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1. The progression of disorder-specific brain pattern expression in schizophrenia over 9 years

Author

Lieslehto, J. (Johannes), Jääskeläinen, E. (Erika), Kiviniemi, V. (Vesa), Haapea, M. (Marianne), Jones, P. B. (Peter B.), Murray, G. K. (Graham K.), Veijola, J. (Juha), Dannlowski, U. (Udo), Grotegerd, D. (Dominik), Meinert, S. (Susanne), Hahn, T. (Tim), Ruef, A. (Anne), Isohanni, M. (Matti), Falkai, P. (Peter), Miettunen, J. (Jouko), Dwyer, D. B. (Dominic B.), Koutsouleris, N. (Nikolaos), Lieslehto, J. (Johannes), Jääskeläinen, E. (Erika), Kiviniemi, V. (Vesa), Haapea, M. (Marianne), Jones, P. B. (Peter B.), Murray, G. K. (Graham K.), Veijola, J. (Juha), Dannlowski, U. (Udo), Grotegerd, D. (Dominik), Meinert, S. (Susanne), Hahn, T. (Tim), Ruef, A. (Anne), Isohanni, M. (Matti), Falkai, P. (Peter), Miettunen, J. (Jouko), Dwyer, D. B. (Dominic B.), and Koutsouleris, N. (Nikolaos)

Abstract

Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models’ predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model’s schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern’s progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.

