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

Author

Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Krämer, B, Overs, B, Hartberg, CB, Abé, C, Dima, D, Grotegerd, D, Sprooten, E, Bøen, E, Jimenez, E, Howells, FM, Delvecchio, G, Temmingh, H, Starke, J, Almeida, JRC, Goikolea, JM, Houenou, J, Beard, LM, Rauer, L, Abramovic, L, Bonnin, M, Ponteduro, MF, Keil, M, Rive, MM, Yao, N, Yalin, N, Najt, P, Rosa, PG, Redlich, R, Trost, S, Hagenaars, S, Fears, SC, Alonso-Lana, S, van Erp, TGM, Nickson, T, Chaim-Avancini, TM, Meier, TB, Elvsåshagen, T, Haukvik, UK, Lee, WH, Schene, AH, Lloyd, AJ, Young, AH, Nugent, A, Dale, AM, Pfennig, A, McIntosh, AM, Lafer, B, Baune, BT, Ekman, CJ, Zarate, CA, Bearden, CE, Henry, C, Simhandl, C, McDonald, C, Bourne, C, Stein, DJ, Wolf, DH, Cannon, DM, Glahn, DC, Veltman, DJ, Pomarol-Clotet, E, Vieta, E, Canales-Rodriguez, EJ, Nery, FG, Duran, FLS, Busatto, GF, Roberts, G, Pearlson, GD, Goodwin, GM, Kugel, H, Whalley, HC, Ruhe, HG, Soares, JC, Fullerton, JM, Rybakowski, JK, Savitz, J, Chaim, KT, Fatjó-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landén, M, and Lawrence, NS

Subjects
Brain, Cerebral Cortex, Frontal Lobe, Prefrontal Cortex, Temporal Lobe, Humans, Magnetic Resonance Imaging, Case-Control Studies, Bipolar Disorder, Psychotic Disorders, Age Factors, Sex Factors, Adolescent, Adult, Middle Aged, Female, Male, Young Adult, Neuroimaging, Gray Matter, Clinical Research, Serious Mental Illness, Biomedical Imaging, Brain Disorders, Mental Health, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

Published
2018

2. Characterization of technological properties of matá-matá wood (Eschweilera coriacea [DC.] S.A. Mori, E. odora Poepp. [Miers] and E. truncata A.C. Sm.) by Near Infrared Spectroscopy

Author

do Nascimento CS, do Nascimento CC, de Araújo RD, Rodrigues Soares JC, and Higuchi N

Subjects
Eschweilera, Amazonian Woods, Wood Chemistry, Physico-mechanical Properties, NIR Spectroscopy, Forest Management, Forestry, SD1-669.5
Abstract

The aim of this study was to determine the technological properties (chemical, mechanical and physical) of Eschweilera sp. woods using near infrared spectroscopy (NIRS). NIR spectroscopy proved to be efficient for chemical analysis (extractives, lignin and carbohydrates) and physical-mechanical testing (moisture content, basic density - BD, modulus of elasticity - MOE and modulus of rupture - MOR) of wood, providing a powerful tool for use in sustainable forest management activities in the Amazon. Wood samples from three trees of each Eschweilera species were collected from the Experimental Station of Tropical Forestry/INPA/Brazil. Specimens were extracted from the cross-sectional area (20 × 20 × 30 mm) in the direction sapwood-heartwood. NIR spectra (4.000-10.000 cm-1) were then obtained from the samples (moisture 12%) using Fourier-transform spectrometry. The Partial Least Squares (PLS) regression prediction models for the chemical, mechanical and physical properties of Amazonian woods were used for quantification. The results for total extractives (both in toluene and ethanol) and hot water solubility showed a maximum extractive concentration of 7.66% and 3.13% for E. odora (yellow matá-matá), including several compounds with low molecular weight, such as resins, gums, terpenes, alkaloids, flavonoids, and tannins. The highest concentration of phenolic substances (tannins) was found in E. Truncata (black matá-matá, 10.00%). The macrocomponents (primary metabolism) of Eschweilera species were in the range of 44.20-46.33% for cellulose and 28.89-31.21% for lignin. Mineral compounds (ash) were quantified in concentrations < 0.70%. The predictive results for the physical and mechanical properties of matá-matá wood are in the standard range for tropical woods. The higher calorific value (HCV) varied from 4.993-5.033 cal g-1 and the BD from 0.78-0.88 g m-3. Regarding moisture, the highest content was observed in E. truncata (13.46%). Values for mechanical resistance were in the range of 14,253-17,447 MPa for MOE and 146.04-175.73 MPa for MOR, with the greatest strength attributed to E. truncata wood. The values obtained for the wood technological properties of E. coriacea (white matá-matá), E. odora (yellow matá-matá) and E. truncata (black matá-matá) were compatible with those obtained by destructive determination of tropical species and also for other species of the genus Eschweilera.

Published
2021
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3. 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

4. Subcortical volumetric abnormalities in bipolar disorder.

Author

Hibar, DP, Westlye, LT, van Erp, TGM, Rasmussen, J, Leonardo, CD, Faskowitz, J, Haukvik, UK, Hartberg, CB, Doan, NT, Agartz, I, Dale, AM, Gruber, O, Krämer, B, Trost, S, Liberg, B, Abé, C, Ekman, CJ, Ingvar, M, Landén, M, Fears, SC, Freimer, NB, Bearden, CE, Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes, Sprooten, E, Glahn, DC, Pearlson, GD, Emsell, L, Kenney, J, Scanlon, C, McDonald, C, Cannon, DM, Almeida, J, Versace, A, Caseras, X, Lawrence, NS, Phillips, ML, Dima, D, Delvecchio, G, Frangou, S, Satterthwaite, TD, Wolf, D, Houenou, J, Henry, C, Malt, UF, Bøen, E, Elvsåshagen, T, Young, AH, Lloyd, AJ, Goodwin, GM, Mackay, CE, Bourne, C, Bilderbeck, A, Abramovic, L, Boks, MP, van Haren, NEM, Ophoff, RA, Kahn, RS, Bauer, M, Pfennig, A, Alda, M, Hajek, T, Mwangi, B, Soares, JC, Nickson, T, Dimitrova, R, Sussmann, JE, Hagenaars, S, Whalley, HC, McIntosh, AM, Thompson, PM, and Andreassen, OA

