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1. 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

2. Comparison of the Accuracy of the 7-Item HADS Depression Subscale and 14-Item Total HADS for Screening for Major Depression: A Systematic Review and Individual Participant Data Meta-Analysis

Author

Wu, Y, Levis, B, Daray, FM, Ioannidis, JPA, Patten, SB, Cuijpers, P, Ziegelstein, RC, Gilbody, S, Fischer, FH, Fan, S, Sun, Y, He, C, Krishnan, A, Neupane, D, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Boruff, JT, McMillan, D, Kloda, LA, Markham, S, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Al-Adawi, S, Beck, KR, Beraldi, A, Bernstein, CN, Boye, B, Buel-Drabe, N, Bunevicius, A, Can, C, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Costa-Requena, G, Cukor, D, Dabscheck, E, De Souza, J, Downing, M, Feinstein, A, Ferentinos, PP, Flint, AJ, Gallagher, P, Gandy, M, Grassi, L, Haerter, M, Hernando, A, Jackson, ML, Jenewein, J, Jette, N, Juliao, M, Kjaergaard, M, Kohler, S, Konig, H-H, Krishna, LKR, Lee, Y, Loebner, M, Loosman, WL, Love, AW, Loewe, B, Malt, UF, Marrie, RA, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Nelson, CJ, Ng, CG, O'Donnell, ML, O'Rourke, SJ, Ozturk, A, Pabst, A, Pasco, JA, Peceliuniene, J, Pintor, L, Ponsford, JL, Pulido, F, Quinn, TJ, Reme, SE, Reuter, K, Riedel-Heller, SG, Rooney, AG, Sanchez-Gonzalez, R, Saracino, RM, Schellekens, MPJ, Scherer, M, Schwarzbold, ML, Cankorur, VS, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Strobe, NA, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, Weyerer, SB, White, J, Wiese, B, Williams, LJ, Wong, L-Y, Benedetti, A, Thombsi, BD, Wu, Y, Levis, B, Daray, FM, Ioannidis, JPA, Patten, SB, Cuijpers, P, Ziegelstein, RC, Gilbody, S, Fischer, FH, Fan, S, Sun, Y, He, C, Krishnan, A, Neupane, D, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Boruff, JT, McMillan, D, Kloda, LA, Markham, S, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Al-Adawi, S, Beck, KR, Beraldi, A, Bernstein, CN, Boye, B, Buel-Drabe, N, Bunevicius, A, Can, C, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Costa-Requena, G, Cukor, D, Dabscheck, E, De Souza, J, Downing, M, Feinstein, A, Ferentinos, PP, Flint, AJ, Gallagher, P, Gandy, M, Grassi, L, Haerter, M, Hernando, A, Jackson, ML, Jenewein, J, Jette, N, Juliao, M, Kjaergaard, M, Kohler, S, Konig, H-H, Krishna, LKR, Lee, Y, Loebner, M, Loosman, WL, Love, AW, Loewe, B, Malt, UF, Marrie, RA, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Nelson, CJ, Ng, CG, O'Donnell, ML, O'Rourke, SJ, Ozturk, A, Pabst, A, Pasco, JA, Peceliuniene, J, Pintor, L, Ponsford, JL, Pulido, F, Quinn, TJ, Reme, SE, Reuter, K, Riedel-Heller, SG, Rooney, AG, Sanchez-Gonzalez, R, Saracino, RM, Schellekens, MPJ, Scherer, M, Schwarzbold, ML, Cankorur, VS, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Strobe, NA, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, Weyerer, SB, White, J, Wiese, B, Williams, LJ, Wong, L-Y, Benedetti, A, and Thombsi, BD

