Author: "Hatton, SN" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Hatton, SN"' showing total 123 results

Start Over Author "Hatton, SN"

123 results on '"Hatton, SN"'

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

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

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

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

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

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

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

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

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

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

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

Author: Ge, R, Yu, Y, Qi, YX, Fan, YN, Chen, S, Gao, C, Haas, SS, New, F, Boomsma, DI, Brodaty, H ; https://orcid.org/0000-0001-9487-6617, Brouwer, RM, Buckner, R, Caseras, X, Crivello, F, Crone, EA, Erk, S, Fisher, SE, Franke, B, Glahn, DC, Dannlowski, U, Grotegerd, D, Gruber, O, Hulshoff Pol, HE, Schumann, G, Tamnes, CK, Walter, H, Wierenga, LM, Jahanshad, N, Thompson, PM, Frangou, S, Agartz, I, Asherson, P, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Baumeister, S, Bertolino, A, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Buitelaar, JK, Cannon, DM, Cervenka, S, Conrod, PJ, Crespo-Facorro, B, Davey, CG, de Haan, L, de Zubicaray, GI, Di Giorgio, A, Frodl, T, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hatton, SN, Hickie, I, Howells, FM, Huyser, C, Jernigan, TL, Jiang, J ; https://orcid.org/0000-0002-2147-6302, Joska, JA, Kahn, RS, Kalnin, AJ, Kochan, NA ; https://orcid.org/0000-0002-8630-6398, Koops, S, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lochner, C, Martin, NG, Mazoyer, B, McDonald, BC, McDonald, C, McMahon, KL, Medland, S, Modabbernia, A, Mwangi, B, Nakao, T, Nyberg, L, Piras, F, Portella, MJ, Qiu, J, Roffman, JL, Sachdev, PS ; https://orcid.org/0000-0002-9595-3220, Sanford, N, Satterthwaite, TD, Saykin, AJ, Sellgren, CM, Sim, K, Smoller, JW, Soares, JC, Sommer, IE, Spalletta, G, Stein, DJ, Thomopoulos, SI, Tomyshev, AS, Tordesillas-Gutiérrez, D, Trollor, JN ; https://orcid.org/0000-0002-7685-2977, Wen, Wei ; https://orcid.org/0000-0003-2753-3870, Ge, R, Yu, Y, Qi, YX, Fan, YN, Chen, S, Gao, C, Haas, SS, New, F, Boomsma, DI, Brodaty, H ; https://orcid.org/0000-0001-9487-6617, Brouwer, RM, Buckner, R, Caseras, X, Crivello, F, Crone, EA, Erk, S, Fisher, SE, Franke, B, Glahn, DC, Dannlowski, U, Grotegerd, D, Gruber, O, Hulshoff Pol, HE, Schumann, G, Tamnes, CK, Walter, H, Wierenga, LM, Jahanshad, N, Thompson, PM, Frangou, S, Agartz, I, Asherson, P, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Baumeister, S, Bertolino, A, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Buitelaar, JK, Cannon, DM, Cervenka, S, Conrod, PJ, Crespo-Facorro, B, Davey, CG, de Haan, L, de Zubicaray, GI, Di Giorgio, A, Frodl, T, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hatton, SN, Hickie, I, Howells, FM, Huyser, C, Jernigan, TL, Jiang, J ; https://orcid.org/0000-0002-2147-6302, Joska, JA, Kahn, RS, Kalnin, AJ, Kochan, NA ; https://orcid.org/0000-0002-8630-6398, Koops, S, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lochner, C, Martin, NG, Mazoyer, B, McDonald, BC, McDonald, C, McMahon, KL, Medland, S, Modabbernia, A, Mwangi, B, Nakao, T, Nyberg, L, Piras, F, Portella, MJ, Qiu, J, Roffman, JL, Sachdev, PS ; https://orcid.org/0000-0002-9595-3220, Sanford, N, Satterthwaite, TD, Saykin, AJ, Sellgren, CM, Sim, K, Smoller, JW, Soares, JC, Sommer, IE, Spalletta, G, Stein, DJ, Thomopoulos, SI, Tomyshev, AS, Tordesillas-Gutiérrez, D, Trollor, JN ; https://orcid.org/0000-0002-7685-2977, and Wen, Wei ; https://orcid.org/0000-0003-2753-3870
Abstract: The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3–90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.
Published: 2024

7. Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy

Author: Park, B-Y, Lariviere, S, Rodriguez-Cruces, R, Royer, J, Tavakol, S, Wang, Y, Caciagli, L, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Alvim, MKM, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Kreilkamp, BAK, Domin, M, von Podewils, F, Langner, S, Rummel, C, Rebsamen, M, Wiest, R, Martin, P, Kotikalapudi, R, Bender, B, O'Brien, TJ, Law, M, Sinclair, B, Vivash, L, Kwan, P, Desmond, PM, Malpas, CB, Lui, E, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, 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, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Parodi, C, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Galovic, M, Whelan, CD, Bargallo, N, Pariente, J, Conde-Blanco, E, Vaudano, AE, Tondelli, M, Meletti, S, Kong, X-Z, Francks, C, Fisher, SE, Caldairou, B, Ryten, M, Labate, A, Sisodiya, SM, Thompson, PM, McDonald, CR, Bernasconi, A, Bernasconi, N, Bernhardt, BC, Park, B-Y, Lariviere, S, Rodriguez-Cruces, R, Royer, J, Tavakol, S, Wang, Y, Caciagli, L, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Alvim, MKM, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Kreilkamp, BAK, Domin, M, von Podewils, F, Langner, S, Rummel, C, Rebsamen, M, Wiest, R, Martin, P, Kotikalapudi, R, Bender, B, O'Brien, TJ, Law, M, Sinclair, B, Vivash, L, Kwan, P, Desmond, PM, Malpas, CB, Lui, E, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, 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, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Parodi, C, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Galovic, M, Whelan, CD, Bargallo, N, Pariente, J, Conde-Blanco, E, Vaudano, AE, Tondelli, M, Meletti, S, Kong, X-Z, Francks, C, Fisher, SE, Caldairou, B, Ryten, M, Labate, A, Sisodiya, SM, Thompson, PM, McDonald, CR, Bernasconi, A, Bernasconi, N, and Bernhardt, BC
Abstract: Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine ou
Published: 2022

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

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

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

Author: Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Frangou, S, Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Frangou, S
Abstract: Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
Published: 2022

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

Author: Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Dima, D, Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Dima, D
Abstract: Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
Published: 2022