Subjects
Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes, Brain, Humans, Magnetic Resonance Imaging, Organ Size, Case-Control Studies, Retrospective Studies, Bipolar Disorder, Adult, Middle Aged, Female, Male, Clinical Research, Mental Health, Brain Disorders, Serious Mental Illness, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Mental health, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case-control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen's d=-0.232; P=3.50 × 10-7) and thalamus (d=-0.148; P=4.27 × 10-3) and enlarged lateral ventricles (d=-0.260; P=3.93 × 10-5) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.

Published
2016

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

Author

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

Abstract

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

Published
2024

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

Author

Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, Van der Wee, NJA, Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, and Van der Wee, NJA

Abstract

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.

Published
2023

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

Author

Ching, CRK, Hibar, DP, Gurholt, TP, Nunes, A, Thomopoulos, SI, Abe, C, Agartz, I, Brouwer, RM, Cannon, DM, de Zwarte, SMC, Eyler, LT, Favre, P, Hajek, T, Haukvik, UK, Houenou, J, Landen, M, Lett, TA, McDonald, C, Nabulsi, L, Patel, Y, Pauling, ME, Paus, T, Radua, J, Soeiro-de-Souza, MG, Tronchin, G, van Haren, NEM, Vieta, E, Walter, H, Zeng, L-L, Alda, M, Almeida, J, Alnaes, D, Alonso-Lana, S, Altimus, C, Bauer, M, Baune, BT, Bearden, CE, Bellani, M, Benedetti, F, Berk, M, Bilderbeck, AC, Blumberg, HP, Boen, E, Bollettini, I, del Mar Bonnin, C, Brambilla, P, Canales-Rodriguez, EJ, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Dima, D, Duchesnay, E, Elvsashagen, T, Fears, SC, Frangou, S, Fullerton, JM, Glahn, DC, Goikolea, JM, Green, MJ, Grotegerd, D, Gruber, O, Haarman, BCM, Henry, C, Howells, FM, Ives-Deliperi, V, Jansen, A, Kircher, TTJ, Knoechel, C, Kramer, B, Lafer, B, Lopez-Jaramillo, C, Machado-Vieira, R, MacIntosh, BJ, Melloni, EMT, Mitchell, PB, Nenadic, I, Nery, F, Nugent, AC, Oertel, V, Ophoff, RA, Ota, M, Overs, BJ, Pham, DL, Phillips, ML, Pineda-Zapata, JA, Poletti, S, Polosan, M, Pomarol-Clotet, E, Pouchon, A, Quide, Y, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Schene, AH, Sim, K, Soares, JC, Staeblein, M, Stein, DJ, Tamnes, CK, Thomaidis, GV, Upegui, CV, Veltman, DJ, Wessa, M, Westlye, LT, Whalley, HC, Wolf, DH, Wu, M-J, Yatham, LN, Zarate, CA, Thompson, PM, Andreassen, OA, Ching, CRK, Hibar, DP, Gurholt, TP, Nunes, A, Thomopoulos, SI, Abe, C, Agartz, I, Brouwer, RM, Cannon, DM, de Zwarte, SMC, Eyler, LT, Favre, P, Hajek, T, Haukvik, UK, Houenou, J, Landen, M, Lett, TA, McDonald, C, Nabulsi, L, Patel, Y, Pauling, ME, Paus, T, Radua, J, Soeiro-de-Souza, MG, Tronchin, G, van Haren, NEM, Vieta, E, Walter, H, Zeng, L-L, Alda, M, Almeida, J, Alnaes, D, Alonso-Lana, S, Altimus, C, Bauer, M, Baune, BT, Bearden, CE, Bellani, M, Benedetti, F, Berk, M, Bilderbeck, AC, Blumberg, HP, Boen, E, Bollettini, I, del Mar Bonnin, C, Brambilla, P, Canales-Rodriguez, EJ, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Dima, D, Duchesnay, E, Elvsashagen, T, Fears, SC, Frangou, S, Fullerton, JM, Glahn, DC, Goikolea, JM, Green, MJ, Grotegerd, D, Gruber, O, Haarman, BCM, Henry, C, Howells, FM, Ives-Deliperi, V, Jansen, A, Kircher, TTJ, Knoechel, C, Kramer, B, Lafer, B, Lopez-Jaramillo, C, Machado-Vieira, R, MacIntosh, BJ, Melloni, EMT, Mitchell, PB, Nenadic, I, Nery, F, Nugent, AC, Oertel, V, Ophoff, RA, Ota, M, Overs, BJ, Pham, DL, Phillips, ML, Pineda-Zapata, JA, Poletti, S, Polosan, M, Pomarol-Clotet, E, Pouchon, A, Quide, Y, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Schene, AH, Sim, K, Soares, JC, Staeblein, M, Stein, DJ, Tamnes, CK, Thomaidis, GV, Upegui, CV, Veltman, DJ, Wessa, M, Westlye, LT, Whalley, HC, Wolf, DH, Wu, M-J, Yatham, LN, Zarate, CA, Thompson, PM, and Andreassen, OA

