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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. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Author: McWhinney, SR, Hlinka, J, Bakstein, E, Dietze, LMF, Corkum, ELV, Abé, C, Alda, M, Alexander, N, Benedetti, F, Berk, M, Bøen, E, Bonnekoh, LM, Boye, B, Brosch, K, Canales-Rodríguez, EJ, Cannon, DM, Dannlowski, U, Demro, C, Diaz-Zuluaga, A, Elvsåshagen, T, Eyler, LT, Fortea, L, Fullerton, JM, Goltermann, J, Gotlib, IH, Grotegerd, D, Haarman, B, Hahn, T, Howells, FM, Jamalabadi, H, Jansen, A, Kircher, T, Klahn, AL, Kuplicki, R, Lahud, E, Landén, M, Leehr, EJ, Lopez-Jaramillo, C, Mackey, S, Malt, U, Martyn, F, Mazza, E, McDonald, C, McPhilemy, G, Meier, S, Meinert, S, Melloni, E, Mitchell, PB, Nabulsi, L, Nenadić, I, Nitsch, R, Opel, N, Ophoff, RA, Ortuño, M, Overs, BJ, Pineda-Zapata, J, Pomarol-Clotet, E, Radua, J, Repple, J, Roberts, G, Rodriguez-Cano, E, Sacchet, MD, Salvador, R, Savitz, J, Scheffler, F, Schofield, PR, Schürmeyer, N, Shen, C, Sim, K, Sponheim, SR, Stein, DJ, Stein, F, Straube, B, Suo, C, Temmingh, H, Teutenberg, L, Thomas-Odenthal, F, Thomopoulos, SI, Urosevic, S, Usemann, P, van Haren, NEM, Vargas, C, Vieta, E, Vilajosana, E, Vreeker, A, Winter, NR, Yatham, LN, Thompson, PM, Andreassen, OA, Ching, CRK, Hajek, T, McWhinney, SR, Hlinka, J, Bakstein, E, Dietze, LMF, Corkum, ELV, Abé, C, Alda, M, Alexander, N, Benedetti, F, Berk, M, Bøen, E, Bonnekoh, LM, Boye, B, Brosch, K, Canales-Rodríguez, EJ, Cannon, DM, Dannlowski, U, Demro, C, Diaz-Zuluaga, A, Elvsåshagen, T, Eyler, LT, Fortea, L, Fullerton, JM, Goltermann, J, Gotlib, IH, Grotegerd, D, Haarman, B, Hahn, T, Howells, FM, Jamalabadi, H, Jansen, A, Kircher, T, Klahn, AL, Kuplicki, R, Lahud, E, Landén, M, Leehr, EJ, Lopez-Jaramillo, C, Mackey, S, Malt, U, Martyn, F, Mazza, E, McDonald, C, McPhilemy, G, Meier, S, Meinert, S, Melloni, E, Mitchell, PB, Nabulsi, L, Nenadić, I, Nitsch, R, Opel, N, Ophoff, RA, Ortuño, M, Overs, BJ, Pineda-Zapata, J, Pomarol-Clotet, E, Radua, J, Repple, J, Roberts, G, Rodriguez-Cano, E, Sacchet, MD, Salvador, R, Savitz, J, Scheffler, F, Schofield, PR, Schürmeyer, N, Shen, C, Sim, K, Sponheim, SR, Stein, DJ, Stein, F, Straube, B, Suo, C, Temmingh, H, Teutenberg, L, Thomas-Odenthal, F, Thomopoulos, SI, Urosevic, S, Usemann, P, van Haren, NEM, Vargas, C, Vieta, E, Vilajosana, E, Vreeker, A, Winter, NR, Yatham, LN, Thompson, PM, Andreassen, OA, Ching, CRK, and Hajek, T
Abstract: Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associati
Published: 2024

4. Reduced glutamate in white matter of male neonates exposed to alcohol in utero: a 1H-magnetic resonance spectroscopy study

Author: Howells, FM, Donald, KA, Roos, A, Woods, RP, Zar, HJ, Narr, KL, and Stein, DJ
Subjects: Paediatrics, Biomedical and Clinical Sciences, Intellectual and Developmental Disabilities (IDD), Conditions Affecting the Embryonic and Fetal Periods, Clinical Research, Alcoholism, Alcohol Use and Health, Fetal Alcohol Spectrum Disorders (FASD), Neurosciences, Pediatric, Substance Misuse, Perinatal Period - Conditions Originating in Perinatal Period, Brain Disorders, Good Health and Well Being, Alcohol Drinking, Cohort Studies, Female, Glutamic Acid, Humans, Infant, Newborn, Magnetic Resonance Spectroscopy, Male, Pregnancy, Prenatal Exposure Delayed Effects, Protons, Sex Characteristics, White Matter, MRS, Alcohol exposure, Gray matter, Oligodendrocytes, Thyroid hormone, Clinical Sciences, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract: In utero exposure to alcohol leads to a spectrum of fetal alcohol related disorders (FASD). However, few studies used have used proton magnetic resonance spectroscopy ((1)H-MRS) to understand how neurochemical disturbances relate to the pathophysiology of FASD. Further, no studies to date have assessed brain metabolites in infants exposed to alcohol in utero. We hypothesize that neonates exposed to alcohol in utero will show decreased glutamatergic activity, pre-emptive of their clinical diagnosis or behavioural phenotype. Single voxel (1)H-MRS data, sampled in parietal white and gray matter, were acquired from 36 neonates exposed to alcohol in utero, and 31 control unexposed healthy neonates, in their 2nd-4th week of life. Metabolites relative to creatine with phosophocreatine and metabolites absolute concentrations using a water reference are reported. Male infants exposed to alcohol in utero were found to have reduced concentration of glutamate with glutamine (Glx) in their parietal white matter (PWM), compared to healthy male infants (p = 0.02). Further, male infants exposed to alcohol in utero had reduced concentration and ratio for glutamate (Glu) in their PWM (p = 0.02), compared to healthy male infants and female infants exposed to alcohol in utero. Female infants showed higher relative Glx and Glu ratios for parietal gray matter (PGM, p
Published: 2016

5. Large- scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Author: Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, Francks, C, Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, and Francks, C
Abstract: Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.
Published: 2023

6. In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

Author: Haukvik, UK, Gurholt, TP, Nerland, S, Elvsåshagen, T, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, Michael, Bettella, F, Bøen, E, Bonnín, CM, Brambilla, P, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, van Erp, TGM, Fatjó-Vilas, M, Foley, SF, Förster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Hülsmann, C, Jahanshad, N, Jørgensen, KN, Kircher, T, Krämer, B, Krug, A, Kuplicki, R, Lagerberg, TV, Lancaster, TM, Lenroot, RK, Lonning, V, López-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, van der Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarró, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Stäblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, SI, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Sämann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, Agartz, I, Haukvik, UK, Gurholt, TP, Nerland, S, Elvsåshagen, T, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, Michael, Bettella, F, Bøen, E, Bonnín, CM, Brambilla, P, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Díaz-Zuluaga, AM, van Erp, TGM, Fatjó-Vilas, M, Foley, SF, Förster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Hülsmann, C, Jahanshad, N, Jørgensen, KN, Kircher, T, Krämer, B, Krug, A, Kuplicki, R, Lagerberg, TV, Lancaster, TM, Lenroot, RK, Lonning, V, López-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, van der Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarró, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Stäblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, SI, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Sämann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, and Agartz, I
Published: 2022

7. 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: 2022

8. Greater male than female variability in regional brain structure across the lifespan

Author: Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, Tamnes, CK, Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, and Tamnes, CK
Abstract: For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
Published: 2022

9. In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics throughMeta-AnalysisBipolar Disorder Working Group

Author: Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, Agartz, I, Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, and Agartz, I
Abstract: The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.
Published: 2022

10. 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: 2022

11. 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: 2022

12. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author: Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, Paus, T, Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, and Paus, T
Abstract: BACKGROUND: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life. METHODS: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed. RESULTS: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth. CONCLUSIONS: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from t
Published: 2022

13. 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

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

Author: Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, Schmaal, L, Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, and Schmaal, L
Abstract: Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.
Published: 2021

15. 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

16. 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

17. Correction: Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals (Neuropsychopharmacology, (2019), 44, 13, (2285-2293), 10.1038/s41386-019-0485-6)

Author: Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abé, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodríguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarró, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, JF, Henry, C, Duchesnay, E, Houenou, J, Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abé, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodríguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarró, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, JF, Henry, C, Duchesnay, E, and Houenou, J
Abstract: This Article was originally published under NPG's License to Publish, but has now been made available under a [CC BY 4.0] license. The PDF and HTML versions of the Article have been modified accordingly.
Published: 2019

18. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author: Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abé, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodríguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarró, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, JF, Henry, C, Duchesnay, E, Houenou, J, Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abé, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodríguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarró, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, JF, Henry, C, Duchesnay, E, and Houenou, J
Abstract: Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr < 0.001) and cingulum (right: R2 = 0.041, left: R2 = 0.040, Pcorr < 0.001). Lithium medication, later onset and short disease duration were related to higher FA along multiple ROIs. Results of the meta-analysis showed similar effects. We demonstrated widespread WM abnormalities in BD and highlighted that altered WM connectivity within the corpus callosum and the cingulum are strongly associated with BD. These brain abnormalities could represent a biomarker for use in the diagnosis of BD. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
Published: 2019

