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3. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Author

Constantinides, C, Han, LKM, Alloza, C, Antonucci, LA, Arango, C, Ayesa-Arriola, R, Banaj, N, Bertolino, A, Borgwardt, S, Bruggemann, J, Bustillo, J, Bykhovski, O, Calhoun, V, Carr, V, Catts, S, Chung, Y-C, Crespo-Facorro, B, Diaz-Caneja, CM, Donohoe, G, Du Plessis, S, Edmond, J, Ehrlich, S, Emsley, R, Eyler, LT, Fuentes-Claramonte, P, Georgiadis, F, Green, M, Guerrero-Pedraza, A, Ha, M, Hahn, T, Henskens, FA, Holleran, L, Homan, S, Homan, P, Jahanshad, N, Janssen, J, Ji, E, Kaiser, S, Kaleda, V, Kim, M, Kim, W-S, Kirschner, M, Kochunov, P, Kwak, YB, Kwon, JS, Lebedeva, I, Liu, J, Mitchie, P, Michielse, S, Mothersill, D, Mowry, B, de la Foz, VO-G, Pantelis, C, Pergola, G, Piras, F, Pomarol-Clotet, E, Preda, A, Quide, Y, Rasser, PE, Rootes-Murdy, K, Salvador, R, Sangiuliano, M, Sarro, S, Schall, U, Schmidt, A, Scott, RJ, Selvaggi, P, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Thomopoulos, S, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, van Amelsvoort, T, Vazquez-Bourgon, J, Vecchio, D, Voineskos, A, Weickert, CS, Weickert, T, Thompson, PM, Schmaal, L, van Erp, TGM, Turner, J, Cole, JH, Dima, D, Walton, E, Constantinides, C, Han, LKM, Alloza, C, Antonucci, LA, Arango, C, Ayesa-Arriola, R, Banaj, N, Bertolino, A, Borgwardt, S, Bruggemann, J, Bustillo, J, Bykhovski, O, Calhoun, V, Carr, V, Catts, S, Chung, Y-C, Crespo-Facorro, B, Diaz-Caneja, CM, Donohoe, G, Du Plessis, S, Edmond, J, Ehrlich, S, Emsley, R, Eyler, LT, Fuentes-Claramonte, P, Georgiadis, F, Green, M, Guerrero-Pedraza, A, Ha, M, Hahn, T, Henskens, FA, Holleran, L, Homan, S, Homan, P, Jahanshad, N, Janssen, J, Ji, E, Kaiser, S, Kaleda, V, Kim, M, Kim, W-S, Kirschner, M, Kochunov, P, Kwak, YB, Kwon, JS, Lebedeva, I, Liu, J, Mitchie, P, Michielse, S, Mothersill, D, Mowry, B, de la Foz, VO-G, Pantelis, C, Pergola, G, Piras, F, Pomarol-Clotet, E, Preda, A, Quide, Y, Rasser, PE, Rootes-Murdy, K, Salvador, R, Sangiuliano, M, Sarro, S, Schall, U, Schmidt, A, Scott, RJ, Selvaggi, P, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Thomopoulos, S, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, van Amelsvoort, T, Vazquez-Bourgon, J, Vecchio, D, Voineskos, A, Weickert, CS, Weickert, T, Thompson, PM, Schmaal, L, van Erp, TGM, Turner, J, Cole, JH, Dima, D, and Walton, E

Abstract

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

Published
2023

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

Author

Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, Francks, C, Schijven, D, Postema, MC, Fukunaga, M, Matsumoto, J, Miura, K, de Zwarte, SMC, van Haren, NEM, Cahn, W, Pol, HEH, Kahn, RS, Ayesa-Arriola, R, de la Foz, VO-G, Tordesillas-Gutierrez, D, Vazquez-Bourgon, J, Crespo-Facorro, B, Alnaes, D, Dahl, A, Westlye, LT, Agartz, I, Andreassen, OA, Jonsson, EG, Kochunov, P, Bruggemann, JM, Catts, SV, Michie, PT, Mowry, BJ, Quide, Y, Rasser, PE, Schall, U, Scott, RJ, Carr, VJ, Green, MJ, Henskens, FA, Loughland, CM, Pantelis, C, Weickert, CS, Weickert, TW, De Haan, L, Brosch, K, Pfarr, J-K, Ringwald, KG, Stein, F, Jansen, A, Kircher, TTJ, Nenadic, I, Kramer, B, Gruber, O, Satterthwaite, TD, Bustillo, J, Mathalon, DH, Preda, A, Calhoun, VD, Ford, JM, Potkin, SG, Chen, J, Tan, Y, Wang, Z, Xiang, H, Fan, F, Bernardoni, F, Ehrlich, S, Fuentes-Claramonte, P, Garcia-Leon, MA, Guerrero-Pedraza, A, Salvador, R, Sarro, S, Pomarol-Clotet, E, Ciullo, V, Piras, F, Vecchio, D, Banaj, N, Spalletta, G, Michielse, S, van Amelsvoort, T, Dickie, EW, Voineskos, AN, Sim, K, Ciufolini, S, Dazzan, P, Murray, RM, Kim, W-S, Chung, Y-C, Andreou, C, Schmidt, A, Borgwardt, S, McIntosh, AM, Whalley, HC, Lawrie, SM, Du Plessis, S, Luckhoff, HK, Scheffler, F, Emsley, R, Grotegerd, D, Lencer, R, Dannlowski, U, Edmond, JT, Rootes-Murdy, K, Stephen, JM, Mayer, AR, Antonucci, LA, Fazio, L, Pergola, G, Bertolino, A, Diaz-Caneja, CM, Janssen, J, Lois, NG, Arango, C, Tomyshev, AS, Lebedeva, I, Cervenkav, S, Sellgrenv, CM, Georgiadis, F, Kirschner, M, Kaiser, S, Hajek, T, Skoch, A, Spaniel, F, Kim, M, Bin Kwak, Y, Oh, S, Kwon, JS, James, A, Bakker, G, Knochel, C, Stablein, M, Oertel, V, Uhlmann, A, Howells, FM, Stein, DJ, Temmingh, HS, Diaz-Zuluaga, AM, Pineda-Zapata, JA, Lopez-Jaramillo, C, Homan, S, Ji, E, Surbeck, W, Homan, P, Fishera, SE, Franke, B, Glahn, DC, Gur, RC, Hashimoto, R, Jahanshad, N, Luders, E, Medland, SE, Thompson, PM, Turner, JA, van Erp, TGM, and Francks, C