Abstract

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Published
2022

9. Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Author

van Velzen, LS, Dauvermann, MR, Colic, L, Villa, LM, Savage, HS, Toenders, YJ, Zhu, AH, Bright, JK, Campos, A, Salminen, LE, Ambrogi, S, Ayesa-Arriola, R, Banaj, N, Basgoze, Z, Bauer, J, Blair, K, Blair, RJ, Brosch, K, Cheng, Y, Colle, R, Connolly, CG, Corruble, E, Couvy-Duchesne, B, Crespo-Facorro, B, Cullen, KR, Dannlowski, U, Davey, CG, Dohm, K, Fullerton, JM, Gonul, AS, Gotlib, IH, Grotegerd, D, Hahn, T, Harrison, BJ, He, M, Hickie, IB, Ho, TC, Iorfino, F, Jansen, A, Jollant, F, Kircher, T, Klimes-Dougan, B, Klug, M, Leehr, EJ, Lippard, ETC, McLaughlin, KA, Meinert, S, Miller, AB, Mitchell, PB, Mwangi, B, Nenadic, I, Ojha, A, Overs, BJ, Pfarr, J-K, Piras, F, Ringwald, KG, Roberts, G, Romer, G, Sanches, M, Sheridan, MA, Soares, JC, Spalletta, G, Stein, F, Teresi, G, Tordesillas-Gutierrez, D, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJ, Vermeiren, RRJM, Winter, A, Wu, M-J, Yang, TT, Thompson, PM, Renteria, ME, Jahanshad, N, Blumberg, HP, Van Harmelen, A-L, Schmaal, L, van Velzen, LS, Dauvermann, MR, Colic, L, Villa, LM, Savage, HS, Toenders, YJ, Zhu, AH, Bright, JK, Campos, A, Salminen, LE, Ambrogi, S, Ayesa-Arriola, R, Banaj, N, Basgoze, Z, Bauer, J, Blair, K, Blair, RJ, Brosch, K, Cheng, Y, Colle, R, Connolly, CG, Corruble, E, Couvy-Duchesne, B, Crespo-Facorro, B, Cullen, KR, Dannlowski, U, Davey, CG, Dohm, K, Fullerton, JM, Gonul, AS, Gotlib, IH, Grotegerd, D, Hahn, T, Harrison, BJ, He, M, Hickie, IB, Ho, TC, Iorfino, F, Jansen, A, Jollant, F, Kircher, T, Klimes-Dougan, B, Klug, M, Leehr, EJ, Lippard, ETC, McLaughlin, KA, Meinert, S, Miller, AB, Mitchell, PB, Mwangi, B, Nenadic, I, Ojha, A, Overs, BJ, Pfarr, J-K, Piras, F, Ringwald, KG, Roberts, G, Romer, G, Sanches, M, Sheridan, MA, Soares, JC, Spalletta, G, Stein, F, Teresi, G, Tordesillas-Gutierrez, D, Uyar-Demir, A, van der Wee, NJA, van der Werff, SJ, Vermeiren, RRJM, Winter, A, Wu, M-J, Yang, TT, Thompson, PM, Renteria, ME, Jahanshad, N, Blumberg, HP, Van Harmelen, A-L, and Schmaal, L

Abstract

Identifying brain alterations associated with suicidal thoughts and behaviors (STBs) in young people is critical to understanding their development and improving early intervention and prevention. The ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium analyzed neuroimaging data harmonized across sites to examine brain morphology associated with STBs in youth. We performed analyses in three separate stages, in samples ranging from most to least homogeneous in terms of suicide assessment instrument and mental disorder. First, in a sample of 577 young people with mood disorders, in which STBs were assessed with the Columbia Suicide Severity Rating Scale (C-SSRS). Second, in a sample of young people with mood disorders, in which STB were assessed using different instruments, MRI metrics were compared among healthy controls without STBs (HC; N = 519), clinical controls with a mood disorder but without STBs (CC; N = 246) and young people with current suicidal ideation (N = 223). In separate analyses, MRI metrics were compared among HCs (N = 253), CCs (N = 217), and suicide attempters (N = 64). Third, in a larger transdiagnostic sample with various assessment instruments (HC = 606; CC = 419; Ideation = 289; HC = 253; CC = 432; Attempt=91). In the homogeneous C-SSRS sample, surface area of the frontal pole was lower in young people with mood disorders and a history of actual suicide attempts (N = 163) than those without a lifetime suicide attempt (N = 323; FDR-p = 0.035, Cohen's d = 0.34). No associations with suicidal ideation were found. When examining more heterogeneous samples, we did not observe significant associations. Lower frontal pole surface area may represent a vulnerability for a (non-interrupted and non-aborted) suicide attempt; however, more research is needed to understand the nature of its relationship to suicide risk.

Published
2022

10. The Role of Educational Attainment and Brain Morphology in Major Depressive Disorder: Findings From the ENIGMA Major Depressive Disorder Consortium

Author

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

Abstract

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

Published
2022

11. Multi-Site Meta-Analysis of Morphometry

Author

Jahanshad, N, Faskowitz, J, Roshchupkin, Gennady, Hibar, DP, Gutman, B A, Tustison, NJ, Adams, Hieab, Niessen, Wiro, Vernooij, Meike, Ikram, Arfan, Zwiers, MP, Vasquez, AA, Franke, B, Kroll, JL, Mwangi, B, Soares, JC, Ing, A, Desrivieres, S, Schumann, G, Hansell, NK, de Zubicaray, GI, McMahon, KL, Martin, NG, Wright, MJ, Thompson, PM, Alzheimer's Dis Neuroimaging, I, Epidemiology, Medical Informatics, Radiology & Nuclear Medicine, and Neurology

