Hechenberger, Stefanie; Helmlinger, Birgit; Penner, Iris-Katharina; Pirpamer, Lukas; Fruhwirth, Viktoria; Heschl, Bettina; Ropele, Stefan; Wurth, Sebastian; Damulina, Anna; Eppinger, Sebastian; Demjaha, Rina; Khalil, Michael; Pinter, Daniela; Enzinger, Christian (2023). Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis. Journal of the neurological sciences, 454(120833), p. 120833. Elsevier 10.1016/j.jns.2023.120833
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology |
UniBE Contributor: |
Penner, Iris-Katharina |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
0022-510X |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
24 Oct 2023 14:56 |
Last Modified: |
20 Nov 2023 00:16 |
Publisher DOI: |
10.1016/j.jns.2023.120833 |
PubMed ID: |
37866195 |
Uncontrolled Keywords: |
Brain MRI Fatigue Multiple sclerosis Prediction Psychological factors |
BORIS DOI: |
10.48350/187369 |
URI: |
https://boris.unibe.ch/id/eprint/187369 |
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