11. 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

12. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression

Author: Lariviere, S, Royer, J, Rodriguez-Cruces, R, Paquola, C, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, CL, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Lui, E, Vaudano, AE, Meletti, S, Tondelli, M, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Winston, GP, Griffin, A, Singh, A, Tiwari, VK, Kreilkamp, BAK, Lenge, M, Guerrini, R, Hamandi, K, Foley, S, Ruber, T, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Kaestner, E, Hatton, SN, Vos, SB, Caciagli, L, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, Bernhardt, BC, Lariviere, S, Royer, J, Rodriguez-Cruces, R, Paquola, C, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, CL, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Lui, E, Vaudano, AE, Meletti, S, Tondelli, M, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Winston, GP, Griffin, A, Singh, A, Tiwari, VK, Kreilkamp, BAK, Lenge, M, Guerrini, R, Hamandi, K, Foley, S, Ruber, T, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Kaestner, E, Hatton, SN, Vos, SB, Caciagli, L, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, and Bernhardt, BC
Abstract: Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.
Published: 2022

13. Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts

Author: Campos, A, Thompson, PM, Veltman, DJ, Pozzi, E, van Veltzen, LS, Jahanshad, N, Adams, MJ, Baune, BT, Berger, K, Brosch, K, Bulow, R, Connolly, CG, Dannlowski, U, Davey, CG, de Zubicaray, G, Dima, D, Erwin-Grabner, T, Evans, JW, Fu, CHY, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Grotegerd, D, Harris, MA, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Kircher, T, Krug, A, Lagopoulos, J, Lemke, H, McMahon, K, MacMaster, FP, Martin, NG, McIntosh, AM, Medland, SE, Meinert, S, Meller, T, Nenadic, I, Opel, N, Redlich, R, Reneman, L, Repple, J, Sacchet, MD, Schmitt, S, Schrantee, A, Sim, K, Singh, A, Stein, F, Strike, LT, van Der Wee, NJA, van Der Werff, SJA, Volzke, H, Waltemate, L, Whalley, HC, Wittfeld, K, Wright, MJ, Yang, TT, Zarate, CA, Schmaal, L, Renteria, ME, Campos, A, Thompson, PM, Veltman, DJ, Pozzi, E, van Veltzen, LS, Jahanshad, N, Adams, MJ, Baune, BT, Berger, K, Brosch, K, Bulow, R, Connolly, CG, Dannlowski, U, Davey, CG, de Zubicaray, G, Dima, D, Erwin-Grabner, T, Evans, JW, Fu, CHY, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Grotegerd, D, Harris, MA, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Kircher, T, Krug, A, Lagopoulos, J, Lemke, H, McMahon, K, MacMaster, FP, Martin, NG, McIntosh, AM, Medland, SE, Meinert, S, Meller, T, Nenadic, I, Opel, N, Redlich, R, Reneman, L, Repple, J, Sacchet, MD, Schmitt, S, Schrantee, A, Sim, K, Singh, A, Stein, F, Strike, LT, van Der Wee, NJA, van Der Werff, SJA, Volzke, H, Waltemate, L, Whalley, HC, Wittfeld, K, Wright, MJ, Yang, TT, Zarate, CA, Schmaal, L, and Renteria, ME
Abstract: BACKGROUND: Neuroimaging studies of suicidal behavior have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD Working Group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior. METHODS: Here, we pooled data from 18 international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy control subjects and 6448 people with depression, of whom 694 had attempted suicide). We compared regional cortical thickness and surface area and measures of subcortical, lateral ventricular, and intracranial volumes between suicide attempters, clinical control subjects (nonattempters with depression), and healthy control subjects. RESULTS: We identified 25 regions of interest with statistically significant (false discovery rate < .05) differences between groups. Post hoc examinations identified neuroimaging markers associated with suicide attempt including smaller volumes of the left and right thalamus and the right pallidum and lower surface area of the left inferior parietal lobe. CONCLUSIONS: This study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt and further demonstrates the need for well-powered samples and collaborative efforts. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidal behaviors may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk.
Published: 2021

14. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders (May, 2020, 10.1038/s41380-020-0774-9)

Author: Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Samann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, IV, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, van der Auwera, S, Wittfeld, K, Hosten, N, Volzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, Dannlowski, U, Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Samann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, IV, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, van der Auwera, S, Wittfeld, K, Hosten, N, Volzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, and Dannlowski, U
Published: 2021

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

Author: Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Saemann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, I, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, Van der Auwera, S, Wittfeld, K, Hosten, N, Voelzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, Dannlowski, U, Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Saemann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, I, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, Van der Auwera, S, Wittfeld, K, Hosten, N, Voelzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, and Dannlowski, U
Abstract: Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = -0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.
Published: 2021

16. Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Author: Gleichgerrcht, E, Munsell, BC, Alhusaini, S, Alvim, MKM, Bargallo, N, Bender, B, Bernasconi, A, Bernasconi, N, Bernhardt, B, Blackmon, K, Caligiuri, ME, Cendes, F, Concha, L, Desmond, PM, Devinsky, O, Doherty, CP, Domin, M, Duncan, JS, Focke, NK, Gambardella, A, Gong, B, Guerrini, R, Hatton, SN, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kreilkamp, BAK, Labate, A, Langner, S, Lariviere, S, Lenge, M, Lui, E, Martin, P, Mascalchi, M, Meletti, S, O'Brien, TJ, Pardoe, HR, Pariente, JC, Rao, JX, Richardson, MP, Rodriguez-Cruces, R, Ruber, T, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Vaudano, AE, Vivash, L, von Podewills, F, Vos, SB, Weber, B, Yao, Y, Yasuda, CL, Zhang, J, Thompson, PM, Sisodiya, SM, McDonald, CR, Bonilha, L, Gleichgerrcht, E, Munsell, BC, Alhusaini, S, Alvim, MKM, Bargallo, N, Bender, B, Bernasconi, A, Bernasconi, N, Bernhardt, B, Blackmon, K, Caligiuri, ME, Cendes, F, Concha, L, Desmond, PM, Devinsky, O, Doherty, CP, Domin, M, Duncan, JS, Focke, NK, Gambardella, A, Gong, B, Guerrini, R, Hatton, SN, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kreilkamp, BAK, Labate, A, Langner, S, Lariviere, S, Lenge, M, Lui, E, Martin, P, Mascalchi, M, Meletti, S, O'Brien, TJ, Pardoe, HR, Pariente, JC, Rao, JX, Richardson, MP, Rodriguez-Cruces, R, Ruber, T, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Vaudano, AE, Vivash, L, von Podewills, F, Vos, SB, Weber, B, Yao, Y, Yasuda, CL, Zhang, J, Thompson, PM, Sisodiya, SM, McDonald, CR, and Bonilha, L
Abstract: Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.
Published: 2021

