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1. The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

Author: Weiner, MW, Aisen, P, Petersen, R, Jack, CR, Jagust, W, Trojanowki, JQ, Toga, AW, Beckett, L, Green, RC, Saykin, AJ, Morris, JC, Perrin, RJ, Shaw, LM, Khachaturian, Z, Carrillo, M, Potter, W, Barnes, L, Bernard, M, González, H, Ho, C, Hsiao, JK, Jackson, J, Masliah, E, Masterman, D, Okonkwo, O, Perrin, R, Ryan, L, Silverberg, N, Fleisher, A, Sacrey, DT, Fockler, J, Conti, C, Veitch, D, Neuhaus, J, Jin, C, Nosheny, R, Ashford, M, Flenniken, D, Kormos, A, Monine, T, Rafii, M, Raman, R, Jimenez, G, Donohue, M, Gessert, D, Salazar, J, Zimmerman, C, Cabrera, Y, Walter, S, Miller, G, Coker, G, Clanton, T, Hergesheimer, L, Smith, S, Adegoke, O, Mahboubi, P, Moore, S, Pizzola, J, Shaffer, E, Sloan, B, Harvey, D, Forghanian-Arani, A, Borowski, B, Ward, C, Schwarz, C, Jones, D, Gunter, J, Kantarci, K, Senjem, M, Vemuri, P, Reid, R, Fox, NC, Malone, I, Thompson, P, Thomopoulos, SI, Nir, TM, Jahanshad, N, DeCarli, C, Knaack, A, Fletcher, E, Tosun-Turgut, D, Chen, SR, Choe, M, Crawford, K, Yushkevich, PA, Das, S, Koeppe, RA, Reiman, EM, Chen, K, Mathis, C, Landau, S, Cairns, NJ, Householder, E, Franklin, E, Bernhardt, H, Taylor-Reinwald, L, Tojanowki, JQ, Korecka, M, and Figurski, M
Subjects: Biological Sciences, Genetics, Prevention, Human Genome, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Knowledge Bases, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Risk Factors, Alzheimer’s Disease Neuroimaging Initiative, Biological sciences, Biomedical and clinical sciences
Abstract: The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.
Published: 2022

2. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Author: Hettwer, MD, Larivière, S, Park, BY, van den Heuvel, OA, Schmaal, L, Andreassen, OA, Ching, CRK, Hoogman, M, Buitelaar, J, van Rooij, D, Veltman, DJ, Stein, DJ, Franke, B, van Erp, TGM, Jahanshad, N, Thompson, PM, Thomopoulos, SI, Bethlehem, RAI, Bernhardt, BC, Eickhoff, SB, and Valk, SL
Subjects: Mental Health, Brain Disorders, Neurosciences, Behavioral and Social Science, Mental health, Humans, Cerebral Cortex, Connectome, Mental Disorders, Cerebellum, Attention, Magnetic Resonance Imaging, ENIGMA ADHD Working Group, ENIGMA Autism Working Group, ENIGMA Bipolar Disorder Working Group, ENIGMA Major Depression Working Group, ENIGMA OCD Working Group, ENIGMA Schizophrenia Working Group
Abstract: Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.
Published: 2022

3. Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis

Author: Haas, SS, Ge, R, Agartz, I, Amminger, GP, Andreassen, OA, Bachman, P, Baeza, I, Choi, S, Colibazzi, T, Cropley, VL, de la Fuente-Sandoval, C, Ebdrup, BH, Fortea, A, Fusar-Poli, P, Glenthoj, BY, Glenthoj, LB, Haut, KM, Hayes, RA, Heekeren, K, Hooker, CI, Hwang, WJ, Jahanshad, N, Kaess, M, Kasai, K, Katagiri, N, Kim, M, Kindler, J, Koike, S, Kristensen, TD, Kwon, JS, Lawrie, SM, Lebedeva, I, Lee, J, Lemmers-Jansen, ILJ, Lin, A, Ma, X, Mathalon, DH, McGuire, P, Michel, C, Mizrahi, R, Mizuno, M, Moller, P, Mora-Duran, R, Nelson, B, Nemoto, T, Nordentoft, M, Nordholm, D, Omelchenko, MA, Pantelis, C, Pariente, JC, Raghava, J, Reyes-Madrigal, F, Rossberg, JI, Rossler, W, Salisbury, DF, Sasabayashi, D, Schall, U, Smigielski, L, Sugranyes, G, Suzuki, M, Takahashi, T, Tamnes, CK, Theodoridou, A, Thomopoulos, SI, Thompson, PM, Tomyshev, AS, Uhlhaas, PJ, Vaernes, TG, van Amelsvoort, TAMJ, van Erp, TGM, Waltz, JA, Wenneberg, C, Westlye, LT, Wood, SJ, Zhou, JH, Hernaus, D, Jalbrzikowski, M, Kahn, RS, Corcoran, CM, Frangou, S, Haas, SS, Ge, R, Agartz, I, Amminger, GP, Andreassen, OA, Bachman, P, Baeza, I, Choi, S, Colibazzi, T, Cropley, VL, de la Fuente-Sandoval, C, Ebdrup, BH, Fortea, A, Fusar-Poli, P, Glenthoj, BY, Glenthoj, LB, Haut, KM, Hayes, RA, Heekeren, K, Hooker, CI, Hwang, WJ, Jahanshad, N, Kaess, M, Kasai, K, Katagiri, N, Kim, M, Kindler, J, Koike, S, Kristensen, TD, Kwon, JS, Lawrie, SM, Lebedeva, I, Lee, J, Lemmers-Jansen, ILJ, Lin, A, Ma, X, Mathalon, DH, McGuire, P, Michel, C, Mizrahi, R, Mizuno, M, Moller, P, Mora-Duran, R, Nelson, B, Nemoto, T, Nordentoft, M, Nordholm, D, Omelchenko, MA, Pantelis, C, Pariente, JC, Raghava, J, Reyes-Madrigal, F, Rossberg, JI, Rossler, W, Salisbury, DF, Sasabayashi, D, Schall, U, Smigielski, L, Sugranyes, G, Suzuki, M, Takahashi, T, Tamnes, CK, Theodoridou, A, Thomopoulos, SI, Thompson, PM, Tomyshev, AS, Uhlhaas, PJ, Vaernes, TG, van Amelsvoort, TAMJ, van Erp, TGM, Waltz, JA, Wenneberg, C, Westlye, LT, Wood, SJ, Zhou, JH, Hernaus, D, Jalbrzikowski, M, Kahn, RS, Corcoran, CM, and Frangou, S
Abstract: IMPORTANCE: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals. OBJECTIVE: To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. DESIGN, SETTING, AND PARTICIPANTS: This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022. MAIN OUTCOMES AND MEASURES: For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores. RESULTS: Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD
Published: 2024

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

Author: Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, ENIGMA Major Depressive Disorder working group, Belov, V, Erwin-Grabner, T, Aghajani, M, Aleman, A, Amod, AR, Basgoze, Z, Benedetti, F, Besteher, B, Bülow, R, Ching, CRK, Connolly, CG, Cullen, K, Davey, CG, Dima, D, Dols, A, Evans, JW, Fu, CHY, Gonul, AS, Gotlib, IH, Grabe, HJ, Groenewold, N, Hamilton, JP, Harrison, BJ, Ho, TC, Mwangi, B, Jaworska, N, Jahanshad, N, Klimes-Dougan, B, Koopowitz, S-M, Lancaster, T, Li, M, Linden, DEJ, MacMaster, FP, Mehler, DMA, Melloni, E, Mueller, BA, Ojha, A, Oudega, ML, Penninx, BWJH, Poletti, S, Pomarol-Clotet, E, Portella, MJ, Pozzi, E, Reneman, L, Sacchet, MD, Sämann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Thomopoulos, SI, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJA, Völzke, H, Whittle, S, Wittfeld, K, Wright, MJ, Wu, M-J, Yang, TT, Zarate, C, Veltman, DJ, Schmaal, L, Thompson, PM, Goya-Maldonado, R, and ENIGMA Major Depressive Disorder working group
Abstract: Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.
Published: 2024

5. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Author: Jiang, Y, Luo, C, Wang, J, Palaniyappan, L, Chang, X, Xiang, S, Zhang, J, Duan, M, Huang, H, Gaser, C, Nemoto, K, Miura, K, Hashimoto, R, Westlye, LT, Richard, G, Fernandez-Cabello, S, Parker, N, Andreassen, OA, Kircher, T, Nenadić, I, Stein, F, Thomas-Odenthal, F, Teutenberg, L, Usemann, P, Dannlowski, U, Hahn, T, Grotegerd, D, Meinert, S, Lencer, R, Tang, Y, Zhang, T, Li, C, Yue, W, Zhang, Y, Yu, X, Zhou, E, Lin, C-P, Tsai, S-J, Rodrigue, AL, Glahn, D, Pearlson, G, Blangero, J, Karuk, A, Pomarol-Clotet, E, Salvador, R, Fuentes-Claramonte, P, Garcia-León, MÁ, Spalletta, G, Piras, F, Vecchio, D, Banaj, N, Cheng, J, Liu, Z, Yang, J, Gonul, AS, Uslu, O, Burhanoglu, BB, Uyar Demir, A, Rootes-Murdy, K, Calhoun, VD, Sim, K, Green, M, Quidé, Y, Chung, YC, Kim, W-S, Sponheim, SR, Demro, C, Ramsay, IS, Iasevoli, F, de Bartolomeis, A, Barone, A, Ciccarelli, M, Brunetti, A, Cocozza, S, Pontillo, G, Tranfa, M, Park, MTM, Kirschner, M, Georgiadis, F, Kaiser, S, Van Rheenen, TE, Rossell, SL, Hughes, M, Woods, W, Carruthers, SP, Sumner, P, Ringin, E, Spaniel, F, Skoch, A, Tomecek, D, Homan, P, Homan, S, Omlor, W, Cecere, G, Nguyen, DD, Preda, A, Thomopoulos, SI, Jahanshad, N, Cui, L-B, Yao, D, Thompson, PM, Turner, JA, van Erp, TGM, Cheng, W, ENIGMA Schizophrenia Consortium, Feng, J, ZIB Consortium, Jiang, Y, Luo, C, Wang, J, Palaniyappan, L, Chang, X, Xiang, S, Zhang, J, Duan, M, Huang, H, Gaser, C, Nemoto, K, Miura, K, Hashimoto, R, Westlye, LT, Richard, G, Fernandez-Cabello, S, Parker, N, Andreassen, OA, Kircher, T, Nenadić, I, Stein, F, Thomas-Odenthal, F, Teutenberg, L, Usemann, P, Dannlowski, U, Hahn, T, Grotegerd, D, Meinert, S, Lencer, R, Tang, Y, Zhang, T, Li, C, Yue, W, Zhang, Y, Yu, X, Zhou, E, Lin, C-P, Tsai, S-J, Rodrigue, AL, Glahn, D, Pearlson, G, Blangero, J, Karuk, A, Pomarol-Clotet, E, Salvador, R, Fuentes-Claramonte, P, Garcia-León, MÁ, Spalletta, G, Piras, F, Vecchio, D, Banaj, N, Cheng, J, Liu, Z, Yang, J, Gonul, AS, Uslu, O, Burhanoglu, BB, Uyar Demir, A, Rootes-Murdy, K, Calhoun, VD, Sim, K, Green, M, Quidé, Y, Chung, YC, Kim, W-S, Sponheim, SR, Demro, C, Ramsay, IS, Iasevoli, F, de Bartolomeis, A, Barone, A, Ciccarelli, M, Brunetti, A, Cocozza, S, Pontillo, G, Tranfa, M, Park, MTM, Kirschner, M, Georgiadis, F, Kaiser, S, Van Rheenen, TE, Rossell, SL, Hughes, M, Woods, W, Carruthers, SP, Sumner, P, Ringin, E, Spaniel, F, Skoch, A, Tomecek, D, Homan, P, Homan, S, Omlor, W, Cecere, G, Nguyen, DD, Preda, A, Thomopoulos, SI, Jahanshad, N, Cui, L-B, Yao, D, Thompson, PM, Turner, JA, van Erp, TGM, Cheng, W, ENIGMA Schizophrenia Consortium, Feng, J, and ZIB Consortium
Abstract: Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
Published: 2024

6. 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

7. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author: Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, Van der Wee, NJA, Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, and Van der Wee, NJA
Abstract: There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.
Published: 2023

8. Progressive Spinal Cord Degeneration in Friedreich's Ataxia: Results from ENIGMA-Ataxia.

Author: Rezende, TJR, Adanyeguh, IM, Arrigoni, F, Bender, B, Cendes, F, Corben, LA, Deistung, A, Delatycki, M, Dogan, I, Egan, GF, Göricke, SL, Georgiou-Karistianis, N, Henry, P-G, Hutter, D, Jahanshad, N, Joers, JM, Lenglet, C, Lindig, T, Martinez, ARM, Martinuzzi, A, Paparella, G, Peruzzo, D, Reetz, K, Romanzetti, S, Schöls, L, Schulz, JB, Synofzik, M, Thomopoulos, SI, Thompson, PM, Timmann, D, Harding, IH, França, MC, Rezende, TJR, Adanyeguh, IM, Arrigoni, F, Bender, B, Cendes, F, Corben, LA, Deistung, A, Delatycki, M, Dogan, I, Egan, GF, Göricke, SL, Georgiou-Karistianis, N, Henry, P-G, Hutter, D, Jahanshad, N, Joers, JM, Lenglet, C, Lindig, T, Martinez, ARM, Martinuzzi, A, Paparella, G, Peruzzo, D, Reetz, K, Romanzetti, S, Schöls, L, Schulz, JB, Synofzik, M, Thomopoulos, SI, Thompson, PM, Timmann, D, Harding, IH, and França, MC
Published: 2023

9. Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group

Author: Abé, C, Ching, CRK, Liberg, B, Lebedev, AV, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Benedetti, F, Berk, Michael, Bøen, E, Bonnin, CDM, Breuer, F, Brosch, K, Brouwer, RM, Canales-Rodríguez, EJ, Cannon, DM, Chye, Y, Dahl, A, Dandash, O, Dannlowski, U, Dohm, K, Elvsåshagen, T, Fisch, L, Fullerton, JM, Goikolea, JM, Grotegerd, D, Haatveit, B, Hahn, T, Hajek, T, Heindel, W, Ingvar, M, Sim, K, Kircher, TTJ, Lenroot, RK, Malt, UF, McDonald, C, McWhinney, SR, Melle, I, Meller, T, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Opel, N, Overs, BJ, Panicalli, F, Pfarr, JK, Poletti, S, Pomarol-Clotet, E, Radua, J, Repple, J, Ringwald, KG, Roberts, G, Rodriguez-Cano, E, Salvador, R, Sarink, K, Sarró, S, Schmitt, S, Stein, F, Suo, C, Thomopoulos, SI, Tronchin, G, Vieta, E, Westlye, LT, White, AG, Yatham, LN, Zak, N, Thompson, PM, Andreassen, OA, Landén, M, Abé, C, Ching, CRK, Liberg, B, Lebedev, AV, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Benedetti, F, Berk, Michael, Bøen, E, Bonnin, CDM, Breuer, F, Brosch, K, Brouwer, RM, Canales-Rodríguez, EJ, Cannon, DM, Chye, Y, Dahl, A, Dandash, O, Dannlowski, U, Dohm, K, Elvsåshagen, T, Fisch, L, Fullerton, JM, Goikolea, JM, Grotegerd, D, Haatveit, B, Hahn, T, Hajek, T, Heindel, W, Ingvar, M, Sim, K, Kircher, TTJ, Lenroot, RK, Malt, UF, McDonald, C, McWhinney, SR, Melle, I, Meller, T, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Opel, N, Overs, BJ, Panicalli, F, Pfarr, JK, Poletti, S, Pomarol-Clotet, E, Radua, J, Repple, J, Ringwald, KG, Roberts, G, Rodriguez-Cano, E, Salvador, R, Sarink, K, Sarró, S, Schmitt, S, Stein, F, Suo, C, Thomopoulos, SI, Tronchin, G, Vieta, E, Westlye, LT, White, AG, Yatham, LN, Zak, N, Thompson, PM, Andreassen, OA, and Landén, M
Published: 2022

10. 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

11. Intracranial and subcortical volumes in adolescents with early-onset psychosis: A multisite mega-analysis from the ENIGMA consortium

Author: Gurholt, TP, Lonning, V, Nerland, S, Jørgensen, KN, Haukvik, UK, Alloza, C, Arango, C, Barth, C, Bearden, CE, Berk, Michael, Bohman, H, Dandash, O, Díaz-Caneja, CM, Edbom, CT, van Erp, TGM, Fett, AKJ, Frangou, S, Goldstein, BI, Grigorian, A, Jahanshad, N, James, AC, Janssen, J, Johannessen, C, Karlsgodt, KH, Kempton, MJ, Kochunov, P, Krabbendam, L, Kyriakopoulos, M, Lundberg, M, MacIntosh, BJ, Rund, BR, Smelror, RE, Sultan, A, Tamnes, CK, Thomopoulos, SI, Vajdi, A, Wedervang-Resell, K, Myhre, AM, Andreassen, OA, Thompson, PM, Agartz, I, Gurholt, TP, Lonning, V, Nerland, S, Jørgensen, KN, Haukvik, UK, Alloza, C, Arango, C, Barth, C, Bearden, CE, Berk, Michael, Bohman, H, Dandash, O, Díaz-Caneja, CM, Edbom, CT, van Erp, TGM, Fett, AKJ, Frangou, S, Goldstein, BI, Grigorian, A, Jahanshad, N, James, AC, Janssen, J, Johannessen, C, Karlsgodt, KH, Kempton, MJ, Kochunov, P, Krabbendam, L, Kyriakopoulos, M, Lundberg, M, MacIntosh, BJ, Rund, BR, Smelror, RE, Sultan, A, Tamnes, CK, Thomopoulos, SI, Vajdi, A, Wedervang-Resell, K, Myhre, AM, Andreassen, OA, Thompson, PM, and Agartz, I
Published: 2022

12. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author: Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, Hulshoff Pol, HE, Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, and Hulshoff Pol, HE
Abstract: Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.
Published: 2022

13. An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration

Author: van den Heuvel, OA, Boedhoe, PSW, Bertolin, S, Bruin, WB, Francks, C, Ivanov, I, Jahanshad, N, Kong, X-Z, Kwon, JS, O'Neill, J, Paus, T, Patel, Y, Piras, F, Schmaal, L, Soriano-Mas, C, Spalletta, G, van Wingen, GA, Yun, J-Y, Vriend, C, Simpson, HB, van Rooij, D, Hoexter, MQ, Hoogman, M, Buitelaar, JK, Arnold, P, Beucke, JC, Benedetti, F, Bollettini, I, Bose, A, Brennan, BP, De Nadai, AS, Fitzgerald, K, Gruner, P, Gruenblatt, E, Hirano, Y, Huyser, C, James, A, Koch, K, Kvale, G, Lazaro, L, Lochner, C, Marsh, R, Mataix-Cols, D, Morgado, P, Nakamae, T, Nakao, T, Narayanaswamy, JC, Nurmi, E, Pittenger, C, Reddy, YCJ, Sato, JR, Soreni, N, Stewart, SE, Taylor, SF, Tolin, D, Thomopoulos, SI, Veltman, DJ, Venkatasubramanian, G, Walitza, S, Wang, Z, Thompson, PM, Stein, DJ, van den Heuvel, OA, Boedhoe, PSW, Bertolin, S, Bruin, WB, Francks, C, Ivanov, I, Jahanshad, N, Kong, X-Z, Kwon, JS, O'Neill, J, Paus, T, Patel, Y, Piras, F, Schmaal, L, Soriano-Mas, C, Spalletta, G, van Wingen, GA, Yun, J-Y, Vriend, C, Simpson, HB, van Rooij, D, Hoexter, MQ, Hoogman, M, Buitelaar, JK, Arnold, P, Beucke, JC, Benedetti, F, Bollettini, I, Bose, A, Brennan, BP, De Nadai, AS, Fitzgerald, K, Gruner, P, Gruenblatt, E, Hirano, Y, Huyser, C, James, A, Koch, K, Kvale, G, Lazaro, L, Lochner, C, Marsh, R, Mataix-Cols, D, Morgado, P, Nakamae, T, Nakao, T, Narayanaswamy, JC, Nurmi, E, Pittenger, C, Reddy, YCJ, Sato, JR, Soreni, N, Stewart, SE, Taylor, SF, Tolin, D, Thomopoulos, SI, Veltman, DJ, Venkatasubramanian, G, Walitza, S, Wang, Z, Thompson, PM, and Stein, DJ
Abstract: Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.
Published: 2022

14. The Enhancing NeuroImaging Genetics through Meta-Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping

Author: Thompson, PM, Jahanshad, N, Schmaal, L, Turner, JA, Winkler, AM, Thomopoulos, SI, Egan, GF, Kochunov, P, Thompson, PM, Jahanshad, N, Schmaal, L, Turner, JA, Winkler, AM, Thomopoulos, SI, Egan, GF, and Kochunov, P
Abstract: This Special Issue of Human Brain Mapping is dedicated to a 10-year anniversary of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large-scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task-based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well-powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large-scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke.
Published: 2022

15. 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

16. ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders.

Author: Bas-Hoogendam, JM, Groenewold, NA, Aghajani, M, Freitag, GF, Harrewijn, A, Hilbert, K, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Veltman, DJ, Winkler, AM, Lueken, U, Pine, DS, van der Wee, NJA, Stein, DJ, ENIGMA-Anxiety Working Group, Bas-Hoogendam, JM, Groenewold, NA, Aghajani, M, Freitag, GF, Harrewijn, A, Hilbert, K, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Veltman, DJ, Winkler, AM, Lueken, U, Pine, DS, van der Wee, NJA, Stein, DJ, and ENIGMA-Anxiety Working Group
Abstract: Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.
Published: 2022

17. Intracranial and subcortical volumes in adolescents withearly-onsetpsychosis: A multisitemega-analysisfrom theENIGMAconsortium

Author: Gurholt, TP, Lonning, V, Nerland, S, Jorgensen, KN, Haukvik, UK, Alloza, C, Arango, C, Barth, C, Bearden, CE, Berk, M, Bohman, H, Dandash, O, Diaz-Caneja, CM, Edbom, CT, van Erp, TGM, Fett, A-KJ, Frangou, S, Goldstein, BI, Grigorian, A, Jahanshad, N, James, AC, Janssen, J, Johannessen, C, Karlsgodt, KH, Kempton, MJ, Kochunov, P, Krabbendam, L, Kyriakopoulos, M, Lundberg, M, MacIntosh, BJ, Rund, BR, Smelror, RE, Sultan, A, Tamnes, CK, Thomopoulos, SI, Vajdi, A, Wedervang-Resell, K, Myhre, AM, Andreassen, OA, Thompson, PM, Agartz, I, Gurholt, TP, Lonning, V, Nerland, S, Jorgensen, KN, Haukvik, UK, Alloza, C, Arango, C, Barth, C, Bearden, CE, Berk, M, Bohman, H, Dandash, O, Diaz-Caneja, CM, Edbom, CT, van Erp, TGM, Fett, A-KJ, Frangou, S, Goldstein, BI, Grigorian, A, Jahanshad, N, James, AC, Janssen, J, Johannessen, C, Karlsgodt, KH, Kempton, MJ, Kochunov, P, Krabbendam, L, Kyriakopoulos, M, Lundberg, M, MacIntosh, BJ, Rund, BR, Smelror, RE, Sultan, A, Tamnes, CK, Thomopoulos, SI, Vajdi, A, Wedervang-Resell, K, Myhre, AM, Andreassen, OA, Thompson, PM, and Agartz, I
Abstract: Early-onset psychosis disorders are serious mental disorders arising before the age of 18 years. Here, we investigate the largest neuroimaging dataset, to date, of patients with early-onset psychosis and healthy controls for differences in intracranial and subcortical brain volumes. The sample included 263 patients with early-onset psychosis (mean age: 16.4 ± 1.4 years, mean illness duration: 1.5 ± 1.4 years, 39.2% female) and 359 healthy controls (mean age: 15.9 ± 1.7 years, 45.4% female) with magnetic resonance imaging data, pooled from 11 clinical cohorts. Patients were diagnosed with early-onset schizophrenia (n = 183), affective psychosis (n = 39), or other psychotic disorders (n = 41). We used linear mixed-effects models to investigate differences in intracranial and subcortical volumes across the patient sample, diagnostic subgroup and antipsychotic medication, relative to controls. We observed significantly lower intracranial (Cohen's d = -0.39) and hippocampal (d = -0.25) volumes, and higher caudate (d = 0.25) and pallidum (d = 0.24) volumes in patients relative to controls. Intracranial volume was lower in both early-onset schizophrenia (d = -0.34) and affective psychosis (d = -0.42), and early-onset schizophrenia showed lower hippocampal (d = -0.24) and higher pallidum (d = 0.29) volumes. Patients who were currently treated with antipsychotic medication (n = 193) had significantly lower intracranial volume (d = -0.42). The findings demonstrate a similar pattern of brain alterations in early-onset psychosis as previously reported in adult psychosis, but with notably low intracranial volume. The low intracranial volume suggests disrupted neurodevelopment in adolescent early-onset psychosis.
Published: 2022