Subjects
Adult, Male, Databases, Factual, Imaging genetics, Computer science, 0206 medical engineering, Big data, Neuroimaging, Genome-wide association study, Genomics, 02 engineering and technology, computer.software_genre, Polymorphism, Single Nucleotide, Article, 150 000 MR Techniques in Brain Function, Young Adult, All institutes and research themes of the Radboud University Medical Center, Meta-Analysis as Topic, Alzheimer Disease, Genetics, Humans, Aged, Aged, 80 and over, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], business.industry, Applied Mathematics, Brain, Computational Biology, 220 Statistical Imaging Neuroscience, Middle Aged, Magnetic Resonance Imaging, Data sharing, Meta-analysis, Female, Data mining, business, computer, 020602 bioinformatics, Genome-Wide Association Study, Biotechnology
Abstract

Genome-wide association studies (GWAS) link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses (e.g., tensor-based morphometry; TBM) may help better localize statistical effects. This can lead to $10^{13}$1013 tests for GWAS and become underpowered. We developed an analytical framework for multi-site TBM by performing multi-channel registration to cohort-specific templates. Our results highlight the reliability of the method and the added power over alternative options while preserving single site specificity and opening the doors for well-powered image-wide genome-wide discoveries.

Published
2019
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12. Implications of the COVID-19 pandemic for people with bipolar disorders: A scoping review

Author

Fornaro, M, De Prisco, M, Billeci, M, Ermini, E, Young, AH, Lafer, B, Soares, JC, Vieta, E, Quevedo, J, de Bartolomeis, A, Sim, K, Yatham, LN, Bauer, M, Stein, DJ, Solmi, M, Berk, Michael, Carvalho, AF, Fornaro, M, De Prisco, M, Billeci, M, Ermini, E, Young, AH, Lafer, B, Soares, JC, Vieta, E, Quevedo, J, de Bartolomeis, A, Sim, K, Yatham, LN, Bauer, M, Stein, DJ, Solmi, M, Berk, Michael, and Carvalho, AF

Published
2021

13. 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

14. 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

15. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

Author

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

Abstract

IMPORTANCE: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood. OBJECTIVE: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia. DESIGN, SETTING, AND PARTICIPANTS: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244. MAIN OUTCOMES AND MEASURES: Interregional profiles of group difference in cortical thickness between cases and controls. RESULTS: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene

Published
2021

16. 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

17. ENIGMA-Sleep: Challenges, opportunities, and the road map

Author

Tahmasian, M, Aleman, A, Andreassen, OA, Arab, Z, Baillet, M, Benedetti, F, Bresser, T, Bright, J, Chee, MWL, Chylinski, D, Cheng, W, Deantoni, M, Dresler, M, Eickhoff, SB, Eickhoff, CR, Elvsashagen, T, Feng, J, Foster-Dingley, JC, Ganjgahi, H, Grabe, HJ, Groenewold, NA, Ho, TC, Hong, SB, Houenou, J, Irungu, B, Jahanshad, N, Khazaie, H, Kim, H, Koshmanova, E, Kocevska, D, Kochunov, P, Lakbila-Kamal, O, Leerssen, J, Li, M, Luik, A, Muto, V, Narbutas, J, Nilsonne, G, O'Callaghan, VS, Olsen, A, Osorio, RS, Poletti, S, Poudel, G, Reesen, JE, Reneman, L, Reyt, M, Riemann, D, Rosenzweig, I, Rostampour, M, Saberi, A, Schiel, J, Schmidt, C, Schrantee, A, Sciberras, E, Silk, TJ, Sim, K, Smevik, H, Soares, JC, Spiegelhalder, K, Stein, DJ, Talwar, P, Tamm, S, Teresi, GL, Valk, SL, Van Someren, E, Vandewalle, G, Van Egroo, M, Volzke, H, Walter, M, Wassing, R, Weber, FD, Weihs, A, Westlye, LT, Wright, MJ, Wu, M-J, Zak, N, Zarei, M, Tahmasian, M, Aleman, A, Andreassen, OA, Arab, Z, Baillet, M, Benedetti, F, Bresser, T, Bright, J, Chee, MWL, Chylinski, D, Cheng, W, Deantoni, M, Dresler, M, Eickhoff, SB, Eickhoff, CR, Elvsashagen, T, Feng, J, Foster-Dingley, JC, Ganjgahi, H, Grabe, HJ, Groenewold, NA, Ho, TC, Hong, SB, Houenou, J, Irungu, B, Jahanshad, N, Khazaie, H, Kim, H, Koshmanova, E, Kocevska, D, Kochunov, P, Lakbila-Kamal, O, Leerssen, J, Li, M, Luik, A, Muto, V, Narbutas, J, Nilsonne, G, O'Callaghan, VS, Olsen, A, Osorio, RS, Poletti, S, Poudel, G, Reesen, JE, Reneman, L, Reyt, M, Riemann, D, Rosenzweig, I, Rostampour, M, Saberi, A, Schiel, J, Schmidt, C, Schrantee, A, Sciberras, E, Silk, TJ, Sim, K, Smevik, H, Soares, JC, Spiegelhalder, K, Stein, DJ, Talwar, P, Tamm, S, Teresi, GL, Valk, SL, Van Someren, E, Vandewalle, G, Van Egroo, M, Volzke, H, Walter, M, Wassing, R, Weber, FD, Weihs, A, Westlye, LT, Wright, MJ, Wu, M-J, Zak, N, and Zarei, M

Abstract

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.