19. 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, Pol, HEH, Kahn, RS, Ophoff, RA, van Haren, NEM, Andreassen, OA, Dale, AM, Nhat, TD, Gurholt, TP, Hartberg, CB, Haukvik, UK, Jorgensen, KN, Lagerberg, T, Melle, I, Westlye, LT, Gruber, O, Kraemer, B, Richter, A, Zilles, D, Calhoun, VD, Crespo-Facorro, B, Roiz-Santianez, R, Tordesillas-Gutierrez, 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, Quide, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Weickert, CS, Weickert, TW, Morris, DW, Hong, E, Kochunov, P, Beard, LM, Gur, RE, Gur, RC, Satterthwaite, TD, Wolf, DH, Belger, A, Brown, GG, Ford, JM, Macciardi, F, Mathalon, DH, O'Leary, DS, Potkin, SG, Preda, A, Voyvodic, J, Lim, KO, McEwen, S, Yang, F, Tan, Y, Tan, S, Wang, Z, Fan, F, Chen, J, Xiang, H, Tang, S, Guo, H, Wan, P, Wei, D, Bockholt, HJ, Ehrlich, S, Wolthusen, RPF, King, MD, Shoemaker, JM, Sponheim, SR, De Haan, L, Koenders, L, Machielsen, MW, van Amelsvoort, T, Veltman, DJ, Assogna, F, Banaj, N, de Rossi, P, Iorio, M, Piras, F, Spalletta, G, McKenna, PJ, Pomarol-Clotet, E, Salvador, R, Corvin, A, Donohoe, G, Kelly, S, Whelan, CD, Dickie, EW, Rotenberg, D, Voineskos, AN, Ciufolini, S, Radua, J, Dazzan, P, Murray, R, Marques, TR, Simmons, A, Borgwardt, S, Egloff, L, Harrisberger, F, Riecher-Roessler, A, Smieskova, R, Alpert, K, Wang, L, Jonsson, EG, Koops, S, Sommer, IEC, Bertolino, A, Bonvino, A, Di Giorgio, A, Neilson, E, Mayer, AR, Stephen, JM, Kwon, JS, Yun, J-Y, Cannon, DM, McDonald, C, Lebedeva, I, Tomyshev, AS, Akhadov, T, Kaleda, V, Fatouros-Bergman, H, Flyckt, L, Busatto, GF, Rosa, PGP, Serpa, MH, Zanetti, M, Hoschl, C, Skoch, A, Spaniel, F, Tomecek, D, Hagenaars, SP, McIntosh, AM, Whalley, HC, Lawrie, SM, Knoechel, C, Oertel-Knoechel, V, Staeblein, M, Howells, FM, Stein, DJ, Temmingh, HS, Uhlmann, A, Lopez-Jaramillo, C, Dima, D, McMahon, A, Faskowitz, J, Gutman, BA, Jahanshad, N, Thompson, PM, Turner, JA, 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, Pol, HEH, Kahn, RS, Ophoff, RA, van Haren, NEM, Andreassen, OA, Dale, AM, Nhat, TD, Gurholt, TP, Hartberg, CB, Haukvik, UK, Jorgensen, KN, Lagerberg, T, Melle, I, Westlye, LT, Gruber, O, Kraemer, B, Richter, A, Zilles, D, Calhoun, VD, Crespo-Facorro, B, Roiz-Santianez, R, Tordesillas-Gutierrez, 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, Quide, Y, Schall, U, Scott, RJ, Cairns, MJ, Seal, M, Tooney, PA, Rasser, PE, Cooper, G, Weickert, CS, Weickert, TW, Morris, DW, Hong, E, Kochunov, P, Beard, LM, Gur, RE, Gur, RC, Satterthwaite, TD, Wolf, DH, Belger, A, Brown, GG, Ford, JM, Macciardi, F, Mathalon, DH, O'Leary, DS, Potkin, SG, Preda, A, Voyvodic, J, Lim, KO, McEwen, S, Yang, F, Tan, Y, Tan, S, Wang, Z, Fan, F, Chen, J, Xiang, H, Tang, S, Guo, H, Wan, P, Wei, D, Bockholt, HJ, Ehrlich, S, Wolthusen, RPF, King, MD, Shoemaker, JM, Sponheim, SR, De Haan, L, Koenders, L, Machielsen, MW, van Amelsvoort, T, Veltman, DJ, Assogna, F, Banaj, N, de Rossi, P, Iorio, M, Piras, F, Spalletta, G, McKenna, PJ, Pomarol-Clotet, E, Salvador, R, Corvin, A, Donohoe, G, Kelly, S, Whelan, CD, Dickie, EW, Rotenberg, D, Voineskos, AN, Ciufolini, S, Radua, J, Dazzan, P, Murray, R, Marques, TR, Simmons, A, Borgwardt, S, Egloff, L, Harrisberger, F, Riecher-Roessler, A, Smieskova, R, Alpert, K, Wang, L, Jonsson, EG, Koops, S, Sommer, IEC, Bertolino, A, Bonvino, A, Di Giorgio, A, Neilson, E, Mayer, AR, Stephen, JM, Kwon, JS, Yun, J-Y, Cannon, DM, McDonald, C, Lebedeva, I, Tomyshev, AS, Akhadov, T, Kaleda, V, Fatouros-Bergman, H, Flyckt, L, Busatto, GF, Rosa, PGP, Serpa, MH, Zanetti, M, Hoschl, C, Skoch, A, Spaniel, F, Tomecek, D, Hagenaars, SP, McIntosh, AM, Whalley, HC, Lawrie, SM, Knoechel, C, Oertel-Knoechel, V, Staeblein, M, Howells, FM, Stein, DJ, Temmingh, HS, Uhlmann, A, Lopez-Jaramillo, C, Dima, D, McMahon, A, Faskowitz, J, Gutman, BA, Jahanshad, N, Thompson, PM, and Turner, JA
Abstract: 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

20. 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

21. White matter microstructure in obesity and bipolar disorders: an ENIGMA bipolar disorder working group study in 2186 individuals.

Author: Dietze LMF, McWhinney SR, Favre P, Abé C, Alexander N, Barkhau C, Benedetti F, Berk M, Bøen E, Boye B, Brosch K, Canales-Rodríguez EJ, Cannon DM, Carruthers SP, Corkum ELV, Dannlowski U, Díaz-Zuluaga AM, Dohm K, Elvsåshagen T, Flinkenflügel K, Fortea L, Furlong LS, Goldstein BI, Grotegerd D, Gruber M, Haarman BCM, Howells FM, Jahanshad N, Jamalabadi H, Jansen A, Karantonis JA, Kennedy KG, Kircher TTJ, Klahn AL, Kochunov P, Kraus A, Landén M, López-Jaramillo C, MacIntosh BJ, Mazza E, McDonald C, McIntosh AM, Meinert H, Meinert S, Melloni EMT, Mitchell PB, Nenadić I, Opel N, Phillips M, Piguet C, Polosan M, Pomarol-Clotet E, Pouchon A, Radua J, Roberts G, Ross AJ, Rossell SL, Salvador R, Sim K, Soares JC, Zunta-Soares GB, Stein F, Straube B, Suo C, Teutenberg L, Thomas-Odenthal F, Thomopoulos SI, Usemann P, Van Rheenen TE, Versace A, Vieta E, Vilajosana E, Mwangi B, Wen W, Whalley HC, Wu MJ, Andreassen OA, Ching CRK, Thompson PM, Houenou J, and Hajek T
Abstract: Although specific risk factors for brain alterations in bipolar disorders (BD) are currently unknown, obesity impacts the brain and is highly prevalent in BD. Gray matter correlates of obesity in BD have been well documented, but we know much less about brain white matter abnormalities in people who have both obesity and BD. We obtained body mass index (BMI) and diffusion tensor imaging derived fractional anisotropy (FA) from 22 white matter tracts in 899 individuals with BD, and 1287 control individuals from 20 cohorts in the ENIGMA-BD working group. In a mega-analysis, we investigated the associations between BMI, diagnosis or medication and FA. Lower FA was associated with both BD and BMI in six white matter tracts, including the corpus callosum and thalamic radiation. Higher BMI or BD were uniquely associated with lower FA in three and six white matter tracts, respectively. People not receiving lithium treatment had a greater negative association between FA and BMI than people treated with lithium in the posterior thalamic radiation and sagittal stratum. In three tracts BMI accounted for 10.5 to 17% of the negative association between the number of medication classes other than lithium and FA. Both overweight/obesity and BD demonstrated lower FA in some of the same regions. People prescribed lithium had a weaker association between BMI and FA than people not on lithium. In contrast, greater weight contributed to the negative associations between medications and FA. Obesity may add to brain alterations in BD and may play a role in effects of medications on the brain., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)
Published: 2024
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22. Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Author: McWhinney SR, Hlinka J, Bakstein E, Dietze LMF, Corkum ELV, Abé C, Alda M, Alexander N, Benedetti F, Berk M, Bøen E, Bonnekoh LM, Boye B, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Demro C, Diaz-Zuluaga A, Elvsåshagen T, Eyler LT, Fortea L, Fullerton JM, Goltermann J, Gotlib IH, Grotegerd D, Haarman B, Hahn T, Howells FM, Jamalabadi H, Jansen A, Kircher T, Klahn AL, Kuplicki R, Lahud E, Landén M, Leehr EJ, Lopez-Jaramillo C, Mackey S, Malt U, Martyn F, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Melloni E, Mitchell PB, Nabulsi L, Nenadić I, Nitsch R, Opel N, Ophoff RA, Ortuño M, Overs BJ, Pineda-Zapata J, Pomarol-Clotet E, Radua J, Repple J, Roberts G, Rodriguez-Cano E, Sacchet MD, Salvador R, Savitz J, Scheffler F, Schofield PR, Schürmeyer N, Shen C, Sim K, Sponheim SR, Stein DJ, Stein F, Straube B, Suo C, Temmingh H, Teutenberg L, Thomas-Odenthal F, Thomopoulos SI, Urosevic S, Usemann P, van Haren NEM, Vargas C, Vieta E, Vilajosana E, Vreeker A, Winter NR, Yatham LN, Thompson PM, Andreassen OA, Ching CRK, and Hajek T
Subjects: Humans, Adult, Female, Male, Middle Aged, Schizophrenia diagnostic imaging, Schizophrenia pathology, Schizophrenia drug therapy, Schizophrenia physiopathology, Cerebral Cortex diagnostic imaging, Cerebral Cortex pathology, Cluster Analysis, Young Adult, Brain diagnostic imaging, Brain pathology, Bipolar Disorder diagnostic imaging, Bipolar Disorder drug therapy, Bipolar Disorder pathology, Principal Component Analysis, Magnetic Resonance Imaging methods, Obesity diagnostic imaging
Abstract: Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2024
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23. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