18. The ENIGMA-Epilepsy working group: Mapping disease from large data sets

Author: Sisodiya, SM, Whelan, CD, Hatton, SN, Huynh, K, Altmann, A, Ryten, M, Vezzani, A, Caligiuri, ME, Labate, A, Gambardella, A, Ives-Deliperi, V, Meletti, S, Munsell, BC, Bonilha, L, Tondelli, M, Rebsamen, M, Rummel, C, Vaudano, AE, Wiest, R, Balachandra, AR, Bargallo, N, Bartolini, E, Bernasconi, A, Bernasconi, N, Bernhardt, B, Caldairou, B, Carr, SJA, Cavalleri, GL, Cendes, F, Concha, L, Desmond, PM, Domin, M, Duncan, JS, Focke, NK, Guerrini, R, Hamandi, K, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kowalczyk, MA, Kreilkamp, BAK, Kwan, P, Lariviere, S, Lenge, M, Lopez, SM, Martin, P, Mascalchi, M, Moreira, JCV, Morita-Sherman, ME, Pardoe, HR, Pariente, JC, Raviteja, K, Rocha, CS, Rodriguez-Cruces, R, Seeck, M, Semmelroch, MKHG, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Thomopoulos, SI, Velakoulis, D, Vivash, L, Weber, B, Yasuda, CL, Zhang, J, Thompson, PM, McDonald, CR, Sisodiya, SM, Whelan, CD, Hatton, SN, Huynh, K, Altmann, A, Ryten, M, Vezzani, A, Caligiuri, ME, Labate, A, Gambardella, A, Ives-Deliperi, V, Meletti, S, Munsell, BC, Bonilha, L, Tondelli, M, Rebsamen, M, Rummel, C, Vaudano, AE, Wiest, R, Balachandra, AR, Bargallo, N, Bartolini, E, Bernasconi, A, Bernasconi, N, Bernhardt, B, Caldairou, B, Carr, SJA, Cavalleri, GL, Cendes, F, Concha, L, Desmond, PM, Domin, M, Duncan, JS, Focke, NK, Guerrini, R, Hamandi, K, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kowalczyk, MA, Kreilkamp, BAK, Kwan, P, Lariviere, S, Lenge, M, Lopez, SM, Martin, P, Mascalchi, M, Moreira, JCV, Morita-Sherman, ME, Pardoe, HR, Pariente, JC, Raviteja, K, Rocha, CS, Rodriguez-Cruces, R, Seeck, M, Semmelroch, MKHG, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Thomopoulos, SI, Velakoulis, D, Vivash, L, Weber, B, Yasuda, CL, Zhang, J, Thompson, PM, and McDonald, CR
Abstract: Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.
Published: 2022

19. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities.

Author: Hansen, JY, Shafiei, G, Vogel, JW, Smart, K, Bearden, CE, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, McDonald, CR, Sisodiya, SM, Schmaal, L, Veltman, DJ, van den Heuvel, OA, Stein, DJ, van Erp, TGM, Ching, CRK, Andreassen, OA, Hajek, T, Opel, N, Modinos, G, Aleman, A, van der Werf, Y, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Carson, RE, Dagher, A, Misic, B, Hansen, JY, Shafiei, G, Vogel, JW, Smart, K, Bearden, CE, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, McDonald, CR, Sisodiya, SM, Schmaal, L, Veltman, DJ, van den Heuvel, OA, Stein, DJ, van Erp, TGM, Ching, CRK, Andreassen, OA, Hajek, T, Opel, N, Modinos, G, Aleman, A, van der Werf, Y, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Carson, RE, Dagher, A, and Misic, B
Abstract: Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
Published: 2022

20. 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

21. Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group

Author: Abé, C, Ching, CRK, Liberg, B, Lebedev, AV, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Benedetti, F, Berk, M, Bøen, E, Bonnin, CDM, Breuer, F, Brosch, K, Brouwer, RM, Canales-Rodríguez, EJ, Cannon, DM, Chye, Y, Dahl, A, Dandash, O, Dannlowski, U, Dohm, K, Elvsåshagen, T, Fisch, L, Fullerton, JM, Goikolea, JM, Grotegerd, D, Haatveit, B, Hahn, T, Hajek, T, Heindel, W, Ingvar, M, Sim, K, Kircher, TTJ, Lenroot, RK, Malt, UF, McDonald, C, McWhinney, SR, Melle, I, Meller, T, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Opel, N, Overs, BJ, Panicalli, F, Pfarr, J-K, Poletti, S, Pomarol-Clotet, E, Radua, J, Repple, J, Ringwald, KG, Roberts, G, Rodriguez-Cano, E, Salvador, R, Sarink, K, Sarró, S, Schmitt, S, Stein, F, Suo, C, Thomopoulos, SI, Tronchin, G, Vieta, E, Westlye, LT, White, AG, Yatham, LN, Zak, N, Thompson, PM, Andreassen, OA, Landén, M, Abé, C, Ching, CRK, Liberg, B, Lebedev, AV, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Benedetti, F, Berk, M, Bøen, E, Bonnin, CDM, Breuer, F, Brosch, K, Brouwer, RM, Canales-Rodríguez, EJ, Cannon, DM, Chye, Y, Dahl, A, Dandash, O, Dannlowski, U, Dohm, K, Elvsåshagen, T, Fisch, L, Fullerton, JM, Goikolea, JM, Grotegerd, D, Haatveit, B, Hahn, T, Hajek, T, Heindel, W, Ingvar, M, Sim, K, Kircher, TTJ, Lenroot, RK, Malt, UF, McDonald, C, McWhinney, SR, Melle, I, Meller, T, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadić, I, Opel, N, Overs, BJ, Panicalli, F, Pfarr, J-K, Poletti, S, Pomarol-Clotet, E, Radua, J, Repple, J, Ringwald, KG, Roberts, G, Rodriguez-Cano, E, Salvador, R, Sarink, K, Sarró, S, Schmitt, S, Stein, F, Suo, C, Thomopoulos, SI, Tronchin, G, Vieta, E, Westlye, LT, White, AG, Yatham, LN, Zak, N, Thompson, PM, Andreassen, OA, and Landén, M
Published: 2021

22. 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, Silk, Timothy, 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, and Silk, Timothy
Published: 2021

23. 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

24. 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

25. The genetic architecture of the human cerebral cortex

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

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

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

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

Author: Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, Veltman, DJ, Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, and Veltman, DJ
Abstract: A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.
Published: 2020

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

Author: García-Marín LM, Campos AI, Diaz-Torres S, Rabinowitz JA, Ceja Z, Mitchell BL, Grasby KL, Thorp JG, Agartz I, Alhusaini S, Ames D, Amouyel P, Andreassen OA, Arfanakis K, Arias-Vasquez A, Armstrong NJ, Athanasiu L, Bastin ME, Beiser AS, Bennett DA, Bis JC, Boks MPM, Boomsma DI, Brodaty H, Brouwer RM, Buitelaar JK, Burkhardt R, Cahn W, Calhoun VD, Carmichael OT, Chakravarty M, Chen Q, Ching CRK, Cichon S, Crespo-Facorro B, Crivello F, Dale AM, Smith GD, de Geus EJC, De Jager PL, de Zubicaray GI, Debette S, DeCarli C, Depondt C, Desrivières S, Djurovic S, Ehrlich S, Erk S, Espeseth T, Fernández G, Filippi I, Fisher SE, Fleischman DA, Fletcher E, Fornage M, Forstner AJ, Francks C, Franke B, Ge T, Goldman AL, Grabe HJ, Green RC, Grimm O, Groenewold NA, Gruber O, Gudnason V, Håberg AK, Haukvik UK, Heinz A, Hibar DP, Hilal S, Himali JJ, Ho BC, Hoehn DF, Hoekstra PJ, Hofer E, Hoffmann W, Holmes AJ, Homuth G, Hosten N, Ikram MK, Ipser JC, Jack CR Jr, Jahanshad N, Jönsson EG, Kahn RS, Kanai R, Klein M, Knol MJ, Launer LJ, Lawrie SM, Hellard SL, Lee PH, Lemaître H, Li S, Liewald DCM, Lin H, Longstreth WT Jr, Lopez OL, Luciano M, Maillard P, Marquand AF, Martin NG, Martinot JL, Mather KA, Mattay VS, McMahon KL, Mecocci P, Melle I, Meyer-Lindenberg A, Mirza-Schreiber N, Milaneschi Y, Mosley TH, Mühleisen TW, Müller-Myhsok B, Maniega SM, Nauck M, Nho K, Niessen WJ, Nöthen MM, Nyquist PA, Oosterlaan J, Pandolfo M, Paus T, Pausova Z, Penninx BWJH, Pike GB, Psaty BM, Pütz B, Reppermund S, Rietschel MD, Risacher SL, Romanczuk-Seiferth N, Romero-Garcia R, Roshchupkin GV, Rotter JI, Sachdev PS, Sämann PG, Saremi A, Sargurupremraj M, Saykin AJ, Schmaal L, Schmidt H, Schmidt R, Schofield PR, Scholz M, Schumann G, Schwarz E, Shen L, Shin J, Sisodiya SM, Smith AV, Smoller JW, Soininen HS, Steen VM, Stein DJ, Stein JL, Thomopoulos SI, Toga AW, Tordesillas-Gutiérrez D, Trollor JN, Valdes-Hernandez MC, van T Ent D, van Bokhoven H, van der Meer D, van der Wee NJA, Vázquez-Bourgon J, Veltman DJ, Vernooij MW, Villringer A, Vinke LN, Völzke H, Walter H, Wardlaw JM, Weinberger DR, Weiner MW, Wen W, Westlye LT, Westman E, White T, Witte AV, Wolf C, Yang J, Zwiers MP, Ikram MA, Seshadri S, Thompson PM, Satizabal CL, Medland SE, and Rentería ME
Subjects: Humans, Female, Male, Organ Size genetics, Attention Deficit Disorder with Hyperactivity genetics, Attention Deficit Disorder with Hyperactivity pathology, Parkinson Disease genetics, Parkinson Disease pathology, Polymorphism, Single Nucleotide, Genomics methods, Adult, Genetic Predisposition to Disease, Middle Aged, White People genetics, Genome-Wide Association Study, Multifactorial Inheritance genetics, Brain pathology
Abstract: Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Published: 2024
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30. The Pattern and Staging of Brain Atrophy in Spinocerebellar Ataxia Type 2 (SCA2): MRI Volumetrics from ENIGMA-Ataxia.