Abstract

The seven-item Hospital Anxiety and Depression Scale Depression subscale (HADS-D) and the total score of the 14-item HADS (HADS-T) are both used for major depression screening. Compared to the HADS-D, the HADS-T includes anxiety items and requires more time to complete. We compared the screening accuracy of the HADS-D and HADS-T for major depression detection. We conducted an individual participant data meta-analysis and fit bivariate random effects models to assess diagnostic accuracy among participants with both HADS-D and HADS-T scores. We identified optimal cutoffs, estimated sensitivity and specificity with 95% confidence intervals, and compared screening accuracy across paired cutoffs via two-stage and individual-level models. We used a 0.05 equivalence margin to assess equivalency in sensitivity and specificity. 20,700 participants (2,285 major depression cases) from 98 studies were included. Cutoffs of ≥7 for the HADS-D (sensitivity 0.79 [0.75, 0.83], specificity 0.78 [0.75, 0.80]) and ≥15 for the HADS-T (sensitivity 0.79 [0.76, 0.82], specificity 0.81 [0.78, 0.83]) minimized the distance to the top-left corner of the receiver operating characteristic curve. Across all sets of paired cutoffs evaluated, differences of sensitivity between HADS-T and HADS-D ranged from -0.05 to 0.01 (0.00 at paired optimal cutoffs), and differences of specificity were within 0.03 for all cutoffs (0.02-0.03). The pattern was similar among outpatients, although the HADS-T was slightly (not nonequivalently) more specific among inpatients. The accuracy of HADS-T was equivalent to the HADS-D for detecting major depression. In most settings, the shorter HADS-D would be preferred. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Published
2023

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

Author

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

Published
2022

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

Author

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

Published
2022

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

Author

Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, Hulshoff Pol, HE, Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, and Hulshoff Pol, HE

Abstract

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.

Published
2022

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

Author

Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, Agartz, I, Haukvik, UK, Gurholt, TP, Nerland, S, Elvsashagen, T, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Bauer, J, Baune, BT, Benedetti, F, Berk, M, Bettella, F, Boen, E, Bonnin, CM, Brambilla, P, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Dandash, O, Dannlowski, U, Delvecchio, G, Diaz-Zuluaga, AM, Erp, TGM, Fatjo-Vilas, M, Foley, SF, Foerster, K, Fullerton, JM, Goikolea, JM, Grotegerd, D, Gruber, O, Haarman, BCM, Haatveit, B, Hajek, T, Hallahan, B, Harris, M, Hawkins, EL, Howells, FM, Huelsmann, C, Jahanshad, N, Jorgensen, KN, Kircher, T, Kraemer, B, Krug, A, Kuplicki, R, Lagerberg, T, Lancaster, TM, Lenroot, RK, Lonning, V, Lopez-Jaramillo, C, Malt, UF, McDonald, C, McIntosh, AM, McPhilemy, G, Meer, D, Melle, I, Melloni, EMT, Mitchell, PB, Nabulsi, L, Nenadic, I, Oertel, V, Oldani, L, Opel, N, Otaduy, MCG, Overs, BJ, Pineda-Zapata, JA, Pomarol-Clotet, E, Radua, J, Rauer, L, Redlich, R, Repple, J, Rive, MM, Roberts, G, Ruhe, HG, Salminen, LE, Salvador, R, Sarro, S, Savitz, J, Schene, AH, Sim, K, Soeiro-de-Souza, MG, Staeblein, M, Stein, DJ, Stein, F, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Veltman, DJ, Vieta, E, Waltemate, L, Westlye, LT, Whalley, HC, Saemann, PG, Thompson, PM, Ching, CRK, Andreassen, OA, and Agartz, I

Abstract

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Published
2022

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

Author

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

Published
2021

8. 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

9. 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

12. 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, Kraemer, B, Overs, B, Hartberg, CB, Abe, C, Dima, D, Grotegerd, D, Sprooten, E, Boen, 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, Elvsashagen, 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, Fatjo-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landen, M, Lawrence, NS, van Haren, NEM, Horn, NR, Freimer, NB, Gruber, O, Schofield, PR, Mitchell, PB, Kahn, RS, Lenroot, R, Machado-Vieira, R, Ophoff, RA, Sarro, S, Frangou, S, Satterthwaite, TD, Hajek, T, Dannlowski, U, Malt, UF, Arolt, V, Gattaz, WF, Drevets, WC, Caseras, X, Agartz, I, Thompson, PM, Andreassen, OA, Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Kraemer, B, Overs, B, Hartberg, CB, Abe, C, Dima, D, Grotegerd, D, Sprooten, E, Boen, 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, Elvsashagen, 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, Fatjo-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landen, M, Lawrence, NS, van Haren, NEM, Horn, NR, Freimer, NB, Gruber, O, Schofield, PR, Mitchell, PB, Kahn, RS, Lenroot, R, Machado-Vieira, R, Ophoff, RA, Sarro, S, Frangou, S, Satterthwaite, TD, Hajek, T, Dannlowski, U, Malt, UF, Arolt, V, Gattaz, WF, Drevets, WC, Caseras, X, Agartz, I, Thompson, PM, and Andreassen, OA