Abstract

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Published
2023

5. Brain correlates of cognitive impairment in patients with schizophrenia

Author

García-León, M.Á., primary, Barbosa, L., additional, Fuentes-Claramonte, P., additional, Soler-Vidal, J., additional, Ramiro, N., additional, Del Olmo, P., additional, Salgado-Pineda, P., additional, Llanos-Torres, M., additional, Guerrero-Pedraza, A., additional, Tristany, J., additional, Canut, P., additional, Sarro, S., additional, Salvador, R., additional, McKenna, P., additional, and Pomarol-Clotet, E., additional

Published
2023
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10. Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group

Author

Abe, C, Ching, CRK, Liberg, B, V. Lebedev, A, Agartz, I, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Benedetti, F, Berk, M, Boen, E, Bonnin, CDM, Breuer, F, Brosch, K, Brouwer, RM, Canales-Rodriguez, EJ, Cannon, DM, Chye, Y, Dahl, A, Dandash, O, Dannlowski, U, Dohm, K, Elvsashagen, 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, Nenadic, 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, Sarro, 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, Landen, M, Abe, C, Ching, CRK, Liberg, B, V. Lebedev, A, Agartz, I, Akudjedu, TN, Alda, M, Alnaes, D, Alonso-Lana, S, Benedetti, F, Berk, M, Boen, E, Bonnin, CDM, Breuer, F, Brosch, K, Brouwer, RM, Canales-Rodriguez, EJ, Cannon, DM, Chye, Y, Dahl, A, Dandash, O, Dannlowski, U, Dohm, K, Elvsashagen, 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, Nenadic, 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, Sarro, 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 Landen, M

Abstract

BACKGROUND: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD. METHODS: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables. RESULTS: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18

Published
2022

11. 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

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

Author

Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, Tamnes, CK, Wierenga, LM, Doucet, GE, Dima, D, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andreassen, OA, Anticevic, A, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, den Braber, A, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Calhoun, VD, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Chaim-Avancini, TM, Ching, CRK, Clark, VP, Conrod, PJ, Conzelmann, A, Crivello, F, Davey, CG, Dickie, EW, Ehrlich, S, Van't Ent, D, Fisher, SE, Fouche, J-P, Franke, B, Fuentes-Claramonte, P, de Geus, EJC, Di Giorgio, A, Glahn, DC, Gotlib, IH, Grabe, HJ, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Gurholt, TP, de Haan, L, Haatveit, B, Harrison, BJ, Hartman, CA, Hatton, SN, Heslenfeld, DJ, van den Heuvel, OA, Hickie, IB, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, AC, Jiang, J, Jonsson, EG, Joska, JA, Kalnin, AJ, Klein, M, Koenders, L, Kolskar, KK, Kramer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, IS, Lee, PH, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, BC, McDonald, C, McIntosh, AM, McMahon, KL, McPhilemy, G, van der Meer, D, Menchon, JM, Naaijen, J, Nyberg, L, Oosterlaan, J, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Radua, J, Reif, A, Richard, G, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Sim, K, Simmons, A, Smoller, JW, Sommer, IE, Soriano-Mas, C, Stein, DJ, Strike, LT, Szeszko, PR, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Trollor, JN, Uhlmann, A, Veer, IM, Veltman, DJ, Voineskos, A, Volzke, H, Walter, H, Wang, L, Wang, Y, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, HC, Williams, SCR, Wittfeld, K, Wolf, DH, Wright, MJ, Yoncheva, YN, Zanetti, M, Ziegler, GC, de Zubicaray, G, Thompson, PM, Crone, EA, Frangou, S, and Tamnes, CK

Abstract

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Published
2022

13. 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

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

Author

Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Frangou, S, Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Frangou, S

Abstract

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Published
2022

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

Author

Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Dima, D, Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Dima, D

Abstract

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Published
2022

16. 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

17. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, Paus, T, Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, and Paus, T

Abstract

BACKGROUND: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life. METHODS: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed. RESULTS: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth. CONCLUSIONS: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from t

Published
2022

18. A regional-based newborn hearing screening program: the Emilia-Romagna model after ten years of legislation.

Author

Bianchin, G., Palma, S., Polizzi, V., Kaleci, S., Stagi, P., Cappai, M., Baiocchi, M. P., Benincasa, P., Brandolini, C., Casadio, L., Di Sarro, S., Farneti, D., Galli, A., Ghiselli, S., Iadicicco, P., Landuzzi, E., Limarzi, M., Locatelli, C., Murri, A., and Nanni, L.

Subjects
NEWBORN infants, CONGENITAL disorders, HEALTH outcome assessment, EPIDEMIOLOGY, DEAFNESS
Abstract

Copyright of Annali di Igiene, Medicina Preventiva e di Comunità is the property of Societa Editrice Universo s.r.l. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
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19. Relapse of first-episode schizophrenia patients and neurocognitive impairment: The role of dopaminergic and anticholinergic burden

Author

Cuesta M, Ballesteros A, Sanchez-Torres A, Amoretti S, Lopez-Ilundain J, Merchan-Naranjo J, Gonzalez-Ortega I, Salgado P, Rodriguez-Jimenez R, Roldan-Bejarano A, Sarro S, Ibanez A, Usall J, Escarti M, Moreno-Izco L, Mezquida G, Parellada M, Gonzalez-Pinto A, Berrocoso E, and Bernardo M

Abstract

BACKGROUND: The prevention of relapse may be a key factor to diminish the cognitive impairment of first-episode schizophrenia (FES) patients. We aimed to ascertain the effects of relapse, and dopaminergic and anticholinergic treatment burdens on cognitive functioning in the follow-up.; METHODS: Ninety-nine FES patients participated in this study. Cognitive assessments were performed at baseline and after 3years of follow-up or, in those patients who relapsed, after >2months of stabilization of the new acute psychotic episode. The primary outcomes were final cognitive dimensions.; RESULTS: Repeated measures MANOVA analyses showed improvements in the whole sample on the end-point assessments in processing speed and social cognition. However, only impairment in social cognition showed a significant interaction with relapse by time in this sample. Relapse in FES patients was significantly associated with poor performance on end-point assessments of working memory, social cognition and global cognitive score. Anticholinergic burden, but not dopaminergic burden, was associated with verbal memory impairment. These significant associations resulted after controlling for baseline cognitive functioning, relapse and dopaminergic burden.; CONCLUSIONS: The relationship between relapse and cognitive impairment in recovered FES patients seems to be particularly complex at the short-term follow-up of these patients. While relapse was associated with working memory, social cognition impairments and global cognitive score, anticholinergic burden might play an additional worsening effect on verbal memory. Thus, tailoring or changing antipsychotics and other drugs to reduce their anticholinergic burden may be a potential modifiable factor to diminish cognitive impairment at this stage of the illness. Copyright © 2022 Elsevier B.V. All rights reserved.

