EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery

Abstract

Background: Quantitative EEG (qEEG) can capture changes in brain activity that follow a stroke. Accordingly, EEG metrics could be used to monitor patients state and recovery. Although qEEG metrics traditionally focus on oscillatory activity, recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. Objective: To assess neurophysiological impairment and recovery from mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD). To relate neurophysiological recovery with patients functional outcome. Methods: Eighteen patients (n=18) with Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n=16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients. Results: At T0, stroke patients showed significantly more negative SE values than HC (p=0.003), reflecting broad-band EEG slowing. Moreover, SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1 in our patient sample. This SE renormalization significantly correlated with NIHSS improvement (R= 0.63, p=0.005). Conclusions: SE is a reliable readout of the electric changes occurring in the brain after an ischaemic cortical lesion. Moreover, SE holds the promise to be a robust method to assess stroke impairment as well as to monitor and predict functional outcome.

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness

“…The strong evidence that the aperiodic EEG component distinguishes states of wakefulness from sleep (Lendner et al ., 2020) and general anesthesia (Colombo et al ., 2019; Lendner et al ., 2020) motivated investigations of diagnostic value of this signal for unresponsive patients. In a clinical population, Lanzone et al (2022) proposed the aperiodic slope as an index for longitudinal recovery from stroke. To date, only a single study by Alnes et al (2021) assessed the spectral properties of pathologically unresponsive individuals and showed that the aperiodic component of the EEG is altered in patients in a coma, compared to healthy adults.…”

Normalization of Aperiodic Electrocorticography Components Indicates Fine Motor Recovery After Sensory Cortical Stroke in Mice

“…39,40 Aperiodic EEG measures are under investigation in patients. 8 Further, E/I imbalance has been demonstrated in the subacute phase after stroke in humans and transcranial stimulation at gamma frequency was able to reduce the degree of inhibition and improve performance in a motor task. 41,42 Alternatively, pharmacological agents to modify E/I ratio are available.…”

“…4–6 However, it was not until recently that the aperiodic, nonrhythmic component of neurophysiological signals gained more attention in animal and human data. 7,8 The aperiodic component defines the shape of PSD that typically shows exponentially decreasing power with increasing frequency and, accordingly, is largely dominated by a 1/f n -function. 7,9–12 The exponent n relates to the slope of the spectrum in log-log space.…”

Normalization of aperiodic ECoG components indicates fine motor recovery after sensory cortical stroke in mice

“…Electrophysiological observations in mice after stroke are not unlikely to occur in humans 39,40 . Spectral exponents in human stroke patients are under investigation 10 . A joint analysis of periodic and aperiodic parameters might enable future studies to further reveal their interference and prognostic value in stroke rehabilitation.…”

“…delta-alpha ratio) [4][5][6][7][8] . However, it was not until recently that the aperiodic, non-rhythmic component of neurophysiological signals gained more attention in animal and human data 9,10 . The aperiodic component defines the shape of PSD that typically shows exponentially decreasing power with increasing frequency and, accordingly, is largely dominated by a 1/f n -function 9,[11][12][13][14] .…”