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

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

Author

Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, O, Binder, EB, Blangero, J, Bockholt, HJ, Boen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, Djurovic, S, Dong, H, Donohoe, G, Duggirala, R, Dyer, TD, Ehrlich, S, Ekman, CJ, Elvsashagen, T, Emsell, L, Erk, S, Espeseth, T, Fagerness, J, Fears, S, Fedko, I, Fernandez, G, Fisher, SE, Foroud, T, Fox, PT, Francks, C, Frangou, S, Frey, EM, Frodl, T, Frouin, V, Garavan, H, Giddaluru, S, Glahn, DC, Godlewska, B, Goldstein, RZ, Gollub, RL, Grabe, HJ, Grimm, O, Gruber, O, Guadalupe, T, Gur, RE, Gur, RC, Goering, HHH, Hagenaars, S, Hajek, T, Hall, GB, Hall, J, Hardy, J, Hartman, CA, Hass, J, Hatton, SN, Haukvik, UK, Hegenscheid, K, Heinz, A, Hickie, IB, Ho, B-C, Hoehn, D, Hoekstra, PJ, Hollinshead, M, Holmes, AJ, Homuth, G, Hoogman, M, Hong, LE, Hosten, N, Hottenga, J-J, Pol, HEH, Hwang, KS, Jack, CR, Jenkinson, M, Johnston, C, Joensson, EG, Kahn, RS, Kasperaviciute, D, Kelly, S, Kim, S, Kochunov, P, Koenders, L, Kraemer, B, Kwok, JBJ, Lagopoulos, J, Laje, G, Landen, M, Landman, BA, Lauriello, J, Lawrie, SM, Lee, PH, Le Hellard, S, Lemaitre, H, Leonardo, CD, Li, C-S, Liberg, B, Liewald, DC, Liu, X, Lopez, LM, Loth, E, Lourdusamy, A, Luciano, M, Macciardi, F, Machielsen, MWJ, MacQueen, GM, Malt, UF, Mandl, R, Manoach, DS, Martinot, J-L, Matarin, M, Mather, KA, Mattheisen, M, Mattingsdal, M, Meyer-Lindenberg, A, McDonald, C, McIntosh, AM, McMahon, FJ, McMahon, KL, Meisenzahl, E, Melle, I, Milaneschi, Y, Mohnke, S, Montgomery, GW, Morris, DW, Moses, EK, Mueller, BA, Munoz Maniega, S, Muehleisen, TW, Mueller-Myhsok, B, Mwangi, B, Nauck, M, Nho, K, Nichols, TE, Nilsson, L-G, Nugent, AC, Nyberg, L, Olvera, RL, Oosterlaan, J, Ophoff, RA, Pandolfo, M, Papalampropoulou-Tsiridou, M, Papmeyer, M, Paus, T, Pausova, Z, Pearlson, GD, Penninx, BW, Peterson, CP, Pfennig, A, Phillips, M, Pike, GB, Poline, J-B, Potkin, SG, Puetz, B, Ramasamy, A, Rasmussen, J, Rietschel, M, Rijpkema, M, Risacher, SL, Roffman, JL, Roiz-Santianez, R, Romanczuk-Seiferth, N, Rose, EJ, Royle, NA, Rujescu, D, Ryten, M, Sachdev, PS, Salami, A, Satterthwaite, TD, Savitz, J, Saykin, AJ, Scanlon, C, Schmaal, L, Schnack, HG, Schork, AJ, Schulz, SC, Schuer, R, Seidman, L, Shen, L, Shoemaker, JM, Simmons, A, Sisodiya, SM, Smith, C, Smoller, JW, Soares, JC, Sponheim, SR, Sprooten, E, Starr, JM, Steen, VM, Strakowski, S, Strike, L, Sussmann, J, Saemann, PG, Teumer, A, Toga, AW, Tordesillas-Gutierrez, D, Trabzuni, D, Trost, S, Turner, J, Van den Heuvel, M, van der Wee, NJ, van Eijk, K, van Erp, TGM, van Haren, NEM, van 't Ent, D, van