Schmidt H, Schmidt R, Schofield PR, Scholz M, Schumann G, Schwarz E, Shen L, Shin J, Sisodiya SM, Smith AV, Smoller JW, Soininen HS, Steen VM, Stein DJ, Stein JL, Thomopoulos SI, Toga AW, Tordesillas-Gutiérrez D, Trollor JN, Valdes-Hernandez MC, van T Ent D, van Bokhoven H, van der Meer D, van der Wee NJA, Vázquez-Bourgon J, Veltman DJ, Vernooij MW, Villringer A, Vinke LN, Völzke H, Walter H, Wardlaw JM, Weinberger DR, Weiner MW, Wen W, Westlye LT, Westman E, White T, Witte AV, Wolf C, Yang J, Zwiers MP, Ikram MA, Seshadri S, Thompson PM, Satizabal CL, Medland SE, and Rentería ME
Abstract: Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Published: 2024
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47. Real-world survival outcomes in patients with locally advanced or metastatic NTRK fusion-positive solid tumors receiving standard-of-care therapies other than targeted TRK inhibitors.

Author: Hibar DP, Demetri GD, Peters S, Davies J, Humblet O, Maund SL, and Perez L
Subjects: Gene Fusion, Humans, Oncogene Proteins, Fusion genetics, Protein Kinase Inhibitors therapeutic use, Receptor, trkA genetics, Tropomyosin genetics, Neoplasms drug therapy, Neoplasms genetics, Neoplasms pathology, Neoplasms, Second Primary drug therapy
Abstract: The clinical profiles and outcomes of patients with neurotrophic tropomyosin receptor kinase fusion-positive (NTRK+) solid tumors receiving standard of care other than tropomyosin receptor kinase inhibitor (TRKi) targeted therapy have not been well documented. Here, we describe the clinical characteristics of patients with NTRK+ tumors treated in clinical practice using information from a United States electronic health record-derived clinicogenomic database. We also compared survival outcomes in NTRK+ patients and matched NTRK fusion-negative (NTRK-) patients and investigated the clinical prognostic value of NTRK fusions. NTRK positivity was defined by the presence of a fusion or rearrangement involving NTRK1/2/3, determined using NGS (Foundation Medicine, Inc.). NTRK+ patients (n = 28) were diagnosed with locally advanced/metastatic solid tumors between January 1, 2011 and December 31, 2019 and had received no TRKis (e.g., entrectinib or larotrectinib) during their patient journey. The unselected NTRK-population comprised 24,903 patients, and the matched NTRK-cohort included 280 patients. NTRK+ patients tended to be younger, were more commonly not smokers, and had a shorter time from advanced diagnosis to first NGS report, compared with unselected NTRK-patients; however, these differences were not significant. Median overall survival (OS) from advanced/metastatic diagnosis was 10.2 months (95% CI, 7.2-14.1) for the NTRK+ cohort versus 10.4 months (95% CI, 6.7-14.3) for the matched NTRK-cohort; hazard ratio for death in NTRK+ versus matched NTRK-patients was 1.6 (95% CI, 1.0-2.5; P = 0.05). Genomic co-alterations were rare in the NTRK+ cohort (only two of 28 patients had a co-alteration). Overall, while hazard ratios suggest NTRK fusions may be a negative prognostic factor of survival, there are no significant indications of any favorable impact of NTRK fusions on patient outcomes. TRKis, with their high response rate and good tolerability, are likely to improve outcomes for patients compared with existing standard-of-care treatments., Competing Interests: This study was funded by F. Hoffmann-La Roche Ltd/Genentech, Inc.: F. Hoffmann-La Roche used Flatiron Health’s services for data acquisition and preparation. The funders and Flatiron Health, Inc. also provided support in the form of salaries for authors and funding for medical writing assistance. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare. DPH and SLM report employment from Genentech, Inc. GDD reports honoraria from M.J. Hennessey / OncLive, MEDSCAPE, AACR/ASCO; consulting/advisory role for F. Hoffmann-La Roche Ltd, Ikena Therapeutics, Genentech, Inc., Bayer, EMD-Serono, Daiichi-Sankyo, WCG/Arsenal Capital, Blueprint Medicines, PharmaMar, Pfizer, Novartis, GlaxoSmithKline, Janssen, Mirati, MEDSCAPE, Relay Therapeutics, CellCarta; research funding to institution from F. Hoffmann-La Roche Ltd, Bayer, Loxo Oncology at Lilly, Daiichi-Sankyo, Janssen, Pfizer, Novartis; travel, accommodations, expenses from F. Hoffmann-La Roche Ltd; shareholder/stakeholder/stock options from Blueprint Medicines, G1 Therapeutics, Ikena Therapeutics, CARIS Life Sciences, Bessor Pharmaceuticals, ERASCA Pharmaceuticals, Translate BIO, Relay Therapeutics, CellCarta; licensing/royalties from Novartis to Dana-Farber Cancer Institute; board of directors membership for Blueprint Medicines, Translate BIO; non-remunerated activities for Alexandria Real Estate Equities. SP reports honoraria from F. Hoffmann-La Roche Ltd, Bristol-Myers Squibb, Novartis, Pfizer, MSD, AstraZeneca, Takeda, Illumina; consulting/advisory role for F. Hoffmann-La Roche Ltd/Genentech, Inc., Novartis, Bristol-Myers Squibb, Pfizer, MSD, Amgen, AstraZeneca, Janssen, Regeneron, Merck Serono, Boehringer Ingelheim, Takeda, Lilly, Abbvie, Bayer, Biocartis, Debiopharm Group, Illumina, PharmaMar, Sanofi, Seattle Genetics, Blueprint Medicines, Daiichi Sankyo, Incyte, Bioinvent, Clovis Oncology, Vaccibody; research funding from F. Hoffmann-La Roche Ltd, Bristol-Myers Squibb, MSD, Amgen, Lilly, AstraZeneca, Pfizer, Illumina, Merck Serono, Novartis, Biodesix, Boehringer Ingelheim, Iovance Biotherapeutics; travel, accommodations, expenses from F. Hoffmann-La Roche Ltd, Bristol-Myers Squibb, MSD, Sanofi, Incyte; non-remunerated activities for Journal of Thoracic Oncology, ESMO, European Thoracic Oncology Platform (ETOP), Annals of Oncology. JD and LP report employment at F. Hoffmann-La Roche Ltd. OH reports employment at Flatiron Health, Inc., which is an independent subsidiary of the Roche Group, as well as stock ownership in Roche.
Published: 2022
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48. A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies.

Author: Altmann A, Ryten M, Di Nunzio M, Ravizza T, Tolomeo D, Reynolds RH, Somani A, Bacigaluppi M, Iori V, Micotti E, Di Sapia R, Cerovic M, Palma E, Ruffolo G, Botía JA, Absil J, Alhusaini S, Alvim MKM, Auvinen P, Bargallo N, Bartolini E, Bender B, Bergo FPG, Bernardes T, Bernasconi A, Bernasconi N, Bernhardt BC, Blackmon K, Braga B, Caligiuri ME, Calvo A, Carlson C, Carr SJA, Cavalleri GL, Cendes F, Chen J, Chen S, Cherubini A, Concha L, David P, Delanty N, Depondt C, Devinsky O, Doherty CP, Domin M, Focke NK, Foley S, Franca W, Gambardella A, Guerrini R, Hamandi K, Hibar DP, Isaev D, Jackson GD, Jahanshad N, Kälviäinen R, Keller SS, Kochunov P, Kotikalapudi R, Kowalczyk MA, Kuzniecky R, Kwan P, Labate A, Langner S, Lenge M, Liu M, Martin P, Mascalchi M, Meletti S, Morita-Sherman ME, O'Brien TJ, Pariente JC, Richardson MP, Rodriguez-Cruces R, Rummel C, Saavalainen T, Semmelroch MK, Severino M, Striano P, Thesen T, Thomas RH, Tondelli M, Tortora D, Vaudano AE, Vivash L, von Podewils F, Wagner J, Weber B, Wiest R, Yasuda CL, Zhang G, Zhang J, Leu C, Avbersek A, Thom M, Whelan CD, Thompson P, McDonald CR, Vezzani A, and Sisodiya SM
Subjects: Animals, Brain, Endothelial Cells, Mice, Seizures, Epilepsy metabolism, Microglia metabolism
Abstract: Aims: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis., Methods: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy., Results: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia., Conclusions: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control., (© 2021 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society.)