Author: Robertson JW, Adanyeguh I, Bender B, Boesch S, Brunetti A, Cocozza S, Coutinho L, Deistung A, Diciotti S, Dogan I, Durr A, Fernandez-Ruiz J, Göricke SL, Grisoli M, Han S, Mariotti C, Marzi C, Mascalchi M, Mochel F, Nachbauer W, Nanetti L, Nigri A, Ono SE, Onyike CU, Prince JL, Reetz K, Romanzetti S, Saccà F, Synofzik M, Ghizoni Teive HA, Thomopoulos SI, Thompson PM, Timmann D, Ying SH, Harding IH, and Hernandez-Castillo CR
Abstract: Objective: Spinocerebellar ataxia type 2 (SCA2) is a rare, inherited neurodegenerative disease characterised by progressive deterioration in both motor coordination and cognitive function. Atrophy of the cerebellum, brainstem, and spinal cord are core features of SCA2, however the evolution and pattern of whole-brain atrophy in SCA2 remain unclear. We undertook a multi-site, structural magnetic resonance imaging (MRI) study to comprehensively characterize the neurodegeneration profile of SCA2., Methods: Voxel-based morphometry analyses of 110 participants with SCA2 and 128 controls were undertaken to assess groupwise differences in whole-brain volume. Correlations with clinical severity and genotype, and cross-sectional profiling of atrophy patterns at different disease stages, were also performed., Results: Atrophy in SCA2 relative to controls was greatest (Cohen's d >2.5) in the cerebellar white matter (WM), middle cerebellar peduncle, pons, and corticospinal tract. Very large effects ( d >1.5) were also evident in the superior cerebellar, inferior cerebellar, and cerebral peduncles. In cerebellar grey matter (GM), large effects ( d >0.8) mapped to areas related to both motor coordination and cognitive tasks. Strong correlations (| r |>0.4) between volume and disease severity largely mirrored these groupwise outcomes. Stratification by disease severity showed a degeneration pattern beginning in cerebellar and pontine WM in pre-clinical subjects; spreading to the cerebellar GM and cerebro-cerebellar/corticospinal WM tracts; then finally involving the thalamus, striatum, and cortex in severe stages., Interpretation: The magnitude and pattern of brain atrophy evolves over the course of SCA2, with widespread, non-uniform involvement across the brainstem, cerebellar tracts, and cerebellar cortex; and late involvement of the cerebral cortex and striatum., Competing Interests: Potential Conflicts of Interest The authors have no conflicts of interest to declare with respect to this study.
Published: 2024
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31. Comparison of Explainable AI Models for MRI-based Alzheimer's Disease Classification.

Author: Chattopadhyay T, Joshy NA, Jagad C, Gleave EJ, Thomopoulos SI, Feng Y, Villalón-Reina JE, Laltoo E, Joshi H, Venkatasubramanian G, John JP, Ver Steeg G, Ambite JL, and Thompson PM
Abstract: Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from 3D T1-weighted brain MRI scans. Here, we examine the value of adding occlusion sensitivity analysis (OSA) and gradient-weighted class activation mapping (Grad-CAM) to these models to make the results more interpretable. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) or National Alzheimer's Coordinating Center (NACC), which assess people of North American, predominantly European ancestry, so we examine how well models trained on these data generalize to a new population dataset from India (NIMHANS cohort). We also evaluate the benefit of using a combined dataset to train the CNN models. Our experiments show feature localization consistent with knowledge of AD from other methods. OSA and Grad-CAM resolve features at different scales to help interpret diagnostic inferences made by CNNs.
Published: 2024
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32. A worldwide study of white matter microstructural alterations in people living with Parkinson's disease.

Author: Owens-Walton C, Nir TM, Al-Bachari S, Ambrogi S, Anderson TJ, Aventurato ÍK, Cendes F, Chen YL, Ciullo V, Cook P, Dalrymple-Alford JC, Dirkx MF, Druzgal J, Emsley HCA, Guimarães R, Haroon HA, Helmich RC, Hu MT, Johansson ME, Kim HB, Klein JC, Laansma M, Lawrence KE, Lochner C, Mackay C, McMillan CT, Melzer TR, Nabulsi L, Newman B, Opriessnig P, Parkes LM, Pellicano C, Piras F, Piras F, Pirpamer L, Pitcher TL, Poston KL, Roos A, Silva LS, Schmidt R, Schwingenschuh P, Shahid-Besanti M, Spalletta G, Stein DJ, Thomopoulos SI, Tosun D, Tsai CC, van den Heuvel OA, van Heese E, Vecchio D, Villalón-Reina JE, Vriend C, Wang JJ, Wu YR, Yasuda CL, Thompson PM, Jahanshad N, and van der Werf Y
Abstract: The progression of Parkinson's disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20-89 years; 33% female) and 885 controls (age: 19-84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen's d effect sizes reached d = -1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder., (© 2024. The Author(s).)
Published: 2024
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33. Amyloid, Tau, and APOE in Alzheimer's Disease: Impact on White Matter Tracts.

Author: Chandio BQ, Villalon-Reina JE, Nir TM, Thomopoulos SI, Feng Y, Benavidez S, Jahanshad N, Harezlak J, Garyfallidis E, and Thompson PM
Abstract: Alzheimer's disease (AD) is characterized by cognitive decline and memory loss due to the abnormal accumulation of amyloid-beta (A β ) plaques and tau tangles in the brain; its onset and progression also depend on genetic factors such as the apolipoprotein E (APOE) genotype. Understanding how these factors affect the brain's neural pathways is important for early diagnostics and interventions. Tractometry is an advanced technique for 3D quantitative assessment of white matter tracts, localizing microstructural abnormalities in diseased populations in vivo . In this work, we applied BUAN (Bundle Analytics) tractometry to 3D diffusion MRI data from 730 participants in ADNI3 (phase 3 of the Alzheimer's Disease Neuroimaging Initiative; age range: 55-95 years, 349M/381F, 214 with mild cognitive impairment, 69 with AD, and 447 cognitively healthy controls). Using along-tract statistical analysis, we assessed the localized impact of amyloid, tau, and APOE genetic variants on the brain's neural pathways. BUAN quantifies microstructural properties of white matter tracts, supporting along-tract statistical analyses that identify factors associated with brain microstructure. We visualize the 3D profile of white matter tract associations with tau and amyloid burden in Alzheimer's disease; strong associations near the cortex may support models of disease propagation along neural pathways. Relative to the neutral genotype, APOE ϵ 3/ ϵ 3, carriers of the AD-risk conferring APOE ϵ 4 genotype show microstructural abnormalities, while carriers of the protective ϵ 2 genotype also show subtle differences. Of all the microstructural metrics, mean diffusivity (MD) generally shows the strongest associations with AD pathology, followed by axial diffusivity (AxD) and radial diffusivity (RD), while fractional anisotropy (FA) is typically the least sensitive metric. Along-tract microstructural metrics are sensitive to tau and amyloid accumulation, showing the potential of diffusion MRI to track AD pathology and map its impact on neural pathways.
Published: 2024
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34. Large-scale analysis of structural brain asymmetries during neurodevelopment: Associations with age and sex in 4265 children and adolescents.

