Lower cortical volume is associated with poor sleep quality after traumatic brain injury.

Traumatic brain injury (TBI) is known to be associated with poor sleep. In this report, we aimed to identify associations between differences in cortical volume and sleep quality post-TBI. MRI anatomical scans from 88 cases with TBI were analyzed in this report. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Voxel Based Morphometry (VBM), was used to obtain statistical maps of the association between PSQI and cortical volume in gray matter and white matter voxels. Higher PSQI total scores (poor sleep quality) were strongly associated with smaller gray matter volume in the cerebellum. White matter volume was not associated with total PSQI. The sleep disturbance subcomponent showed a significant association with gray and white matter volumes in the cerebellum. Although not significant, cortical areas such as the cingulate and medial frontal regions were associated with sleep quality. The cerebellum with higher contribution to motor and autonomic systems was associated strongly with poor sleep quality. Additionally, regions that play critical roles in inhibitory brain function and suppress mind wandering (i.e., default mode network including medial frontal and cingulate regions) were associated (although to a lesser extent) with sleep. Our findings suggest that poor sleep quality following TBI is significantly associated with lower cerebellar volume, with trending relationships in regions associated with inhibitory function.

Compounding Effects of Traumatic Brain Injury, Military Status, and Other Factors on Pittsburgh Sleep Quality Index: A Meta-analysis.

INTRODUCTION: Traumatic brain injury (TBI) or concussion is a known risk factor for multiple adverse health outcomes, including disturbed sleep. Although prior studies show adverse effects of TBI on sleep quality, its compounding effect with other factors on sleep is unknown. This meta-analysis aimed to quantify the effects of TBI on subjective sleep quality in the context of military status and other demographic factors. MATERIALS AND METHODS: A programmatic search of PubMed database from inception to June 2020 was conducted to identify studies that compared subjective sleep quality measured using Pittsburgh Sleep Quality Index (PSQI) in individuals with TBI relative to a control group. The meta-analysis included group-wise standard mean difference (SMD) and 95% CI. Pooled means and SDs were obtained for TBI and non-TBI groups with and without military service, and meta-regression was conducted to test for group effects. Exploratory analysis was performed to test for the effect of TBI, non-head injury, military status, sex, and age on sleep quality across studies. RESULTS: Twenty-six articles were included, resulting in a combined total of 5,366 individuals (2,387 TBI and 2,979 controls). Overall, individuals with TBI self-reported poorer sleep quality compared to controls (SMD = 0.63, 95% CI: 0.45 to 0.80). Subgroup analysis revealed differences in the overall effect of TBI on PSQI, with a large effect observed in the civilian subgroup (SMD: 0.80, 95% CI: 0.57 to 1.03) and a medium effect in the civilian subgroup with orthopedic injuries (SMD: 0.40, 95% CI: 0.13 to 0.65) and military/veteran subgroup (SMD: 0.43, 95% CI: 0.14 to 0.71). Exploratory analysis revealed that age and history of military service significantly impacted global PSQI scores. CONCLUSIONS: Poor sleep quality in TBI cohorts may be due to the influence of multiple factors. Military/veteran samples had poorer sleep quality compared to civilians even in the absence of TBI, possibly reflecting unique stressors associated with prior military experiences and the sequelae of these stressors or other physical and/or psychological traumas that combine to heightened vulnerability. These findings suggest that military service members and veterans with TBI are particularly at a higher risk of poor sleep and its associated adverse health outcomes. Additional research is needed to identify potential exposures that may further heighten vulnerability toward poorer sleep quality in those with TBI across both civilian and military/veteran populations.

Compensatory Neural Responses to Cognitive Fatigue in Young and Older Adults.

Prolonged performance of a demanding cognitive task induces cognitive fatigue. We examined the behavioral and neural responses to fatigue-induced cognitive impairments in young and older adults. Particular emphasis was placed on whether the brain exhibited compensatory neural activity in response to cognitive fatigue. High-density EEG was recorded from a young (n = 16; 18-33 years of age) and an older (n = 18; 60-87 years of age) cohort who performed a Stroop task continuously for ∼2 h with no breaks. In the young cohort, behavioral performance declined as the experiment progressed, reflecting the deleterious effects of cognitive fatigue. Neurophysiologically, in addition to declining neural activity as cognitive fatigue developed, there is also evidence of region- and time-specific increase in neural activity, suggesting neural compensation. The compensatory activities followed patterns paralleling that of posterior-anterior shift in aging (PASA) and early to late shift in aging (ELSA) observed in cognitive aging and helped to moderate fatigue-induced behavioral deterioration. In the older cohort, behavioral performance did not decline as the experiment progressed, and neural activity either declined or stayed unchanged, showing no evidence of neural compensation, in contrast to the young. These results suggest that young and older adults coped with cognitive fatigue differently by exhibiting differential responses as a function of time-on-task at both the behavioral level and the neural level.

Task-Based Cognitive Fatigability for Older Adults and Validation of Mental Fatigability Subscore of Pittsburgh Fatigability Scale.