Published
2021

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

Author

Nunes, A, Schnack, HG, Ching, CRK, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Bøen, E, Bonnin, CDM, Busatto, GF, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Chaim-Avancini, TM, Dannlowski, U, Díaz-Zuluaga, AM, Dietsche, B, Doan, NT, Duchesnay, E, Elvsåshagen, T, Emden, D, Eyler, LT, Fatjó-Vilas, M, Favre, P, Foley, SF, Fullerton, JM, Glahn, DC, Goikolea, JM, Grotegerd, D, Hahn, T, Henry, C, Hibar, DP, Houenou, J, Howells, FM, Jahanshad, N, Kaufmann, T, Kenney, J, Kircher, TTJ, Krug, A, Lagerberg, TV, Lenroot, RK, López-Jaramillo, C, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Mwangi, B, Nabulsi, L, Opel, N, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Redlich, R, Roberts, G, Rosa, PG, Salvador, R, Satterthwaite, TD, Soares, JC, Stein, DJ, Temmingh, HS, Trappenberg, T, Uhlmann, A, van Haren, NEM, Vieta, E, Westlye, LT, Wolf, DH, Yüksel, D, Zanetti, MV, Andreassen, OA, Thompson, PM, Hajek, T, Nunes, A, Schnack, HG, Ching, CRK, Agartz, I, Akudjedu, TN, Alda, M, Alnæs, D, Alonso-Lana, S, Bauer, J, Baune, BT, Bøen, E, Bonnin, CDM, Busatto, GF, Canales-Rodríguez, EJ, Cannon, DM, Caseras, X, Chaim-Avancini, TM, Dannlowski, U, Díaz-Zuluaga, AM, Dietsche, B, Doan, NT, Duchesnay, E, Elvsåshagen, T, Emden, D, Eyler, LT, Fatjó-Vilas, M, Favre, P, Foley, SF, Fullerton, JM, Glahn, DC, Goikolea, JM, Grotegerd, D, Hahn, T, Henry, C, Hibar, DP, Houenou, J, Howells, FM, Jahanshad, N, Kaufmann, T, Kenney, J, Kircher, TTJ, Krug, A, Lagerberg, TV, Lenroot, RK, López-Jaramillo, C, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Mwangi, B, Nabulsi, L, Opel, N, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Redlich, R, Roberts, G, Rosa, PG, Salvador, R, Satterthwaite, TD, Soares, JC, Stein, DJ, Temmingh, HS, Trappenberg, T, Uhlmann, A, van Haren, NEM, Vieta, E, Westlye, LT, Wolf, DH, Yüksel, D, Zanetti, MV, Andreassen, OA, Thompson, PM, and Hajek, T

Abstract

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Published
2020

20. 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

21. 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

22. The Association Between Familial Risk and Brain Abnormalities Is Disease Specific: An ENIGMA-Relatives Study of Schizophrenia and Bipolar Disorder

Author

de Zwarte, SMC, Brouwer, RM, Agartz, I, Alda, M, Aleman, A, Alpert, KI, Bearden, CE, Bertolino, A, Bois, C, Bonvino, A, Bramon, E, Buimer, EEL, Cahn, W, Cannon, DM, Cannon, TD, Caseras, X, Castro-Fornieles, J, Chen, Q, Chung, Y, De la Serna, E, Di Giorgio, A, Doucet, GE, Eker, MC, Erk, S, Fears, SC, Foley, SF, Frangou, S, Frankland, A, Fullerton, JM, Glahn, DC, Goghari, VM, Goldman, AL, Gonul, AS, Gruber, O, de Haan, L, Hajek, T, Hawkins, EL, Heinz, A, Hillegers, MHJ, Hulshoff Pol, HE, Hultman, CM, Ingvar, M, Johansson, V, Jönsson, EG, Kane, F, Kempton, MJ, Koenis, MMG, Kopecek, M, Krabbendam, L, Krämer, B, Lawrie, SM, Lenroot, RK, Marcelis, M, Marsman, JBC, Mattay, VS, McDonald, C, Meyer-Lindenberg, A, Michielse, S, Mitchell, PB, Moreno, D, Murray, RM, Mwangi, B, Najt, P, Neilson, E, Newport, J, van Os, J, Overs, B, Ozerdem, A, Picchioni, MM, Richter, A, Roberts, G, Aydogan, AS, Schofield, PR, Simsek, F, Soares, JC, Sugranyes, G, Toulopoulou, T, Tronchin, G, Walter, H, Wang, L, Weinberger, DR, Whalley, HC, Yalin, N, Andreassen, OA, Ching, CRK, van Erp, TGM, Turner, JA, Jahanshad, N, Thompson, PM, Kahn, RS, van Haren, NEM, de Zwarte, SMC, Brouwer, RM, Agartz, I, Alda, M, Aleman, A, Alpert, KI, Bearden, CE, Bertolino, A, Bois, C, Bonvino, A, Bramon, E, Buimer, EEL, Cahn, W, Cannon, DM, Cannon, TD, Caseras, X, Castro-Fornieles, J, Chen, Q, Chung, Y, De la Serna, E, Di Giorgio, A, Doucet, GE, Eker, MC, Erk, S, Fears, SC, Foley, SF, Frangou, S, Frankland, A, Fullerton, JM, Glahn, DC, Goghari, VM, Goldman, AL, Gonul, AS, Gruber, O, de Haan, L, Hajek, T, Hawkins, EL, Heinz, A, Hillegers, MHJ, Hulshoff Pol, HE, Hultman, CM, Ingvar, M, Johansson, V, Jönsson, EG, Kane, F, Kempton, MJ, Koenis, MMG, Kopecek, M, Krabbendam, L, Krämer, B, Lawrie, SM, Lenroot, RK, Marcelis, M, Marsman, JBC, Mattay, VS, McDonald, C, Meyer-Lindenberg, A, Michielse, S, Mitchell, PB, Moreno, D, Murray, RM, Mwangi, B, Najt, P, Neilson, E, Newport, J, van Os, J, Overs, B, Ozerdem, A, Picchioni, MM, Richter, A, Roberts, G, Aydogan, AS, Schofield, PR, Simsek, F, Soares, JC, Sugranyes, G, Toulopoulou, T, Tronchin, G, Walter, H, Wang, L, Weinberger, DR, Whalley, HC, Yalin, N, Andreassen, OA, Ching, CRK, van Erp, TGM, Turner, JA, Jahanshad, N, Thompson, PM, Kahn, RS, and van Haren, NEM

Abstract

Background: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. Methods: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. Results: FDRs-BD had significantly larger ICV (d = +0.16, q <.05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = −0.12, q <.05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < −0.09, q <.05 corrected); and third ventricle was larger (d = +0.15, q <.05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. Conclusions: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct.

Published
2019

23. Treatment-resistant and Multi-therapy resistant criteria for bipolar depression: A consensus definition - CORRIGENDUM

Author

Hidalgo-Mazzei, D, Berk, Michael, Cipriani, A, Cleare, AJ, Di Florio, A, Dietch, D, Geddes, JR, Goodwin, GM, Grunze, H, Hayes, JF, Jones, I, Kasper, S, MacRitchie, K, Hamish McAllister-Williams, R, Morriss, R, Nayrouz, S, Pappa, S, Soares, JC, Smith, DJ, Suppes, T, Talbot, P, Vieta, E, Watson, S, Yatham, LN, Young, AH, Stokes, PRA, Hidalgo-Mazzei, D, Berk, Michael, Cipriani, A, Cleare, AJ, Di Florio, A, Dietch, D, Geddes, JR, Goodwin, GM, Grunze, H, Hayes, JF, Jones, I, Kasper, S, MacRitchie, K, Hamish McAllister-Williams, R, Morriss, R, Nayrouz, S, Pappa, S, Soares, JC, Smith, DJ, Suppes, T, Talbot, P, Vieta, E, Watson, S, Yatham, LN, Young, AH, and Stokes, PRA

Published
2019

24. What is the optimal serum level for lithium in the maintenance treatment of bipolar disorder? A systematic review and recommendations from the ISBD/IGSLI Task Force on treatment with lithium

Author

Nolen, WA, Licht, RW, Young, AH, Malhi, GS, Tohen, M, Vieta, E, Kupka, RW, Zarate, C, Nielsen, RE, Baldessarini, RJ, Severus, E, Alda, M, Bauer, M, Berghoefer, A, Berk, M, Bocchetta, A, Calkin, CV, Duffy, A, Fountoulakis, KN, Gonzalez-Pinto, A, Hajek, T, Kessing, LV, Lopez-Jaramillo, C, Machado-Vieira, R, Post, RM, Rybakowski, JK, Simhandl, C, Soares, JC, Tondo, L, Nolen, WA, Licht, RW, Young, AH, Malhi, GS, Tohen, M, Vieta, E, Kupka, RW, Zarate, C, Nielsen, RE, Baldessarini, RJ, Severus, E, Alda, M, Bauer, M, Berghoefer, A, Berk, M, Bocchetta, A, Calkin, CV, Duffy, A, Fountoulakis, KN, Gonzalez-Pinto, A, Hajek, T, Kessing, LV, Lopez-Jaramillo, C, Machado-Vieira, R, Post, RM, Rybakowski, JK, Simhandl, C, Soares, JC, and Tondo, L

Abstract

AIMS: To systematically review the existing trials on optimal serum levels for lithium for maintenance treatment of bipolar disorder and to develop clinical recommendations. METHODS: Systematic literature search. Discussion of major characteristics, limitations, methodological quality, and results of selected trials. Delphi survey consisting of clinical questions and corresponding statements. For statements endorsed by at least 80% of the members, consensus was considered as having been achieved. RESULTS: With strict inclusion criteria no studies could be selected, making it difficult to formulate evidence-based recommendations. After loosening the inclusion criteria 7 trials were selected addressing our aims at least to some extent. Four of these studies suggest better efficacy being associated with lithium serum levels in a range above a lower threshold around 0.45/0.60 and up to 0.80/1.00 mmol/L. These findings support the outcome of the Delphi survey. CONCLUSIONS: For adults with bipolar disorder there was consensus that the standard lithium serum level should be 0.60-0.80 mmol/L with the option to reduce it to 0.40-0.60 mmol/L in case of good response but poor tolerance or to increase it to 0.80-1.00 mmol/L in case of insufficient response and good tolerance. For children and adolescents there was no consensus, but the majority of the members endorsed the same recommendation. For the elderly there was also no consensus, but the majority of the members endorsed a more conservative approach: usually 0.40-0.60 mmol/L, with the option to go to maximally 0.70 or 0.80 mmol/L at ages 65-79 years, and to maximally 0.70 mmol/L over age 80 years.

Published
2019

25. The use of component-wise gradient boosting to assess the possible role of cognitive measures as markers of vulnerability to pediatric bipolar disorder.

