Cognitive Fatigue and Mood in Multiple Sclerosis: A Voxel-Based Morphometry Study

Background: Multiple Sclerosis (MS) is a chronic autoimmune disease that has a wide range of clinical manifestations, including sensory, motor, physiological, cognitive, and mood symptoms. Subjective cognitive fatigue (CF) – i.e., the perceived sensation of mental exhaustion – and mood disturbances are often associated with MS and have a significant impact on the quality of life. Several studies have investigated their brain correlates in MS: for example, mood disturbances are frequently associated with functional and structural alterations in structures belonging to the limbic system (e.g., the posterior cingulate cortex, the hippocampus, and the amygdala). Such alterations, together with other risk factors (e.g. psychosocial stressors related to the chronicity of MS symptoms, such as fatigue, motor disability, and cognitive impairment) are believed to contribute to the etiopathogenesis of mood symptoms in MS. On the other hand, CF has been linked to structural and functional changes in the striatal-thalamic-frontal cortical network. Some theoretical models suggest that CF in MS could emerge from an imbalance between the perceived effort needed to complete a cognitive task and its expected reward. This imbalance may be the result of MS-related structural and functional changes in brain networks associated with interoception and reward processing. Aim of the study: This study aims to investigate the structural brain correlates of CF and mood symptoms – specifically depression and anxiety – in MS patients by means of a computational morphometry approach. A cross-sectional design involving both MS patients and healthy controls (HC) has been devised with the purpose of: (a) assessing the association between CF and mood disturbances in MS; (b) exploring the relationship between mood disturbances and gray matter (GM) volume; and (c) investigating whether MS patients with higher levels of CF exhibit distinct GM volume alterations compared to those with lower fatigue levels and to HC. Methods: A sample of 57 individuals took part in the present study: 14 healthy individuals and 43 MS patients. CF was assessed using the Modified Fatigue Impact Scale (MFIS), while depressive and anxiety symptoms were measured with Beck’s Depression Inventory-II (BDI-II) and State-Trait Anxiety Inventory (STAI-Y1/Y2) respectively. MS patients were divided into two subgroups – specifically with low (LCF) and high (HCF) CF levels – based on the median score of the MFIS. All participants underwent high-resolution T1-weighted MRI scanning. Correlations between cognitive and behavioral measures were computed to explore the association between mood and CF in MS, while multiple regression models were employed to investigate the structural brain correlates of mood symptoms (i.e., depression and anxiety). Specifically, three separate multiple regression models were computed, with mood measures (i.e., BDI-II, STAI-Y1, and STAI-Y2) as predictors, and local GM volumes of MS participants as the dependent variables. Finally, a full factorial model (ANCOVA) was used to compare GM volumes of the three study groups to assess the structural brain correlates of different levels of CF. For all VBM analyses age, sex, CRIq, and total intracranial volume (TIV) were set as covariates. Results and conclusions: The analysis revealed a significant positive correlation between CF and both depression and anxiety symptoms in MS patients. No significant relationships were observed between mood symptoms and GM volume in MS patients. The ANCOVA showed a significant GM reduction in the right anterior cingulate cortex (ACC) in HCF compared to HC. No GM differences were found between LCF and HCF, as well as between LCF and HC. These findings highlight the link between mood symptoms and CF in MS, suggesting that mood-targeted interventions may help alleviate fatigue and enhance quality of life. Implications and limitations of this study are discussed.