Author: Kurth F, Schijven D, van den Heuvel OA, Hoogman M, van Rooij D, Stein DJ, Buitelaar JK, Bölte S, Auzias G, Kushki A, Venkatasubramanian G, Rubia K, Bollmann S, Isaksson J, Jaspers-Fayer F, Marsh R, Batistuzzo MC, Arnold PD, Bressan RA, Stewart SE, Gruner P, Sorensen L, Pan PM, Silk TJ, Gur RC, Cubillo AI, Haavik J, O'Gorman Tuura RL, Hartman CA, Calvo R, McGrath J, Calderoni S, Jackowski A, Chantiluke KC, Satterthwaite TD, Busatto GF, Nigg JT, Gur RE, Retico A, Tosetti M, Gallagher L, Szeszko PR, Neufeld J, Ortiz AE, Ghisleni C, Lazaro L, Hoekstra PJ, Anagnostou E, Hoekstra L, Simpson B, Plessen JK, Deruelle C, Soreni N, James A, Narayanaswamy J, Reddy JY, Fitzgerald J, Bellgrove MA, Salum GA, Janssen J, Muratori F, Vila M, Giral MG, Ameis SH, Bosco P, Remnélius KL, Huyser C, Pariente JC, Jalbrzikowski M, Rosa PG, O'Hearn KM, Ehrlich S, Mollon J, Zugman A, Christakou A, Arango C, Fisher SE, Kong X, Franke B, Medland SE, Thomopoulos SI, Jahanshad N, Glahn DC, Thompson PM, Francks C, and Luders E
Subjects: Humans, Adolescent, Male, Child, Female, Child, Preschool, Infant, Age Factors, Child Development physiology, Functional Laterality physiology, Adolescent Development physiology, Sex Characteristics, Magnetic Resonance Imaging, Brain diagnostic imaging, Brain growth & development, Brain anatomy & histology
Abstract: Only a small number of studies have assessed structural differences between the two hemispheres during childhood and adolescence. However, the existing findings lack consistency or are restricted to a particular brain region, a specific brain feature, or a relatively narrow age range. Here, we investigated associations between brain asymmetry and age as well as sex in one of the largest pediatric samples to date (n = 4265), aged 1-18 years, scanned at 69 sites participating in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our study revealed that significant brain asymmetries already exist in childhood, but their magnitude and direction depend on the brain region examined and the morphometric measurement used (cortical volume or thickness, regional surface area, or subcortical volume). With respect to effects of age, some asymmetries became weaker over time while others became stronger; sometimes they even reversed direction. With respect to sex differences, the total number of regions exhibiting significant asymmetries was larger in females than in males, while the total number of measurements indicating significant asymmetries was larger in males (as we obtained more than one measurement per cortical region). The magnitude of the significant asymmetries was also greater in males. However, effect sizes for both age effects and sex differences were small. Taken together, these findings suggest that cerebral asymmetries are an inherent organizational pattern of the brain that manifests early in life. Overall, brain asymmetry appears to be relatively stable throughout childhood and adolescence, with some differential effects in males and females., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2024
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35. Cortical and Subcortical Brain Alterations in Specific Phobia and Its Animal and Blood-Injection-Injury Subtypes: A Mega-Analysis From the ENIGMA Anxiety Working Group.

Author: Hilbert K, Boeken OJ, Langhammer T, Groenewold NA, Bas-Hoogendam JM, Aghajani M, Zugman A, Åhs F, Arolt V, Beesdo-Baum K, Björkstrand J, Blackford JU, Blanco-Hinojo L, Böhnlein J, Bülow R, Cano M, Cardoner N, Caseras X, Dannlowski U, Domschke K, Fehm L, Feola B, Fredrikson M, Goossens L, Grabe HJ, Grotegerd D, Gur RE, Hamm AO, Harrewijn A, Heinig I, Herrmann MJ, Hofmann D, Jackowski AP, Jansen A, Kaczkurkin AN, Kindt M, Kingsley EN, Kircher T, Klahn AL, Koelkebeck K, Krug A, Kugel H, Larsen B, Leehr EJ, Leonhardt L, Lotze M, Margraf J, Michałowski J, Muehlhan M, Nenadić I, Pan PM, Pauli P, Peñate W, Pittig A, Plag J, Pujol J, Richter J, Rivero FL, Salum GA, Satterthwaite TD, Schäfer A, Schäfer J, Schienle A, Schneider S, Schrammen E, Schruers K, Schulz SM, Seidl E, Stark RM, Stein F, Straube B, Straube T, Ströhle A, Suchan B, Thomopoulos SI, Ventura-Bort C, Visser R, Völzke H, Wabnegger A, Wannemüller A, Wendt J, Wiemer J, Wittchen HU, Wittfeld K, Wright B, Yang Y, Zilverstand A, Zwanzger P, Veltman DJ, Winkler AM, Pine DS, Jahanshad N, Thompson PM, Stein DJ, Van der Wee NJA, and Lueken U
Subjects: Humans, Adult, Female, Male, Child, Adolescent, Young Adult, Middle Aged, Brain pathology, Brain diagnostic imaging, Aged, Child, Preschool, Aged, 80 and over, Cerebral Cortex pathology, Cerebral Cortex diagnostic imaging, Animals, Case-Control Studies, Phobic Disorders pathology, Magnetic Resonance Imaging
Abstract: Objective: Specific phobia is a common anxiety disorder, but the literature on associated brain structure alterations exhibits substantial gaps. The ENIGMA Anxiety Working Group examined brain structure differences between individuals with specific phobias and healthy control subjects as well as between the animal and blood-injection-injury (BII) subtypes of specific phobia. Additionally, the authors investigated associations of brain structure with symptom severity and age (youths vs. adults)., Methods: Data sets from 31 original studies were combined to create a final sample with 1,452 participants with phobia and 2,991 healthy participants (62.7% female; ages 5-90). Imaging processing and quality control were performed using established ENIGMA protocols. Subcortical volumes as well as cortical surface area and thickness were examined in a preregistered analysis., Results: Compared with the healthy control group, the phobia group showed mostly smaller subcortical volumes, mixed surface differences, and larger cortical thickness across a substantial number of regions. The phobia subgroups also showed differences, including, as hypothesized, larger medial orbitofrontal cortex thickness in BII phobia (N=182) compared with animal phobia (N=739). All findings were driven by adult participants; no significant results were observed in children and adolescents., Conclusions: Brain alterations associated with specific phobia exceeded those of other anxiety disorders in comparable analyses in extent and effect size and were not limited to reductions in brain structure. Moreover, phenomenological differences between phobia subgroups were reflected in diverging neural underpinnings, including brain areas related to fear processing and higher cognitive processes. The findings implicate brain structure alterations in specific phobia, although subcortical alterations in particular may also relate to broader internalizing psychopathology.
Published: 2024
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36. Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers.

Author: Olafson ER, Sperber C, Jamison KW, Bowren MD Jr, Boes AD, Andrushko JW, Borich MR, Boyd LA, Cassidy JM, Conforto AB, Cramer SC, Dula AN, Geranmayeh F, Hordacre B, Jahanshad N, Kautz SA, Tavenner BP, MacIntosh BJ, Piras F, Robertson AD, Seo NJ, Soekadar SR, Thomopoulos SI, Vecchio D, Weng TB, Westlye LT, Winstein CJ, Wittenberg GF, Wong KA, Thompson PM, Liew SL, and Kuceyeski AF
Abstract: Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested ( R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract ( R 2 = 0.132, P < 0.001) and of multiple descending motor tracts ( R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps ( R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps ( R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations., Competing Interests: S.C.C. serves as a consultant for Abbvie, Constant Therapeutics, BrainQ, Myomo, MicroTransponder, Neurolutions, Panaxium, NeuExcell, Elevian, Helius, Omniscient, Brainsgate, Nervgen, Battelle, and TRCare. B.H. has a clinical partnership with Fourier Intelligence. N.J.S. is an inventor for a patent US 10,071,015 B2. C.J. W. is a consultant for Microtransponder, BrainQ, and MedRhythm. G.F.W. sits on Advisory Boards for Myomo and Neuro-innovators., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)
Published: 2024
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37. ENIGMA-Chronic Pain: a worldwide initiative to identify brain correlates of chronic pain.