Author: Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, Haas SS, Modabbernia A, New F, Agartz I, Asherson P, Ayesa-Arriola R, Banaj N, Banaschewski T, Baumeister S, Bertolino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buckner R, Buitelaar JK, Cannon DM, Caseras X, Cervenka S, Conrod PJ, Crespo-Facorro B, Crivello F, Crone EA, de Haan L, de Zubicaray GI, Di Giorgio A, Erk S, Fisher SE, Franke B, Frodl T, Glahn DC, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Harrison BJ, Hatton SN, Hickie I, Howells FM, Pol HEH, Huyser C, Jernigan TL, Jiang J, Joska JA, Kahn RS, Kalnin AJ, Kochan NA, Koops S, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lochner C, Martin NG, Mazoyer B, McDonald BC, McDonald C, McMahon KL, Nakao T, Nyberg L, Piras F, Portella MJ, Qiu J, Roffman JL, Sachdev PS, Sanford N, Satterthwaite TD, Saykin AJ, Schumann G, Sellgren CM, Sim K, Smoller JW, Soares J, Sommer IE, Spalletta G, Stein DJ, Tamnes CK, Thomopolous SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, van 't Ent D, van den Heuvel OA, van Erp TG, van Haren NE, Vecchio D, Veltman DJ, Walter H, Wang Y, Weber B, Wei D, Wen W, Westlye LT, Wierenga LM, Williams SC, Wright MJ, Medland S, Wu MJ, Yu K, Jahanshad N, Thompson PM, and Frangou S
Abstract: We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/)., Competing Interests: Declaration of interests SSH is supported by NIH National Institute of Mental Health (T32MH122394), and received a travel award from the Society of Biological Psychiatry to attend the annual meeting in 2023. HB declares an institutional grant from the National Health and Medical Research Council; has received compensation for being on an advisory board or a consultant to Biogen, Eisai, Eli Lilly, Roche, and Skin2Neuron; payment for being on the Cranbrook Care Medical Advisory Board, and honoraria for being on the Montefiore Homes Clinical Advisory Board. RMB and HEHP declare partial funding through the Geestkracht programme of the Dutch Health Research Council (Zon-Mw, grant No 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical center The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGzE, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal and Delta), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO 51.02.061 to H.H., NWO 51.02.062 to D. B., NWO–NIHC Programs of excellence 433-09-220 to H.H., NWO-MagW 480-04-004 to D. B., and NWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European Research Council (ERC-230374 to D. B.); and Universiteit Utrecht (High Potential Grant to H. H.). RB declares funding by NIH National Institute on Aging (R01AG067420); compensation for being on the scientific advisory board from Alkermes and Cognito Therapeutics with no conflict to the present work; honoraria from academic institutions for talks all under $1000 and $1000 for speaking at MGH/HMS course; travel fees for services to attend the annual meeting from the Simons Foundation; serves as a Director on the Simons Foundation collaborative initiative on aging (SCPAB); is a paid scientific advisory board member for philanthropic grants for The Foundation for OCD Research and the Klarman Family Foundation. BF has received educational speaking fees from Medice. DG reports funding from the NIH. UD is funded through the German Research Foundation (DFG; DA 1151/9- 1, DA 1151/10- 1, DA 1151/11- 1). GS declares funding from the European Commission, DFG, and NSFC. CKT has received grants from the Research Council of Norway and the Norwegian Regional Health Authority, unrelated to the current work. HW reports funding from the German Research Foundation (WA 1539/11-1). NJ reports funding from the NIH and compensation from the International Neuropsychological Society. PT declares a grant from the NIH and travel funded by NIH grants. All other authors declare no competing interests.
Published: 2023
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24. Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses.

Author: Banaj N, Vecchio D, Piras F, De Rossi P, Bustillo J, Ciufolini S, Dazzan P, Di Forti M, Dickie EW, Ford JM, Fuentes-Claramonte P, Gruber O, Guerrero-Pedraza A, Hamilton HK, Howells FM, Kraemer B, Lawrie SM, Mathalon DH, Murray R, Pomarol-Clotet E, Potkin SG, Preda A, Radua J, Richter A, Salvador R, Sawa A, Scheffler F, Sim K, Spaniel F, Stein DJ, Temmingh HS, Thomopoulos SI, Tomecek D, Uhlmann A, Voineskos A, Yang K, Jahanshad N, Thompson PM, Van Erp TGM, Turner JA, Spalletta G, and Piras F
Subjects: Humans, Magnetic Resonance Imaging, Neuroimaging, Parietal Lobe, Syndrome, Cerebral Cortex diagnostic imaging, Schizophrenia genetics
Abstract: Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis., (© 2023. The Author(s).)
Published: 2023
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25. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium.

Author: Schijven D, Postema MC, Fukunaga M, Matsumoto J, Miura K, de Zwarte SMC, van Haren NEM, Cahn W, Hulshoff Pol HE, Kahn RS, Ayesa-Arriola R, Ortiz-García de la Foz V, Tordesillas-Gutierrez D, Vázquez-Bourgon J, Crespo-Facorro B, Alnæs D, Dahl A, Westlye LT, Agartz I, Andreassen OA, Jönsson EG, Kochunov P, Bruggemann JM, Catts SV, Michie PT, Mowry BJ, Quidé Y, Rasser PE, Schall U, Scott RJ, Carr VJ, Green MJ, Henskens FA, Loughland CM, Pantelis C, Weickert CS, Weickert TW, de Haan L, Brosch K, Pfarr JK, Ringwald KG, Stein F, Jansen A, Kircher TTJ, Nenadić I, Krämer B, Gruber O, Satterthwaite TD, Bustillo J, Mathalon DH, Preda A, Calhoun VD, Ford JM, Potkin SG, Chen J, Tan Y, Wang Z, Xiang H, Fan F, Bernardoni F, Ehrlich S, Fuentes-Claramonte P, Garcia-Leon MA, Guerrero-Pedraza A, Salvador R, Sarró S, Pomarol-Clotet E, Ciullo V, Piras F, Vecchio D, Banaj N, Spalletta G, Michielse S, van Amelsvoort T, Dickie EW, Voineskos AN, Sim K, Ciufolini S, Dazzan P, Murray RM, Kim WS, Chung YC, Andreou C, Schmidt A, Borgwardt S, McIntosh AM, Whalley HC, Lawrie SM, du Plessis S, Luckhoff HK, Scheffler F, Emsley R, Grotegerd D, Lencer R, Dannlowski U, Edmond JT, Rootes-Murdy K, Stephen JM, Mayer AR, Antonucci LA, Fazio L, Pergola G, Bertolino A, Díaz-Caneja CM, Janssen J, Lois NG, Arango C, Tomyshev AS, Lebedeva I, Cervenka S, Sellgren CM, Georgiadis F, Kirschner M, Kaiser S, Hajek T, Skoch A, Spaniel F, Kim M, Kwak YB, Oh S, Kwon JS, James A, Bakker G, Knöchel C, Stäblein M, Oertel V, Uhlmann A, Howells FM, Stein DJ, Temmingh HS, Diaz-Zuluaga AM, Pineda-Zapata JA, López-Jaramillo C, Homan S, Ji E, Surbeck W, Homan P, Fisher SE, Franke B, Glahn DC, Gur RC, Hashimoto R, Jahanshad N, Luders E, Medland SE, Thompson PM, Turner JA, van Erp TGM, and Francks C
Subjects: Male, Female, Humans, Case-Control Studies, Brain diagnostic imaging, Cerebral Cortex, Magnetic Resonance Imaging methods, Functional Laterality, Schizophrenia diagnostic imaging
Abstract: Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.
Published: 2023
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26. Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

Author: McWhinney SR, Abé C, Alda M, Benedetti F, Bøen E, Del Mar Bonnin C, Borgers T, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Diaz-Zuluaga AM, Dietze LMF, Elvsåshagen T, Eyler LT, Fullerton JM, Goikolea JM, Goltermann J, Grotegerd D, Haarman BCM, Hahn T, Howells FM, Ingvar M, Jahanshad N, Kircher TTJ, Krug A, Kuplicki RT, Landén M, Lemke H, Liberg B, Lopez-Jaramillo C, Malt UF, Martyn FM, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Meller T, Melloni EMT, Mitchell PB, Nabulsi L, Nenadic I, Opel N, Ophoff RA, Overs BJ, Pfarr JK, Pineda-Zapata JA, Pomarol-Clotet E, Raduà J, Repple J, Richter M, Ringwald KG, Roberts G, Ross A, Salvador R, Savitz J, Schmitt S, Schofield PR, Sim K, Stein DJ, Stein F, Temmingh HS, Thiel K, Thomopoulos SI, van Haren NEM, Vargas C, Vieta E, Vreeker A, Waltemate L, Yatham LN, Ching CRK, Andreassen OA, Thompson PM, and Hajek T
Abstract: Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact., Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations., Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI., Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.
Published: 2023
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27. Virtual Ontogeny of Cortical Growth Preceding Mental Illness.