Published
2022

21. P.0788 Surface-based morphometry abnormality in schizophrenia and its relation to positive, negative and disorganized syndromes

Author

Garcia-Leon, M.A., primary, Fuentes-Claramonte, P., additional, Canales-Rodriguez, E., additional, Soler, J., additional, Santo, A., additional, Ramiro, N., additional, Torres, M.L., additional, Aquino, A., additional, Bosque, C., additional, Saló, L., additional, Portillo, F., additional, Panicali, F., additional, Argila, I., additional, Salgado-Pineda, P., additional, Sarro, S., additional, Salvador, R., additional, McKenna, P., additional, and Pomarol-Clotet, E., additional

Published
2021
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22. A longitudinal study of gene expression in first-episode schizophrenia; exploring relapse mechanisms by co-expression analysis in peripheral blood

Author

Gasso P, Rodriguez N, Martinez-Pinteno A, Mezquida G, Ribeiro M, Gonzalez-Penas J, Zorrilla I, Martinez-Sadurni L, Rodriguez-Jimenez R, Corripio I, Sarro S, Ibanez A, Usall J, Lobo A, Moren C, Cuesta M, Parellada M, Gonzalez-Pinto A, Berrocoso E, Bernardo M, Mas S, 2EPs Grp, and 2EPs Group

Abstract

Little is known about the pathophysiological mechanisms of relapse in first-episode schizophrenia, which limits the study of potential biomarkers. To explore relapse mechanisms and identify potential biomarkers for relapse prediction, we analyzed gene expression in peripheral blood in a cohort of first-episode schizophrenia patients with less than 5 years of evolution who had been evaluated over a 3-year follow-up period. A total of 91 participants of the 2EPs project formed the sample for baseline gene expression analysis. Of these, 67 provided biological samples at follow-up (36 after 3 years and 31 at relapse). Gene expression was assessed using the Clariom S Human Array. Weighted gene co-expression network analysis was applied to identify modules of co-expressed genes and to analyze their preservation after 3 years of follow-up or at relapse. Among the 25 modules identified, one module was semi-conserved at relapse (DarkTurquoise) and was enriched with risk genes for schizophrenia, showing a dysregulation of the TCF4 gene network in the module. Two modules were semi-conserved both at relapse and after 3 years of follow-up (DarkRed and DarkGrey) and were found to be biologically associated with protein modification and protein location processes. Higher expression of DarkRed genes was associated with higher risk of suffering a relapse and early appearance of relapse (p = 0.045). Our findings suggest that a dysregulation of the TCF4 network could be an important step in the biological process that leads to relapse and suggest that genes related to the ubiquitin proteosome system could be potential biomarkers of relapse.

Published
2021

23. The prevention of relapses in first episodes of schizophrenia: The 2EPs Project, background, rationale and study design

Author

Bernardo M, Amoretti S, Cuesta M, Parellada M, Mezquida G, Gonzalez-Pinto A, Berge D, Lobo A, Aguilar E, Usall J, Corripio I, Bobes J, Rodriguez-Jimenez R, Sarro S, Contreras F, Ibanez A, Gutierrez M, Mico J, and 2EPs Group

Subjects
2EPs Project, PEPs Project, Schizophrenia, Esquizofrenia, First-episode of schizophrenia, Relapse, Primer episodio de esquizofrenia, Recaída, Proyecto 2EPs, Proyecto PEPs
Abstract

Up to 80% of first-episode psychosis patients suffer a relapse within five years of the remission. Relapse should be an important focus of prevention given the potential harm to the patient and family. It threatens to disrupt their psychosocial recovery, increases the risk of resistance to treatment and has been associated with greater direct and indirect costs for society. Based on a previous project entitled "Genotype-phenotype and environment. Application to a predictive model in first psychotic episodes" (PEPs Project), the project "Clinical and neurobiological determinants of second episodes of schizophrenia. Longitudinal study of first episode of psychosis" was designed, also known as the 2EPs Project. It aimed to identify and characterize those factors that predict a relapse within the years immediately following a first episode. This project has focused on following the clinical course, with neuropsychological assessments, biological and neuroanatomical measures, genetic adherence and physical health monitoring in order to compare a subgroup of patients with a second episode to another group of patients which remains in remission. The main objective of the present article is to describe the rationale of the 2EPs Project, explaining the measurement approach adopted and providing an overview of the selected clinical and functional measures. 2EPs Project is a multicenter, coordinated, naturalistic, longitudinal follow-up study over three years in a Spanish sample of patients in remission after a first-psychotic episode of schizophrenia. It is closely monitoring the clinical course of the cases recruited to compare the subgroup of patients with a second episode to that which remains in remission. The sample is composed of 223 subjects recruited from 15 clinical centres in Spain with experience of the preceding PEPs Study project, albeit 2EPs being an expanded version with new basic groups in biological research. From the total sample recruited, 63 patients presented a relapse (44%). 2EPs arose to characterize first episodes in an exhaustive, novel and multimodal way, thus contributing towards the development of a predictive model of relapse. Identifying the characteristics of patients who relapse could improve early detection and intervention.

