Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis

Stefanie Hechenberger; Birgit Helmlinger; Iris-Katharina Penner; Lukas Pirpamer; Viktoria Fruhwirth; Bettina Heschl; Stefan Ropele; Sebastian Wurth; Anna Damulina; Sebastian Eppinger; Rina Demjaha; Michael Khalil; Daniela Pinter; Christian Enzinger

doi:10.1016/j.jns.2023.120833

Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis

J Neurol Sci. 2023 Nov 15:454:120833. doi: 10.1016/j.jns.2023.120833. Epub 2023 Oct 10.

Affiliations

¹ Medical University of Graz, Research Unit for Neuronal Plasticity and Repair, Graz, Austria; Medical University of Graz, Department of Neurology, Graz, Austria.
² Department of Neurology. Inselspital, Bern University Hospital, University of Bern, Switzerland.
³ Medical University of Graz, Department of Neurology, Graz, Austria; Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
⁴ Medical University of Graz, Department of Neurology, Graz, Austria.
⁵ Medical University of Graz, Department of Neurology, Graz, Austria; Medical University of Graz, Division of Neuroradiology & Interventional Radiology, Department of Radiology, Graz, Austria.
⁶ Medical University of Graz, Department of Neurology, Graz, Austria; Medical University of Graz, Neurology Biomarker Research Unit, Graz, Austria.
⁷ Medical University of Graz, Research Unit for Neuronal Plasticity and Repair, Graz, Austria; Medical University of Graz, Department of Neurology, Graz, Austria. Electronic address: daniela.pinter@medunigraz.at.

PMID: 37866195
DOI: 10.1016/j.jns.2023.120833

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.

Keywords: Brain MRI; Fatigue; Multiple sclerosis; Prediction; Psychological factors.

MeSH terms

Brain / diagnostic imaging
Brain / pathology
Cognition
Depression
Female
Humans
Magnetic Resonance Imaging
Multiple Sclerosis* / complications
Multiple Sclerosis* / diagnostic imaging
Multiple Sclerosis* / pathology