NN04
Correlation of Fatigue and BRAIN Network Disruptions in Patients with Multiple Sclerosis Compared to Healthy Controls
Fatigue severity is common debilitating problem associated with multiple sclerosis (MS). Data suggest that certain brain networks are disrupted with increased levels of fatigue. To date, research has not determined what brain network is perturbed in people that endorse fatigue and if these changes can be targeted to change clinical outcomes with treatment.
Objectives:
Our study evaluated two hypotheses: 1) whether patients with RRMS would exhibit disruptions in brain connectivity in the frontal-parietal (FP) network during an n-back task; and 2) whether those FP network disruptions in brain connectivity were associated with fatigue.
Methods:
A pilot cross-sectional study was completed with a convenience sample of 10 MS participants with relapsing remitting MS (RRMS) and 8 healthy controls. Electroencephalogram data were recorded from recorded during an NBack task (0/2 Back) using Geodesic software (version 5.4) with a 256-channel cap. Self-reported Fatigue Severity Scale [FSS]) was used to measure these variables to all participants before the task. Descriptive statistics and analyses were also applied.After transformation of qEEG data to Exact Low-Resolution Electromagnetic Tomography (eLORETA), the data were analyzed by the t-test and multivariable linear regression methods to model the brain dynamics in our sample.
Results:
Mean age was 43 for the MS group; and 49.8 for the healthy controls. Mean FSS was 41.7 for MS group, and 32.94 for healthy controls. For the MS group, time since diagnosis in years was 8.5 (SD =7.3) and the mean SR-EDSS was 3 (SD = .55). Phase connectivity measures obtained during the n-back task (p = .04) were significantly different between groups, and the magnitude of the connectivity was positively related to fatigue levels (p = .05) in the frontal-parietal network, especially in the delta and alpha-1 frequency bands.
Conclusions:
Conclusions: The results indicate that the decreased efficiency caused by the neural disruption in RRMS leads to increased compensatory neural dynamics to meet environmental demands. We conclude that this inefficiency in information processing capability supports a model of brain function in RRMS fatigue whereby the hubs and connections in brain networks must rely on less efficient resources, due to structural disconnections within the pathways, playing a significant role in central cognitive fatigue of RRMS.