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Papazova, I.* ; Wunderlich, S.* ; Papazov, B.* ; Vogelmann, U.* ; Keeser, D.* ; Karali, T.* ; Falkai, P.* ; Rospleszcz, S. ; Maurus, I.* ; Schmitt, A.* ; Hasan, A.* ; Malchow, B.* ; Stöcklein, S.*

Characterizing cognitive subtypes in schizophrenia using cortical curvature.

J. Psychiatr. Res. 173, 131-138 (2024)
Verlagsversion Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Cognitive deficits are a core symptom of schizophrenia, but research on their neural underpinnings has been challenged by the heterogeneity in deficits' severity among patients. Here, we address this issue by combining logistic regression and random forest to classify two neuropsychological profiles of patients with high (HighCog) and low (LowCog) cognitive performance in two independent samples. We based our analysis on the cortical features grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC) of N = 57 patients (discovery sample) and validated the classification in an independent sample (N = 52). We investigated which cortical feature would yield the best classification results and expected that the 10 most important features would include frontal and temporal brain regions. The model based on MC had the best performance with area under the curve (AUC) values of 76% and 73%, and identified fronto-temporal and occipital brain regions as the most important features for the classification. Moreover, subsequent comparison analyses could reveal significant differences in MC of single brain regions between the two cognitive profiles. The present study suggests MC as a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cognitive Subtypes ; Cortical Curvature ; Schizophrenia; Surface-based Analysis; Human Cerebral-cortex; Neurocognitive Deficits; Intrinsic Curvature; Longitudinal Course; Brain Structure; Thickness; Gyrification; Classification; Connectivity
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0022-3956
e-ISSN 1879-1379
Zeitschrift Journal of Psychiatric Research
Quellenangaben Band: 173, Heft: , Seiten: 131-138 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504000-010
Förderungen Federal Ministry of Education and Research (BMBF)
DOI 10.1016/j.jpsychires.2024.03.019
WOS ID 001290921600001
Scopus ID 85188700308
PubMed ID 38531143
Erfassungsdatum 2024-04-25
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