Author

Bauer, IE, Suchting, R, Van Rheenen, TE, Wu, M-J, Mwangi, B, Spiker, D, Zunta-Soares, GB, Soares, JC, Bauer, IE, Suchting, R, Van Rheenen, TE, Wu, M-J, Mwangi, B, Spiker, D, Zunta-Soares, GB, and Soares, JC

Abstract

BACKGROUND AND AIMS: Cognitive impairments are primary hallmarks symptoms of bipolar disorder (BD). Whether these deficits are markers of vulnerability or symptoms of the disease is still unclear. This study used a component-wise gradient (CGB) machine learning algorithm to identify cognitive measures that could accurately differentiate pediatric BD, unaffected offspring of BD parents, and healthy controls. METHODS: 59 healthy controls (HC; 11.19 ± 3.15 yo; 30 girls), 119 children and adolescents with BD (13.31 ± 3.02 yo, 52 girls) and 49 unaffected offspring of BD parents (UO; 9.36 ± 3.18 yo; 22 girls) completed the CANTAB cognitive battery. RESULTS: CGB achieved accuracy of 73.2% and an AUROC of 0.785 in classifying individuals as either BD or non-BD on a dataset held out for validation for testing. The strongest cognitive predictors of BD were measures of processing speed and affective processing. Measures of cognition did not differentiate between UO and HC. CONCLUSIONS: Alterations in processing speed and affective processing are markers of BD in pediatric populations. Longitudinal studies should determine whether UO with a cognitive profile similar to that of HC are at less or equal risk for mood disorders. Future studies should include relevant measures for BD such as verbal memory and genetic risk scores.

Published
2019

26. Skin picking disorder: Prevalence, correlates, and associations with quality of life in a large sample

Author

Machado, MO, Köhler, CA, Stubbs, B, Nunes-Neto, PR, Koyanagi, A, Quevedo, J, Soares, JC, Hyphantis, TN, Marazziti, D, Maes, Michael, Stein, DJ, Carvalho, AF, Machado, MO, Köhler, CA, Stubbs, B, Nunes-Neto, PR, Koyanagi, A, Quevedo, J, Soares, JC, Hyphantis, TN, Marazziti, D, Maes, Michael, Stein, DJ, and Carvalho, AF

Abstract

Objective Evidence suggests that skin picking disorder (SPD) could be a prevalent condition associated with comorbidity and psychosocial dysfunction. However, just a few studies have assessed the prevalence and correlates of SPD in samples from low-and middle-income countries. In addition, the impact of SPD on quality of life (QoL) dimension after multivariable adjustment to potential confounders remains unclear.Methods Data were obtained from a Brazilian anonymous Web-based research platform. Participants provided sociodemographic data and completed the modified Skin Picking-Stanford questionnaire, the Hypomania Checklist (HCL-32), the Patient Health Questionnaire-9 (PHQ-9), the Fagerström Test for Nicotine Dependence, Alcohol Use Disorder Identification Test (AUDIT), Symptom Checklist-90-Revised inventory (SCL-90R), early trauma inventory self report-short form, and the World Health Organization quality of life abbreviated scale (WHOQOL-Bref). Associations were adjusted to potential confounders through multivariable models.Results For our survey, 7639 participants took part (71.3% females; age: 27.2±7.9 years). The prevalence of SPD was 3.4% (95% CI: 3.0-3.8%), with a female preponderance (P<0.001). In addition, SPD was associated with a positive screen for a major depressive episode, nicotine dependence, and alcohol dependence, as well as suicidal ideation. Physical and psychological QoL was significantly more impaired in participants with SPD compared to those without SPD, even after adjustment for comorbidity.Conclusions In this large sample, SPD was a prevalent condition associated with co-occurring depression, nicotine, and alcohol dependence. In addition, SPD was independently associated with impaired physical and psychological QoL. Public health efforts toward the early recognition and treatment of SPD are warranted.

Published
2018

27. An exploratory study of the heterogeneity of the jealousy phenomenon and its associations with affective temperaments and psychopathological dimensions in a large Brazilian sample

Author

Lima AB, Köhler CA, Stubbs B, Quevedo J, Hyphantis TN, Koyanagi A, Marazziti D, Soares JC, Vieta E, and Carvalho AF

Subjects
Personality, Jealousy, Psychiatry, Survey, Affective temperaments, Psychopathology
Abstract

BACKGROUND: Jealousy is a heterogenous emotion on a spectrum from normality to psychopathology. The relationship between different jealousy subtypes/dimensions and affective temperaments remain unknown. In addition, few large surveys have investigated the associations between jealousy subtypes and psychopathological dimensions. METHODS: A Brazilian Portuguese version of the "Questionario della Gelosia" (QUEGE) was developed. We obtained data from an anonymous web-based research platform. Socio-demographic data was obtained and participants answered the QUEGE, the TEMPS-Rio de Janeiro, and the Symptom Checklist-90-Revised (SCL-90-R). RESULTS: 2042 participants (29% men, 71% female, mean age+SD: 28.9±8.8 years), took part in this survey. Confirmatory factor analysis provided a five-factor model for the QUEGE with self-esteem, paranoia, interpersonal sensitivity, fear of being abandoned, and obsessive dimensions. The anxious, irritable, cyclothymic, and depressive temperaments were independently associated with jealousy dimensions, whereas the hyperthymic temperament was associated with lower scores on the self-esteem jealousy dimension (N=2042, P

Published
2017

28. Lifestyle interventions targeting dietary habits and exercise in bipolar disorder: A systematic review

Author

Bauer IE, Galvez JF, Hamilton JE, Balanza-Martinez V, Zunta-Soares GB, Soares JC, and Meyer TD

Subjects
Lifestyle intervention, Bipolar disorder, Physical activity, Wellbeing, Psychosocial intervention, Nutrition
Abstract