Tol, M-J, Hernandez, MCV, Veltman, DJ, Versace, A, Voelzke, H, Walker, R, Walter, H, Wang, L, Wardlaw, JM, Weale, ME, Weiner, MW, Wen, W, Westlye, LT, Whalley, HC, Whelan, CD, White, T, Winkler, AM, Wittfeld, K, Woldehawariat, G, Wolf, C, Zilles, D, Zwiers, MP, Thalamuthu, A, Schofield, PR, Freimer, NB, Lawrence, NS, Drevets, W, Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, O, Binder, EB, Blangero, J, Bockholt, HJ, Boen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, Djurovic, S, Dong, H, Donohoe, G, Duggirala, R, Dyer, TD, Ehrlich, S, Ekman, CJ, Elvsashagen, T, Emsell, L, Erk, S, Espeseth, T, Fagerness, J, Fears, S, Fedko, I, Fernandez, G, Fisher, SE, Foroud, T, Fox, PT, Francks, C, Frangou, S, Frey, EM, Frodl, T, Frouin, V, Garavan, H, Giddaluru, S, Glahn, DC, Godlewska, B, Goldstein, RZ, Gollub, RL, Grabe, HJ, Grimm, O, Gruber, O, Guadalupe, T, Gur, RE, Gur, RC, Goering, HHH, Hagenaars, S, Hajek, T, Hall, GB, Hall, J, Hardy, J, Hartman, CA, Hass, J, Hatton, SN, Haukvik, UK, Hegenscheid, K, Heinz, A, Hickie, IB, Ho, B-C, Hoehn, D, Hoekstra, PJ, Hollinshead, M, Holmes, AJ, Homuth, G, Hoogman, M, Hong, LE, Hosten, N, Hottenga, J-J, Pol, HEH, Hwang, KS, Jack, CR, Jenkinson, M, Johnston, C, Joensson, EG, Kahn, RS, Kasperaviciute, D, Kelly, S, Kim, S, Kochunov, P, Koenders, L, Kraemer, B, Kwok, JBJ, Lagopoulos, J, Laje, G, Landen, M, Landman, BA, Lauriello, J, Lawrie, SM, Lee, PH, Le Hellard, S, Lemaitre, H, Leonardo, CD, Li, C-S, Liberg, B, Liewald, DC, Liu, X, Lopez, LM, Loth, E, Lourdusamy, A, Luciano, M, Macciardi, F, Machielsen, MWJ, MacQueen, GM, Malt, UF, Mandl, R, Manoach, DS, Martinot, J-L, Matarin, M, Mather, KA, Mattheisen, M, Mattingsdal, M, Meyer-Lindenberg, A, McDonald, C, McIntosh, AM, McMahon, FJ, McMahon, KL, Meisenzahl, E, Melle, I, Milaneschi, Y, Mohnke, S, Montgomery, GW, Morris, DW, Moses, EK, Mueller, BA, Munoz Maniega, S, Muehleisen, TW, Mueller-Myhsok, B, Mwangi, B, Nauck, M, Nho, K, Nichols, TE, Nilsson, L-G, Nugent, AC, Nyberg, L, Olvera, RL, Oosterlaan, J, Ophoff, RA, Pandolfo, M, Papalampropoulou-Tsiridou, M, Papmeyer, M, Paus, T, Pausova, Z, Pearlson, GD, Penninx, BW, Peterson, CP, Pfennig, A, Phillips, M, Pike, GB, Poline, J-B, Potkin, SG, Puetz, B, Ramasamy, A, Rasmussen, J, Rietschel, M, Rijpkema, M, Risacher, SL, Roffman, JL, Roiz-Santianez, R, Romanczuk-Seiferth, N, Rose, EJ, Royle, NA, Rujescu, D, Ryten, M, Sachdev, PS, Salami, A, Satterthwaite, TD, Savitz, J, Saykin, AJ, Scanlon, C, Schmaal, L, Schnack, HG, Schork, AJ, Schulz, SC, Schuer, R, Seidman, L, Shen, L, Shoemaker, JM, Simmons, A, Sisodiya, SM, Smith, C, Smoller, JW, Soares, JC, Sponheim, SR, Sprooten, E, Starr, JM, Steen, VM, Strakowski, S, Strike, L, Sussmann, J, Saemann, PG, Teumer, A, Toga, AW, Tordesillas-Gutierrez, D, Trabzuni, D, Trost, S, Turner, J, Van den Heuvel, M, van der Wee, NJ, van Eijk, K, van Erp, TGM, van Haren, NEM, van 't Ent, D, van Tol, M-J, Hernandez, MCV, Veltman, DJ, Versace, A, Voelzke, H, Walker, R, Walter, H, Wang, L, Wardlaw, JM, Weale, ME, Weiner, MW, Wen, W, Westlye, LT, Whalley, HC, Whelan, CD, White, T, Winkler, AM, Wittfeld, K, Woldehawariat, G, Wolf, C, Zilles, D, Zwiers, MP, Thalamuthu, A, Schofield, PR, Freimer, NB, Lawrence, NS, and Drevets, W

