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Status: Bibliographieeintrag

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Verfasst von:Peretzke, Robin [VerfasserIn]   i
 Neher, Peter [VerfasserIn]   i
 Brandt, Geva A. [VerfasserIn]   i
 Fritze, Stefan [VerfasserIn]   i
 Volkmer, Sebastian [VerfasserIn]   i
 Daub, Jonas [VerfasserIn]   i
 Northoff, Georg [VerfasserIn]   i
 Bohn, Jonas [VerfasserIn]   i
 Kirchhoff, Yannick [VerfasserIn]   i
 Roy, Saikat [VerfasserIn]   i
 Maier-Hein, Klaus H. [VerfasserIn]   i
 Meyer-Lindenberg, Andreas [VerfasserIn]   i
 Hirjak, Dusan [VerfasserIn]   i
Titel:Deciphering white matter microstructural alterations in catatonia according to ICD-11
Titelzusatz:replication and machine learning analysis
Verf.angabe:Robin Peretzke, Peter F. Neher, Geva A. Brandt, Stefan Fritze, Sebastian Volkmer, Jonas Daub, Georg Northoff, Jonas Bohn, Yannick Kirchhoff, Saikat Roy, Klaus H. Maier-Hein, Andreas Meyer-Lindenberg and Dusan Hirjak
E-Jahr:2024
Jahr:02 December 2024
Umfang:13 S.
Fussnoten:Gesehen am 24.03.2025
Titel Quelle:Enthalten in: Molecular psychiatry
Ort Quelle:[London] : Springer Nature, 1997
Jahr Quelle:2025
Band/Heft Quelle:30(2025), 5, Seite 2095-2107
ISSN Quelle:1476-5578
Abstract:Catatonia is a severe psychomotor disorder characterized by motor, affective and cognitive-behavioral abnormalities. Although previous magnetic resonance imaging (MRI) studies suggested white matter (WM) dysconnectivity in the pathogenesis of catatonia, it is unclear whether microstructural alterations of WM tracts connecting psychomotor regions might contribute to a better classification of catatonia patients. Here, diffusion-weighted MRI data were collected from two independent cohorts (whiteCAT/replication cohort) of patients with (n = 45/n = 13) and without (n = 56/n = 26) catatonia according to ICD-11 criteria. Catatonia severity was examined using the Northoff (NCRS) and Bush-Francis (BFCRS) Catatonia Rating Scales. We used tract-based spatial statistics (TBSS), tractometry (TractSeg) and machine-learning (ML) to classify catatonia patients from tractometry values as well as tractomics features generated by the newly developed tool RadTract. Catatonia patients showed fractional anisotropy (FA) alterations measured via TractSeg in different corpus callosum segments (CC_1, CC_3, CC_4, CC_5 and CC_6) compared to non-catatonia patients across both cohorts. Our classification results indicated a higher level of performance when trained on tractomics as opposed to traditional tractometry values. Moreover, in the CC_6, we successfully trained two classifiers using the tractomics features identified in the whiteCAT data. These classifiers were applied separately to the whiteCAT and replication cohorts, demonstrating comparable performance with Area Under the Receiver Operating Characteristics (AUROC) values of 0.79 for the whiteCAT cohort and 0.76 for the replication cohort. In contrast, training on FA tractometry resulted in lower AUROC values of 0.66 for the whiteCAT cohort and 0.51 for the replication cohort. In conclusion, these findings underscore the significance of CC WM microstructural alterations in the pathophysiology of catatonia. The successful use of an ML based classification model to identify catatonia patients has the potential to improve diagnostic precision.
DOI:doi:10.1038/s41380-024-02821-0
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1038/s41380-024-02821-0
 kostenfrei: Volltext: http://www.nature.com/articles/s41380-024-02821-0
 DOI: https://doi.org/10.1038/s41380-024-02821-0
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Diagnostic markers
 Neuroscience
K10plus-PPN:1920425349
Verknüpfungen:→ Zeitschrift

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