Background: Bipolar disorder (BD) is a serious mental illness associated with a high risk of medical comorbidities, long-term disability and premature death. This systematic review examined the current literature on therapeutic interventions targeting nutrition, physical activity and wellness in BD and collecting health-related measures such as mood and course of illness. Methods: Scopus (all databases), Pubmed and Ovid Medline were systematically searched with no language or year restrictions, up to June 2015, for studies focusing on lifestyle interventions in BD. Search terms were related to bipolar disorder, nutrition, physical activity, wellbeing, psychosocial interventions and course of illness. We hand searched content pages of Bipolar Disorders and Journal of Affective Disorders and checked references of relevant reviews and dissertations to identify additional papers. Results: After applying inclusion and exclusion criteria to identified hits, this literature search retrieved six papers. Overall findings point towards a beneficial role of lifestyle interventions on mood, weight, blood pressure, lipid profile, physical activity and overall wellbeing. Methodological limitations include small sample size, gender ratio imbalance, inconsistencies in terms of laboratory measures, and lack of randomized controlled trials and absence of follow-up and longitudinal studies to determine the benefits of these factors on clinical and functional outcomes over time Conclusions: Lifestyle interventions in BD targeting nutrition, exercise, wellbeing alongside beliefs, coping strategies and attitudes towards health show promise in reducing the risk of comorbid ailments in BD. There is still a strong need for studies a) developing interventions which are informed by the patient's input and b) examining the effectiveness of such interventions targeting general wellness using well-controlled trials. Published by Elsevier Ltd.

Published
2016

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

Author

Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, Francks, C, Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, and Francks, C

Abstract

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

Published
2017

30. Perturbations in the apoptotic pathway and mitochondrial network dynamics in peripheral blood mononuclear cells from bipolar disorder patients

Author

Scaini, G, Fries, GR, Valvassori, SS, Zeni, CP, Zunta-Soares, G, Berk, M, Soares, JC, Quevedo, J, Scaini, G, Fries, GR, Valvassori, SS, Zeni, CP, Zunta-Soares, G, Berk, M, Soares, JC, and Quevedo, J

Abstract

Bipolar disorder (BD) is a severe psychiatric disorder characterized by phasic changes of mood and can be associated with progressive structural brain change and cognitive decline. The numbers and sizes of glia and neurons are reduced in several brain areas, suggesting the involvement of apoptosis in the pathophysiology of BD. Because the changes in mitochondrial dynamics are closely related with the early process of apoptosis and the specific processes of apoptosis and mitochondrial dynamics in BD have not been fully elucidated, we measured the apoptotic pathway and the expression of mitochondrial fission/fusion proteins from BD patients and healthy controls. We recruited 16 patients with BD type I and sixteen well-matched healthy controls and investigated protein levels of several pro-apoptotic and anti-apoptotic factors, as well as the expression of mitochondrial fission/fusion proteins in peripheral blood mononuclear cells (PBMCs). Our results showed that the levels of the anti-apoptotic proteins Bcl-xL, survivin and Bcl-xL/Bak dimer were significantly decreased, while active caspase-3 protein levels were significantly increased in PBMCs from BD patients. Moreover, we observed the downregulation of the mitochondrial fusion-related proteins Mfn2 and Opa1 and the upregulation of the fission protein Fis1 in PBMCs from BD patients, both in terms of gene expression and protein levels. We also showed a significantly decrease in the citrate synthase activity. Finally, we found a positive correlation between Mfn2 and Opa1 with mitochondrial content markers, as well as a negative correlation between mitochondrial fission/fusion proteins and apoptotic markers. Overall, data reported here are consistent with the working hypothesis that apoptosis may contribute to cellular dysfunction, brain volume loss and progressive cognitive in BD. Moreover, we show an important relationship between mitochondrial dynamics and the cell death pathway activation in BD patients, supporting

Published
2017

33. Peripheral brain-derived neurotrophic factor (BDNF) as a biomarker in bipolar disorder: a meta-analysis of 52 studies

Author

Fernandes, BS, Molendijk, ML, Koehler, CA, Soares, JC, Leite, CMGS, Machado-Vieira, R, Ribeiro, TL, Silva, JC, Sales, PMG, Quevedo, J, Oertel-Knoechel, V, Vieta, E, Gonzalez-Pinto, A, Berk, M, Carvalho, AF, Fernandes, BS, Molendijk, ML, Koehler, CA, Soares, JC, Leite, CMGS, Machado-Vieira, R, Ribeiro, TL, Silva, JC, Sales, PMG, Quevedo, J, Oertel-Knoechel, V, Vieta, E, Gonzalez-Pinto, A, Berk, M, and Carvalho, AF

Abstract

BACKGROUND: The neurotrophic hypothesis postulates that mood disorders such as bipolar disorder (BD) are associated with a lower expression of brain-derived neurotrophic factor (BDNF). However, its role in peripheral blood as a biomarker of disease activity and of stage for BD, transcending pathophysiology, is still disputed. In the last few years an increasing number of clinical studies assessing BDNF in serum and plasma have been published. Therefore, it is now possible to analyse the association between BDNF levels and the severity of affective symptoms in BD as well as the effects of acute drug treatment of mood episodes on BDNF levels. METHODS: We conducted a systematic review and meta-analysis of all studies on serum and plasma BDNF levels in bipolar disorder. RESULTS: Through a series of meta-analyses including a total of 52 studies with 6,481 participants, we show that, compared to healthy controls, peripheral BDNF levels are reduced to the same extent in manic (Hedges' g = -0.57, P = 0.010) and depressive (Hedges' g = -0.93, P = 0.001) episodes, while BDNF levels are not significantly altered in euthymia. In meta-regression analyses, BDNF levels additionally negatively correlate with the severity of both manic and depressive symptoms. We found no evidence for a significant impact of illness duration on BDNF levels. In addition, in plasma, but not serum, peripheral BDNF levels increase after the successful treatment of an acute mania episode, but not of a depressive one. CONCLUSIONS: In summary, our data suggest that peripheral BDNF levels, more clearly in plasma than in serum, is a potential biomarker of disease activity in BD, but not a biomarker of stage. We suggest that peripheral BDNF may, in future, be used as a part of a blood protein composite measure to assess disease activity in BD.

Published
2015

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