Author: Patel Y, Shin J, Abé C, Agartz I, Alloza C, Alnæs D, Ambrogi S, Antonucci LA, Arango C, Arolt V, Auzias G, Ayesa-Arriola R, Banaj N, Banaschewski T, Bandeira C, Başgöze Z, Cupertino RB, Bau CHD, Bauer J, Baumeister S, Bernardoni F, Bertolino A, Bonnin CDM, Brandeis D, Brem S, Bruggemann J, Bülow R, Bustillo JR, Calderoni S, Calvo R, Canales-Rodríguez EJ, Cannon DM, Carmona S, Carr VJ, Catts SV, Chenji S, Chew QH, Coghill D, Connolly CG, Conzelmann A, Craven AR, Crespo-Facorro B, Cullen K, Dahl A, Dannlowski U, Davey CG, Deruelle C, Díaz-Caneja CM, Dohm K, Ehrlich S, Epstein J, Erwin-Grabner T, Eyler LT, Fedor J, Fitzgerald J, Foran W, Ford JM, Fortea L, Fuentes-Claramonte P, Fullerton J, Furlong L, Gallagher L, Gao B, Gao S, Goikolea JM, Gotlib I, Goya-Maldonado R, Grabe HJ, Green M, Grevet EH, Groenewold NA, Grotegerd D, Gruber O, Haavik J, Hahn T, Harrison BJ, Heindel W, Henskens F, Heslenfeld DJ, Hilland E, Hoekstra PJ, Hohmann S, Holz N, Howells FM, Ipser JC, Jahanshad N, Jakobi B, Jansen A, Janssen J, Jonassen R, Kaiser A, Kaleda V, Karantonis J, King JA, Kircher T, Kochunov P, Koopowitz SM, Landén M, Landrø NI, Lawrie S, Lebedeva I, Luna B, Lundervold AJ, MacMaster FP, Maglanoc LA, Mathalon DH, McDonald C, McIntosh A, Meinert S, Michie PT, Mitchell P, Moreno-Alcázar A, Mowry B, Muratori F, Nabulsi L, Nenadić I, O'Gorman Tuura R, Oosterlaan J, Overs B, Pantelis C, Parellada M, Pariente JC, Pauli P, Pergola G, Piarulli FM, Picon F, Piras F, Pomarol-Clotet E, Pretus C, Quidé Y, Radua J, Ramos-Quiroga JA, Rasser PE, Reif A, Retico A, Roberts G, Rossell S, Rovaris DL, Rubia K, Sacchet M, Salavert J, Salvador R, Sarró S, Sawa A, Schall U, Scott R, Selvaggi P, Silk T, Sim K, Skoch A, Spalletta G, Spaniel F, Stein DJ, Steinsträter O, Stolicyn A, Takayanagi Y, Tamm L, Tavares M, Teumer A, Thiel K, Thomopoulos SI, Tomecek D, Tomyshev AS, Tordesillas-Gutiérrez D, Tosetti M, Uhlmann A, Van Rheenen T, Vazquez-Bourgón J, Vernooij MW, Vieta E, Vilarroya O, Weickert C, Weickert T, Westlye LT, Whalley H, Willinger D, Winter A, Wittfeld K, Yang TT, Yoncheva Y, Zijlmans JL, Hoogman M, Franke B, van Rooij D, Buitelaar J, Ching CRK, Andreassen OA, Pozzi E, Veltman D, Schmaal L, van Erp TGM, Turner J, Castellanos FX, Pausova Z, Thompson P, and Paus T
Subjects: Cerebral Cortex, Child, Female, Humans, Infant, Newborn, Magnetic Resonance Imaging methods, Pregnancy, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder genetics, Autism Spectrum Disorder pathology, Bipolar Disorder, Depressive Disorder, Major pathology, Premature Birth pathology
Abstract: Background: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., Methods: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., Results: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., Conclusions: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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28. Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.

Author: McWhinney SR, Abé C, Alda M, Benedetti F, Bøen E, Del Mar Bonnin C, Borgers T, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Diaz-Zuluaga AM, Dietze L, Elvsåshagen T, Eyler LT, Fullerton JM, Goikolea JM, Goltermann J, Grotegerd D, Haarman BCM, Hahn T, Howells FM, Ingvar M, Kircher TTJ, Krug A, Kuplicki RT, Landén M, Lemke H, Liberg B, Lopez-Jaramillo C, Malt UF, Martyn FM, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Meller T, Melloni EMT, Mitchell PB, Nabulsi L, Nenadic I, Opel N, Ophoff RA, Overs BJ, Pfarr JK, Pineda-Zapata JA, Pomarol-Clotet E, Raduà J, Repple J, Richter M, Ringwald KG, Roberts G, Ross A, Salvador R, Savitz J, Schmitt S, Schofield PR, Sim K, Stein DJ, Stein F, Temmingh HS, Thiel K, Thomopoulos SI, van Haren NEM, Van Gestel H, Vargas C, Vieta E, Vreeker A, Waltemate L, Yatham LN, Ching CRK, Andreassen OA, Thompson PM, and Hajek T
Subjects: Body Mass Index, Cluster Analysis, Humans, Magnetic Resonance Imaging, Obesity complications, Obesity diagnostic imaging, Temporal Lobe pathology, Bipolar Disorder diagnosis
Abstract: Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry., Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles., Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex., Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)
Published: 2022
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29. Capacity for cortical excitation is reduced in psychotic disorders: An investigation of the TMS-EMG cortical silent period.

Author: Howells FM, Hsieh JH, Temmingh HS, Baldwin DS, and Stein DJ
Subjects: Humans, Psychotic Disorders, Schizophrenia
Published: 2022
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30. 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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31. Greater male than female variability in regional brain structure across the lifespan.

Author: Wierenga LM, Doucet GE, Dima D, Agartz I, Aghajani M, Akudjedu TN, Albajes-Eizagirre A, Alnaes D, Alpert KI, Andreassen OA, Anticevic A, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur-Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, den Braber A, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Calhoun VD, Canales-Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Chaim-Avancini TM, Ching CR, Clark VP, Conrod PJ, Conzelmann A, Crivello F, Davey CG, Dickie EW, Ehrlich S, Van't Ent D, Fisher SE, Fouche JP, Franke B, Fuentes-Claramonte P, de Geus EJ, Di Giorgio A, Glahn DC, Gotlib IH, Grabe HJ, Gruber O, Gruner P, Gur RE, Gur RC, Gurholt TP, de Haan L, Haatveit B, Harrison BJ, Hartman CA, Hatton SN, Heslenfeld DJ, van den Heuvel OA, Hickie IB, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James AC, Jiang J, Jönsson EG, Joska JA, Kalnin AJ, Klein M, Koenders L, Kolskår KK, Krämer B, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lee PH, Lochner C, Machielsen MW, Maingault S, Martin NG, Martínez-Zalacaín I, Mataix-Cols D, Mazoyer B, McDonald BC, McDonald C, McIntosh AM, McMahon KL, McPhilemy G, van der Meer D, Menchón JM, Naaijen J, Nyberg L, Oosterlaan J, Paloyelis Y, Pauli P, Pergola G, Pomarol-Clotet E, Portella MJ, Radua J, Reif A, Richard G, Roffman JL, Rosa PG, Sacchet MD, Sachdev PS, Salvador R, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Sim K, Simmons A, Smoller JW, Sommer IE, Soriano-Mas C, Stein DJ, Strike LT, Szeszko PR, Temmingh HS, Thomopoulos SI, Tomyshev AS, Trollor JN, Uhlmann A, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Wang L, Wang Y, Weber B, Wen W, West JD, Westlye LT, Whalley HC, Williams SC, Wittfeld K, Wolf DH, Wright MJ, Yoncheva YN, Zanetti MV, Ziegler GC, de Zubicaray GI, Thompson PM, Crone EA, Frangou S, and Tamnes CK
Subjects: Female, Humans, Male, Brain Cortical Thickness, Cerebral Cortex anatomy & histology, Cerebral Cortex diagnostic imaging, Biological Variation, Population physiology, Brain anatomy & histology, Brain diagnostic imaging, Human Development physiology, Magnetic Resonance Imaging, Neuroimaging, Sex Characteristics
Abstract: For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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32. In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group.