Published
2021

24. 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

25. 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

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

Author

Favre P., Pauling M., Stout J., Hozer F., Sarrazin S., Abe C., Alda M., Alloza C., Alonso-Lana S., Andreassen O. A., Baune B. T., Benedetti F., Busatto G. F., Canales-Rodriguez E. J., Caseras X., Chaim-Avancini T. M., Ching C. R. K., Dannlowski U., Deppe M., Eyler L. T., Fatjo-Vilas M., Foley S. F., Grotegerd D., Hajek T., Haukvik U. K., Howells F. M., Jahanshad N., Kugel H., Lagerberg T. V., Lawrie S. M., Linke J. O., McIntosh A., Melloni E. M. T., Mitchell P. B., Polosan M., Pomarol-Clotet E., Repple J., Roberts G., Roos A., Rosa P. G. P., Salvador R., Sarro S., Schofield P. R., Serpa M. H., Sim K., Stein D. J., Sussmann J. E., Temmingh H. S., Thompson P. M., Verdolini N., Vieta E., Wessa M., Whalley H. C., Zanetti M. V., Leboyer M., Mangin J. -F., Henry C., Duchesnay E., Houenou J., Favre, P., Pauling, M., Stout, J., Hozer, F., Sarrazin, S., Abe, C., Alda, M., Alloza, C., Alonso-Lana, S., Andreassen, O. A., Baune, B. T., Benedetti, F., Busatto, G. F., Canales-Rodriguez, E. J., Caseras, X., Chaim-Avancini, T. M., Ching, C. R. K., Dannlowski, U., Deppe, M., Eyler, L. T., Fatjo-Vilas, M., Foley, S. F., Grotegerd, D., Hajek, T., Haukvik, U. K., Howells, F. M., Jahanshad, N., Kugel, H., Lagerberg, T. V., Lawrie, S. M., Linke, J. O., Mcintosh, A., Melloni, E. M. T., Mitchell, P. B., Polosan, M., Pomarol-Clotet, E., Repple, J., Roberts, G., Roos, A., Rosa, P. G. P., Salvador, R., Sarro, S., Schofield, P. R., Serpa, M. H., Sim, K., Stein, D. J., Sussmann, J. E., Temmingh, H. S., Thompson, P. M., Verdolini, N., Vieta, E., Wessa, M., Whalley, H. C., Zanetti, M. V., Leboyer, M., Mangin, J. -F., Henry, C., Duchesnay, E., and Houenou, J.

Abstract

This Article was originally published under NPG's License to Publish, but has now been made available under a [CC BY 4.0] license. The PDF and HTML versions of the Article have been modified accordingly.

Published
2019

28. Personalized medicine begins with the phenotype: identifying antipsychotic response phenotypes in a first-episode psychosis cohort

Author

Mas, S, Gasso, P, Rodriguez, N, Cabrera, B, Mezquida, G, Lobo, A, Gonzalez-Pinto, A, Parellada, M, Corripio, I, Vieta, E, Castro-Fornieles, J, Bobes, J, Usall, J, Saiz-Ruiz, J, Contreras, F, Parellada, E, Bernardo, M, Lafuente, A, Bioque, M, Diaz-Caneja, CM, Gonzalez-Penas, J, Solis, AA, Rebella, M, Gonzalez-Ortega, I, Besga, A, SanJuan, J, Nacher, J, Morro, L, Montserrat, C, Jimenez, E, Da Costa, SG, Baeza, I, de la Serna, E, Rivas, S, Diaz, C, Saiz, PA, Garcia-Alvarez, L, Fraile, MG, Rabadan, AZ, Torio, I, Rodriguez-Jimenez, R, Butjosa, A, Pardo, M, Sarro, S, Pomarol-Clotet, E, Cuadrado, AI, and Cuesta, MJ

Subjects
predictive factors, psychosis, personalized medicine, first-episode, antipsychotic, clustering
Abstract

Aims Here, we present a clustering strategy to identify phenotypes of antipsychotic (AP) response by using longitudinal data from patients presenting first-episode psychosis (FEP). Method One hundred and ninety FEP with complete data were selected from the PEPs project. The efficacy was assessed using total PANSS, and adverse effects using total UKU, during one-year follow-up. We used the Klm3D method to cluster longitudinal data. Results We identified four clusters: cluster A, drug not toxic and beneficial; cluster B, drug beneficial but toxic; cluster C, drug neither toxic nor beneficial; and cluster D, drug toxic and not beneficial. These groups significantly differ in baseline demographics, clinical, and neuropsychological characteristics (PAS, total PANSS, DUP, insight, pIQ, age of onset, cocaine use and family history of mental illness). Conclusions The results presented here allow the identification of phenotypes of AP response that differ in well-known simple and classic clinical variables opening the door to clinical prediction and application of personalized medicine.

Published
2020

29. Association study of candidate genes with obesity and metabolic traits in antipsychotic-treated patients with first-episode psychosis over a 2-year period

Author

Gasso P, Arnaiz J, Mas S, Lafuente A, Bioque M, Cuesta M, Diaz-Caneja C, Garcia C, Lobo A, Gonzalez-Pinto A, Parellada M, Corripio I, Vieta E, Castro-Fornieles J, Mane A, Rodriguez N, Bolac D, Saiz-Ruiz J, Bernardo M, Mezquida G, Amoretti S, Pina-Camacho L, Gonzalez-Penas J, Alonso-Solis A, Rabetla M, Zorrilla I, Garcia S, Barcones F, Modrego P, Sanjuan J, Nacher J, Berge D, Monserrat C, Verdolini N, Gil-Badenes J, Baeza I, de la Serna E, Contreras F, Saiz-Masvidal C, Garcia-Portilla M, Bobes J, Gutierrez M, Segarra R, Dompablo M, Rodriguez-Jimenez R, Usall J, Butjosa A, Sarro S, Pomarol-Clotet E, Ibanez A, Ribeiro M, Selva-Vera G, and PEPs Grp

Subjects
Antipsychotic, obesity, first episode of psychosis, gene, side-effect, polymorphism
Abstract

Aims: Patients with a first episode of psychosis (FEP) often display different metabolic disturbances even independently of drug therapy. However, antipsychotic (AP) treatment, especially with second-generation APs, is strongly linked to weight gain, which increases patients' risk of developing obesity and other metabolic diseases. There is an important genetic risk component that can contribute to the appearance of these disturbances. The aim of the present study was to evaluate the effect of polymorphisms in selected candidate genes on obesity and other anthropometric and metabolic traits in 320 AP-treated FEP patients over the course of a 2-year follow-up. Methods: These patients were recruited in the multicentre PEPs study (Phenotype-genotype and environmental interaction; Application of a predictive model in first psychotic episodes). A total of 127 validated single nucleotide polymorphisms (SNPs) in 18 candidate genes were included in the genetic analysis. Results: After Bonferroni correction, SNPs in ADRA2A, FTO, CNR1, DRD2, DRD3, LEPR and BDNF were associated with obesity, abdominal circumference, triglycerides, HDL cholesterol, and/or percentage of glycated haemoglobin. Conclusions: Although our results should be interpreted as exploratory, they support previous evidence of the impact of these candidate genes on obesity and metabolic status. Further research is required to gain a better knowledge of the genetic variants that can be considered relevant metabolic risk factors. The ability to identify FEP patients at higher risk for these metabolic disturbances would enable clinicians to better select and control their AP treatment.