Cognitive fatigue and cognitive fatigability are distinct constructs. Cognitive fatigue reflects perception of cognitive fatigue outside of the context of activity level and duration and can be reliably assessed via established instruments such as the Fatigue Severity Scale (FSS) and the Modified Fatigue Impact Scale (MFIS). In contrast, cognitive fatigability reflects change in fatigue levels quantified within the context of the level and duration of cognitive activity, and currently there are no reliable measures of cognitive fatigability. A recently published scale, the Pittsburgh Fatigability Scale (PFS), attempts to remedy this problem with a focus on the aged population. While the physical fatigability subscore of PFS has been validated using physical activity derived measures, the mental fatigability subscore of PFS remains to be tested against equivalent measures derived from cognitive activities. To this end, we recruited 35 older, healthy adult participants (mean age 73.77 ± 5.9) to complete the PFS as well as a prolonged continuous performance of a Stroop task (>2 h). Task-based assessments included time-on-task changes in self-reported fatigue scores (every 20 min), reaction time, and pupil diameter. Defining subjective fatigability, behavioral fatigability, and physiologic/autonomic fatigability to be the slope of change over time-on-task in the above three assessed variables, we found that the PFS mental subscore was not correlated with any of the three task-based fatigability measures. Instead, the PFS mental subscore was correlated with trait level fatigue measures FSS (ρ = 0.63, p < 0.001), and MFIS cognitive subsection (ρ = 0.36, p = 0.03). This finding suggested that the PFS mental fatigability subscore may not be an adequate measure of how fatigued one becomes after a given amount of mental work. Further development efforts are needed to create a self-report scale that reliably captures cognitive fatigability in older adults.

The frequency of alpha oscillations: Task-dependent modulation and its functional significance.

Power (amplitude) and frequency are two important characteristics of EEG alpha oscillations (8-12 Hz). There is an extensive literature showing that alpha power can be modulated in a goal-oriented manner to either enhance or suppress sensory information processing. Only a few studies to date have examined the task-dependent modulation of alpha frequency. Instead, alpha frequency is often viewed as a trait variable, and used to characterize individual differences in cognitive functioning. We performed two experiments to examine the task-dependent modulation of alpha frequency and its functional significance. In the first experiment, high-density EEG was recorded from 21 participants performing a Sternberg working memory task. The results showed that: (1) during memory encoding, alpha frequency decreased with increasing memory load, whereas during memory retention and retrieval, alpha frequency increased with increasing memory load, (2) higher alpha frequency prior to the onset of probe was associated with longer reaction time, and (3) higher alpha frequency prior to the onset of cue or probe was associated with weaker early cue-evoked or probe-evoked neural responses. In the second experiment, simultaneous EEG-fMRI was recorded from 59 participants during resting state. An EEG-informed fMRI analysis revealed that the spontaneous fluctuations of alpha frequency, but not alpha power, were inversely associated with BOLD activity in the visual cortex. Taken together, these findings suggest that alpha frequency is task-dependent, may serve as an indicator of cortical excitability, and along with alpha power, provides more comprehensive indexing of sensory gating.

Brain's compensatory response to drug-induced cognitive impairment.

INTRODUCTION: Topiramate (TPM), a frequently prescribed antiseizure medication, can cause severe cognitive side-effects. Though these side-effects have been studied behaviorally, the underlying neural mechanisms are unknown. In a double-blind, randomized, placebo-controlled, crossover study of TPM's impact on cognition, nine healthy volunteers completed three study sessions: a no-drug baseline session and two sessions during which they received either TPM or placebo. Electroencephalogram was recorded during each session while subjects performed a working-memory task with three memory-loads. RESULTS: Comparing TPM with baseline we found the following results. (a) TPM administration led to declines in behavioral performance. (b) Fronto-central event-related potentials (ERP) elicited by probe stimuli, representing the primary task network activity, showed strong memory-load modulations at baseline, but the magnitude of these load-dependent modulations was significantly reduced during TPM session, suggesting drug-induced impairments of the primary task network. (c) ERP responses over bilateral fronto-temporal electrodes, which were not load sensitive at baseline, showed significant memory-load modulations after TPM administration, suggesting the drug-related recruitment of additional neural resources. (d) At fronto-central scalp sites, there was significant increase in response amplitude for low memory-load during TPM session compared to baseline, and the amplitude increase was dependent on TPM plasma concentration, suggesting that the primary task network became less efficient under TPM impact. (e) At bilateral fronto-temporal electrodes, there were no ERP differences when comparing low memory-load trials, but TPM administration led to an increase in ERP responses to high load, the magnitude of which was positively correlated with task performance, suggesting that the recruited neural resources were beneficial for task performance. Placebo-TPM comparison yielded similar effects albeit with generally reduced significance and effect sizes. CONCLUSION: Our findings support the hypothesis that TPM impairs the primary task network by reducing its efficiency, which triggers compensatory recruitment of additional resources to maintain task performance.