Published
2021

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

Author

Thompson, P.M. (Paul), Jahanshad, N. (Neda), Ching, C.R.K. (Christopher), Salminen, L.E. (Lauren E.), Thomopoulos, S.I. (Sophia I.), Bright, J. (Joanna), Baune, B.T., Bertolín, S. (Sara), Bralten, L.B.C. (Linda), Bruin, W.B. (Willem B.), Bülow, R. (Robin), Chen, J. (Jian), Chye, Y. (Yann), Dannlowski, U. (Udo), de Kovel, C.G.F. (Carolien G F), Donohoe, D.J. (Dennis), Eyler, L.T. (Lisa T.), Faraone, S.V. (Stephen), Favre, P. (Pauline), Filippi, C.A. (Courtney A.), Frodl, T. (Thomas), Garijo, D. (Daniel), Gil, Y. (Yolanda), Grabe, H.J. (Hans Jörgen), Grasby, K.L. (Katrina L.), Hajek, T. (Tomas), Han, L.K.M. (Laura K M), Hatton, W., Hilbert, K. (Kevin), Ho, T.C. (Tiffany C.), Holleran, L. (Laurena), Homuth, G. (Georg), Hosten, N. (Norbert), Houenou, J. (Josselin), Ivanov, I. (Iliyan), Jia, T. (Tianye), Kelly, S. (Sinead), Klein, M. (Marieke), Kwon, J.S. (Jun Soo), Laansma, M.A. (Max A.), Leerssen, J. (Jeanne), Lueken, U. (Ulrike), Nunes, A. (Abraham), Neill, J.O. (Joseph O'), Opel, N. (Nils), Piras, F. (Fabrizio), Piras, F. (Federica), Postema, M.C. (Merel C.), Pozzi, E. (Elena), Shatokhina, N. (Natalia), Soriano-Mas, C. (Carles), Spalletta, G. (Gianfranco), Sun, D. (Daqiang), Teumer, A. (Alexander), Tilot, A.K. (Amanda K.), Tozzi, L. (Leonardo), van der Merwe, C. (Celia), Someren, E.J.W. (Eus) van, van Wingen, G.A. (Guido A.), Völzke, H. (Henry), Walton, E. (Esther), Wang, L. (Lei), Winkler, A.M. (Anderson), Wittfeld, K. (Katharina), Wright, M.J. (Margaret), Yun, J.-Y. (Je-Yeon), Zhang, G. (Guohao), Zhang-James, Y. (Yanli), Adhikari, B.M. (Bhim M.), Agartz, I. (Ingrid), Aghajani, M. (Moji), Aleman, A. (André), Althoff, R.R. (Robert R.), Altmann, A. (A.), Andreassen, O.A. (Ole), Baron, D.A. (David A.), Bartnik-Olson, B.L. (Brenda L.), Marie Bas-Hoogendam, J. (Janna), Baskin-Sommers, A.R. (Arielle R.), Bearden, C.E. (Carrie), Berner, L.A. (Laura A.), Boedhoe, P.S.W. (Premika S W), Brouwer, R.M. (Rachel), Buitelaar, J.K. (Jan), Caeyenberghs, K. (Karen), Cecil, C.A.M. (Charlotte), Cohen, R.A. (Ronald A.), Cole, J.H. (James H.), Conrod, P. (Patricia), De Brito, S.A. (Stephane A.), de Zwarte, S.M.C. (Sonja M C), Dennis, E.L. (Emily L.), Desrivieres, S. (Sylvane), Dima, D. (Danai), Ehrlich, S.M. (Stefan), Esopenko, C. (Carrie), Fairchild, G. (Graeme), Fisher, S.E. (Simon), Fouche, J.-P. (Jean-Paul), Francks, C. (Clyde), Frangou, S. (Sophia), Franke, B. (Barbara), Garavan, H.P. (Hugh P.), Glahn, D.C. (David), Groenewold, N.A. (Nynke A.), Gurholt, T.P. (Tiril P.), Gutman, B.A. (Boris A.), Hahn, T. (Tim), Harding, I.H. (Ian H.), Hernaus, D. (Dennis), Hibar, D.P. (Derrek P.), Hillary, F.G. (Frank G.), Hoogman, M. (Martine), Hulshoff Pol, H.E. (Hilleke E.), Jalbrzikowski, M. (Maria), Karkashadze, G.A. (George A.), Klapwijk, E.T. (Eduard T.), Knickmeyer, R.C. (Rebecca C.), Kochunov, P. (Peter), Koerte, I.K. (Inga K.), Kong, X.-Z. (Xiang-Zhen), Liew, S.-L. (Sook-Lei), Lin, A.P. (Alexander P.), Logue, M.W. (Mark W.), Luders, E. (Eileen), Macciardi, F. (Fabio), Mackey, S. (Scott), Mayer, A.R. (Andrew R.), McDonald, C.R. (Carrie R.), McMahon, A.B. (Agnes B.), Medland, S.E. (Sarah), Modinos, G. (Gemma), Morey, R.A. (Rajendra A.), Mueller, S.C. (Sven C.), Mukherjee, P. (Pratik), Namazova-Baranova, L. (L.), Nir, T.M. (Talia M.), Olsen, A. (Alexander), Paschou, P. (Peristera), Pine, D.S. (Daniel S.), Pizzagalli, F. (Fabrizio), Rentería, M.E. (Miguel), Rohrer, J.D. (Jonathan D.), Sämann, P.G. (Philipp), Schmaal, L. (Lianne), Schumann, G. (Gunter), Shiroishi, M.S. (Mark S.), Sisodiya, S.M. (Sanjay), Smit, D.J.A. (Dirk J A), Sønderby, I.E. (Ida E.), Stein, D.J. (Dan J.), Stein, J.L., Tahmasian, M. (Masoud), Tate, D.F. (David F.), Turner, J. (Jessica), Heuvel, O.A. (Odile A.), Wee, N.J. (Nic) van der, van der Werf, Y.D. (Ysbrand), Erp, T.G.M. (Theo G.) van, van Haren, N.E.M. (Neeltje E M), van Rooij, D. (Daan), Van Velzen, L.S., Veer, I.M. (Ilya), Veltman, D.J. (Dick), Villalon-Reina, J.E. (Julio E.), Walter, H.J. (Henrik), Whelan, C.D. (Christopher), Wilde, E.A. (Elisabeth