Event-Related Potential Markers of Subject Cognitive Decline and Mild Cognitive Impairment during a sustained visuo-attentive task

Abstract

AbstractINTRODUCTIONSubjective cognitive decline (SCD), mild cognitive impairment (MCI), or severe Alzheimer’s disease stages are still lacking clear electrophysiological correlates.METHODSIn 145 subjects (86 SCD, 40 MCI, and 19 healthy subjects (HS)), we analysed event-related potentials observed during a sustained visual attention task, aiming to distinguish biomarkers associated with group conditions and performance.RESULTSWe observed distinct patterns among group conditions in the occipital P1 and N1 components during the stimulus encoding phase, as well as in the central P3 component during the stimulus decision phase. The order of ERP components was non-monotonic, indicating a closer resemblance between MCI and HS. ERP features from occipital channels exhibited greater differences between SCD and MCI. Task performance was significantly enhanced in the central channels during the decision phase.DISCUSSIONThose results support evidence of early stage, neural anomalies linked to visuo-attentive alterations in cognitive decline as candidate EEG biomarkers.Research in contextTHE SYSTEMATIC REVIEWThe researchers examined existing literature by referring to conventional sources like PubMed, Scopus, and Google Scholar. Keywords used: e.g., “EEG & Dementia”; “Visual Evoked Potential & SCD or MCI”.References are properly cited and almost half of them are from the last ten years.THE INTERPRETATIONResults proposed early dynamics of visual processing ERP being insightful biomarkers for SCD and MCI patients. Those components reflect evoked potential patterns, suggesting the power of few milliseconds in being informative about the underlying neural dysfunctionalities associated with visuo-attentive mechanisms.FUTURE DIRECTIONSWe enrolled 100+ subjects. By even expanding the sample size and conducting follow-up assessments, we aim to assess the extracted ERP features, as well as by training and testing machine learning algorithms. The goal is to support clinical decision-making, and to prioritise patients with an abnormal neural signal over manifest cognitive symptomatology, tracking the cognitive decline trajectory effectively.