Author: Haukvik UK, Gurholt TP, Nerland S, Elvsåshagen T, Akudjedu TN, Alda M, Alnaes D, Alonso-Lana S, Bauer J, Baune BT, Benedetti F, Berk M, Bettella F, Bøen E, Bonnín CM, Brambilla P, Canales-Rodríguez EJ, Cannon DM, Caseras X, Dandash O, Dannlowski U, Delvecchio G, Díaz-Zuluaga AM, van Erp TGM, Fatjó-Vilas M, Foley SF, Förster K, Fullerton JM, Goikolea JM, Grotegerd D, Gruber O, Haarman BCM, Haatveit B, Hajek T, Hallahan B, Harris M, Hawkins EL, Howells FM, Hülsmann C, Jahanshad N, Jørgensen KN, Kircher T, Krämer B, Krug A, Kuplicki R, Lagerberg TV, Lancaster TM, Lenroot RK, Lonning V, López-Jaramillo C, Malt UF, McDonald C, McIntosh AM, McPhilemy G, van der Meer D, Melle I, Melloni EMT, Mitchell PB, Nabulsi L, Nenadić I, Oertel V, Oldani L, Opel N, Otaduy MCG, Overs BJ, Pineda-Zapata JA, Pomarol-Clotet E, Radua J, Rauer L, Redlich R, Repple J, Rive MM, Roberts G, Ruhe HG, Salminen LE, Salvador R, Sarró S, Savitz J, Schene AH, Sim K, Soeiro-de-Souza MG, Stäblein M, Stein DJ, Stein F, Tamnes CK, Temmingh HS, Thomopoulos SI, Veltman DJ, Vieta E, Waltemate L, Westlye LT, Whalley HC, Sämann PG, Thompson PM, Ching CRK, Andreassen OA, and Agartz I
Subjects: Bipolar Disorder drug therapy, Genetics, Hippocampus drug effects, Humans, Bipolar Disorder diagnostic imaging, Bipolar Disorder pathology, Hippocampus diagnostic imaging, Hippocampus pathology, Magnetic Resonance Imaging, Neuroimaging
Abstract: The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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33. 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 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, Dannlowski U, Dale AM, 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, Williams SCR, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, and Frangou S
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Young Adult, Amygdala anatomy & histology, Amygdala diagnostic imaging, Corpus Striatum anatomy & histology, Corpus Striatum diagnostic imaging, Hippocampus anatomy & histology, Hippocampus diagnostic imaging, Human Development physiology, Neuroimaging, Thalamus anatomy & histology, Thalamus diagnostic imaging
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., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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34. 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, Alnaes 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, Soares JC, Stäblein M, Stein DJ, Tamnes CK, Thomaidis GV, Upegui CV, Veltman DJ, Wessa M, Westlye LT, Whalley HC, Wolf DH, Wu MJ, Yatham LN, Zarate CA, Thompson PM, and Andreassen OA
Subjects: Humans, Meta-Analysis as Topic, Multicenter Studies as Topic, Bipolar Disorder diagnostic imaging, Bipolar Disorder pathology, Cerebral Cortex diagnostic imaging, Cerebral Cortex pathology, Magnetic Resonance Imaging, Neuroimaging
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., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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35. Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

Author: McWhinney SR, Abé C, Alda M, Benedetti F, Bøen E, Del Mar Bonnin C, Borgers T, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Díaz-Zuluaga AM, Elvsåshagen T, Eyler LT, Fullerton JM, Goikolea JM, Goltermann J, Grotegerd D, Haarman BCM, Hahn T, Howells FM, Ingvar M, Kircher TTJ, Krug A, Kuplicki RT, Landén M, Lemke H, Liberg B, Lopez-Jaramillo C, Malt UF, Martyn FM, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Meller T, Melloni EMT, Mitchell PB, Nabulsi L, Nenadic I, Opel N, Ophoff RA, Overs BJ, Pfarr JK, Pineda-Zapata JA, Pomarol-Clotet E, Raduà J, Repple J, Richter M, Ringwald KG, Roberts G, Salvador R, Savitz J, Schmitt S, Schofield PR, Sim K, Stein DJ, Stein F, Temmingh HS, Thiel K, van Haren NEM, Gestel HV, Vargas C, Vieta E, Vreeker A, Waltemate L, Yatham LN, Ching CRK, Andreassen O, Thompson PM, and Hajek T
Subjects: Amygdala, Body Mass Index, Brain, Humans, Magnetic Resonance Imaging methods, Bipolar Disorder
Abstract: Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression., (© 2021. The Author(s).)
Published: 2021
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36. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.

Author: Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, Aghajani M, Aleman A, Baune BT, Berger K, Brak I, Filho GB, Carballedo A, Connolly CG, Couvy-Duchesne B, Cullen KR, Dannlowski U, Davey CG, Dima D, Duran FLS, Enneking V, Filimonova E, Frenzel S, Frodl T, Fu CHY, Godlewska BR, Gotlib IH, Grabe HJ, Groenewold NA, Grotegerd D, Gruber O, Hall GB, Harrison BJ, Hatton SN, Hermesdorf M, Hickie IB, Ho TC, Hosten N, Jansen A, Kähler C, Kircher T, Klimes-Dougan B, Krämer B, Krug A, Lagopoulos J, Leenings R, MacMaster FP, MacQueen G, McIntosh A, McLellan Q, McMahon KL, Medland SE, Mueller BA, Mwangi B, Osipov E, Portella MJ, Pozzi E, Reneman L, Repple J, Rosa PGP, Sacchet MD, Sämann PG, Schnell K, Schrantee A, Simulionyte E, Soares JC, Sommer J, Stein DJ, Steinsträter O, Strike LT, Thomopoulos SI, van Tol MJ, Veer IM, Vermeiren RRJM, Walter H, van der Wee NJA, van der Werff SJA, Whalley H, Winter NR, Wittfeld K, Wright MJ, Wu MJ, Völzke H, Yang TT, Zannias V, de Zubicaray GI, Zunta-Soares GB, Abé C, Alda M, Andreassen OA, Bøen E, Bonnin CM, Canales-Rodriguez EJ, Cannon D, Caseras X, Chaim-Avancini TM, Elvsåshagen T, Favre P, Foley SF, Fullerton JM, Goikolea JM, Haarman BCM, Hajek T, Henry C, Houenou J, Howells FM, Ingvar M, Kuplicki R, Lafer B, Landén M, Machado-Vieira R, Malt UF, McDonald C, Mitchell PB, Nabulsi L, Otaduy MCG, Overs BJ, Polosan M, Pomarol-Clotet E, Radua J, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Savitz J, Schene AH, Schofield PR, Serpa MH, Sim K, Soeiro-de-Souza MG, Sutherland AN, Temmingh HS, Timmons GM, Uhlmann A, Vieta E, Wolf DH, Zanetti MV, Jahanshad N, Thompson PM, Veltman DJ, Penninx BWJH, Marquand AF, Cole JH, and Schmaal L
Subjects: Adolescent, Adult, Aged, Aging, Brain diagnostic imaging, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Depressive Disorder, Major
Abstract: Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates., (© 2020. The Author(s).)
Published: 2021
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37. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.