A.), Zarei, M. (Mojtaba), Zelman, V. (Vladimir), Thompson, P.M. (Paul), Jahanshad, N. (Neda), Ching, C.R.K. (Christopher), Salminen, L.E. (Lauren E.), Thomopoulos, S.I. (Sophia I.), Bright, J. (Joanna), Baune, B.T., Bertolín, S. (Sara), Bralten, L.B.C. (Linda), Bruin, W.B. (Willem B.), Bülow, R. (Robin), Chen, J. (Jian), Chye, Y. (Yann), Dannlowski, U. (Udo), de Kovel, C.G.F. (Carolien G F), Donohoe, D.J. (Dennis), Eyler, L.T. (Lisa T.), Faraone, S.V. (Stephen), Favre, P. (Pauline), Filippi, C.A. (Courtney A.), Frodl, T. (Thomas), Garijo, D. (Daniel), Gil, Y. (Yolanda), Grabe, H.J. (Hans Jörgen), Grasby, K.L. (Katrina L.), Hajek, T. (Tomas), Han, L.K.M. (Laura K M), Hatton, W., Hilbert, K. (Kevin), Ho, T.C. (Tiffany C.), Holleran, L. (Laurena), Homuth, G. (Georg), Hosten, N. (Norbert), Houenou, J. (Josselin), Ivanov, I. (Iliyan), Jia, T. (Tianye), Kelly, S. (Sinead), Klein, M. (Marieke), Kwon, J.S. (Jun Soo), Laansma, M.A. (Max A.), Leerssen, J. (Jeanne), Lueken, U. (Ulrike), Nunes, A. (Abraham), Neill, J.O. (Joseph O'), Opel, N. (Nils), Piras, F. (Fabrizio), Piras, F. (Federica), Postema, M.C. (Merel C.), Pozzi, E. (Elena), Shatokhina, N. (Natalia), Soriano-Mas, C. (Carles), Spalletta, G. (Gianfranco), Sun, D. (Daqiang), Teumer, A. (Alexander), Tilot, A.K. (Amanda K.), Tozzi, L. (Leonardo), van der Merwe, C. (Celia), Someren, E.J.W. (Eus) van, van Wingen, G.A. (Guido A.), Völzke, H. (Henry), Walton, E. (Esther), Wang, L. (Lei), Winkler, A.M. (Anderson), Wittfeld, K. (Katharina), Wright, M.J. (Margaret), Yun, J.-Y. (Je-Yeon), Zhang, G. (Guohao), Zhang-James, Y. (Yanli), Adhikari, B.M. (Bhim M.), Agartz, I. (Ingrid), Aghajani, M. (Moji), Aleman, A. (André), Althoff, R.R. (Robert R.), Altmann, A. (A.), Andreassen, O.A. (Ole), Baron, D.A. (David A.), Bartnik-Olson, B.L. (Brenda L.), Marie Bas-Hoogendam, J. (Janna), Baskin-Sommers, A.R. (Arielle R.), Bearden, C.E. (Carrie), Berner, L.A. (Laura A.), Boedhoe, P.S.W. (Premika S W), Brouwer, R.M. (Rachel), Buitelaar, J.K. (Jan), Caeyenberghs, K. (Karen), Cecil, C.A.M. (Charlotte), Cohen, R.A. (Ronald A.), Cole, J.H. (James H.), Conrod, P. (Patricia), De Brito, S.A. (Stephane A.), de Zwarte, S.M.C. (Sonja M C), Dennis, E.L. (Emily L.), Desrivieres, S. (Sylvane), Dima, D. (Danai), Ehrlich, S.M. (Stefan), Esopenko, C. (Carrie), Fairchild, G. (Graeme), Fisher, S.E. (Simon), Fouche, J.-P. (Jean-Paul), Francks, C. (Clyde), Frangou, S. (Sophia), Franke, B. (Barbara), Garavan, H.P. (Hugh P.), Glahn, D.C. (David), Groenewold, N.A. (Nynke A.), Gurholt, T.P. (Tiril P.), Gutman, B.A. (Boris A.), Hahn, T. (Tim), Harding, I.H. (Ian H.), Hernaus, D. (Dennis), Hibar, D.P. (Derrek P.), Hillary, F.G. (Frank G.), Hoogman, M. (Martine), Hulshoff Pol, H.E. (Hilleke E.), Jalbrzikowski, M. (Maria), Karkashadze, G.A. (George A.), Klapwijk, E.T. (Eduard T.), Knickmeyer, R.C. (Rebecca C.), Kochunov, P. (Peter), Koerte, I.K. (Inga K.), Kong, X.-Z. (Xiang-Zhen), Liew, S.-L. (Sook-Lei), Lin, A.P. (Alexander P.), Logue, M.W. (Mark W.), Luders, E. (Eileen), Macciardi, F. (Fabio), Mackey, S. (Scott), Mayer, A.R. (Andrew R.), McDonald, C.R. (Carrie R.), McMahon, A.B. (Agnes B.), Medland, S.E. (Sarah), Modinos, G. (Gemma), Morey, R.A. (Rajendra A.), Mueller, S.C. (Sven C.), Mukherjee, P. (Pratik), Namazova-Baranova, L. (L.), Nir, T.M. (Talia M.), Olsen, A. (Alexander), Paschou, P. (Peristera), Pine, D.S. (Daniel S.), Pizzagalli, F. (Fabrizio), Rentería, M.E. (Miguel), Rohrer, J.D. (Jonathan D.), Sämann, P.G. (Philipp), Schmaal, L. (Lianne), Schumann, G. (Gunter), Shiroishi, M.S. (Mark S.), Sisodiya, S.M. (Sanjay), Smit, D.J.A. (Dirk J A), Sønderby, I.E. (Ida E.), Stein, D.J. (Dan J.), Stein, J.L., Tahmasian, M. (Masoud), Tate, D.F. (David F.), Turner, J. (Jessica), Heuvel, O.A. (Odile A.), Wee, N.J. (Nic) van der, van der Werf, Y.D. (Ysbrand), Erp, T.G.M. (Theo G.) van, van Haren, N.E.M. (Neeltje E M), van Rooij, D. (Daan), Van Velzen, L.S., Veer, I.M. (Ilya), Veltman, D.J. (Dick), Villalon-Reina, J.E. (Julio E.), Walter, H.J. (Henrik), Whelan, C.D. (Christopher), Wilde, E.A. (Elisabeth A.), Zarei, M. (Mojtaba), and Zelman, V. (Vladimir)