Published
2020

30. Personalized medicine begins with the phenotype: identifying antipsychotic response phenotypes in a first-episode psychosis cohort

Author

Mas S, Gasso P, Rodriguez N, Cabrera B, Mezquida G, Lobo A, Gonzalez-Pinto A, Parellada M, Corripio I, Vieta E, Castro-Fornieles J, Bobes J, Usall J, Saiz-Ruiz J, Contreras F, Parellada E, Bernardo M, Lafuente A, Bioque M, Diaz-Caneja C, Gonzalez-Penas J, Solis A, Rebella M, Gonzalez-Ortega I, Besga A, Sanjuan J, Nacher J, Morro L, Montserrat C, Jimenez E, Da Costa S, Baeza I, de la Serna E, Rivas S, Diaz C, Saiz P, Garcia-Alvarez L, Fraile M, Rabadan A, Torio I, Rodriguez-Jimenez R, Butjosa A, Pardo M, Sarro S, Pomarol-Clotet E, Cuadrado A, Cuesta M, and PEPs Grp

Subjects
predictive factors, psychosis, personalized medicine, first-episode, antipsychotic, clustering
Abstract

Aims Here, we present a clustering strategy to identify phenotypes of antipsychotic (AP) response by using longitudinal data from patients presenting first-episode psychosis (FEP). Method One hundred and ninety FEP with complete data were selected from the PEPs project. The efficacy was assessed using total PANSS, and adverse effects using total UKU, during one-year follow-up. We used the Klm3D method to cluster longitudinal data. Results We identified four clusters: cluster A, drug not toxic and beneficial; cluster B, drug beneficial but toxic; cluster C, drug neither toxic nor beneficial; and cluster D, drug toxic and not beneficial. These groups significantly differ in baseline demographics, clinical, and neuropsychological characteristics (PAS, total PANSS, DUP, insight, pIQ, age of onset, cocaine use and family history of mental illness). Conclusions The results presented here allow the identification of phenotypes of AP response that differ in well-known simple and classic clinical variables opening the door to clinical prediction and application of personalized medicine.

Published
2020

31. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Author

Radua, J, Vieta, E, Shinohara, R, Kochunov, P, Quidé, Y, Green, MJ, Weickert, CS, Weickert, T, Bruggemann, J, Kircher, T, Nenadić, I, Cairns, MJ, Seal, M, Schall, U, Henskens, F, Fullerton, JM, Mowry, B, Pantelis, C, Lenroot, R, Cropley, V, Loughland, C, Scott, R, Wolf, D, Satterthwaite, TD, Tan, Y, Sim, K, Piras, F, Spalletta, G, Banaj, N, Pomarol-Clotet, E, Solanes, A, Albajes-Eizagirre, A, Canales-Rodríguez, EJ, Sarro, S, Di Giorgio, A, Bertolino, A, Stäblein, M, Oertel, V, Knöchel, C, Borgwardt, S, du Plessis, S, Yun, JY, Kwon, JS, Dannlowski, U, Hahn, T, Grotegerd, D, Alloza, C, Arango, C, Janssen, J, Díaz-Caneja, C, Jiang, W, Calhoun, V, Ehrlich, S, Yang, K, Cascella, NG, Takayanagi, Y, Sawa, A, Tomyshev, A, Lebedeva, I, Kaleda, V, Kirschner, M, Hoschl, C, Tomecek, D, Skoch, A, van Amelsvoort, T, Bakker, G, James, A, Preda, A, Weideman, A, Stein, DJ, Howells, F, Uhlmann, A, Temmingh, H, López-Jaramillo, C, Díaz-Zuluaga, A, Fortea, L, Martinez-Heras, E, Solana, E, Llufriu, S, Jahanshad, N, Thompson, P, Turner, J, van Erp, T, Glahn, D, Pearlson, G, Hong, E, Krug, A, Carr, V, Tooney, P, Cooper, G, Rasser, P, Michie, P, Catts, S, Gur, R, Yang, F, Fan, F, Chen, J, Guo, H, Tan, S, Radua, J, Vieta, E, Shinohara, R, Kochunov, P, Quidé, Y, Green, MJ, Weickert, CS, Weickert, T, Bruggemann, J, Kircher, T, Nenadić, I, Cairns, MJ, Seal, M, Schall, U, Henskens, F, Fullerton, JM, Mowry, B, Pantelis, C, Lenroot, R, Cropley, V, Loughland, C, Scott, R, Wolf, D, Satterthwaite, TD, Tan, Y, Sim, K, Piras, F, Spalletta, G, Banaj, N, Pomarol-Clotet, E, Solanes, A, Albajes-Eizagirre, A, Canales-Rodríguez, EJ, Sarro, S, Di Giorgio, A, Bertolino, A, Stäblein, M, Oertel, V, Knöchel, C, Borgwardt, S, du Plessis, S, Yun, JY, Kwon, JS, Dannlowski, U, Hahn, T, Grotegerd, D, Alloza, C, Arango, C, Janssen, J, Díaz-Caneja, C, Jiang, W, Calhoun, V, Ehrlich, S, Yang, K, Cascella, NG, Takayanagi, Y, Sawa, A, Tomyshev, A, Lebedeva, I, Kaleda, V, Kirschner, M, Hoschl, C, Tomecek, D, Skoch, A, van Amelsvoort, T, Bakker, G, James, A, Preda, A, Weideman, A, Stein, DJ, Howells, F, Uhlmann, A, Temmingh, H, López-Jaramillo, C, Díaz-Zuluaga, A, Fortea, L, Martinez-Heras, E, Solana, E, Llufriu, S, Jahanshad, N, Thompson, P, Turner, J, van Erp, T, Glahn, D, Pearlson, G, Hong, E, Krug, A, Carr, V, Tooney, P, Cooper, G, Rasser, P, Michie, P, Catts, S, Gur, R, Yang, F, Fan, F, Chen, J, Guo, H, and Tan, S

Abstract

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Published
2020

32. Increased power by harmonizing structural MRI site differences with the ComBat batch method in ENIGMA

Author

Radua, J, Vieta, E, Shinohara, R, Kochunov, P, Quide, Y, Green, MJ, Weickert, CS, Weickert, T, Bruggemann, J, Kircher, T, Nenadic, I, Cairns, MJ, Seal, M, Schall, U, Henskens, F, Fullerton, JM, Mowry, B, Pantelis, C, Lenroot, R, Cropley, V, Loughland, C, Scott, R, Wolf, D, Satterthwaite, TD, Tan, Y, Sim, K, Piras, F, Spalletta, G, Banaj, N, Pomarol-Clotet, E, Solanes, A, Albajes-Eizagirre, A, Canales-Rodriguez, EJ, Sarro, S, Di Giorgio, A, Bertolino, A, Staeblein, M, Oertel, V, Knoechel, C, Borgwardt, S, du Plessis, S, Yun, J-Y, Kwon, JS, Dannlowski, U, Hahn, T, Grotegerd, D, Alloza, C, Arango, C, Janssen, J, Diaz-Caneja, C, Jiang, W, Calhoun, V, Ehrlich, S, Yang, K, Cascella, NG, Takayanagi, Y, Sawa, A, Tomyshev, A, Lebedeva, I, Kaleda, V, Kirschner, M, Hoschl, C, Tomecek, D, Skoch, A, van Amelsvoort, T, Bakker, G, James, A, Preda, A, Weideman, A, Stein, DJ, Howells, F, Uhlmann, A, Temmingh, H, Lopez-Jaramillo, C, Diaz-Zuluaga, A, Fortea, L, Martinez-Heras, E, Solana, E, Llufriu, S, Jahanshad, N, Thompson, P, Turner, J, van Erp, T, Radua, J, Vieta, E, Shinohara, R, Kochunov, P, Quide, Y, Green, MJ, Weickert, CS, Weickert, T, Bruggemann, J, Kircher, T, Nenadic, I, Cairns, MJ, Seal, M, Schall, U, Henskens, F, Fullerton, JM, Mowry, B, Pantelis, C, Lenroot, R, Cropley, V, Loughland, C, Scott, R, Wolf, D, Satterthwaite, TD, Tan, Y, Sim, K, Piras, F, Spalletta, G, Banaj, N, Pomarol-Clotet, E, Solanes, A, Albajes-Eizagirre, A, Canales-Rodriguez, EJ, Sarro, S, Di Giorgio, A, Bertolino, A, Staeblein, M, Oertel, V, Knoechel, C, Borgwardt, S, du Plessis, S, Yun, J-Y, Kwon, JS, Dannlowski, U, Hahn, T, Grotegerd, D, Alloza, C, Arango, C, Janssen, J, Diaz-Caneja, C, Jiang, W, Calhoun, V, Ehrlich, S, Yang, K, Cascella, NG, Takayanagi, Y, Sawa, A, Tomyshev, A, Lebedeva, I, Kaleda, V, Kirschner, M, Hoschl, C, Tomecek, D, Skoch, A, van Amelsvoort, T, Bakker, G, James, A, Preda, A, Weideman, A, Stein, DJ, Howells, F, Uhlmann, A, Temmingh, H, Lopez-Jaramillo, C, Diaz-Zuluaga, A, Fortea, L, Martinez-Heras, E, Solana, E, Llufriu, S, Jahanshad, N, Thompson, P, Turner, J, and van Erp, T

Abstract

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Published
2020

34. Auditory hallucinations in first-episode psychosis: A voxel-based morphometry study

Author

Escarti, MJ, Garcia-Marti, G, Sanz-Requena, R, Marti-Bonmati, L, Cabrera, B, Vieta, E, Lobo, A, Castro-Fornieles, J, Gonzalez-Pinto, A, Cortizo, R, Pina-Camacho, L, Parellada, M, Bernardo, M, Sanjuan, J, Mezquida, G, Bioque, M, Janssen, J, Arango, C, Zorilla, I, Gonzalez, I, de-la-Camara, C, Barcones, MF, Garcia-Iturrospe, EJA, Mane, A, Montserrat, C, Bonnin, CM, Martinez-Aran, A, de la Serna, E, Baeza, I, Contreras, F, Menchon, JM, Gonzalez-Blanco, L, Garcia-Alvarez, L, Rodriguez-Jimenez, R, Morales-Munoz, I, Usall, J, Butjosa, A, Sarro, S, Pomarol-Clotet, E, and PEPs Grp

Abstract

Background: Auditory hallucinations (AH) are a core symptom of psychosis. The brain abnormalities responsible for AH remain controversial due to inconsistent and conflicting findings across studies, with substantial confounding factors, such as chronicity. Few studies have examined the pathological changes that occur in the gray matter (GM) of patients with first-episode psychosis (FEP) and AH. The present study aims to validate the presence and characteristics of these structural abnormalities in relation to the intensity of psychotic symptoms and AH in a larger homogeneous sample than those of previous studies. Methods: A magnetic resonance voxel-based morphometric analysis was applied to a group of 215 patients with FEP (93 patients with AH and 122 patients without AH) and 177 healthy controls. The patients were evaluated using the PANSS scale. Results: Patients with FEP exhibited greater reductions in GM concentrations in the temporal, frontal, cingulate and insular areas than the healthy controls did. No specific differences were found between the patients with FEP and AH and the patients without AH. In addition, total scores on the PANSS were negatively correlated with GM reductions in the FEP group. No correlations were found between the severity of the AH and the GM volumes. Conclusions: As in previous studies, reductions in the GM concentrations in patients with FEP suggest that alterations are present in the early stages of psychosis, and these alterations are correlated with the severity of the illness. The GM reductions were not found to be related to the presence or severity of AH. (C) 2019 Elsevier B.V. All rights reserved.

Published
2019

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

Author

Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abe, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodriguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarro, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, J-F, Henry, C, Duchesnay, E, Houenou, J, Favre, P, Pauling, M, Stout, J, Hozer, F, Sarrazin, S, Abe, C, Alda, M, Alloza, C, Alonso-Lana, S, Andreassen, OA, Baune, BT, Benedetti, F, Busatto, GF, Canales-Rodriguez, EJ, Caseras, X, Chaim-Avancini, TM, Ching, CRK, Dannlowski, U, Deppe, M, Eyler, LT, Fatjo-Vilas, M, Foley, SF, Grotegerd, D, Hajek, T, Haukvik, UK, Howells, FM, Jahanshad, N, Kugel, H, Lagerberg, TV, Lawrie, SM, Linke, JO, McIntosh, A, Melloni, EMT, Mitchell, PB, Polosan, M, Pomarol-Clotet, E, Repple, J, Roberts, G, Roos, A, Rosa, PGP, Salvador, R, Sarro, S, Schofield, PR, Serpa, MH, Sim, K, Stein, DJ, Sussmann, JE, Temmingh, HS, Thompson, PM, Verdolini, N, Vieta, E, Wessa, M, Whalley, HC, Zanetti, MV, Leboyer, M, Mangin, J-F, Henry, C, Duchesnay, E, and Houenou, J

Abstract

Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr < 0.001) and cingulum (right: R2 = 0.041, left: R2 = 0.040, Pcorr < 0.001). Lithium medication, later onset and short disease duration were related to higher FA along multiple ROIs. Results of the meta-analysis showed similar effects. We demonstrated widespread WM abnormalities in BD and highlighted that altered WM connectivity within the corpus callosum and the cingulum are strongly associated with BD. These brain abnormalities could represent a biomarker for use in the diagnosis of BD. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.

Published
2019

36. Smoking does not impact social and non-social cognition in patients with first episode psychosis

Author

Sanchez-Gutierrez, T, Garcia-Portilla, MP, Parellada, M, Bobes, J, Calvo, A, Moreno-Izco, L, Gonzalez-Pinto, A, Lobo, A, de la Serna, E, Cabrera, B, Torrent, C, Roldan, L, Sanjuan, J, Ibanez, A, Sanchez-Torres, AM, Corripio, I, Bernardo, M, Cuesta, MJ, Vieta, E, Martinez-Aran, A, Castro-Fornieles, J, Baeza, I, Bioque, M, Mezquida, G, Lopez-Ilundain, JM, Alonso, A, Rabela, M, Lopez, P, Zorrilla, I, Arbej, J, Rivero, G, Aguilar, EJ, Mane, A, Berge, D, Contreras, F, Albacete, A, Garcia-Alvarez, L, Al-Halabi, S, Gutierrez, M, Segarra, R, Morales-Munoz, I, Rodriguez-Jimenez, R, Butjosa, A, Usall, J, Sarro, S, Landin-Romero, R, Ruiz, JS, and Balanza, V

Subjects
Tobacco, Schizophrenia, Non-social cognition, First-episode psychosis, Neurocognition, Social cognition
Abstract

Background: Many studies having shown significant improvements in non-social and social cognitive performance in smoking FEP patients compared to non-smoking FEP patients. The findings are controversial. This study analyzed the effects of tobacco use on non-social and social cognitive function in a large group of FEP patients and a matched healthy control group. Methods: A sample of 335 patients with FEP and 253 healthy controls was divided into four subgroups: control tobacco users (CTU), control non-tobacco users (CNTU), patient tobacco users (PTU) and patient non-tobacco users (PNTU). Demographic variables, tobacco use variables (presence or absence, frequency and duration of tobacco use), neurocognitive (non-social) performance and social cognition were assessed. Results: Comparison of 4 subgroups in non-social cognitive function revealed significant differences after controlling for covariables in executive functions (F = 13.45; p

Published
2018

37. Individual trajectories of cognitive performance in first episode psychosis: a 2-year follow-up study

Author

Sanchez-Torres, AM, Moreno-Izco, L, Lorente-Omenaca, R, Cabrera, B, Lobo, A, Gonzalez-Pinto, AM, Merchan-Naranjo, J, Corripio, I, Vieta, E, de la Serna, E, Butjosa, A, Contreras, F, Sarro, S, Mezquida, G, Ribeiro, M, Bernardo, M, and Cuesta, MJ

Subjects
First episode psychosis, Cognition, Longitudinal, Schizophrenia, Reliable change index, Clinically significant change
Abstract

Individual changes over time in cognition in patients with psychotic disorders have been studied very little, especially in the case of first episode psychosis (FEP). We aimed to establish whether change in individual trajectories in cognition over 2years of a sample of 159 FEP patients was reliable and clinically significant, using the reliable change index (RCI) and clinically significant change (CSC) methods. We also studied a sample of 151 matched healthy controls. Patients and controls were assessed with a set of neuropsychological tests, as well as premorbid, clinical and functionality measures. We analysed the course of cognitive measures over time, using analysis of variance, and the individual trajectories in the cognitive measures with the regression-based RCI (RCISRB) and the CSC. The RCISRB showed that between 5.4 and 31.2% of the patients showed deterioration patterns, and between 0.6 and 8.8% showed improvement patterns in these tests over time. Patients showing better cognitive profiles according to RCISRB (worsening in zero to two cognitive measures) showed better premorbid, clinical and functional profiles than patients showing deterioration patterns in more than three tests. When combining RCISRB and CSC values, we found that less than 10% of patients showed improvement or deterioration patterns in executive function and attention measures. These results support the view that cognitive impairments are stable over the first 2years of illness, but also that the analysis of individual trajectories could help to identify a subgroup of patients with particular phenotypes, who may require specific interventions.

Published
2018

38. Cognitive reserve as an outcome predictor: first-episode affective versus non-affective psychosis

Author

Amoretti, S, Cabrera, B, Torrent, C, Mezquida, G, Lobo, A, Gonzalez-Pinto, A, Parellada, M, Corripio, I, Vieta, E, de la Serna, E, Butjosa, A, Contreras, F, Sarro, S, Penades, R, Sanchez-Torres, AM, Cuesta, M, and Bernardo, M

Subjects
cognition, neuropsychology, cognitive remediation, cognitive reserve, first episode
Abstract

ObjectiveMethodCognitive reserve (CR) refers to the brain's capacity to cope with pathology in order to minimize the symptoms. CR is associated with different outcomes in severe mental illness. This study aimed to analyze the impact of CR according to the diagnosis of first-episode affective or non-affective psychosis (FEP). A total of 247 FEP patients (211 non-affective and 36 affective) and 205 healthy controls were enrolled. To assess CR, common proxies have been integrated (premorbid IQ; education-occupation; leisure activities). The groups were divided into high and low CR. ResultsConclusionIn non-affective patients, those with high CR were older, had higher socioeconomic status (SES), shorter duration of untreated psychosis, and a later age of onset. They also showed greater performance in most cognitive domains. In affective patients, those with a greater CR showed a higher SES, better functioning, and greater verbal memory performance. CR plays a differential role in the outcome of psychoses according to the diagnosis. Specifically, in order to address the needs of non-affective patients with low CR, cognitive rehabilitation treatments will need to be enriched' by adding pro-cognitive pharmacological agents or using more sophisticated approaches. However, a functional remediation therapy may be of choice for those with an affective psychosis and low CR.