Author: Patel Y, Parker N, Shin J, Howard D, French L, Thomopoulos SI, Pozzi E, Abe Y, Abé C, Anticevic A, Alda M, Aleman A, Alloza C, Alonso-Lana S, Ameis SH, Anagnostou E, McIntosh AA, Arango C, Arnold PD, Asherson P, Assogna F, Auzias G, Ayesa-Arriola R, Bakker G, Banaj N, Banaschewski T, Bandeira CE, Baranov A, Bargalló N, Bau CHD, Baumeister S, Baune BT, Bellgrove MA, Benedetti F, Bertolino A, Boedhoe PSW, Boks M, Bollettini I, Del Mar Bonnin C, Borgers T, Borgwardt S, Brandeis D, Brennan BP, Bruggemann JM, Bülow R, Busatto GF, Calderoni S, Calhoun VD, Calvo R, Canales-Rodríguez EJ, Cannon DM, Carr VJ, Cascella N, Cercignani M, Chaim-Avancini TM, Christakou A, Coghill D, Conzelmann A, Crespo-Facorro B, Cubillo AI, Cullen KR, Cupertino RB, Daly E, Dannlowski U, Davey CG, Denys D, Deruelle C, Di Giorgio A, Dickie EW, Dima D, Dohm K, Ehrlich S, Ely BA, Erwin-Grabner T, Ethofer T, Fair DA, Fallgatter AJ, Faraone SV, Fatjó-Vilas M, Fedor JM, Fitzgerald KD, Ford JM, Frodl T, Fu CHY, Fullerton JM, Gabel MC, Glahn DC, Roberts G, Gogberashvili T, Goikolea JM, Gotlib IH, Goya-Maldonado R, Grabe HJ, Green MJ, Grevet EH, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Guerrero-Pedraza A, Gur RE, Gur RC, Haar S, Haarman BCM, Haavik J, Hahn T, Hajek T, Harrison BJ, Harrison NA, Hartman CA, Whalley HC, Heslenfeld DJ, Hibar DP, Hilland E, Hirano Y, Ho TC, Hoekstra PJ, Hoekstra L, Hohmann S, Hong LE, Höschl C, Høvik MF, Howells FM, Nenadic I, Jalbrzikowski M, James AC, Janssen J, Jaspers-Fayer F, Xu J, Jonassen R, Karkashadze G, King JA, Kircher T, Kirschner M, Koch K, Kochunov P, Kohls G, Konrad K, Krämer B, Krug A, Kuntsi J, Kwon JS, Landén M, Landrø NI, Lazaro L, Lebedeva IS, Leehr EJ, Lera-Miguel S, Lesch KP, Lochner C, Louza MR, Luna B, Lundervold AJ, MacMaster FP, Maglanoc LA, Malpas CB, Portella MJ, Marsh R, Martyn FM, Mataix-Cols D, Mathalon DH, McCarthy H, McDonald C, McPhilemy G, Meinert S, Menchón JM, Minuzzi L, Mitchell PB, Moreno C, Morgado P, Muratori F, Murphy CM, Murphy D, Mwangi B, Nabulsi L, Nakagawa A, Nakamae T, Namazova L, Narayanaswamy J, Jahanshad N, Nguyen DD, Nicolau R, O'Gorman Tuura RL, O'Hearn K, Oosterlaan J, Opel N, Ophoff RA, Oranje B, García de la Foz VO, Overs BJ, Paloyelis Y, Pantelis C, Parellada M, Pauli P, Picó-Pérez M, Picon FA, Piras F, Piras F, Plessen KJ, Pomarol-Clotet E, Preda A, Puig O, Quidé Y, Radua J, Ramos-Quiroga JA, Rasser PE, Rauer L, Reddy J, Redlich R, Reif A, Reneman L, Repple J, Retico A, Richarte V, Richter A, Rosa PGP, Rubia KK, Hashimoto R, Sacchet MD, Salvador R, Santonja J, Sarink K, Sarró S, Satterthwaite TD, Sawa A, Schall U, Schofield PR, Schrantee A, Seitz J, Serpa MH, Setién-Suero E, Shaw P, Shook D, Silk TJ, Sim K, Simon S, Simpson HB, Singh A, Skoch A, Skokauskas N, Soares JC, Soreni N, Soriano-Mas C, Spalletta G, Spaniel F, Lawrie SM, Stern ER, Stewart SE, Takayanagi Y, Temmingh HS, Tolin DF, Tomecek D, Tordesillas-Gutiérrez D, Tosetti M, Uhlmann A, van Amelsvoort T, van der Wee NJA, van der Werff SJA, van Haren NEM, van Wingen GA, Vance A, Vázquez-Bourgon J, Vecchio D, Venkatasubramanian G, Vieta E, Vilarroya O, Vives-Gilabert Y, Voineskos AN, Völzke H, von Polier GG, Walton E, Weickert TW, Weickert CS, Weideman AS, Wittfeld K, Wolf DH, Wu MJ, Yang TT, Yang K, Yoncheva Y, Yun JY, Cheng Y, Zanetti MV, Ziegler GC, Franke B, Hoogman M, Buitelaar JK, van Rooij D, Andreassen OA, Ching CRK, Veltman DJ, Schmaal L, Stein DJ, van den Heuvel OA, Turner JA, van Erp TGM, Pausova Z, Thompson PM, and Paus T
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Attention Deficit Disorder with Hyperactivity diagnostic imaging, Autism Spectrum Disorder diagnostic imaging, Bipolar Disorder diagnostic imaging, Case-Control Studies, Cerebral Cortex cytology, Cerebral Cortex diagnostic imaging, Cerebral Cortex growth & development, Child, Child, Preschool, Cohort Studies, Computational Biology, Depressive Disorder, Major diagnostic imaging, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Obsessive-Compulsive Disorder diagnostic imaging, Principal Component Analysis, Schizophrenia diagnostic imaging, Young Adult, Attention Deficit Disorder with Hyperactivity pathology, Autism Spectrum Disorder pathology, Bipolar Disorder pathology, Cerebral Cortex pathology, Depressive Disorder, Major pathology, Fetal Development physiology, Gene Expression physiology, Human Development physiology, Obsessive-Compulsive Disorder pathology, Schizophrenia pathology
Abstract: Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood., Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia., Design, Setting, and Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244., Main Outcomes and Measures: Interregional profiles of group difference in cortical thickness between cases and controls., Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders., Conclusions and Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.
Published: 2021
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38. 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, and Hajek T
Subjects: Brain diagnostic imaging, Humans, Machine Learning, Magnetic Resonance Imaging, Neuroimaging, Bipolar Disorder diagnostic imaging
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
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39. The prevalence and clinical correlates of substance use disorders in patients with psychotic disorders from an Upper-Middle-Income Country.

Author: Temmingh HS, Mall S, Howells FM, Sibeko G, and Stein DJ
Abstract: Background: Substance use disorders (SUDs) occur frequently in patients with psychotic disorders and have been associated with various demographic and clinical correlates. There is an absence of research on the prevalence and clinical correlates of SUDs in psychotic disorders in low-and-middle-income countries (LMICs)., Aim: We aimed to determine the prevalence and correlates of SUDs in psychotic disorders., Setting: Patients attending a large secondary-level psychiatric hospital in Cape Town South Africa., Methods: We used the Structured Clinical Interview for DSM-IV (SCID-I) to determine psychiatric and substance use diagnoses, depressive, anxiety, obsessive-compulsive and post-traumatic symptoms. We used logistic regression models to determine significant predictors of SUDs., Results: In total sample ( N = 248), 55.6% of participants had any SUD, 34.3% had cannabis use disorders, 30.6% alcohol use disorders, 27.4% methamphetamine use disorders, 10.4% methaqualone use disorders and 4.8% had other SUDs. There were significant associations with male sex for most SUDs, with younger age and Coloured ethnicity for methamphetamine use disorders, and with lower educational attainment for cannabis use disorders. Anxiety symptoms and suicide attempts were significantly associated with alcohol use disorders; a diagnosis of a substance induced psychosis with cannabis and methamphetamine use disorders. Across most SUDs legal problems and criminal involvement were significantly increased., Conclusion: This study found a high prevalence and wide distribution of SUDs in patients with psychotic disorders, consistent with previous work from high income countries. Given clinical correlates, in individuals with psychotic disorders and SUDs it is important to assess anxiety symptoms, suicidality and criminal involvement., Competing Interests: The authors declare that no competing interests exist., (© 2020. The Authors.)
Published: 2020
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40. Differential effects of social isolation rearing on glutamate- and GABA-stimulated noradrenaline release in the rat prefrontal cortex and hippocampus.

Author: Atmore KH, Stein DJ, Harvey BH, Russell VA, and Howells FM
Subjects: Animals, Animals, Newborn, Female, Hippocampus drug effects, Male, Prefrontal Cortex drug effects, Rats, Rats, Sprague-Dawley, Glutamic Acid pharmacology, Hippocampus metabolism, Norepinephrine metabolism, Prefrontal Cortex metabolism, Social Isolation psychology, gamma-Aminobutyric Acid pharmacology
Abstract: Social isolation rearing (SIR) provides an excellent model of early life adversity to investigate alterations in brain function. Few studies have investigated the effects of SIR on noradrenaline (NE) projections which arise from the locus coeruleus (LC), a system which regulates arousal and attentional processes, including the processing of novelty. In addition, there is a paucity of information on the effects of SIR in females. In this study we investigated the behavioural response to attentional processing of novelty and glutamate- and GABA-stimulated release of noradrenaline in the prefrontal cortex (PFC) and hippocampus (HC) of male and female rats. Sprague Dawley pups were reared in isolated or socialised housing conditions from weaning on postnatal day 21 (P21). At P78-83 animal behaviour was recorded from the three phases of the novel object recognition (NOR) task. Then at P90-94, NE release was measured in the PFC and HC after stimulating the tissue in vitro with either glutamate or GABA. Behaviourally SIR decreased novelty-related behaviour, male isolates showed effects of SIR during the NOR Test phase while female isolates showed effects of SIR during the Habituation phase. SIR PFC NE release was decreased when glutamate stimulation followed GABA stimulation and tended to increase when GABA stimulation followed glutamate stimulation, differences were predominantly due to male isolates. No SIR differences were found for HC. Early life adversity differentially affects the function of the LCNE system in males and females, evidenced by changes in attentional processing of novelty and stimulated noradrenaline release in the PFC., Competing Interests: Conflict of interest The authors have no conflict of interest to declare in relation to this work and its publication., (Copyright © 2020 Elsevier B.V. and ECNP. All rights reserved.)
Published: 2020
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41. The Relationship Between White Matter Microstructure and General Cognitive Ability in Patients With Schizophrenia and Healthy Participants in the ENIGMA Consortium.

