Synergistic contributions of periodic and aperiodic cortical activity to motor recovery prediction after stroke

Abstract

Functional motor recovery in chronic stroke can be promoted by intensive exercise aided by neurotechnology, yet reliable physiological markers to predict recovery remain limited. Scalp electroencephalography (EEG) is widely used in clinical routines, but conventional band-power metrics conflate oscillatory (periodic) and broadband (aperiodic) activity, limiting their interpretability.

Here, we tested whether EEG spectral profiles predict motor recovery in chronic stroke. Resting-state EEG (1 min) was recorded at admission and discharge using a portable system (N = 41) and parameterized with a spectral decomposition model. Parameters were examined in relation to motor improvement, as indexed by gain in a clinical score, and used to construct a prediction model.

Ipsilesional periodic activity at the individual alpha frequency (IAF) significantly increased after rehabilitation (p = 0.024) and correlated with motor improvement (r = 0.39, p = 0.010). A stepwise regression model identified higher IAF peaks, steeper aperiodic slopes, and their interaction as predictors of recovery (Severe group: R2 = 0.53, p = 0.003; balanced accuracy = 88%). The model outperformed a delta–alpha ratio–based approach commonly used in stroke prognosis.

These findings indicate that the synergistic interaction between periodic and aperiodic activity provides a mechanistically meaningful and clinically relevant biomarker of residual plasticity, supporting individualized rehabilitation strategies in chronic stroke.