17. 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

18. 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

19. Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder: results from the ENIGMA MDD working group

Author: Leerssen, J, Blanken, TF, Pozzi, E, Jahanshad, N, Aftanas, L, Andreassen, OA, Baune, BT, Ching, CRK, Dannlowski, U, Frodl, T, Godlewska, BR, Gotlib, IH, Grotegerd, D, Gruber, O, Hatton, SN, Hickie, IB, Jaworska, N, Kircher, T, Krug, A, Lagopoulos, J, Li, M, MacMaster, FP, McIntosh, AM, Mwangi, B, Osipov, E, Portella, MJ, Sacchet, MD, Samann, PG, Simulionyte, E, Soares, JC, Walter, M, Whalley, HC, Veltman, DJ, Thompson, PM, Schmaal, L, Van Someren, EJW, Leerssen, J, Blanken, TF, Pozzi, E, Jahanshad, N, Aftanas, L, Andreassen, OA, Baune, BT, Ching, CRK, Dannlowski, U, Frodl, T, Godlewska, BR, Gotlib, IH, Grotegerd, D, Gruber, O, Hatton, SN, Hickie, IB, Jaworska, N, Kircher, T, Krug, A, Lagopoulos, J, Li, M, MacMaster, FP, McIntosh, AM, Mwangi, B, Osipov, E, Portella, MJ, Sacchet, MD, Samann, PG, Simulionyte, E, Soares, JC, Walter, M, Whalley, HC, Veltman, DJ, Thompson, PM, Schmaal, L, and Van Someren, EJW
Published: 2020

20. 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

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

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

22. Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study

Author: Lariviere, S, Rodriguez-Cruces, R, Royer, J, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, 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, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, Bernhardt, BC, Lariviere, S, Rodriguez-Cruces, R, Royer, J, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, 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, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, and Bernhardt, BC
Abstract: Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies.
Published: 2020

23. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group

Author: van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, Schmaal, L, van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, and Schmaal, L
Abstract: Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.
Published: 2020

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

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

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

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

26. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

Author: Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, O, Binder, EB, Blangero, J, Bockholt, HJ, Boen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, Djurovic, S, Dong, H, Donohoe, G, Duggirala, R, Dyer, TD, Ehrlich, S, Ekman, CJ, Elvsashagen, T, Emsell, L, Erk, S, Espeseth, T, Fagerness, J, Fears, S, Fedko, I, Fernandez, G, Fisher, SE, Foroud, T, Fox, PT, Francks, C, Frangou, S, Frey, EM, Frodl, T, Frouin, V, Garavan, H, Giddaluru, S, Glahn, DC, Godlewska, B, Goldstein, RZ, Gollub, RL, Grabe, HJ, Grimm, O, Gruber, O, Guadalupe, T, Gur, RE, Gur, RC, Goering, HHH, Hagenaars, S, Hajek, T, Hall, GB, Hall, J, Hardy, J, Hartman, CA, Hass, J, Hatton, SN, Haukvik, UK, Hegenscheid, K, Heinz, A, Hickie, IB, Ho, B-C, Hoehn, D, Hoekstra, PJ, Hollinshead, M, Holmes, AJ, Homuth, G, Hoogman, M, Hong, LE, Hosten, N, Hottenga, J-J, Pol, HEH, Hwang, KS, Jack, CR, Jenkinson, M, Johnston, C, Joensson, EG, Kahn, RS, Kasperaviciute, D, Kelly, S, Kim, S, Kochunov, P, Koenders, L, Kraemer, B, Kwok, JBJ, Lagopoulos, J, Laje, G, Landen, M, Landman, BA, Lauriello, J, Lawrie, SM, Lee, PH, Le Hellard, S, Lemaitre, H, Leonardo, CD, Li, C-S, Liberg, B, Liewald, DC, Liu, X, Lopez, LM, Loth, E, Lourdusamy, A, Luciano, M, Macciardi, F, Machielsen, MWJ, MacQueen, GM, Malt, UF, Mandl, R, Manoach, DS, Martinot, J-L, Matarin, M, Mather, KA, Mattheisen, M, Mattingsdal, M, Meyer-Lindenberg, A, McDonald, C, McIntosh, AM, McMahon, FJ, McMahon, KL, Meisenzahl, E, Melle, I, Milaneschi, Y, Mohnke, S, Montgomery, GW, Morris, DW, Moses, EK, Mueller, BA, Munoz Maniega, S, Muehleisen, TW, Mueller-Myhsok, B, Mwangi, B, Nauck, M, Nho, K, Nichols, TE, Nilsson, L-G, Nugent, AC, Nyberg, L, Olvera, RL, Oosterlaan, J, Ophoff, RA, Pandolfo, M, Papalampropoulou-Tsiridou, M, Papmeyer, M, Paus, T, Pausova, Z, Pearlson, GD, Penninx, BW, Peterson, CP, Pfennig, A, Phillips, M, Pike, GB, Poline, J-B, Potkin, SG, Puetz, B, Ramasamy, A, Rasmussen, J, Rietschel, M, Rijpkema, M, Risacher, SL, Roffman, JL, Roiz-Santianez, R, Romanczuk-Seiferth, N, Rose, EJ, Royle, NA, Rujescu, D, Ryten, M, Sachdev, PS, Salami, A, Satterthwaite, TD, Savitz, J, Saykin, AJ, Scanlon, C, Schmaal, L, Schnack, HG, Schork, AJ, Schulz, SC, Schuer, R, Seidman, L, Shen, L, Shoemaker, JM, Simmons, A, Sisodiya, SM, Smith, C, Smoller, JW, Soares, JC, Sponheim, SR, Sprooten, E, Starr, JM, Steen, VM, Strakowski, S, Strike, L, Sussmann, J, Saemann, PG, Teumer, A, Toga, AW, Tordesillas-Gutierrez, D, Trabzuni, D, Trost, S, Turner, J, Van den Heuvel, M, van der Wee, NJ, van Eijk, K, van Erp, TGM, van Haren, NEM, van 't Ent, D, van Tol, M-J, Hernandez, MCV, Veltman, DJ, Versace, A, Voelzke, H, Walker, R, Walter, H, Wang, L, Wardlaw, JM, Weale, ME, Weiner, MW, Wen, W, Westlye, LT, Whalley, HC, Whelan, CD, White, T, Winkler, AM, Wittfeld, K, Woldehawariat, G, Wolf, C, Zilles, D, Zwiers, MP, Thalamuthu, A, Schofield, PR, Freimer, NB, Lawrence, NS, Drevets, W, Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, O, Binder, EB, Blangero, J, Bockholt, HJ, Boen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, Djurovic, S, Dong, H, Donohoe, G, Duggirala, R, Dyer, TD, Ehrlich, S, Ekman, CJ, Elvsashagen, T, Emsell, L, Erk, S, Espeseth, T, Fagerness, J, Fears, S, Fedko, I, Fernandez, G, Fisher, SE, Foroud, T, Fox, PT, Francks, C, Frangou, S, Frey, EM, Frodl, T, Frouin, V, Garavan, H, Giddaluru, S, Glahn, DC, Godlewska, B, Goldstein, RZ, Gollub, RL, Grabe, HJ, Grimm, O, Gruber, O, Guadalupe, T, Gur, RE, Gur, RC, Goering, HHH, Hagenaars, S, Hajek, T, Hall, GB, Hall, J, Hardy, J, Hartman, CA, Hass, J, Hatton, SN, Haukvik, UK, Hegenscheid, K, Heinz, A, Hickie, IB, Ho, B-C, Hoehn, D, Hoekstra, PJ, Hollinshead, M, Holmes, AJ, Homuth, G, Hoogman, M, Hong, LE, Hosten, N, Hottenga, J-J, Pol, HEH, Hwang, KS, Jack, CR, Jenkinson, M, Johnston, C, Joensson, EG, Kahn, RS, Kasperaviciute, D, Kelly, S, Kim, S, Kochunov, P, Koenders, L, Kraemer, B, Kwok, JBJ, Lagopoulos, J, Laje, G, Landen, M, Landman, BA, Lauriello, J, Lawrie, SM, Lee, PH, Le Hellard, S, Lemaitre, H, Leonardo, CD, Li, C-S, Liberg, B, Liewald, DC, Liu, X, Lopez, LM, Loth, E, Lourdusamy, A, Luciano, M, Macciardi, F, Machielsen, MWJ, MacQueen, GM, Malt, UF, Mandl, R, Manoach, DS, Martinot, J-L, Matarin, M, Mather, KA, Mattheisen, M, Mattingsdal, M, Meyer-Lindenberg, A, McDonald, C, McIntosh, AM, McMahon, FJ, McMahon, KL, Meisenzahl, E, Melle, I, Milaneschi, Y, Mohnke, S, Montgomery, GW, Morris, DW, Moses, EK, Mueller, BA, Munoz Maniega, S, Muehleisen, TW, Mueller-Myhsok, B, Mwangi, B, Nauck, M, Nho, K, Nichols, TE, Nilsson, L-G, Nugent, AC, Nyberg, L, Olvera, RL, Oosterlaan, J, Ophoff, RA, Pandolfo, M, Papalampropoulou-Tsiridou, M, Papmeyer, M, Paus, T, Pausova, Z, Pearlson, GD, Penninx, BW, Peterson, CP, Pfennig, A, Phillips, M, Pike, GB, Poline, J-B, Potkin, SG, Puetz, B, Ramasamy, A, Rasmussen, J, Rietschel, M, Rijpkema, M, Risacher, SL, Roffman, JL, Roiz-Santianez, R, Romanczuk-Seiferth, N, Rose, EJ, Royle, NA, Rujescu, D, Ryten, M, Sachdev, PS, Salami, A, Satterthwaite, TD, Savitz, J, Saykin, AJ, Scanlon, C, Schmaal, L, Schnack, HG, Schork, AJ, Schulz, SC, Schuer, R, Seidman, L, Shen, L, Shoemaker, JM, Simmons, A, Sisodiya, SM, Smith, C, Smoller, JW, Soares, JC, Sponheim, SR, Sprooten, E, Starr, JM, Steen, VM, Strakowski, S, Strike, L, Sussmann, J, Saemann, PG, Teumer, A, Toga, AW, Tordesillas-Gutierrez, D, Trabzuni, D, Trost, S, Turner, J, Van den Heuvel, M, van der Wee, NJ, van Eijk, K, van Erp, TGM, van Haren, NEM, van 't Ent, D, van Tol, M-J, Hernandez, MCV, Veltman, DJ, Versace, A, Voelzke, H, Walker, R, Walter, H, Wang, L, Wardlaw, JM, Weale, ME, Weiner, MW, Wen, W, Westlye, LT, Whalley, HC, Whelan, CD, White, T, Winkler, AM, Wittfeld, K, Woldehawariat, G, Wolf, C, Zilles, D, Zwiers, MP, Thalamuthu, A, Schofield, PR, Freimer, NB, Lawrence, NS, and Drevets, W
Abstract: The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
Published: 2014