Author: Holleran L, Kelly S, Alloza C, Agartz I, Andreassen OA, Arango C, Banaj N, Calhoun V, Cannon D, Carr V, Corvin A, Glahn DC, Gur R, Hong E, Hoschl C, Howells FM, James A, Janssen J, Kochunov P, Lawrie SM, Liu J, Martinez C, McDonald C, Morris D, Mothersill D, Pantelis C, Piras F, Potkin S, Rasser PE, Roalf D, Rowland L, Satterthwaite T, Schall U, Spalletta G, Spaniel F, Stein DJ, Uhlmann A, Voineskos A, Zalesky A, van Erp TGM, Turner JA, Deary IJ, Thompson PM, Jahanshad N, and Donohoe G
Subjects: Adult, Anisotropy, Brain diagnostic imaging, Case-Control Studies, Diffusion Tensor Imaging, Factor Analysis, Statistical, Female, Healthy Volunteers, Humans, Male, Middle Aged, Neural Pathways diagnostic imaging, Principal Component Analysis, Schizophrenia physiopathology, Wechsler Scales, Cognition physiology, Intelligence, Schizophrenia diagnostic imaging, Schizophrenic Psychology, White Matter diagnostic imaging
Abstract: Objective: Schizophrenia has recently been associated with widespread white matter microstructural abnormalities, but the functional effects of these abnormalities remain unclear. Widespread heterogeneity of results from studies published to date preclude any definitive characterization of the relationship between white matter and cognitive performance in schizophrenia. Given the relevance of deficits in cognitive function to predicting social and functional outcomes in schizophrenia, the authors carried out a meta-analysis of available data through the ENIGMA Consortium, using a common analysis pipeline, to elucidate the relationship between white matter microstructure and a measure of general cognitive performance, IQ, in patients with schizophrenia and healthy participants., Methods: The meta-analysis included 760 patients with schizophrenia and 957 healthy participants from 11 participating ENIGMA Consortium sites. For each site, principal component analysis was used to calculate both a global fractional anisotropy component (gFA) and a fractional anisotropy component for six long association tracts (LA-gFA) previously associated with cognition., Results: Meta-analyses of regression results indicated that gFA accounted for a significant amount of variation in cognition in the full sample (effect size [Hedges' g]=0.27, CI=0.17-0.36), with similar effects sizes observed for both the patient (effect size=0.20, CI=0.05-0.35) and healthy participant groups (effect size=0.32, CI=0.18-0.45). Comparable patterns of association were also observed between LA-gFA and cognition for the full sample (effect size=0.28, CI=0.18-0.37), the patient group (effect size=0.23, CI=0.09-0.38), and the healthy participant group (effect size=0.31, CI=0.18-0.44)., Conclusions: This study provides robust evidence that cognitive ability is associated with global structural connectivity, with higher fractional anisotropy associated with higher IQ. This association was independent of diagnosis; while schizophrenia patients tended to have lower fractional anisotropy and lower IQ than healthy participants, the comparable size of effect in each group suggested a more general, rather than disease-specific, pattern of association.
Published: 2020
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42. The relationship between measurement of in vivo brain glutamate and markers of iron metabolism: A proton magnetic resonance spectroscopy study in healthy adults.

Author: Burger A, Kotze MJ, Stein DJ, Janse van Rensburg S, and Howells FM
Subjects: Adult, Biomarkers, Brain diagnostic imaging, Female, Humans, Iron, Magnetic Resonance Imaging, Male, Proton Magnetic Resonance Spectroscopy, Glutamic Acid, Glutamine
Abstract: Fundamental human studies which address associations between glutamate and iron metabolism are needed. Basic research reports associations between glutamate and iron metabolism. Human studies report sex differences in iron metabolism and glutamate concentrations, which suggest that these relationships may differ by sex. We hypothesised associations would be apparent between in vivo glutamate and peripheral markers of iron metabolism, and these associations would differ by sex. To test this, we recruited 40 healthy adults (20 men, 20 women) and measured (a) standard clinical biomarker concentrations for iron metabolism and (b) an in vivo proxy for glutamate concentration, glutamate with glutamine in relation to total creatine containing metabolites using proton magnetic resonance spectroscopy studies with a two-dimensional chemical shift imaging slice, with voxels located in bilateral dorsolateral prefrontal cortices, anterior cingulate cortices and frontal white matter. Only the female group reported significant associations between peripheral markers of iron metabolism and Glx:tCr concentration: (a) right dorsolateral prefrontal cortex Glx:tCr associated positively with serum transferrin (r = .60, p = .006) and negatively with transferrin saturation (r = -.62, p = .004) and (b) right frontal white matter Glx:tCr associated negatively with iron concentration (r = -.59, p = .008) and transferrin saturation (r = -.65, p = .002). Our results support associations between iron metabolism and our proxy for in vivo glutamate concentration (Glx:tCr). These associations were limited to women, suggesting a stronger regulatory control between iron and glutamate metabolism. These associations support additional fundamental research into the molecular mechanisms of this regulatory control., (© 2019 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)
Published: 2020
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43. Correction: Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals.

Author: Favre P, Pauling M, Stout J, Hozer F, Sarrazin S, Abé C, Alda M, Alloza C, Alonso-Lana S, Andreassen OA, Baune BT, Benedetti F, Busatto GF, Canales-Rodríguez EJ, Caseras X, Chaim-Avancini TM, Ching CRK, Dannlowski U, Deppe M, Eyler LT, Fatjo-Vilas M, Foley SF, Grotegerd D, Hajek T, Haukvik UK, Howells FM, Jahanshad N, Kugel H, Lagerberg TV, Lawrie SM, Linke JO, McIntosh A, Melloni EMT, Mitchell PB, Polosan M, Pomarol-Clotet E, Repple J, Roberts G, Roos A, Rosa PGP, Salvador R, Sarró S, Schofield PR, Serpa MH, Sim K, Stein DJ, Sussmann JE, Temmingh HS, Thompson PM, Verdolini N, Vieta E, Wessa M, Whalley HC, Zanetti MV, Leboyer M, Mangin JF, Henry C, Duchesnay E, and Houenou J
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Published: 2019
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44. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals.

Author: Favre P, Pauling M, Stout J, Hozer F, Sarrazin S, Abé C, Alda M, Alloza C, Alonso-Lana S, Andreassen OA, Baune BT, Benedetti F, Busatto GF, Canales-Rodríguez EJ, Caseras X, Chaim-Avancini TM, Ching CRK, Dannlowski U, Deppe M, Eyler LT, Fatjo-Vilas M, Foley SF, Grotegerd D, Hajek T, Haukvik UK, Howells FM, Jahanshad N, Kugel H, Lagerberg TV, Lawrie SM, Linke JO, McIntosh A, Melloni EMT, Mitchell PB, Polosan M, Pomarol-Clotet E, Repple J, Roberts G, Roos A, Rosa PGP, Salvador R, Sarró S, Schofield PR, Serpa MH, Sim K, Stein DJ, Sussmann JE, Temmingh HS, Thompson PM, Verdolini N, Vieta E, Wessa M, Whalley HC, Zanetti MV, Leboyer M, Mangin JF, Henry C, Duchesnay E, and Houenou J
Subjects: Adult, Bipolar Disorder diagnostic imaging, Brain diagnostic imaging, Corpus Callosum diagnostic imaging, Corpus Callosum pathology, Diffusion Tensor Imaging, Female, Humans, Male, Neural Pathways diagnostic imaging, Neural Pathways pathology, White Matter diagnostic imaging, Bipolar Disorder pathology, Brain pathology, White Matter pathology
Abstract: Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R 2 = 0.041, P corr < 0.001) and cingulum (right: R 2 = 0.041, left: R 2 = 0.040, P corr < 0.001). Lithium medication, later onset and short disease duration were related to higher FA along multiple ROIs. Results of the meta-analysis showed similar effects. We demonstrated widespread WM abnormalities in BD and highlighted that altered WM connectivity within the corpus callosum and the cingulum are strongly associated with BD. These brain abnormalities could represent a biomarker for use in the diagnosis of BD. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.
Published: 2019
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45. 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, Rosa PGP, Serpa MH, Zanetti MV, Hoschl C, Skoch A, Spaniel F, Tomecek D, McIntosh AM, Whalley HC, Knöchel C, Oertel-Knöchel V, Howells FM, Stein DJ, Temmingh HS, Uhlmann A, Lopez-Jaramillo C, Dima D, Faskowitz JI, Gutman BA, Jahanshad N, Thompson PM, and Turner JA
Subjects: Brain, Humans, Magnetic Resonance Imaging, Brain Diseases, Schizophrenia
Published: 2019
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46. Food Cue Reactivity and the Brain-Heart Axis During Cognitive Stress Following Clinically Relevant Weight Loss.