Published
2018

39. Impact of NTRK2, DRD2 and ACE polymorphisms on prolactin levels in antipsychotic-treated patients with first-episode psychosis

Author

Gasso, P, Mas, S, Bioque, M, Cabrera, B, Lobo, A, Gonzalez-Pinto, A, Diaz-Caneja, CM, Corripio, I, Vieta, E, Castro-Fornieles, J, Sarro, S, Mane, A, Sanjuan, J, Llerena, A, Lafuente, A, Saiz-Ruiz, J, and Bernardo, M

Subjects
Antipsychotic, prolactin, first-episode of psychosis, gene, side-effect, polymorphism
Abstract

Background: Hyperprolactinemia is a common side-effect of antipsychotics (APs), which may trigger serious secondary problems and compromise the adherence to treatment which is crucial for prognosis, especially in patients presenting with a first-episode of psychosis (FEP). Aims: We evaluated, in some cases for the first time, the effect of polymorphisms in multiple candidate genes on serum prolactin (PRL) levels in an AP-treated FEP cohort recruited in the multicenter PEPs study (Phenotype - genotype and environmental interaction; Application of a predictive model in first psychotic episodes). Methods: PRL concentration was measured in serum from 222 patients. A total of 167 polymorphisms were selected in 23 genes. Genetic association analysis was performed in the whole sample and also in homogenous subgroups of patients treated with APs with a high (N = 101) or low risk (N = 95) of increasing PRL release, which showed significant differences in their PRL levels. Results: After Bonferroni correction, polymorphisms in NTRK2, DRD2 and ACE genes were associated with PRL concentration. Conclusion: Our results give more support to the impact of DRD2, but also of other genes related to dopamine availability such as ACE. Moreover, this study provides the first evidence for the involvement of NTRK2, which suggests that pathways other than the ones related to dopamine or serotonin may participate in the AP-related PRL levels.

Published
2018

40. Functional brain correlates of negative symptoms in schizophrenia assessed using a novel executive paradigm

Author

Santo-Angles, A., primary, Fuentes-Claramonte, P., additional, Lechón, M., additional, Argila, I., additional, Nieto, A., additional, Salgado-Pineda, P., additional, Albacete, A., additional, Martín, M., additional, Aguilar, S., additional, Ramiro, N., additional, Portillo, F., additional, Sarri, C., additional, Salo, L., additional, Bosque, C., additional, Torres, M.L., additional, Sarro, S., additional, Salvador, R., additional, McKenna, P.J., additional, and Pomarol-Clotet, E., additional

Published
2019
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43. 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

44. Differential failure to deactivate the default mode network in unipolar and bipolar depression

Author

Rodriguez-Cano, E, Alonso-Lana, S, Sarro, S, Fernandez-Corcuera, P, Goikolea, JM, Vieta, E, Maristany, T, Salvador, R, McKenna, PJ, Pomarol-Clotet, E, Rodriguez-Cano, E, Alonso-Lana, S, Sarro, S, Fernandez-Corcuera, P, Goikolea, JM, Vieta, E, Maristany, T, Salvador, R, McKenna, PJ, and Pomarol-Clotet, E

Abstract

OBJECTIVES: Neuroimaging studies have revealed evidence of brain functional abnormalities in bipolar depressive disorder (BDD) and major depressive disorder (MDD). However, few studies to date have compared these two mood disorders directly. METHODS: Matched groups of 26 BDD type I patients, 26 MDD patients and 26 healthy controls underwent functional magnetic resonance imaging (fMRI) while performing the n-back working memory task. A whole-brain ANOVA was used to compare the three groups and clusters of significant difference were examined further using region-of-interest (ROI) analysis. RESULTS: The whole-brain ANOVA revealed a single cluster of significant difference in the medial frontal cortex. The BDD and MDD patients both showed failure to deactivate in this area compared to the controls. The BDD patients showed significantly greater failure of deactivation than the MDD patients, which was not accounted for by differences in severity or chronicity of illness between them. CONCLUSIONS: Failure of deactivation, considered to reflect default mode network dysfunction, is present to a greater extent in bipolar than unipolar depression. The study of this network may be useful in the search for brain markers that distinguish the two disorders.

Published
2017

49. L’uso della comunicazione visiva nella scuola primaria: decorativismo o strumento cognitivo?

Author

Menichetti, L. and Sarro, S.

Subjects
comunicazione visiva, scuola primaria, carico cognitivo, apprendimento multimediale, visual communication, primary school, cognitive load, multimedia learning, carico cognitivo, comunicazione visiva, scuola primaria, lcsh:L, lcsh:Education
Abstract

La comunicazione visiva utilizzata a fini didattici è al centro di un interessante dibattito: recenti teorie sull’apprendimento multimediale da un lato ne sottolineano le potenzialità in vista di un apprendimento significativo, dall’altro mettono in guardia contro i rischi di un sovraccarico cognitivo che, all’opposto, si traduce in un ostacolo agli apprendimenti. Nella pratica didattica come vengono usati i visual? Si percepiscono i rischi di un uso puramente decorativo e del correlato sovraccarico cognitivo? Quanta consapevolezza esiste relativamente al potenziale cognitivo implicito? Questo contributo indaga comportamenti e conoscenze di docenti della scuola primaria. Dalla ricerca emergono significative criticità e la necessità di interventi di formazione mirata in questo ambito., Form@re - Open Journal per la formazione in rete, Vol. 15 No. 2 (2015): La comunicazione visiva nella didattica.

Published
2015
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