Published: 2022
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49. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Author: Frangou S, Modabbernia A, Williams SCR, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes-Eizagirre A, Alnaes D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur-Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales-Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim-Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo-Facorro B, Crivello F, Crone EA, Dale AM, Dannlowski U, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros-Bergman H, Fisher SE, Fouche JP, Franke B, Frodl T, Fuentes-Claramonte P, Glahn DC, Gotlib IH, Grabe HJ, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho BC, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch KP, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez-Zalacaín I, Mataix-Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer-Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol-Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez-Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano-Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, van 't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, and Dima D
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Young Adult, Cross-Sectional Studies, Cerebral Cortex anatomy & histology, Cerebral Cortex diagnostic imaging, Human Development physiology, Neuroimaging
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., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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50. Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs.

Author: Sønderby IE, Ching CRK, Thomopoulos SI, van der Meer D, Sun D, Villalon-Reina JE, Agartz I, Amunts K, Arango C, Armstrong NJ, Ayesa-Arriola R, Bakker G, Bassett AS, Boomsma DI, Bülow R, Butcher NJ, Calhoun VD, Caspers S, Chow EWC, Cichon S, Ciufolini S, Craig MC, Crespo-Facorro B, Cunningham AC, Dale AM, Dazzan P, de Zubicaray GI, Djurovic S, Doherty JL, Donohoe G, Draganski B, Durdle CA, Ehrlich S, Emanuel BS, Espeseth T, Fisher SE, Ge T, Glahn DC, Grabe HJ, Gur RE, Gutman BA, Haavik J, Håberg AK, Hansen LA, Hashimoto R, Hibar DP, Holmes AJ, Hottenga JJ, Hulshoff Pol HE, Jalbrzikowski M, Knowles EEM, Kushan L, Linden DEJ, Liu J, Lundervold AJ, Martin-Brevet S, Martínez K, Mather KA, Mathias SR, McDonald-McGinn DM, McRae AF, Medland SE, Moberget T, Modenato C, Monereo Sánchez J, Moreau CA, Mühleisen TW, Paus T, Pausova Z, Prieto C, Ragothaman A, Reinbold CS, Reis Marques T, Repetto GM, Reymond A, Roalf DR, Rodriguez-Herreros B, Rucker JJ, Sachdev PS, Schmitt JE, Schofield PR, Silva AI, Stefansson H, Stein DJ, Tamnes CK, Tordesillas-Gutiérrez D, Ulfarsson MO, Vajdi A, van 't Ent D, van den Bree MBM, Vassos E, Vázquez-Bourgon J, Vila-Rodriguez F, Walters GB, Wen W, Westlye LT, Wittfeld K, Zackai EH, Stefánsson K, Jacquemont S, Thompson PM, Bearden CE, and Andreassen OA
Subjects: Humans, Multicenter Studies as Topic, Brain diagnostic imaging, Brain growth & development, Brain pathology, DNA Copy Number Variations, Magnetic Resonance Imaging, Mental Disorders diagnostic imaging, Mental Disorders genetics, Mental Disorders pathology, Neurodevelopmental Disorders diagnostic imaging, Neurodevelopmental Disorders genetics, Neurodevelopmental Disorders pathology, Neuroimaging
Abstract: The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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