Author: Rauch HGL, Hume DJ, Howells FM, Kroff J, and Lambert EV
Abstract: Successful weight loss maintainers are more vulnerable to stress induced eating. The aim of our study was to determine what effect an attention-demanding cognitive performance task had on brain-heart reactivity to visual food cues in women who maintained clinically relevant weight loss vs. women who had never weight cycled. A clinical weight loss group (CWL, n = 17) and a BMI-matched control group (CTL, n = 23) completed modified Stroop tasks that either included high calorie food pictures (Food Stroop) or excluded food cues (Office Stroop). ECG, breathing rate, and EEG were recorded. CWL participants : The Eating Restraint scores (Three Factor Eating Questionnaire) of the CWL participants correlated negatively with their heart rates recorded during the Food Stroop task ( r = 0.62, p < 0.01). There was no such relationship in CTL participants. The P200 latencies in CWL participants evoked by the Stroop color-word cues at the C3 electrode were positively correlated to the log high frequency power in their cardiac spectrograms during the Food Stroop ( r = 0.63, p < 0.02). There were no such relationships in the Office Stroop task nor in CTL participants. Combined Group s: Participants' heart rates were significantly lower ( p < 0.05) and their RMSSD values and the log Total Power in their cardiac spectrograms were significantly greater during the Food Stroop vs. Office Stroop ( p < 0.01, Bonferroni corrected). In conclusion Eating Restraint scores in CWL participants correlated with their Stroop heart rates, while the P200 latencies evoked by the Stroop cues correlated with the log high frequency power in their cardiac spectrograms (marker of cardiac vagal activation) during the Food Stroop task. This provides evidence that even 12 months after successful weight loss maintenance the cardiac ANS reactivity to food cues while completing a cognitive performance test was still different to that in individuals of normal weight who never weight cycled. Across all participants the cardiac ANS reactivity evoked by performing the Stroop task was lowered by food cues suggesting that the dampening effect of food cues on cardiac ANS reactivity may be one of the drivers of 'stress induced' eating.
Published: 2019
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47. 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, Scott RJ, Cairns MJ, Seal M, Tooney PA, Rasser PE, Cooper G, Shannon Weickert C, Weickert TW, Morris DW, Hong E, Kochunov P, Beard LM, Gur RE, Gur RC, Satterthwaite TD, Wolf DH, Belger A, Brown GG, Ford JM, Macciardi F, Mathalon DH, O'Leary DS, Potkin SG, Preda A, Voyvodic J, Lim KO, McEwen S, Yang F, Tan Y, Tan S, Wang Z, Fan F, Chen J, Xiang H, Tang S, Guo H, Wan P, Wei D, Bockholt HJ, Ehrlich S, Wolthusen RPF, King MD, Shoemaker JM, Sponheim SR, De Haan L, Koenders L, Machielsen MW, van Amelsvoort T, Veltman DJ, Assogna F, Banaj N, de Rossi P, Iorio M, Piras F, Spalletta G, McKenna PJ, Pomarol-Clotet E, Salvador R, Corvin A, Donohoe G, Kelly S, Whelan CD, Dickie EW, Rotenberg D, Voineskos AN, Ciufolini S, Radua J, Dazzan P, Murray R, Reis Marques T, Simmons A, Borgwardt S, Egloff L, Harrisberger F, Riecher-Rössler A, Smieskova R, Alpert KI, Wang L, Jönsson EG, Koops S, Sommer IEC, Bertolino A, Bonvino A, Di Giorgio A, Neilson E, Mayer AR, Stephen JM, Kwon JS, Yun JY, Cannon DM, McDonald C, Lebedeva I, Tomyshev AS, Akhadov T, Kaleda V, Fatouros-Bergman H, Flyckt L, Busatto GF, Rosa PGP, Serpa MH, Zanetti MV, Hoschl C, Skoch A, Spaniel F, Tomecek D, Hagenaars SP, McIntosh AM, Whalley HC, Lawrie SM, Knöchel C, Oertel-Knöchel V, Stäblein M, Howells FM, Stein DJ, Temmingh HS, Uhlmann A, Lopez-Jaramillo C, Dima D, McMahon A, Faskowitz JI, Gutman BA, Jahanshad N, Thompson PM, and Turner JA
Subjects: Adolescent, Adult, Age of Onset, Aged, Brain diagnostic imaging, Case-Control Studies, Child, Female, Frontal Lobe diagnostic imaging, Frontal Lobe pathology, Humans, Linear Models, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Prefrontal Cortex diagnostic imaging, Prefrontal Cortex pathology, Severity of Illness Index, Temporal Lobe diagnostic imaging, Temporal Lobe pathology, Young Adult, Brain pathology, Schizophrenia diagnostic imaging, Schizophrenia pathology
Abstract: 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., (Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2018
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48. Electroencephalographic delta/alpha frequency activity differentiates psychotic disorders: a study of schizophrenia, bipolar disorder and methamphetamine-induced psychotic disorder.

Author: Howells FM, Temmingh HS, Hsieh JH, van Dijen AV, Baldwin DS, and Stein DJ
Subjects: Adult, Biomarkers, Bipolar Disorder diagnosis, Case-Control Studies, Female, Humans, Male, Methamphetamine adverse effects, Psychoses, Substance-Induced diagnosis, Schizophrenia diagnosis, Young Adult, Alpha Rhythm, Bipolar Disorder physiopathology, Delta Rhythm, Psychoses, Substance-Induced physiopathology, Schizophrenia physiopathology
Abstract: Electroencephalography (EEG) has been proposed as a neurophysiological biomarker to delineate psychotic disorders. It is known that increased delta and decreased alpha, which are apparent in psychosis, are indicative of inappropriate arousal state, which leads to reduced ability to attend to relevant information. On this premise, we investigated delta/alpha frequency activity, as this ratio of frequency activity may serve as an effective neurophysiological biomarker. The current study investigated differences in delta/alpha frequency activity, in schizophrenia (SCZ), bipolar I disorder with psychotic features and methamphetamine-induced psychosis. One hundred and nine participants, including individuals with SCZ (n = 28), bipolar I disorder with psychotic features (n = 28), methamphetamine-induced psychotic disorder (MPD) (n = 24) and healthy controls (CON, n = 29). Diagnosis was ascertained with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition disorders and current medication was recorded. EEG was undertaken in three testing conditions: resting eyes open, resting eyes closed and during completion of a simple cognitive task (visual continuous performance task). EEG delta/alpha frequency activity was investigated across these conditions. First, delta/alpha frequency activity during resting eyes closed was higher in SCZ and MPD globally, when compared to CON, then lower for bipolar disorder (BPD) than MPD for right hemisphere. Second, delta/alpha frequency activity during resting eyes open was higher in SCZ, BPD and MPD for all electrodes, except left frontal, when compared to CON. Third, delta/alpha frequency activity during the cognitive task was higher in BPD and MPD for all electrodes, except left frontal, when compared to CON. Assessment of EEG delta/alpha frequency activity supports the delineation of underlying neurophysiological mechanisms present in psychotic disorders, which are likely related to dysfunctional thalamo-cortical connectivity. Delta/alpha frequency activity may provide a useful neurophysiological biomarker to delineate psychotic disorders.
Published: 2018
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49. Progress in neuroscience in Africa: editorial.

Author: Howells FM and Womersley JS
Subjects: Africa, Animals, Humans, Neurosciences, Research
Published: 2018
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50. The impact of acute and short-term methamphetamine abstinence on brain metabolites: A proton magnetic resonance spectroscopy chemical shift imaging study.

Author: Burger A, Brooks SJ, Stein DJ, and Howells FM
Subjects: Adolescent, Adult, Aspartic Acid metabolism, Choline metabolism, Creatine metabolism, Dipeptides metabolism, Glutamic Acid metabolism, Glutamine metabolism, Humans, Inositol metabolism, Magnetic Resonance Imaging, Male, Neurons metabolism, Young Adult, Amphetamine-Related Disorders metabolism, Brain metabolism, Methamphetamine, Proton Magnetic Resonance Spectroscopy
Abstract: Background: Abuse of methamphetamine (MA) is a global health concern. Previous 1 H-MRS studies have found that, with methamphetamine abstinence (MAA), there are changes in n-acetyl-aspartate (NAA/Cr), myo-inositol (mI/Cr), choline (Cho/Cr and Cho/NAA), and glutamate with glutamine (Glx) metabolites. Limited studies have investigated the effect of acute MAA, and acute-to-short-term MAA on brain metabolites., Methods: Adults with chronic MA dependence (n = 31) and healthy controls (n = 22) were recruited. Two-dimensional chemical shift 1 H-MRS imaging (TR2000 ms, TE30 ms) slice was performed and included voxels in bilateral anterior-cingulate (ACC), frontal-white-matter (FWM), and dorsolateral-prefrontal-cortices (DLPFC). Control participants were scanned once. The MA group was scanned twice, with acute (1.5 ± 0.6 weeks, n = 31) and short-term MAA (5.1 ± 0.8 weeks, n = 22). The change in 1 H-MRS metabolites over time (n = 19) was also investigated. Standard 1 H-MRS metabolites are reported relative to Cr + PCr., Results: Acute MAA showed lower n-acetyl-aspartate (NAA) and n-acetyl-aspartate with n-acetyl-aspartyl-glutamate (NAA + NAAG) in left DLPFC, and glycerophosphocholine with phosphocholine (GPC + PCh) in left FWM. Short-term MAA showed lower NAA + NAAG and higher myo-inositol (mI) in right ACC, lower NAA and NAA + NAAG in the left DLPFC, and lower GPC + PCh in left FWM. Over time, MAA showed decreased NAA and NAA + NAAG and increased mI in right ACC, decreased NAA and NAA + NAAG in right FWM, and decreased in mI in left FWM., Conclusion: In acute MAA, there was damage to the integrity of neuronal tissue, which was enhanced with short-term MAA. From acute to short-term MAA, activation of neuroinflammatory processes are suggested. This is the first 1 H-MRS study to report the development of neuroinflammation with loss of neuronal integrity in MAA., (Copyright © 2018 Elsevier B.V. All rights reserved.)
Published: 2018
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