27. ASSOCIATIONS BETWEEN EPILEPSY-RELATED POLYGENIC RISK AND BRAIN MORPHOLOGY IN CHILDHOOD.

Author: Ngo A, Liu L, Larivière S, Kebets V, Fett S, Weber CF, Royer J, Yu E, Rodríguez-Cruces R, Zhang Z, Ooi LQR, Thomas Yeo BT, Frauscher B, Paquola C, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda CL, Bonilha L, Gleichgerrcht E, Focke NK, Kotikalapudi R, O'Brien TJ, Sinclair B, Vivash L, Desmond PM, Lui E, Vaudano AE, Meletti S, Kälviäinen R, Soltanian-Zadeh H, Winston GP, Tiwari VK, Kreilkamp BAK, Lenge M, Guerrini R, Hamandi K, Rüber T, Bauer T, Devinsky O, Striano P, Kaestner E, Hatton SN, Caciagli L, Kirschner M, Duncan JS, Thompson PM, McDonald CR, Sisodiya SM, Bernasconi N, Bernasconi A, Gan-Or Z, and Bernhardt BC
Abstract: Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is associated with a complex genetic architecture, but the translation from genetic risk factors to brain vulnerability remains unclear. Here, we examined associations between epilepsy-related polygenic risk scores for HS (PRS-HS) and brain structure in a large sample of neurotypical children, and correlated these signatures with case-control findings in in multicentric cohorts of patients with TLE-HS. Imaging-genetic analyses revealed PRS-related cortical thinning in temporo-parietal and fronto-central regions, strongly anchored to distinct functional and structural network epicentres. Compared to disease-related effects derived from epilepsy case-control cohorts, structural correlates of PRS-HS mirrored atrophy and epicentre patterns in patients with TLE-HS. By identifying a potential pathway between genetic vulnerability and disease mechanisms, our findings provide new insights into the genetic underpinnings of structural alterations in TLE-HS and highlight potential imaging-genetic biomarkers for early risk stratification and personalized interventions., Competing Interests: POTENTIAL CONFLICTS OF INTEREST All authors declare no competing conflicts of interest.
Published: 2025
Full Text: View/download PDF

28. 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
Full Text: View/download PDF

29. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

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

30. Associations between MRI-assessed locus coeruleus integrity and cortical gray matter microstructure.

Author: Elman JA, Puckett OK, Hagler DJ, Pearce RC, Fennema-Notestine C, Hatton SN, Lyons MJ, McEvoy LK, Panizzon MS, Reas ET, Dale AM, Franz CE, and Kremen WS
Subjects: Aged, Humans, Magnetic Resonance Imaging methods, Male, Norepinephrine, Water, Gray Matter diagnostic imaging, Locus Coeruleus diagnostic imaging
Abstract: The locus coeruleus (LC) is one of the earliest sites of tau pathology, making it a key structure in early Alzheimer's disease (AD) progression. As the primary source of norepinephrine for the brain, reduced LC integrity may have negative consequences for brain health, yet macrostructural brain measures (e.g. cortical thickness) may not be sensitive to early stages of neurodegeneration. We therefore examined whether LC integrity was associated with differences in cortical gray matter microstructure among 435 men (mean age = 67.5; range = 62-71.7). LC structural integrity was indexed by contrast-to-noise ratio (LCCNR) from a neuromelanin-sensitive MRI scan. Restriction spectrum imaging (RSI), an advanced multi-shell diffusion technique, was used to characterize cortical microstructure, modeling total diffusion in restricted, hindered, and free water compartments. Higher LCCNR (greater integrity) was associated with higher hindered and lower free water diffusion in multiple cortical regions. In contrast, no associations between LCCNR and cortical thickness survived correction. Results suggest lower LC integrity is associated with patterns of cortical microstructure that may reflect a reduction in cytoarchitectural barriers due to broader neurodegenerative processes. These findings highlight the potential utility for LC imaging and advanced diffusion measures of cortical microstructure in assessing brain health and early identification of neurodegenerative processes., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Published: 2022
Full Text: View/download PDF

31. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression.

Author: Larivière S, Royer J, Rodríguez-Cruces R, Paquola C, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda CL, Bonilha L, Gleichgerrcht E, Focke NK, Domin M, von Podewills F, Langner S, Rummel C, Wiest R, Martin P, Kotikalapudi R, O'Brien TJ, Sinclair B, Vivash L, Desmond PM, Lui E, 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, Winston GP, Griffin A, Singh A, Tiwari VK, Kreilkamp BAK, Lenge M, Guerrini R, Hamandi K, Foley S, Rüber T, Weber B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Severino M, Striano P, Tortora D, Kaestner E, Hatton SN, Vos SB, Caciagli L, Duncan JS, Whelan CD, Thompson PM, Sisodiya SM, Bernasconi A, Labate A, McDonald CR, Bernasconi N, and Bernhardt BC
Subjects: Adult, Gene Expression, Humans, Immunoglobulin E, Magnetic Resonance Imaging, Nerve Net, Connectome, Epilepsy, Epilepsy, Generalized genetics, Epilepsy, Temporal Lobe diagnosis, Epilepsy, Temporal Lobe genetics
Abstract: Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes., (© 2022. The Author(s).)
Published: 2022
Full Text: View/download PDF

32. Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy.

Author: Park BY, Larivière S, Rodríguez-Cruces R, Royer J, Tavakol S, Wang Y, Caciagli L, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Alvim MKM, Yasuda C, Bonilha L, Gleichgerrcht E, Focke NK, Kreilkamp BAK, Domin M, von Podewils F, Langner S, Rummel C, Rebsamen M, Wiest R, Martin P, Kotikalapudi R, Bender B, O'Brien TJ, Law M, Sinclair B, Vivash L, Kwan P, Desmond PM, Malpas CB, Lui E, 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, Weber B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Severino M, Striano P, Parodi C, Tortora D, Hatton SN, Vos SB, Duncan JS, Galovic M, Whelan CD, Bargalló N, Pariente J, Conde-Blanco E, Vaudano AE, Tondelli M, Meletti S, Kong XZ, Francks C, Fisher SE, Caldairou B, Ryten M, Labate A, Sisodiya SM, Thompson PM, McDonald CR, Bernasconi A, Bernasconi N, and Bernhardt BC
Subjects: Adult, Atrophy pathology, Hippocampus pathology, Humans, Magnetic Resonance Imaging, Connectome, Epilepsy, Temporal Lobe pathology
Abstract: Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)
Published: 2022
Full Text: View/download PDF

33. The Impact of Genes and Environment on Brain Ageing in Males Aged 51 to 72 Years.

Author: Gillespie NA, Hatton SN, Hagler DJ Jr, Dale AM, Elman JA, McEvoy LK, Eyler LT, Fennema-Notestine C, Logue MW, McKenzie RE, Puckett OK, Tu XM, Whitsel N, Xian H, Reynolds CA, Panizzon MS, Lyons MJ, Neale MC, Kremen WS, and Franz C
Abstract: Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimer's Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 51-72 years. Biometrical genetic analyses "twin models" revealed significant and highly correlated estimates of additive genetic heritability ranging from 59 to 75%. Multivariate longitudinal modeling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences., Competing Interests: NAG hold equity in Cassava Sciences, Inc. LKM holds equity in CorTechs Labs, Inc. The remaining 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 © 2022 Gillespie, Hatton, Hagler, Dale, Elman, McEvoy, Eyler, Fennema-Notestine, Logue, McKenzie, Puckett, Tu, Whitsel, Xian, Reynolds, Panizzon, Lyons, Neale, Kremen and Franz.)
Published: 2022
Full Text: View/download PDF

34. Long-term associations of cigarette smoking in early mid-life with predicted brain aging from mid- to late life.

Author: Whitsel N, Reynolds CA, Buchholz EJ, Pahlen S, Pearce RC, Hatton SN, Elman JA, Gillespie NA, Gustavson DE, Puckett OK, Dale AM, Eyler LT, Fennema-Notestine C, Hagler DJ Jr, Hauger RL, McEvoy LK, McKenzie R, Neale MC, Panizzon MS, Sanderson-Cimino M, Toomey R, Tu XM, Williams MKE, Bell T, Xian H, Lyons MJ, Kremen WS, and Franz CE
Subjects: Adolescent, Adult, Aged, Aging, Brain diagnostic imaging, Female, Humans, Male, Middle Aged, Prospective Studies, Nicotiana, Young Adult, Cigarette Smoking epidemiology
Abstract: Background and Aims: Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later., Design: Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated., Setting: Population-based United States sample., Participants/cases: Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves., Measurements: Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19)., Findings: In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD., Conclusions: Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men., (© 2021 Society for the Study of Addiction.)
Published: 2022
Full Text: View/download PDF