Abstract

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Published
2020
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3. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Author

Nunes, A. (Abraham), Schnack, H. (Hugo), Ching, C.R.K. (Christopher), Agartz, I. (Ingrid), Akudjedu, T.N. (Theophilus N.), Alda, M. (Martin), Alnæs, D. (Dag), Alonso-Lana, S. (Silvia), Bauer, J. (Jochen), Baune, B.T., Bøen, E. (Erlend), Bonnin, C.M. (Caterina del Mar), Busatto, G.F. (Geraldo F.), Canales-Rodríguez, E.J. (Erick J.), Cannon, D.M. (Dara), Caseras, X. (Xavier), Chaim-Avancini, T.M. (Tiffany M.), Dannlowski, U. (Udo), Díaz-Zuluaga, A.M. (Ana M.), Dietsche, B. (Bruno), Doan, N.T. (Nhat Trung), Duchesnay, E. (Edouard), Elvsåshagen, T. (Torbjørn), Emden, D. (Daniel), Eyler, L.T. (Lisa T.), Fatjó-Vilas, M. (Mar), Favre, P. (Pauline), Foley, S.F. (Sonya F.), Fullerton, J.M. (Janice M.), Glahn, D.C. (David), Goikolea, J.M. (Jose M.), Grotegerd, D. (Dominik), Hahn, T. (Tim), Henry, C. (C.), Hibar, D.P. (Derrek P.), Houenou, J. (Josselin), Howells, F.M. (Fleur M.), Jahanshad, N. (Neda), Kaufmann, T. (Tobias), Kenney, J. (Joanne), Kircher, T.T.J. (Tilo T. J.), Krug, A. (Axel), Lagerberg, T.V. (Trine V.), Lenroot, R.K. (Rhoshel), López-Jaramillo, C. (Carlos), Machado-Vieira, R. (Rodrigo), Malt, U.F. (Ulrik), McDonald, C. (Colm), Mitchell, P.B. (Philip B.), Mwangi, B. (Benson), Nabulsi, L. (Leila), Opel, N. (Nils), Overs, B.J. (Bronwyn J.), Pineda-Zapata, J.A. (Julian A.), Pomarol-Clotet, E. (Edith), Redlich, R. (Ronny), Roberts, G. (Gloria), Rosa, P.G. (Pedro G.), Salvador, R. (Raymond), Satterthwaite, T.D. (Theodore), Soares, J.C. (Jair C.), Stein, D.J. (Dan), Temmingh, H.S. (Henk S.), Trappenberg, T. (Thomas), Uhlmann, A. (Anne), van Haren, N.E.M. (Neeltje E. M.), Vieta, E. (Eduard), Westlye, L.T. (Lars), Wolf, D.H. (Daniel H.), Yüksel, D. (Dilara), Zanetti, M.V. (Marcus V.), Andreassen, O.A. (Ole), Thompson, P.M. (Paul), Hajek, T. (Tomas), Nunes, A. (Abraham), Schnack, H. (Hugo), Ching, C.R.K. (Christopher), Agartz, I. (Ingrid), Akudjedu, T.N. (Theophilus N.), Alda, M. (Martin), Alnæs, D. (Dag), Alonso-Lana, S. (Silvia), Bauer, J. (Jochen), Baune, B.T., Bøen, E. (Erlend), Bonnin, C.M. (Caterina del Mar), Busatto, G.F. (Geraldo F.), Canales-Rodríguez, E.J. (Erick J.), Cannon, D.M. (Dara), Caseras, X. (Xavier), Chaim-Avancini, T.M. (Tiffany M.), Dannlowski, U. (Udo), Díaz-Zuluaga, A.M. (Ana M.), Dietsche, B. (Bruno), Doan, N.T. (Nhat Trung), Duchesnay, E. (Edouard), Elvsåshagen, T. (Torbjørn), Emden, D. (Daniel), Eyler, L.T. (Lisa T.), Fatjó-Vilas, M. (Mar), Favre, P. (Pauline), Foley, S.F. (Sonya F.), Fullerton, J.M. (Janice M.), Glahn, D.C. (David), Goikolea, J.M. (Jose M.), Grotegerd, D. (Dominik), Hahn, T. (Tim), Henry, C. (C.), Hibar, D.P. (Derrek P.), Houenou, J. (Josselin), Howells, F.M. (Fleur M.), Jahanshad, N. (Neda), Kaufmann, T. (Tobias), Kenney, J. (Joanne), Kircher, T.T.J. (Tilo T. J.), Krug, A. (Axel), Lagerberg, T.V. (Trine V.), Lenroot, R.K. (Rhoshel), López-Jaramillo, C. (Carlos), Machado-Vieira, R. (Rodrigo), Malt, U.F. (Ulrik), McDonald, C. (Colm), Mitchell, P.B. (Philip B.), Mwangi, B. (Benson), Nabulsi, L. (Leila), Opel, N. (Nils), Overs, B.J. (Bronwyn J.), Pineda-Zapata, J.A. (Julian A.), Pomarol-Clotet, E. (Edith), Redlich, R. (Ronny), Roberts, G. (Gloria), Rosa, P.G. (Pedro G.), Salvador, R. (Raymond), Satterthwaite, T.D. (Theodore), Soares, J.C. (Jair C.), Stein, D.J. (Dan), Temmingh, H.S. (Henk S.), Trappenberg, T. (Thomas), Uhlmann, A. (Anne), van Haren, N.E.M. (Neeltje E. M.), Vieta, E. (Eduard), Westlye, L.T. (Lars), Wolf, D.H. (Daniel H.), Yüksel, D. (Dilara), Zanetti, M.V. (Marcus V.), Andreassen, O.A. (Ole), Thompson, P.M. (Paul), and Hajek, T. (Tomas)

