Electrophysiological signatures of resting state networks predict cognitive deficits in stroke. (May 2021)

• Record Type: Journal Article
• Authors: Romeo, Zaira; Mantini, Dante; Durgoni, Eugenia; Passarini, Laura; Meneghello, Francesca; Zorzi, Marco
• Is Part Of: Cortex. Volume 138(2021)
• Page Start: 59
• Page End: 71
• Publication Date: 2021-05
• Subjects: Stroke -- EEG -- Resting state networks -- Functional connectivity -- Cognitive impairments; Neuropsychology -- Periodicals; Nervous system -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Behavior -- Periodicals; Neurology -- Periodicals; 612.825
• DOI: 10.1016/j.cortex.2021.01.019
• Languages: English
• ISSNs: 0010-9452

Abstract

Abstract: Localized damage to different brain regions can cause specific cognitive deficits. However, stroke lesions can also induce modifications in the functional connectivity of intrinsic brain networks, which could be responsible for the behavioral impairment. Though resting state networks (RSNs) are typically mapped using fMRI, it has been recently shown that they can also be detected from high-density EEG. We build on a state-of-the-art approach to extract RSNs from 64-channels EEG activity in a group of right stroke patients and to identify neural predictors of their cognitive performance. Fourteen RSNs previously found in fMRI and high-density EEG studies on healthy participants were successfully reconstructed from our patients' EEG recordings. We then correlated EEG-RSNs functional connectivity with neuropsychological scores, first considering a wide frequency band (1–80 Hz) and then specific frequency ranges in order to examine the association between each EEG rhythm and the behavioral impairment. We found that visuo-spatial and motor impairments were primarily associated with the dorsal attention network, with contribution dependent on the specific EEG band. These findings are in line with the hypothesis that there is a core system of brain networks involved in specific cognitive domains. Moreover, our results pave the way for low-cost EEG-based monitoring of intrinsic brain networks' functioning in neurological patients to complement clinical-behavioral measures.