Is Resting-State EEG Longitudinally Associated With Recovery of Clinical Neurological Impairments Early Poststroke? A Prospective Cohort Study

Abstract

Background. The time course of cortical activation and its relation with clinical measures may elucidate mechanisms underlying spontaneous neurobiological recovery after stroke. Objective. We aimed to investigate (1) the time course of cortical activation as revealed by EEG-based spectral characteristics during awake rest and (2) the development of these spectral characteristics in relation to global neurological and upper-limb motor recovery in the first 6 months poststroke. Methods. Resting-state EEG was measured serially in 41 patients after a first-ever ischemic stroke, within 3 and at 5, 12, and 26 weeks poststroke. We computed the brain symmetry index (BSI) and directional BSI (BSIdir) over different frequency bands (1-25 Hz, delta, theta) and delta/alpha ratio (DAR). The National Institutes of Health Stroke Scale (NIHSS) and Fugl-Meyer motor assessment of the upper extremity (FM-UE) were determined as clinical reflections of spontaneous neurobiological recovery. Longitudinal changes in spectral characteristics and within- and between-subject associations with NIHSS and FM-UE were analyzed with linear mixed models. Results. Spectral characteristics showed a gradual normalization over time, within and beyond 12 weeks poststroke. Significant within- and between-subject associations with NIHSS were found for DAR of the affected hemisphere (DARAH) and BSIdirdelta. BSIdirdelta also demonstrated significant within- and between-subject associations with FM-UE. Conclusions. Changes in spectral characteristics are not restricted to the time window of recovery of clinical neurological impairments. The present study suggests that decreasing DARAH and BSIdirdelta reflect improvement of global neurological impairments, whereas BSIdirdelta was also specifically associated with upper-limb motor recovery early poststroke.

EEG Fractal Analysis Reflects Brain Impairment after Stroke

“…Intra-subject comparison between admission and discharge from Stroke Unit show a non-significant increase of complexity. Indeed, only NIHSS modifications were significant (i.e., on average, 4.75 at T0 vs. 1.80 at T1), whereas FMA modifications were not (i.e., on average, 55 at T0 vs. 60 at T1), most likely due to the ceiling effect of FMA scale already described in studies with a similar design [ 29 ]. Our participants reached almost normal values of both NIHSS and FMA, except for a few severely impaired participants.…”

“…No significant behavioural improvement occurred except for BBT. While other studies [ 29 ] did in fact observe EEG changes over 6 months in a sample of post-stroke persons discharged at home, our sample comprised severely affected persons admitted to a Rehabilitation Unit. The follow up was limited at two months after stroke.…”

Robustness of individualized inferences from longitudinal resting state dynamics

“…Voytek et al (2015) suggest that such changes might indicate a change in the 1/f baseline possibly due to increased physiological noise with aging. Furthermore, systematic longitudinal changes in the power spectrum have been observed following stroke (Giaquinto et al 1994; Saes et al 2020). Thus, the application of this physiological signature to monitor longitudinal RS in clinical populations is an important future priority.…”

“…Tracking how individual human brains change over extended time-scales (e.g., days to years) is crucial to identify the mechanisms of neural plasticity in scenarios ranging from healthy aging (Boersma et al 2011;Cabeza et al 2018;Cassani et al 2018) to stroke recovery (Giaquinto et al 1994;Rehme et al 2011;Wu et al 2016;Bonkhoff et al 2020;Saes et al 2020;van der Vliet et al 2020). Given the many practical challenges that long-term tracking entails, one promising strategy to sample an individual's neural state repeatedly over time is with measures of resting state (RS) activity (Vecchio et al 2013;Carino-Escobar et al 2019;Newbold et al 2020;Pritschet et al 2020;Saes et al 2020). RS activity refers to the ongoing neural activity while individuals maintain a minimally instructed state of "rest" over several minutes.…”

“…Tracking how individual human brains change over extended time-scales (e.g., days to years) is crucial to monitor and modify neural plasticity processes in scenarios ranging from healthy aging (Boersma et al 2011; Cabeza et al 2018; Cassani et al 2018) to stroke recovery (Giaquinto et al 1994; Rehme et al 2011; Wu et al 2016; Bonkhoff et al 2020; Saes et al 2020; van der Vliet et al 2020). A promising strategy to track an individual’s changing neurophysiology is with repeated measurements of resting state (RS) activity, i.e., the ongoing neural oscillatory dynamics over a few minutes of wakeful rest (Vecchio et al 2013; Carino-Escobar et al 2019; Newbold et al 2020; Pritschet et al 2020; Saes et al 2020). RS-activity has been shown to provide reliable indicators of neurobiological organization and integrity (Biswal et al 1995; Damoiseaux and Greicius 2009; Van Den Heuvel et al 2009; Hermundstad et al 2013; Miŝic et al 2016; Hoenig et al 2018; Buckner and DiNicola 2019).…”