Author: Quidé Y, Jahanshad N, Andoh J, Antoniou G, Apkarian AV, Ashar YK, Badran BW, Baird CL, Baxter L, Bell TR, Blanco-Hinojo L, Borckardt J, Cheung CL, Ciampi de Andrade D, Couto BA, Cox SR, Cruz-Almeida Y, Dannlowski U, De Martino E, de Tommaso M, Deus J, Domin M, Egorova-Brumley N, Elliott J, Fanton S, Fauchon C, Flor H, Franz CE, Gatt JM, Gerdhem P, Gilman JM, Gollub RL, Govind V, Graven-Nielsen T, Håkansson G, Hales T, Haswell C, Heukamp NJ, Hu L, Huang L, Hussain A, Jensen K, Kircher T, Kremen WS, Leehr EJ, Lindquist M, Loggia ML, Lotze M, Martucci KT, Meeker TJ, Meinert S, Millard SK, Morey RA, Murillo C, Nees F, Nenadic I, Park HRP, Peng X, Ploner M, Pujol J, Robayo LE, Salan T, Seminowicz DA, Serian A, Slater R, Stein F, Stevens J, Strauss S, Sun D, Vachon-Presseau E, Valdes-Hernandez PA, Vanneste S, Vernon M, Verriotis M, Wager TD, Widerstrom-Noga E, Woodbury A, Zeidan F, Bhatt RR, Ching CRK, Haddad E, Thomopoulos SI, Thompson PM, and Gustin SM
Published: 2024
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38. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Author: Jiang Y, Luo C, Wang J, Palaniyappan L, Chang X, Xiang S, Zhang J, Duan M, Huang H, Gaser C, Nemoto K, Miura K, Hashimoto R, Westlye LT, Richard G, Fernandez-Cabello S, Parker N, Andreassen OA, Kircher T, Nenadić I, Stein F, Thomas-Odenthal F, Teutenberg L, Usemann P, Dannlowski U, Hahn T, Grotegerd D, Meinert S, Lencer R, Tang Y, Zhang T, Li C, Yue W, Zhang Y, Yu X, Zhou E, Lin CP, Tsai SJ, Rodrigue AL, Glahn D, Pearlson G, Blangero J, Karuk A, Pomarol-Clotet E, Salvador R, Fuentes-Claramonte P, Garcia-León MÁ, Spalletta G, Piras F, Vecchio D, Banaj N, Cheng J, Liu Z, Yang J, Gonul AS, Uslu O, Burhanoglu BB, Uyar Demir A, Rootes-Murdy K, Calhoun VD, Sim K, Green M, Quidé Y, Chung YC, Kim WS, Sponheim SR, Demro C, Ramsay IS, Iasevoli F, de Bartolomeis A, Barone A, Ciccarelli M, Brunetti A, Cocozza S, Pontillo G, Tranfa M, Park MTM, Kirschner M, Georgiadis F, Kaiser S, Van Rheenen TE, Rossell SL, Hughes M, Woods W, Carruthers SP, Sumner P, Ringin E, Spaniel F, Skoch A, Tomecek D, Homan P, Homan S, Omlor W, Cecere G, Nguyen DD, Preda A, Thomopoulos SI, Jahanshad N, Cui LB, Yao D, Thompson PM, Turner JA, van Erp TGM, Cheng W, and Feng J
Subjects: Humans, Male, Female, Adult, Machine Learning, Middle Aged, Brain diagnostic imaging, Brain pathology, Cross-Sectional Studies, Europe, Neuroimaging, Reproducibility of Results, North America, Hippocampus diagnostic imaging, Hippocampus pathology, Schizophrenia diagnostic imaging, Schizophrenia pathology, Algorithms, Magnetic Resonance Imaging, Gray Matter diagnostic imaging, Gray Matter pathology
Abstract: Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors., (© 2024. The Author(s).)
Published: 2024
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39. Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Author: Chattopadhyay T, Ozarkar SS, Buwa K, Joshy NA, Komandur D, Naik J, Thomopoulos SI, Ver Steeg G, Ambite JL, and Thompson PM
Abstract: Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chattopadhyay, Ozarkar, Buwa, Joshy, Komandur, Naik, Thomopoulos, Ver Steeg, Ambite and Thompson.)
Published: 2024
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40. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Author: Kennedy E, Liebel SW, Lindsey HM, Vadlamani S, Lei PW, Adamson MM, Alda M, Alonso-Lana S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O'Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz CE, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon J, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold AJ, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O'Brien T, Oertel V, Ollinger J, Olsen A, Ortiz García de la Foz V, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Sponheim SR, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone EB, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vecchio D, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Pugh MJ, Tate DF, Hillary FG, Wilde EA, and Dennis EL
Abstract: Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia ( p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores ( p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.
Published: 2024
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41. Brain Cell-based Genetic Subtyping and Drug Repositioning for Alzheimer Disease.

Author: Sahelijo N, Rajagopalan P, Qian L, Rahman R, Priyadarshi D, Goldstein D, Thomopoulos SI, Bennett DA, Farrer LA, Stein TD, Shen L, Huang H, Nho K, Andrew SJ, Davatzikos C, Thompson PM, Tcw J, and Jun GR
Abstract: Alzheimer's Disease (AD) is characterized by its complex and heterogeneous etiology and gradual progression, leading to high drug failure rates in late-stage clinical trials. In order to better stratify individuals at risk for AD and discern potential therapeutic targets we employed a novel procedure utilizing cell-based co-regulated gene networks and polygenic risk scores (cbPRSs). After defining genetic subtypes using extremes of cbPRS distributions, we evaluated correlations of the genetic subtypes with previously defined AD subtypes defined on the basis of domain-specific cognitive functioning and neuroimaging biomarkers. Employing a PageRank algorithm, we identified priority gene targets for the genetic subtypes. Pathway analysis of priority genes demonstrated associations with neurodegeneration and suggested candidate drugs currently utilized in diabetes, hypertension, and epilepsy for repositioning in AD. Experimental validation utilizing human induced pluripotent stem cell (hiPSC)-derived astrocytes demonstrated the modifying effects of estradiol, levetiracetam, and pioglitazone on expression of APOE and complement C4 genes, suggesting potential repositioning for AD.
Published: 2024
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42. Genotype-specific spinal cord damage in spinocerebellar ataxias: an ENIGMA-Ataxia study.

Author: Rezende TJR, Adanyaguh I, Barsottini OGP, Bender B, Cendes F, Coutinho L, Deistung A, Dogan I, Durr A, Fernandez-Ruiz J, Göricke SL, Grisoli M, Hernandez-Castillo CR, Lenglet C, Mariotti C, Martinez ARM, Massuyama BK, Mochel F, Nanetti L, Nigri A, Ono SE, Öz G, Pedroso JL, Reetz K, Synofzik M, Teive H, Thomopoulos SI, Thompson PM, Timmann D, van de Warrenburg BPC, van Gaalen J, França MC Jr, and Harding IH
Subjects: Humans, Male, Female, Middle Aged, Adult, Genotype, Aged, Spinal Cord pathology, Spinal Cord diagnostic imaging, Cervical Cord diagnostic imaging, Cervical Cord pathology, Severity of Illness Index, Case-Control Studies, Spinocerebellar Ataxias diagnostic imaging, Spinocerebellar Ataxias pathology, Spinocerebellar Ataxias genetics, Magnetic Resonance Imaging
Abstract: Background: Spinal cord damage is a feature of many spinocerebellar ataxias (SCAs), but well-powered in vivo studies are lacking and links with disease severity and progression remain unclear. Here we characterise cervical spinal cord morphometric abnormalities in SCA1, SCA2, SCA3 and SCA6 using a large multisite MRI dataset., Methods: Upper spinal cord (vertebrae C1-C4) cross-sectional area (CSA) and eccentricity (flattening) were assessed using MRI data from nine sites within the ENIGMA-Ataxia consortium, including 364 people with ataxic SCA, 56 individuals with preataxic SCA and 394 nonataxic controls. Correlations and subgroup analyses within the SCA cohorts were undertaken based on disease duration and ataxia severity., Results: Individuals in the ataxic stage of SCA1, SCA2 and SCA3, relative to non-ataxic controls, had significantly reduced CSA and increased eccentricity at all examined levels. CSA showed large effect sizes ( d >2.0) and correlated with ataxia severity (r<-0.43) and disease duration (r<-0.21). Eccentricity correlated only with ataxia severity in SCA2 (r=0.28). No significant spinal cord differences were evident in SCA6. In preataxic individuals, CSA was significantly reduced in SCA2 ( d =1.6) and SCA3 ( d =1.7), and the SCA2 group also showed increased eccentricity ( d =1.1) relative to nonataxic controls. Subgroup analyses confirmed that CSA and eccentricity are abnormal in early disease stages in SCA1, SCA2 and SCA3. CSA declined with disease progression in all, whereas eccentricity progressed only in SCA2., Conclusions: Spinal cord abnormalities are an early and progressive feature of SCA1, SCA2 and SCA3, but not SCA6, which can be captured using quantitative MRI., Competing Interests: Competing interests: TJRR, FC, ARMM, JLP, OB, BKM, IHH, AD, DT, SLG, ID, IA, GO, CM, LN, AN, MG, LC, HAGT, SEO, CRHR, JFR, FM, AD, BW, JG, MS, PMT, SIT: none. The authors declare no competing interests. KR received honoraria for presentations or advisory boards from Biogen and Roche as well as clinical trial grants from Pfizer, Merck, Minoryx, Biogen and Roche. BB is cofounder, shareholder and CTO of AIRAmed GmbH. CL received research grants from Minoryx Therapeutics and research support from Biogen Inc., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)
Published: 2024
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43. 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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44. Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts.

Author: Chattopadhyay T, Joshy NA, Ozarkar SS, Buwa KS, Feng Y, Laltoo E, Thomopoulos SI, Villalon-Reina JE, Joshi H, Venkatasubramanian G, John JP, and Thompson PM
Abstract: Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer's disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting 'brain age' - the task of predicting a person's chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.
Published: 2024
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45. Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry.

Author: Feng Y, Chandio BQ, Villalon-Reina JE, Thomopoulos SI, Nir TM, Benavidez S, Laltoo E, Chattopadhyay T, Joshi H, Venkatasubramanian G, John JP, Jahanshad N, Reid RI, Jack CR, Weiner MW, and Thompson PM
Abstract: Introduction: Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry., Methods: Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure., Results: In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia., Discussion: We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.
Published: 2024
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46. White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

Author: Kim BG, Kim G, Abe Y, Alonso P, Ameis S, Anticevic A, Arnold PD, Balachander S, Banaj N, Bargalló N, Batistuzzo MC, Benedetti F, Bertolín S, Beucke JC, Bollettini I, Brem S, Brennan BP, Buitelaar JK, Calvo R, Castelo-Branco M, Cheng Y, Chhatkuli RB, Ciullo V, Coelho A, Couto B, Dallaspezia S, Ely BA, Ferreira S, Fontaine M, Fouche JP, Grazioplene R, Gruner P, Hagen K, Hansen B, Hanna GL, Hirano Y, Höxter MQ, Hough M, Hu H, Huyser C, Ikuta T, Jahanshad N, James A, Jaspers-Fayer F, Kasprzak S, Kathmann N, Kaufmann C, Kim M, Koch K, Kvale G, Kwon JS, Lazaro L, Lee J, Lochner C, Lu J, Manrique DR, Martínez-Zalacaín I, Masuda Y, Matsumoto K, Maziero MP, Menchón JM, Minuzzi L, Moreira PS, Morgado P, Narayanaswamy JC, Narumoto J, Ortiz AE, Ota J, Pariente JC, Perriello C, Picó-Pérez M, Pittenger C, Poletti S, Real E, Reddy YCJ, van Rooij D, Sakai Y, Sato JR, Segalas C, Shavitt RG, Shen Z, Shimizu E, Shivakumar V, Soreni N, Soriano-Mas C, Sousa N, Sousa MM, Spalletta G, Stern ER, Stewart SE, Szeszko PR, Thomas R, Thomopoulos SI, Vecchio D, Venkatasubramanian G, Vriend C, Walitza S, Wang Z, Watanabe A, Wolters L, Xu J, Yamada K, Yun JY, Zarei M, Zhao Q, Zhu X, Thompson PM, Bruin WB, van Wingen GA, Piras F, Piras F, Stein DJ, van den Heuvel OA, Simpson HB, Marsh R, and Cha J
Subjects: Humans, Male, Female, Adult, Child, Adolescent, Brain pathology, Brain diagnostic imaging, Middle Aged, Young Adult, Obsessive-Compulsive Disorder, Machine Learning, White Matter pathology, White Matter diagnostic imaging, Diffusion Tensor Imaging methods
Abstract: White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research., (© 2024. The Author(s).)
Published: 2024
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47. Childhood trauma moderates schizotypy-related brain morphology: analyses of 1182 healthy individuals from the ENIGMA schizotypy working group.

Author: Quidé Y, Watkeys OJ, Tonini E, Grotegerd D, Dannlowski U, Nenadić I, Kircher T, Krug A, Hahn T, Meinert S, Goltermann J, Gruber M, Stein F, Brosch K, Wroblewski A, Thomas-Odenthal F, Usemann P, Straube B, Alexander N, Leehr EJ, Bauer J, Winter NR, Fisch L, Dohm K, Rössler W, Smigielski L, DeRosse P, Moyett A, Houenou J, Leboyer M, Gilleen J, Thomopoulos SI, Thompson PM, Aleman A, Modinos G, and Green MJ
Subjects: Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Young Adult, Brain diagnostic imaging, Gray Matter, Magnetic Resonance Imaging methods, Adverse Childhood Experiences, Psychological Tests, Schizotypal Personality Disorder diagnostic imaging, Schizotypal Personality Disorder psychology, Self Report
Abstract: Background: Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy., Methods: We addressed this question using data from a total of 1182 healthy adults (age range: 18-65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined., Results: A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure., Conclusions: These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma.
Published: 2024
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48. Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

Author: Kerestes R, Perry A, Vivash L, O'Brien TJ, Alvim MKM, Arienzo D, Aventurato ÍK, Ballerini A, Baltazar GF, Bargalló N, Bender B, Brioschi R, Bürkle E, Caligiuri ME, Cendes F, de Tisi J, Duncan JS, Engel JP Jr, Foley S, Fortunato F, Gambardella A, Giacomini T, Guerrini R, Hall G, Hamandi K, Ives-Deliperi V, João RB, Keller SS, Kleiser B, Labate A, Lenge M, Marotta C, Martin P, Mascalchi M, Meletti S, Owens-Walton C, Parodi CB, Pascual-Diaz S, Powell D, Rao J, Rebsamen M, Reiter J, Riva A, Rüber T, Rummel C, Scheffler F, Severino M, Silva LS, Staba RJ, Stein DJ, Striano P, Taylor PN, Thomopoulos SI, Thompson PM, Tortora D, Vaudano AE, Weber B, Wiest R, Winston GP, Yasuda CL, Zheng H, McDonald CR, Sisodiya SM, and Harding IH
Subjects: Adult, Humans, Phenytoin, Cross-Sectional Studies, Cerebellum diagnostic imaging, Cerebellum pathology, Seizures complications, Magnetic Resonance Imaging methods, Atrophy pathology, Epilepsy, Temporal Lobe complications, Epileptic Syndromes complications
Abstract: Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group., Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness., Results: Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (d max = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (d max = .47), crus I/II (d max = .39), VIIIA (d max = .45), and VIIIB (d max = .40). Earlier age at seizure onset ( η ρ max 2 = .05) and longer epilepsy duration ( η ρ max 2 = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls., Significance: We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy., (© 2024 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)
Published: 2024
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49. Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

Author: Kim BG, Kim G, Abe Y, Alonso P, Ameis S, Anticevic A, Arnold PD, Balachander S, Banaj N, Bargalló N, Batistuzzo MC, Benedetti F, Bertolín S, Beucke JC, Bollettini I, Brem S, Brennan BP, Buitelaar JK, Calvo R, Castelo-Branco M, Cheng Y, Chhatkuli RB, Ciullo V, Coelho A, Couto B, Dallaspezia S, Ely BA, Ferreira S, Fontaine M, Fouche JP, Grazioplene R, Gruner P, Hagen K, Hansen B, Hanna GL, Hirano Y, Höxter MQ, Hough M, Hu H, Huyser C, Ikuta T, Jahanshad N, James A, Jaspers-Fayer F, Kasprzak S, Kathmann N, Kaufmann C, Kim M, Koch K, Kvale G, Kwon JS, Lazaro L, Lee J, Lochner C, Lu J, Manrique DR, Martínez-Zalacaín I, Masuda Y, Matsumoto K, Maziero MP, Menchón JM, Minuzzi L, Moreira PS, Morgado P, Narayanaswamy JC, Narumoto J, Ortiz AE, Ota J, Pariente JC, Perriello C, Picó-Pérez M, Pittenger C, Poletti S, Real E, Reddy YCJ, van Rooij D, Sakai Y, Sato JR, Segalas C, Shavitt RG, Shen Z, Shimizu E, Shivakumar V, Soreni N, Soriano-Mas C, Sousa N, Sousa MM, Spalletta G, Stern ER, Stewart SE, Szeszko PR, Thomas R, Thomopoulos SI, Vecchio D, Venkatasubramanian G, Vriend C, Walitza S, Wang Z, Watanabe A, Wolters L, Xu J, Yamada K, Yun JY, Zarei M, Zhao Q, Zhu X, Thompson PM, Bruin WB, van Wingen GA, Piras F, Piras F, Stein DJ, van den Heuvel OA, Simpson HB, Marsh R, and Cha J
Published: 2024
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50. A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN.

Author: Chen J, Ngo A, Rodríguez-Cruces R, Royer J, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda CL, Alvim MKM, Bonilha L, Gleichgerrcht E, Focke NK, Kreilkamp B, Domin M, von Podewils F, Langner S, Rummel C, Wiest R, Martin P, Kotikalapudi R, Bender B, O'Brien TJ, Sinclair B, Vivash L, Kwan P, Desmond PM, Lui E, Duma GM, Bonanni P, Ballerini A, Vaudano AE, Meletti S, Tondelli M, Alhusaini S, Doherty CP, Cavalleri GL, Delanty N, Kälviäinen R, Jackson GD, Kowalczyk M, Mascalchi M, Semmelroch M, Thomas RH, Soltanian-Zadeh H, Davoodi-Bojd E, Zhang J, Lenge M, Guerrini R, Bartolini E, Hamandi K, Foley S, Rüber T, Bauer T, Weber B, Caldairou B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Pardoe H, Severino M, Striano P, Tortora D, Kaestner E, Hatton SN, Arienzo D, Vos SB, Ryten M, Taylor PN, Duncan JS, Whelan CD, Galovic M, Winston GP, Thomopoulos SI, Thompson PM, Sisodiya SM, Labate A, McDonald CR, Caciagli L, Bernasconi N, Bernasconi A, Larivière S, Schrader D, and Bernhardt BC
Abstract: Objectives: Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments., Methods: We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves., Results: In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions., Conclusions: This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.
Published: 2024
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