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

Author: Dima D, Modabbernia A, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes-Eizagirre A, Alnaes D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur-Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales-Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim-Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo-Facorro B, Crivello F, Crone EA, Dannlowski U, Dale AM, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros-Bergman H, Fisher SE, Fouche JP, Franke B, Frodl T, Fuentes-Claramonte P, Glahn DC, Gotlib IH, Grabe HJ, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho BC, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch KP, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez-Zalacaín I, Mataix-Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer-Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol-Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez-Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano-Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, Van't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Williams SCR, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, and Frangou S
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Young Adult, Amygdala anatomy & histology, Amygdala diagnostic imaging, Corpus Striatum anatomy & histology, Corpus Striatum diagnostic imaging, Hippocampus anatomy & histology, Hippocampus diagnostic imaging, Human Development physiology, Neuroimaging, Thalamus anatomy & histology, Thalamus diagnostic imaging
Abstract: Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
Full Text: View/download PDF

36. Paradoxical cognitive trajectories in men from earlier to later adulthood.

Author: Eglit GML, Elman JA, Panizzon MS, Sanderson-Cimino M, Williams ME, Dale AM, Eyler LT, Fennema-Notestine C, Gillespie NA, Gustavson DE, Hatton SN, Hagler DJ Jr, Hauger RL, Jak AJ, Logue MW, McEvoy LK, McKenzie RE, Neale MC, Puckett O, Reynolds CA, Toomey R, Tu XM, Whitsel N, Xian H, Lyons MJ, Franz CE, and Kremen WS
Subjects: Adult, Aged, Aging genetics, Apolipoproteins E metabolism, Humans, Longitudinal Studies, Male, Memory, Memory, Short-Term, Middle Aged, Neuropsychological Tests, Twin Studies as Topic, Twins, Young Adult, Aging psychology, Brain physiology, Cognition, Executive Function
Abstract: Because longitudinal studies of aging typically lack cognitive data from earlier ages, it is unclear how general cognitive ability (GCA) changes throughout the life course. In 1173 Vietnam Era Twin Study of Aging (VETSA) participants, we assessed young adult GCA at average age 20 and current GCA at 3 VETSA assessments beginning at average age 56. The same GCA index was used throughout. Higher young adult GCA and better GCA maintenance were associated with stronger specific cognitive abilities from age 51 to 73. Given equivalent GCA at age 56, individuals who had higher age 20 GCA outperformed those whose GCA remained stable in terms of memory, executive function, and working memory abilities from age 51 to 73. Thus, paradoxically, despite poorer maintenance of GCA, high young adult GCA still conferred benefits. Advanced predicted brain age and the combination of elevated vascular burden and APOE-ε4 status were associated with poorer maintenance of GCA. These findings highlight the importance of distinguishing between peak and current GCA for greater understanding of cognitive aging., Competing Interests: Disclosure statement L.K. McEvoy has stock options in CorTechs Laboratories, Inc. A.M. Dale is a founder of and holds equity in CorTechs Laboratories, Inc and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc and receives funding through research agreements with General Electric Healthcare and Medtronic, Inc. The terms of these arrangements have been reviewed and approved by University of California, San Diego in accordance with conflict of interest policies. The remaining authors disclose no conflicts., (Published by Elsevier Inc.)
Published: 2022
Full Text: View/download PDF

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

Author: Frangou S, Modabbernia A, Williams SCR, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes-Eizagirre A, Alnaes D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur-Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales-Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim-Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo-Facorro B, Crivello F, Crone EA, Dale AM, Dannlowski U, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros-Bergman H, Fisher SE, Fouche JP, Franke B, Frodl T, Fuentes-Claramonte P, Glahn DC, Gotlib IH, Grabe HJ, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho BC, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch KP, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez-Zalacaín I, Mataix-Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer-Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol-Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez-Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano-Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, van 't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, and Dima D
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Young Adult, Cross-Sectional Studies, Cerebral Cortex anatomy & histology, Cerebral Cortex diagnostic imaging, Human Development physiology, Neuroimaging
Abstract: Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
Full Text: View/download PDF

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

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

39. Correction: Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders.

Author: Opel N, Thalamuthu A, Milaneschi Y, Grotegerd D, Flint C, Leenings R, Goltermann J, Richter M, Hahn T, Woditsch G, Berger K, Hermesdorf M, McIntosh A, Whalley HC, Harris MA, MacMaster FP, Walter H, Veer IM, Frodl T, Carballedo A, Krug A, Nenadic I, Kircher T, Aleman A, Groenewold NA, Stein DJ, Soares JC, Zunta-Soares GB, Mwangi B, Wu MJ, Walter M, Li M, Harrison BJ, Davey CG, Cullen KR, Klimes-Dougan B, Mueller BA, Sämann PG, Penninx B, Nawijn L, Veltman DJ, Aftanas L, Brak IV, Filimonova EA, Osipov EA, Reneman L, Schrantee A, Grabe HJ, Van der Auwera S, Wittfeld K, Hosten N, Völzke H, Sim K, Gotlib IH, Sacchet MD, Lagopoulos J, Hatton SN, Hickie I, Pozzi E, Thompson PM, Jahanshad N, Schmaal L, Baune BT, and Dannlowski U
Published: 2021
Full Text: View/download PDF

40. Lifestyle and the aging brain: interactive effects of modifiable lifestyle behaviors and cognitive ability in men from midlife to old age.

Author: Franz CE, Hatton SN, Elman JA, Warren T, Gillespie NA, Whitsel NA, Puckett OK, Dale AM, Eyler LT, Fennema-Notestine C, Hagler DJ Jr, Hauger RL, McKenzie R, Neale MC, Panizzon MS, Pearce RC, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Williams M, Xian H, Lyons MJ, and Kremen WS
Subjects: Adult, Age Factors, Aged, Aging pathology, Alzheimer Disease pathology, Humans, Male, Middle Aged, White Matter pathology, Young Adult, Aging physiology, Aging psychology, Alzheimer Disease prevention & control, Behavior physiology, Cognition physiology, Cognitive Reserve physiology, Healthy Lifestyle physiology, Independent Living psychology, Life Style
Abstract: We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals., (Copyright © 2021. Published by Elsevier Inc.)
Published: 2021
Full Text: View/download PDF

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

Author: Opel N, Thalamuthu A, Milaneschi Y, Grotegerd D, Flint C, Leenings R, Goltermann J, Richter M, Hahn T, Woditsch G, Berger K, Hermesdorf M, McIntosh A, Whalley HC, Harris MA, MacMaster FP, Walter H, Veer IM, Frodl T, Carballedo A, Krug A, Nenadic I, Kircher T, Aleman A, Groenewold NA, Stein DJ, Soares JC, Zunta-Soares GB, Mwangi B, Wu MJ, Walter M, Li M, Harrison BJ, Davey CG, Cullen KR, Klimes-Dougan B, Mueller BA, Sämann PG, Penninx B, Nawijn L, Veltman DJ, Aftanas L, Brak IV, Filimonova EA, Osipov EA, Reneman L, Schrantee A, Grabe HJ, Van der Auwera S, Wittfeld K, Hosten N, Völzke H, Sim K, Gotlib IH, Sacchet MD, Lagopoulos J, Hatton SN, Hickie I, Pozzi E, Thompson PM, Jahanshad N, Schmaal L, Baune BT, and Dannlowski U
Subjects: Aged, Brain diagnostic imaging, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Obesity genetics, Risk Factors, Depressive Disorder, Major genetics
Abstract: Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = -0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions., (© 2020. The Author(s).)
Published: 2021
Full Text: View/download PDF

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

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

43. Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts.

