Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

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SHR Neuro Krebs Kardio Lipid Stoffw Microb

Hechenberger, S; Helmlinger, B; Penner, IK; Pirpamer, L; Fruhwirth, V; Heschl, B; Ropele, S; Wurth, S; Damulina, A; Eppinger, S; Demjaha, R; Khalil, M; Pinter, D; Enzinger, C.
Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis.
J Neurol Sci. 2023; 454:120833 Doi: 10.1016/j.jns.2023.120833
Web of Science PubMed FullText FullText_MUG

Führende Autor*innen der Med Uni Graz: Hechenberger Stefanie Maria Charlotte; Pinter Daniela Theresia
Co-Autor*innen der Med Uni Graz: Damulina Anna; Demjaha Rina; Enzinger Christian; Eppinger Sebastian; Fruhwirth Viktoria Maria; Helmlinger Birgit; Khalil Michael; Pirpamer Lukas; Ropele Stefan; Wurth Sebastian
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Abstract:: BACKGROUND: Besides demographics and clinical factors, psychological variables and brain-tissue changes have been associated with fatigue in persons with multiple sclerosis (pwMS). Identifying predictors of fatigue could help to improve therapeutic approaches for pwMS. Therefore, we investigated predictors of fatigue using a multifactorial approach. METHODS: 136 pwMS and 49 normal controls (NC) underwent clinical, neuropsychological, and magnetic resonance imaging examinations. We assessed fatigue using the "Fatigue Scale for Motor and Cognitive Functions", yielding a total, motor, and cognitive fatigue score. We further analyzed global and subcortical brain volumes, white matter lesions and microstructural changes (examining fractional anisotropy; FA) along the cortico striatal thalamo cortical (CSTC) loop. Potential demographic, clinical, psychological, and magnetic resonance imaging predictors of total, motor, and cognitive fatigue were explored using multifactorial linear regression models. RESULTS: 53% of pwMS and 20% of NC demonstrated fatigue. Besides demographics and clinical data, total fatigue in pwMS was predicted by higher levels of depression and reduced microstructural tissue integrity in the CSTC loop (adjusted R² = 0.52, p < 0.001). More specifically, motor fatigue was predicted by lower education, female sex, higher physical disability, higher levels of depression, and self-efficacy (adjusted R² = 0.54, p < 0.001). Cognitive fatigue was also predicted by higher levels of depression and lower self-efficacy, but in addition by FA reductions in the CSTC loop (adjusted R² = 0.45, p < 0.001). CONCLUSIONS: Our results indicate that depression and self-efficacy strongly predict fatigue in MS. Incremental variance in total and cognitive fatigue was explained by microstructural changes along the CSTC loop, beyond demographics, clinical, and psychological variables.
Find related publications in this database (using NLM MeSH Indexing): Humans - administration & dosage; Female - administration & dosage; Multiple Sclerosis - complications, diagnostic imaging, pathology; Depression - administration & dosage; Brain - diagnostic imaging, pathology; Magnetic Resonance Imaging - administration & dosage; Cognition - administration & dosage
Find related publications in this database (Keywords): Multiple sclerosis; Fatigue; Prediction; Psychological factors; Brain MRI