Abstract

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Published
2014

15. Consultation-Liaison psychiatric service delivery: results from a European study

Author

Huyse, FJ Herzog, T Lobo, A Malt, UF Opmeer, BC and Stein, B de Jonge, P van Dijck, R Creed, F Crespo, MD and Cardoso, G Guimaraes-Lopes, R Mayou, R van Moffaert, M and Rigatelli, M Sakkas, P Tienari, P

Abstract

The reported Endings of the European Consultation-Liaison Workgroup (ECLW) Collaborative Study describe consultation-liaison service delivery by 56 services from ZI European countries aggregated on a C-L service level. During the period of 1 year (1991), the participants applied a standardized, reliability tested method of patient data collection, and data were collected describing pertinent characteristics of the hospital, the C-L service, and the participating consultants. The consultation rate of 1% (median; 1.4% mean) underscores the discrepancy between epidemiology and the services delivered. The core function of C-L services in general hospitals is a quick comprehensive emergency psychiatric function. Reasons to see patients were the following. deliberate self-harm (17%), substance abuse (7.2%), current psychiatric symptoms (38.6%), and unexplained physical complaints (18.6%) (all means). A significant number of patients are old and seriously ill. Mood disorders and organic mental disorders are most predominant (17.7%). Somatoform and dissociative disorders together constitute 7.5%. C-L services in European countries are mainly emergency psychiatric services and perform an important bridge function between primary, general health, and mental health care. (C) 2001 Elsevier Science inc. All rights reserved.

Published
2001

31. Traumatic distress symptoms in early breast cancer I: acute response to diagnosis.

Author

Tjemsland L, Soreide JA, and Malt UF

Abstract

In this study, the concept of `acute traumatic stress response' was applied to breast cancer diagnosis. A total of 106 patients were studied before surgery, by means of a psychiatric interview and questionnaires (Impact of Event Scale, General Health Questionnaire and Clinical Global Impression Scale). The traumatic stress response was related to age, marital status, occurrence of breast cancer in first-degree relatives, previous physical and psychological health parameters, social support and life events during the last year. Of the patients, 44% reported a high level of intrusive symptoms (mean score 17-2) and 29% of avoidance symptoms (mean score 15.0). Younger age and being married were positively correlated with intrusive symptomology while patients with a first-degree relative with breast cancer had less intrusive distress. Previous physical and psychiatric health parameters showed no association to acute traumatic stress symptoms except for those who had experienced `a serious illness/accident/hospitalisation last year' who had some more avoidant symptomology. Multiple regression showed a statistically significant effect for age only on intrusive symptoms when other factors were controlled for in this analysis. [ABSTRACT FROM AUTHOR]

Published
1996
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43. Changes in pain during a depressive episode and relationship to cytokine levels in major depressive disorder.

Author

Dahl J, Ormstad H, Aass HCD, Malt UF, and Andreassen OA

Subjects
Humans, Cytokines, Follow-Up Studies, Treatment Outcome, Pain, Depressive Disorder, Major complications, Depressive Disorder, Major therapy
Abstract

Background: Depressed patients have an increased incidence of pain. A pathophysiological connection between depression and pain is still not revealed. Immunological activation has been found in both depression and pain. There are few studies of pain and immune activation in patients with depression, without inflammatory and autoimmune disorders., Methods: This is a naturalistic follow-up study of 50 patients with a major depressive disorder (MDD) depressive episode, without any inflammatory or autoimmune conditions. We have previously reported on the relationship between depression and cytokine levels. In this study, we obtained data of depression, pain and cytokine levels before and after 12 weeks of depression treatment. All patients were medication-free at inclusion., Results: At inclusion three out of four patients experienced pain, and the pain scores correlated with the depression scores. After treatment, as depression was relieved, the pain scores dropped significantly and were no longer correlated to the depression scores. There were no correlations between pain scores and cytokine levels. Pain level at inclusion did not correlate with depression treatment outcome., Conclusion: Our findings indicate that pain is a feature of depression. Pain levels and cytokine values didn't correlate. Pain at inclusion did not predict depression treatment outcome.

Published
2024
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45. Chronic fatigue syndromes: real illnesses that people can recover from.

Author

Alme TN, Andreasson A, Asprusten TT, Bakken AK, Beadsworth MB, Boye B, Brodal PA, Brodwall EM, Brurberg KG, Bugge I, Chalder T, Due R, Eriksen HR, Fink PK, Flottorp SA, Fors EA, Jensen BF, Fundingsrud HP, Garner P, Havdal LB, Helgeland H, Jacobsen HB, Johnson GE, Jonsjö M, Knoop H, Landmark L, Launes G, Lekander M, Linnros H, Lindsäter E, Liira H, Linnestad L, Loge JH, Lyby PS, Malik S, Malt UF, Moe T, Norlin AK, Pedersen M, Pignatiello SE, Rask CU, Reme SE, Roksund G, Sainio M, Sharpe M, Thorkildsen RF, van Roy B, Vandvik PO, Vogt H, Wyller HB, and Wyller VBB

Subjects
Humans, Fatigue Syndrome, Chronic therapy, Fatigue Syndrome, Chronic diagnosis, Fatigue Syndrome, Chronic etiology
Abstract

The 'Oslo Chronic Fatigue Consortium' consists of researchers and clinicians who question the current narrative that chronic fatigue syndromes, including post-covid conditions, are incurable diseases. Instead, we propose an alternative view, based on research, which offers more hope to patients. Whilst we regard the symptoms of these conditions as real, we propose that they are more likely to reflect the brain's response to a range of biological, psychological, and social factors, rather than a specific disease process. Possible causes include persistent activation of the neurobiological stress response, accompanied by associated changes in immunological, hormonal, cognitive and behavioural domains. We further propose that the symptoms are more likely to persist if they are perceived as threatening, and all activities that are perceived to worsen them are avoided. We also question the idea that the best way to cope with the illness is by prolonged rest, social isolation, and sensory deprivation.Instead, we propose that recovery is often possible if patients are helped to adopt a less threatening understanding of their symptoms and are supported in a gradual return to normal activities. Finally, we call for a much more open and constructive dialogue about these conditions. This dialogue should include a wider range of views, including those of patients who have recovered from them.

Published
2023
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46. Baseline long-term potentiation-like cortical plasticity is associated with longitudinal cortical thinning in healthy adults and in adults with bipolar disorder type II.

Author

Zak N, Moberget T, Bøen E, Boye B, Rygvold TW, Malt UF, Andreassen OA, Andersson S, Westlye LT, and Elvsåshagen T

Subjects
Humans, Adult, Long-Term Potentiation, Evoked Potentials, Visual, Cerebral Cortex diagnostic imaging, Magnetic Resonance Imaging, Cerebral Cortical Thinning, Bipolar Disorder diagnostic imaging
Abstract

The precise neurobiological processes underlying cerebral cortical thinning in aging and psychiatric illnesses remain undetermined, yet aging- and synaptic dysfunction-related loss of synapses are potentially important mechanisms. We used long-term potentiation-like plasticity of the visual evoked potential as an index of synaptic function in the cortex and hypothesized that plasticity at baseline would be negatively associated with future cortical thinning in healthy adults and in adults with bipolar disorder type II. Thirty-two healthy adults and 15 adults with bipolar disorder type II underwent electroencephalography-based measurement of visual evoked potential plasticity and 3T magnetic resonance imaging of the brain at baseline and a follow-up brain scan on average 2.3 years later. The relationships between visual evoked potential plasticity at baseline and longitudinal cortical thickness changes were examined using Freesurfer and the Permutation Analysis of Linear Models tool. The analyses showed a negative association between the plasticity of the N1 visual evoked potential amplitude at baseline and thinning rate in the medial and lateral parietal and medial occipital cortices in healthy adults and in the right medial occipital cortex in the total sample of healthy adults and adults with bipolar disorder type II, indicating greater thinning over time in subjects with less N1 plasticity (p FWER  < .05). Although preliminary, the results indicate an association between visual evoked potential plasticity and the future rate of cortical thinning in healthy adults and in bipolar disorder type II, supporting the hypothesis that cortical thinning might be related to synaptic dysfunction., (© 2023 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Published
2023
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47. Lower circulating neuron-specific enolase concentrations in adults and adolescents with severe mental illness.

Author

Andreou D, Steen NE, Jørgensen KN, Smelror RE, Wedervang-Resell K, Nerland S, Westlye LT, Nærland T, Myhre AM, Joa I, Reitan SMK, Vaaler A, Morken G, Bøen E, Elvsåshagen T, Boye B, Malt UF, Aukrust P, Skrede S, Kroken RA, Johnsen E, Djurovic S, Andreassen OA, Ueland T, and Agartz I

Subjects
Male, Female, Humans, Adult, Adolescent, Neurons, Phosphopyruvate Hydratase, Mental Disorders, Schizophrenia, Bipolar Disorder
Abstract

Background: Both neurodegenerative and neurodevelopmental abnormalities have been suggested to be part of the etiopathology of severe mental illness (SMI). Neuron-specific enolase (NSE), mainly located in the neuronal cytoplasm, may indicate the process as it is upregulated after neuronal injury while a switch from non-neuronal enolase to NSE occurs during neuronal maturation., Methods: We included 1132 adult patients with SMI [schizophrenia (SZ) or bipolar spectrum disorders], 903 adult healthy controls (HC), 32 adolescent patients with SMI and 67 adolescent HC. Plasma NSE concentrations were measured by enzyme immunoassay. For 842 adults and 85 adolescents, we used total grey matter volume (TGMV) based on T1-weighted magnetic resonance images processed in FreeSurfer v6.0. We explored NSE case-control differences in adults and adolescents separately. To investigate whether putative case-control differences in NSE were TGMV-dependent we controlled for TGMV., Results: We found significantly lower NSE concentrations in both adult ( p < 0.001) and adolescent patients with SMI ( p = 0.007) compared to HC. The results remained significant after controlling for TGMV. Among adults, both patients with SZ spectrum ( p < 0.001) and bipolar spectrum disorders ( p = 0.005) had lower NSE than HC. In both patient subgroups, lower NSE levels were associated with increased symptom severity. Among adults ( p < 0.001) and adolescents ( p = 0.040), females had lower NSE concentrations than males., Conclusion: We found lower NSE concentrations in adult and adolescent patients with SMI compared to HC. The results suggest the lack of progressive neuronal injury, and may reflect abnormal neuronal maturation. This provides further support of a neurodevelopmental rather than a neurodegenerative mechanism in SMI.

Published
2023
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48. Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

Author

McWhinney SR, Abé C, Alda M, Benedetti F, Bøen E, Del Mar Bonnin C, Borgers T, Brosch K, Canales-Rodríguez EJ, Cannon DM, Dannlowski U, Diaz-Zuluaga AM, Dietze LMF, Elvsåshagen T, Eyler LT, Fullerton JM, Goikolea JM, Goltermann J, Grotegerd D, Haarman BCM, Hahn T, Howells FM, Ingvar M, Jahanshad N, Kircher TTJ, Krug A, Kuplicki RT, Landén M, Lemke H, Liberg B, Lopez-Jaramillo C, Malt UF, Martyn FM, Mazza E, McDonald C, McPhilemy G, Meier S, Meinert S, Meller T, Melloni EMT, Mitchell PB, Nabulsi L, Nenadic I, Opel N, Ophoff RA, Overs BJ, Pfarr JK, Pineda-Zapata JA, Pomarol-Clotet E, Raduà J, Repple J, Richter M, Ringwald KG, Roberts G, Ross A, Salvador R, Savitz J, Schmitt S, Schofield PR, Sim K, Stein DJ, Stein F, Temmingh HS, Thiel K, Thomopoulos SI, van Haren NEM, Vargas C, Vieta E, Vreeker A, Waltemate L, Yatham LN, Ching CRK, Andreassen OA, Thompson PM, and Hajek T

