Clinical NeurophysiologyNeurosciencePeer-reviewed journal article
EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery.
Jacopo LanzoneiD, M.A. Colombo, Simone SarassoiD, Filippo ZappasodiiD, Mario RosanovaiD, Marcello MassiminiiD, Vincenzo Di LazzaroiD, Giovanni AssenzaiD
Abstract
BackgroundQuantitative 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. ObjectiveTo 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. MethodsEighteen 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. ResultsAt 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). ConclusionsSE 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.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- Citations
- 57
- FWCI
- 4.74
- Field percentile
- top 5%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Normalization of aperiodic ECoG components indicates fine motor recovery after sensory cortical stroke in mice
Jonatan Biskamp, Sara Isla Cainzos, … Tim Ullrich Magnus·bioRxiv·Feb 16, 2022
- Power spectrum slope and motor function recovery after focal cerebral ischemia in the rat
Susan Leemburg, Bo Gao, … Claudio L. Bassetti·SLEEPPeer-reviewed·Aug 24, 2018
- Electrophysiological Signature of Stroke Recovery: Investigating EEG Biomarkers for Prognostic Insights
Majid Khalili‐Ardali, Vivek Sharma, … Nick F. Ramsey·Frontiers in StrokePeer-reviewed·Sep 28, 2026
- Lempel-Ziv complexity of the EEG predicts long-term functional recovery after stroke in rats
Susan Leemburg, Claudio L. Bassetti·bioRxiv·Jan 15, 2018
- Resting bilateral sensorimotor mu rhythm suppression facilitates ipsilesional M1 excitability after stroke
Uttara U Khatri, Tharan Suresh, … Sara J. Hussain·bioRxiv·Aug 10, 2026
- Slow Oscillations Track Acute Stroke Injury but Not Functional Recovery
Jake Lee, Arnav Ajay Jadav, Eric C. Landsness·bioRxiv·Jul 17, 2026