Segregation over time in functional networks in prefrontal cortex for individuals suffering from pathological fatigue...

Pathological fatigue is present when fatigue is perceived to continually interfere with everyday life. Pathological fatigue has been linked with a dysfunction in the cortico-striatal-thalamic circuits. Previous studies have investigated measures of functional connectivity, such as modularity to quantify levels of segregation. However, previous results have shown both increases and decreases in segregation for pathological fatigue. There are multiple factors why previous studies might have differing results, including: (i) Does the functional connectivity of patients with pathological fatigue display more segregation or integration compared to healthy controls? (ii) Do network properties differ depending on whether patients with pathological fatigue perform a task compared to periods of rest? (iii) Are the brain networks of patients with pathological fatigue and healthy controls differently affected by prolonged cognitive activity? We recruited individuals suffering from pathological fatigue after mild traumatic brain injury (n = 20) and age-matched healthy controls (n = 20) to perform cognitive tasks for 2.5 h. We used functional near-infrared spectroscopy (fNIRS) to assess hemodynamic changes in the frontal cortex. The participants had a resting state session before and after the cognitive test session. Cognitive testing included the Digit Symbol Coding test at the beginning and the end of the procedure to measure processing speed. We conducted an exploratory network analysis on these resting state and Digit Symbol Coding sessions with no a priori hypothesis relating to how patients and controls differ in their functional networks since previous research has found results in both directions. Our result showed a Group vs. Time interaction (p = 0.026, η ² = 0.137), with a post hoc test revealing that the TBI patients developed higher modularity toward the end of the cognitive test session. This work helps to identify how functional networks differ under pathological fatigue compared to healthy controls. Further, it shows how the functional networks dynamically change over time as the patient performs tasks over a time scale that affect their fatigue level.