Abstract

Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact., Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations., Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI., Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Published
2023
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49. Comparison of the accuracy of the 7-item HADS Depression subscale and 14-item total HADS for screening for major depression: A systematic review and individual participant data meta-analysis.

Author

Wu Y, Levis B, Daray FM, Ioannidis JPA, Patten SB, Cuijpers P, Ziegelstein RC, Gilbody S, Fischer FH, Fan S, Sun Y, He C, Krishnan A, Neupane D, Bhandari PM, Negeri Z, Riehm KE, Rice DB, Azar M, Yan XW, Imran M, Chiovitti MJ, Boruff JT, McMillan D, Kloda LA, Markham S, Henry M, Ismail Z, Loiselle CG, Mitchell ND, Al-Adawi S, Beck KR, Beraldi A, Bernstein CN, Boye B, Büel-Drabe N, Bunevicius A, Can C, Carter G, Chen CK, Cheung G, Clover K, Conroy RM, Costa-Requena G, Cukor D, Dabscheck E, De Souza J, Downing M, Feinstein A, Ferentinos PP, Flint AJ, Gallagher P, Gandy M, Grassi L, Härter M, Hernando A, Jackson ML, Jenewein J, Jetté N, Julião M, Kjærgaard M, Köhler S, König HH, Krishna LKR, Lee Y, Löbner M, Loosman WL, Love AW, Löwe B, Malt UF, Marrie RA, Massardo L, Matsuoka Y, Mehnert A, Michopoulos I, Misery L, Nelson CJ, Ng CG, O'Donnell ML, O'Rourke SJ, Öztürk A, Pabst A, Pasco JA, Peceliuniene J, Pintor L, Ponsford JL, Pulido F, Quinn TJ, Reme SE, Reuter K, Riedel-Heller SG, Rooney AG, Sánchez-González R, Saracino RM, Schellekens MPJ, Scherer M, Benedetti A, Thombs BD, and Et Al

Subjects
Humans, Depression diagnosis, Psychiatric Status Rating Scales, Sensitivity and Specificity, Anxiety diagnosis, Mass Screening, Depressive Disorder, Major diagnosis
Abstract

The seven-item Hospital Anxiety and Depression Scale Depression subscale (HADS-D) and the total score of the 14-item HADS (HADS-T) are both used for major depression screening. Compared to the HADS-D, the HADS-T includes anxiety items and requires more time to complete. We compared the screening accuracy of the HADS-D and HADS-T for major depression detection. We conducted an individual participant data meta-analysis and fit bivariate random effects models to assess diagnostic accuracy among participants with both HADS-D and HADS-T scores. We identified optimal cutoffs, estimated sensitivity and specificity with 95% confidence intervals, and compared screening accuracy across paired cutoffs via two-stage and individual-level models. We used a 0.05 equivalence margin to assess equivalency in sensitivity and specificity. 20,700 participants (2,285 major depression cases) from 98 studies were included. Cutoffs of ≥7 for the HADS-D (sensitivity 0.79 [0.75, 0.83], specificity 0.78 [0.75, 0.80]) and ≥15 for the HADS-T (sensitivity 0.79 [0.76, 0.82], specificity 0.81 [0.78, 0.83]) minimized the distance to the top-left corner of the receiver operating characteristic curve. Across all sets of paired cutoffs evaluated, differences of sensitivity between HADS-T and HADS-D ranged from -0.05 to 0.01 (0.00 at paired optimal cutoffs), and differences of specificity were within 0.03 for all cutoffs (0.02-0.03). The pattern was similar among outpatients, although the HADS-T was slightly (not nonequivalently) more specific among inpatients. The accuracy of HADS-T was equivalent to the HADS-D for detecting major depression. In most settings, the shorter HADS-D would be preferred. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Published
2023
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