Author: Campos AI, Thompson PM, Veltman DJ, Pozzi E, van Veltzen LS, Jahanshad N, Adams MJ, Baune BT, Berger K, Brosch K, Bülow R, Connolly CG, Dannlowski U, Davey CG, de Zubicaray GI, Dima D, Erwin-Grabner T, Evans JW, Fu CHY, Gotlib IH, Goya-Maldonado R, Grabe HJ, Grotegerd D, Harris MA, Harrison BJ, Hatton SN, Hermesdorf M, Hickie IB, Ho TC, Kircher T, Krug A, Lagopoulos J, Lemke H, McMahon K, MacMaster FP, Martin NG, McIntosh AM, Medland SE, Meinert S, Meller T, Nenadic I, Opel N, Redlich R, Reneman L, Repple J, Sacchet MD, Schmitt S, Schrantee A, Sim K, Singh A, Stein F, Strike LT, van der Wee NJA, van der Werff SJA, Völzke H, Waltemate L, Whalley HC, Wittfeld K, Wright MJ, Yang TT, Zarate CA, Schmaal L, and Rentería ME
Subjects: Brain diagnostic imaging, Humans, Magnetic Resonance Imaging, Neuroimaging, Depressive Disorder, Major diagnostic imaging, Suicide, Attempted
Abstract: Background: Neuroimaging studies of suicidal behavior have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD Working Group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior., Methods: Here, we pooled data from 18 international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy control subjects and 6448 people with depression, of whom 694 had attempted suicide). We compared regional cortical thickness and surface area and measures of subcortical, lateral ventricular, and intracranial volumes between suicide attempters, clinical control subjects (nonattempters with depression), and healthy control subjects., Results: We identified 25 regions of interest with statistically significant (false discovery rate < .05) differences between groups. Post hoc examinations identified neuroimaging markers associated with suicide attempt including smaller volumes of the left and right thalamus and the right pallidum and lower surface area of the left inferior parietal lobe., Conclusions: This study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt and further demonstrates the need for well-powered samples and collaborative efforts. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidal behaviors may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk., (Copyright © 2021 Society of Biological Psychiatry. All rights reserved.)
Published: 2021
Full Text: View/download PDF

44. 12-year prediction of mild cognitive impairment aided by Alzheimer's brain signatures at mean age 56.

Author: Williams ME, Elman JA, McEvoy LK, Andreassen OA, Dale AM, Eglit GML, Eyler LT, Fennema-Notestine C, Franz CE, Gillespie NA, Hagler DJ Jr, Hatton SN, Hauger RL, Jak AJ, Logue MW, Lyons MJ, McKenzie RE, Neale MC, Panizzon MS, Puckett OK, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Whitsel N, Xian H, and Kremen WS
Abstract: Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer's disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer's disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves ( n s = 246-367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51-60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer's disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61-71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer's disease risk and the potential value of mean diffusivity and/or multimodal brain signatures., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)
Published: 2021
Full Text: View/download PDF

45. MRI-assessed locus coeruleus integrity is heritable and associated with multiple cognitive domains, mild cognitive impairment, and daytime dysfunction.

Author: Elman JA, Puckett OK, Beck A, Fennema-Notestine C, Cross LK, Dale AM, Eglit GML, Eyler LT, Gillespie NA, Granholm EL, Gustavson DE, Hagler DJ Jr, Hatton SN, Hauger R, Jak AJ, Logue MW, McEvoy LK, McKenzie RE, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Toomey R, Tu XM, Whitsel N, Williams ME, Xian H, Lyons MJ, Franz CE, and Kremen WS
Subjects: Aged, Aging physiology, Cognitive Dysfunction diagnostic imaging, Disease Progression, Humans, Magnetic Resonance Imaging, Male, Memory Disorders, Neuropsychological Tests statistics & numerical data, Sleep, Cognition physiology, Cognitive Dysfunction pathology, Locus Coeruleus pathology
Abstract: Introduction: The locus coeruleus (LC) undergoes extensive neurodegeneration in early Alzheimer's disease (AD). The LC is implicated in regulating the sleep-wake cycle, modulating cognitive function, and AD progression., Methods: Participants were 481 men (ages 62 to 71.7) from the Vietnam Era Twin Study of Aging. LC structural integrity was indexed by neuromelanin-sensitive magnetic resonance imaging (MRI) contrast-to-noise ratio (LC CNR ). We examined LC CNR , cognition, amnestic mild cognitive impairment (aMCI), and daytime dysfunction., Results: Heritability of LC CNR was .48. Participants with aMCI showed greater daytime dysfunction. Lower LC CNR was associated with poorer episodic memory, general verbal fluency, semantic fluency, and processing speed, as well as increased odds of aMCI and greater daytime dysfunction., Discussion: Reduced LC integrity is associated with widespread differences across cognitive domains, daytime sleep-related dysfunction, and risk for aMCI. These findings in late-middle-aged adults highlight the potential of MRI-based measures of LC integrity in early identification of AD risk., (© 2021 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
Published: 2021
Full Text: View/download PDF