Abstract

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

Published
2018
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4. Erstellung von Machine Learning-Modellen zur Prädiktion einzelner Krankheitscharakteristika der Depression in Abhängigkeit von der kognitiven Leistung und Gehirnstruktur

Author

Schmitt, M.A.D. (Michael), Hahn, T. (Tim), and Universitäts- und Landesbibliothek Münster

Subjects
Machine Learning, Personalisierte Medizin, Prädiktive Modelle, Depression, Affektive Erkrankungen, MRT, Neuropsychologische Testung, Medicine and health, ddc:610, Machine learning, personalized medicine, predictive models, depression, affective disorders, MRI, neuropsychological assessment
Abstract

Bei der Major Depressive Disorder ist das Verständnis des Verlaufs bezüglich der Therapieentscheidung besonders wichtig. Maschinelles Lernen bietet potenziell die Chance individueller Vorhersagen. In dieser Studie wird der kontrovers diskutierte Zusammenhang zwischen kognitiven Defiziten (neuropsychologische Testung) bzw. strukturellen Gehirnveränderungen (MRT) und Krankheitscharakteristika (Diagnose: MDD/Kontrolle; Schweregrad: HAMD, BDI; Verlauf: Hospitalisierung, Rezidive; Funktionsniveau: GAF, SF-36) erforscht. Die Stichprobe umfasste n = 746 bzw. 432 MDD-Patient(inn)en und n = 914 bzw. 615 Kontrollen (MRT-Daten bzw. neuropsych. Daten). Die Prädiktion der Diagnose gelang mit einer gewichteten Genauigkeit von 59.62 bzw. 55.34 %. Darüber hinaus konnten lediglich die SF 36-Werte prädiziert werden (Reduktion des mittleren absoluten Fehlers um ca. 9 bzw. 13 %, Pearson-Korrelation r = 0.24 bzw. 0.35). Dies könnte auf eine motivationale Komponente in der Genese der MDD hinweisen.

Published
2022

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