Low-Frequency Oscillations Are a Biomarker of Injury and Recovery After Stroke

Abstract

Background and Purpose— Low-frequency oscillations reflect brain injury but also contribute to normal behaviors. We examined hypotheses relating electroencephalography measures, including low-frequency oscillations, to injury and motor recovery poststroke. Methods— Patients with stroke completed structural neuroimaging, a resting-state electroencephalography recording and clinical testing. A subset admitted to an inpatient rehabilitation facility also underwent serial electroencephalography recordings. The relationship that electroencephalography measures (power and coherence with leads overlying ipsilesional primary motor cortex [iM1]) had with injury and motor status was assessed, focusing on delta (1–3 Hz) and high-beta (20–30 Hz) bands. Results— Across all patients (n=62), larger infarct volume was related to higher delta band power in bilateral hemispheres and to higher delta band coherence between iM1 and bilateral regions. In chronic stroke, higher delta power bilaterally correlated with better motor status. In subacute stroke, higher delta coherence between iM1 and bilateral areas correlated with poorer motor status. These coherence findings were confirmed in serial recordings from 18 patients in an inpatient rehabilitation facility. Here, interhemispheric coherence between leads overlying iM1 and contralesional M1 was elevated at inpatient rehabilitation facility admission compared with healthy controls (n=22), declining to control levels over time. Decreases in interhemispheric coherence between iM1 and contralesional M1 correlated with better motor recovery. Conclusions— Delta band coherence with iM1 related to greater injury and poorer motor status subacutely, while delta band power related to greater injury and better motor status chronically. Low-frequency oscillations reflect both injury and recovery after stroke and may be useful biomarkers in stroke recovery and rehabilitation.

Rapid development of strong, persistent, spatiotemporally extensive cortical synchrony and underlying oscillations following acute MCA focal ischemia

“…We find that across these studies different frequency bands (typically delta, alpha, and beta), different electrode locations, and different measures (spectral power, coherence, phase synchrony, etc.) have been used to show the potential of EEG in understanding brain injury in stroke [43][44][45][46][47][48][49][50] . These studies are difficult to synthesize because of the heterogeneity of the size and location of strokes in humans and the varying time periods used in experiments (within hours (acute), weeks (subacute) or months (chronic) of the stroke) 50 .…”

“…have been used to show the potential of EEG in understanding brain injury in stroke [43][44][45][46][47][48][49][50] . These studies are difficult to synthesize because of the heterogeneity of the size and location of strokes in humans and the varying time periods used in experiments (within hours (acute), weeks (subacute) or months (chronic) of the stroke) 50 . Indeed, our own experience shows that useful biomarkers in acute patients at the Emergency Room 46 are quite different from subacute and chronic patients 50 .…”

Damage to the structural connectome reflected in resting-state fMRI functional connectivity

“…Our inability to see the influence of damage at every edge modeled likely reflects this additionally heterogeneity in the data. Future work will hopefully examine the relationship between structure and function in even more localized temporal intervals (say within the first week poststroke) or during intervals far beyond initially elevated periods of plasticity to identify its impact (3 or more months poststroke (Cassidy et al, 2020 )). These studies will reveal in greater clarity the structure-function relationship immediately poststroke, and the relationship after the effects of neuroplasticity are expressed.…”

Up and Down States During Slow Oscillations in Slow-Wave Sleep and Different Levels of Anesthesia

“…Slow oscillations are the hallmark of slow-wave sleep (SWS), and much research has been carried out to try to understand their role in sleep-induced plasticity and memory consolidation (Diekelmann and Born, 2010 ; Klinzing et al, 2019 ). However, this emergent pattern of activity consistent in slow oscillations is generated by the cerebral cortex not only during SWS but also in a variety of pharmacologically induced states (e.g., following administration of propofol, ketamine, urethane, or isoflurane; for a review, see Brown et al, 2010 ) and also in pathological conditions such as stroke or traumatic brain injury (Sarasso et al, 2019 ; Cassidy et al, 2020 ). For this reason, it is important to understand the genesis and dynamics of slow waves (Sanchez-Vives, 2020 ), as well as the brain state transitions that lead the cerebral cortex in and out of these dynamic regimes.…”