46. Periventricular and deep abnormal white matter differ in associations with cognitive performance at midlife.

Author: Sanderson-Cimino M, Panizzon MS, Elman JA, Tu X, Gustavson DE, Puckett O, Cross K, Notestine R, Hatton SN, Eyler LT, McEvoy LK, Hagler DJ, Neale MC, Gillespie NA, Lyons MJ, Franz CE, Fennema-Notestine C, and Kremen WS
Subjects: Aged, Cognitive Dysfunction physiopathology, Humans, Independent Living, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Organ Size, Risk Factors, White Matter pathology, Cognition physiology, Cognitive Dysfunction diagnostic imaging, Executive Function, Leukoencephalopathies diagnostic imaging, Memory, Episodic, Memory, Short-Term, White Matter diagnostic imaging
Abstract: Objective: Abnormal white matter (AWM) on magnetic resonance imaging is associated with cognitive performance in older adults. We explored cognitive associations with AWM during late-midlife. Method: Participants were community-dwelling men ( n = 242; M = 61.90 years; range = 56-66). Linear-mixed effects regression models examined associations of total, periventricular, and deep AWM with cognitive performance, controlling for multiple comparisons. Models considering specific cognitive domains controlled for current general cognitive ability (GCA). We hypothesized that total AWM would be associated with worse processing speed, executive function, and current GCA; deep AWM would correlate with GCA and periventricular AWM would relate to specific cognitive abilities. We also assessed the potential influence of cognitive reserve by examining a moderation effect of early life (mean age of 20) cognition. Results: Greater total and deep AWM were associated with poorer current GCA. Periventricular AWM was associated with worse executive function, working memory, and episodic memory. When periventricular and deep AWM were modeled simultaneously, both retained their respective significant associations with cognitive performance. Cognitive reserve did not moderate associations. Conclusions: Our findings suggest that AWM contributes to poorer cognitive function in late-midlife. Examining only total AWM may obscure the potential differential impact of regional AWM. Separating total AWM into subtypes while controlling for current GCA revealed a dissociation in relationships with cognitive performance; deep AWM was associated with nonspecific cognitive ability whereas periventricular AWM was associated with specific frontal-related abilities and memory. Management of vascular or other risk factors that may increase the risk of AWM should begin during or before early late-midlife. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Published: 2021
Full Text: View/download PDF

47. White Matter Integrity According to the Stage of Mental Disorder in Youth.

Author: Sacks DD, Lagopoulos J, Hatton SN, Iorfino F, Carpenter JS, Crouse JJ, Naismith SL, Scott EM, Hickie IB, and Hermens DF
Subjects: Adolescent, Anisotropy, Corpus Callosum diagnostic imaging, Diffusion Tensor Imaging, Humans, Psychotic Disorders, White Matter diagnostic imaging
Abstract: The present study investigated differences in white matter (WM) integrity between 96 young people with affective and/or psychotic symptoms classified at an early stage of mental disorder (i.e. 'attenuated syndrome'; stage 1b), 85 young people classified at a more advanced stage of mental disorder (i.e. 'discrete disorder'; stage 2), and 81 demographically matched healthy controls using diffusion tensor imaging. The relationship between WM integrity (indexed by fractional anisotropy; FA) across the tracts and neuropsychological functioning was also investigated. A significant reduction in FA was identified in those with more advanced disorder in the body of the corpus callosum. Clinical stage groups were associated with significant neuropsychological impairment, which was significantly greater in those with discrete disorders. Compared to those in the earlier stage of disorder, participants at the later clinical stage showed decreased FA in the body of the corpus callosum that was associated with worse performance in attentional set formation maintenance, shifting and flexibility. These results provide further support for clinical staging of mental disorder and highlight the potential for utilising neuroanatomical biomarkers to support the classification of stages of mental disorder in the future., (Copyright © 2020. Published by Elsevier B.V.)
Published: 2021
Full Text: View/download PDF

48. Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.

Author: Gleichgerrcht E, Munsell BC, Alhusaini S, Alvim MKM, Bargalló N, Bender B, Bernasconi A, Bernasconi N, Bernhardt B, Blackmon K, Caligiuri ME, Cendes F, Concha L, Desmond PM, Devinsky O, Doherty CP, Domin M, Duncan JS, Focke NK, Gambardella A, Gong B, Guerrini R, Hatton SN, Kälviäinen R, Keller SS, Kochunov P, Kotikalapudi R, Kreilkamp BAK, Labate A, Langner S, Larivière S, Lenge M, Lui E, Martin P, Mascalchi M, Meletti S, O'Brien TJ, Pardoe HR, Pariente JC, Xian Rao J, Richardson MP, Rodríguez-Cruces R, Rüber T, Sinclair B, Soltanian-Zadeh H, Stein DJ, Striano P, Taylor PN, Thomas RH, Elisabetta Vaudano A, Vivash L, von Podewills F, Vos SB, Weber B, Yao Y, Lin Yasuda C, Zhang J, Thompson PM, Sisodiya SM, McDonald CR, and Bonilha L
Subjects: Artificial Intelligence, Brain diagnostic imaging, Brain pathology, Hippocampus diagnostic imaging, Hippocampus pathology, Humans, Magnetic Resonance Imaging, Sclerosis pathology, Support Vector Machine, Epilepsy, Temporal Lobe diagnostic imaging, Epilepsy, Temporal Lobe pathology
Abstract: Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2021
Full Text: View/download PDF

49. Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder: results from the ENIGMA MDD Working Group.

Author: Leerssen J, Blanken TF, Pozzi E, Jahanshad N, Aftanas L, Andreassen OA, Baune BT, Brack I, Carballedo A, Ching CRK, Dannlowski U, Dohm K, Enneking V, Filimonova E, Fingas SM, Frodl T, Godlewska BR, Goltermann J, Gotlib IH, Grotegerd D, Gruber O, Harris MA, Hatton SN, Hawkins E, Hickie IB, Jaworska N, Kircher T, Krug A, Lagopoulos J, Lemke H, Li M, MacMaster FP, McIntosh AM, McLellan Q, Meinert S, Mwangi B, Nenadić I, Osipov E, Portella MJ, Redlich R, Repple J, Sacchet MD, Sämann PG, Simulionyte E, Soares JC, Walter M, Watanabe N, Whalley HC, Yüksel D, Veltman DJ, Thompson PM, Schmaal L, and Van Someren EJW
Subjects: Adolescent, Adult, Aged, Brain diagnostic imaging, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Middle Aged, Young Adult, Depressive Disorder, Major diagnostic imaging, Sleep Initiation and Maintenance Disorders diagnostic imaging
Abstract: It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD.
Published: 2020
Full Text: View/download PDF

50. Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study.

Author: Larivière S, Rodríguez-Cruces R, Royer J, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda C, Bonilha L, Gleichgerrcht E, Focke NK, Domin M, von Podewills F, Langner S, Rummel C, Wiest R, Martin P, Kotikalapudi R, O'Brien TJ, Sinclair B, Vivash L, Desmond PM, 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, Weber B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Severino M, Striano P, Tortora D, Hatton SN, Vos SB, Duncan JS, Whelan CD, Thompson PM, Sisodiya SM, Bernasconi A, Labate A, McDonald CR, Bernasconi N, and Bernhardt BC
Abstract: Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)
Published: 2020
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Database

Publisher

123 results on '"Hatton, SN"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources