Subjective cognitive fatigue and autonomic abnormalities in multiple sclerosis patients

Contains fulltext : 176209.pdf (publisher's version ) (Open Access)Background: Cognitive fatigue and autonomic abnormalities are frequent symptoms in MS. Our model of MS-related fatigue assumes a shared neural network for cognitive fatigue and autonomic failures, i.e., aberrant vagus nerve activity induced by inflammatory processes. Therefore, they should occur in common. Objective: To explore the relationship between cognitive fatigue and autonomic symptoms in MS patients, using self-reported questionnaires. Methods: In 95 MS patients cognitive fatigue was assessed with the Fatigue Scale for Motor and Cognitive Functions (FSMC), and autonomic abnormalities with the Composite Autonomic Symptom Scale-31 (COMPASS-31). We used exploratory correlational analyses and hierarchical regression analysis, controlling for age, depressive mood, disease status and disease duration, to analyze the relation between autonomic abnormalities and cognitive fatigue. Results: The cognitive fatigue score strongly correlated with the COMPASS-31 score (r=0.47, p<0.001). Regression analysis revealed that a model including the COMPASS-31 domains pupillomotor, orthostatic intolerance and bladder best predict the level of cognitive fatigue (R=0.69, p<0.001) after forcing the covariates into the model. Conclusion: In MS patients cognitive fatigue and autonomic dysfunction share a large proportion of common variance. This supports our model assuming that fatigue might be explained at least partially by inflammation-induced vagus nerve activity.9 p

Relation between cognitive fatigue and circadian or stress related cortisol levels in MS patients

Contains fulltext : 226041.pdf (Publisher’s version ) (Closed access)Background: Cortisol levels are increased in MS patients. However, the relation between cortisol, cognitive fatigue and load is still unknown and is investigated in this study. Method: In 40 MS patients and 20 healthy controls, cortisol levels were assessed (in saliva) in the morning and afternoon, before and after 5 runs of a cognitively demanding divided attention task (lasting in total 25-minutes). MS patients were divided in those suffering from cognitive fatigue (MS-F) or not (MS-NF). Results: MS-NF patients showed elevated cortisol levels in the morning and in the afternoon before the reaction time task compared to healthy controls. Differences in cortisol levels among the four measurements were also larger compared to healthy controls and MS-F patients. These differences could not be explained by medication, EDSS score, MS course, age or gender. MS-NF patients also produced more omissions on the attention task compared to healthy controls and MS-F patients. MS-F patients experienced more fatigue after the attention task, but they did not show a task related performance decline. Conclusion: MS-NF patients, and not MS-F patients, deviate in cortisol release and task performance from healthy controls and from MS-F patients. We suggest that MS-NF patients suffer from a dysregulation of their circadian cortisol level.7 p

Capturing fatigue parameters: The impact of vagal processing in multiple sclerosis related cognitive fatigue

Background: Causes of fatigue in Multiple Sclerosis remain elusive. Recently, we developed a model linking cognitive fatigue to inflammatory processes based on a neuroinflammatory reflex-arc instantiated by the vagus nerve. The relation between experienced autonomic dysfunctions, based on vagal processing, and cognitive fatigue is well-known, but an examination of the association of objectively measured vagal activity and cognitive fatigue is missing. An attempt was made to collect behavioral and physiological evidence that can be associated with experienced autonomic dysfunctions and fatigue in Multiple Sclerosis patients. Methods: Behavioral performance (response bias) and autonomic functioning (Heart rate variability and Skin conductance level) during an acoustic vigilance task were investigated in 53 Multiple Sclerosis patients. We assessed trait fatigue (independent from task), and time-on-task related increase of fatigue. Regression analysis was used to predict the fatigue status with physiological and behavioral scores. Results: Response bias, indicating a reduced responsiveness, and high and very low frequency components of Heart rate variability, indicating an increased parasympathetic activity, contribute to the regression of trait fatigue. Reduced Heart rate variability (SDNN) and increased parasympathetic activity (pNN50) remained in the regression model predicting time-on-task fatigue. Conclusion: Cognitive fatigue in MS is related to parasympathetic activity and reduced responsiveness, supporting our model representing fatigue as inflammatory processes in the brain. Standardized subjective and objective autonomous dysfunction measures might be considered as additional assessments in MS

Can biofeedback-based training alleviate fatigue and vigilance performance in fatigued MS patients?

Item does not contain fulltextMS related fatigue might be related to autonomous nervous system (ANS) dysfunctions or to inflammation related vagal (hyper-) activation. Consequently, influencing ANS status may lead to relieve of fatigue. We used two opposite biofeedback interventions to either increase sympathetic ("self-alert training", SAT) or parasympathetic activation ("progressive muscle relaxation", PMR). We recorded fatigue status of patients before and after a challenging vigilance task, their behavioural performance on this task, their skin conductance response (SCR), and parameters indicating parasympathetic activity concerning heart rate variability (HRV). We repeated these recordings after the biofeedback training sessions. Patients of the SAT group were able to learn to increase their SCR voluntarily. Patients of the PMR group showed increasing parameters indicating parasympathetic modulation of the HRV. The vigilance task increased their feeling of fatigue. However, there was no effect of biofeedback training on either fatigue status or performance on the vigilance task. Our results show that MS patients can learn to change voluntarily their ANS activity using biofeedback instructions based on SCR and this can be used in future studies to test the postulated link between ANS and fatigue. However, in this experimental intervention we were unable to document a relation between ANS activity and fatigue.17 p

Salivary IL-1ß as an objective measure for fatigue in multiple sclerosis?

Contains fulltext : 194103.pdf (publisher's version ) (Open Access)Background: The causes of fatigue in multiple sclerosis (MS) and other inflammatory disorders are not well understood. One possible cause that might explain fatigue in inflammatory disorders appears to be the immunological process itself, triggering neural activity that is experienced as fatigue. Objectives: To investigate whether salivary IL-1 beta concentration, associated with systemic inflammation, is related to subjective fatigue in MS. Methods: 116 MS patients (62 relapsing remitting MS, 54 secondary progressive MS) and 51 healthy controls participated in this study. Salivary concentration of IL-1 beta was determined using a commercially available enzyme-linked immunosorbent assay (ELISA) kit. Fatigue was assessed using various fatigue scales. We compared IL-1 beta concentration between groups and performed regression analyses to investigate which variables best predict fatigue scores. Results: We found that the IL-1 beta concentration best predicts fatigue scores in relapsing remitting MS patients, even though the IL-1 beta concentration did not differ significantly between relapsing remitting MS patients and healthy controls. Secondary progressive MS patients showed a somewhat elevated IL-1 beta concentration compared to relapsing remitting MS patients and healthy controls. Furthermore, disease modifying treatment had a significant effect on the IL-1 beta concentration, with treated patients showing a lower IL-1 beta concentration than non-treated patients. Conclusions: The present study points to a significant relation between the proinflammatory cytokine IL-1ß and fatigue in relapsing remitting MS patients. It also suggests a potential effect of disease modifying treatment on the peripheral IL-1 beta concentration.9 p

Salivary IL-1ß as an objective measure for fatigue in multiple sclerosis?

Background: The causes of fatigue in multiple sclerosis (MS) and other inflammatory disorders are not well understood. One possible cause that might explain fatigue in inflammatory disorders appears to be the immunological process itself, triggering neural activity that is experienced as fatigue. Objectives: To investigate whether salivary IL-1 beta concentration, associated with systemic inflammation, is related to subjective fatigue in MS. Methods: 116 MS patients (62 relapsing remitting MS, 54 secondary progressive MS) and 51 healthy controls participated in this study. Salivary concentration of IL-1 beta was determined using a commercially available enzyme-linked immunosorbent assay (ELISA) kit. Fatigue was assessed using various fatigue scales. We compared IL-1 beta concentration between groups and performed regression analyses to investigate which variables best predict fatigue scores. Results: We found that the IL-1 beta concentration best predicts fatigue scores in relapsing remitting MS patients, even though the IL-1 beta concentration did not differ significantly between relapsing remitting MS patients and healthy controls. Secondary progressive MS patients showed a somewhat elevated IL-1 beta concentration compared to relapsing remitting MS patients and healthy controls. Furthermore, disease modifying treatment had a significant effect on the IL-1 beta concentration, with treated patients showing a lower IL-1 beta concentration than non-treated patients. Conclusions: The present study points to a significant relation between the proinflammatory cytokine IL-1ß and fatigue in relapsing remitting MS patients. It also suggests a potential effect of disease modifying treatment on the peripheral IL-1 beta concentration