In NeuroImage. Clinical
Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.
Pirondini Elvira, Goldshuv-Ezra Nurit, Zinger Nofya, Britz Juliane, Soroker Nachum, Deouell Leon Y, Ville Dimitri Van De
Computer-enhanced measurement, EEG analysis, EEG topography features, Machine learning, Outcome measurement, Rehabilitation, Resting-state EEG biomarkers, Stroke, Unilateral spatial neglect