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“Using EEG to Predict Dementia Risk and Cognitive Resilience in Elderly Patients Undergoing Surgery.”

Poster Session E - Monday, March 9, 2026, 2:30 – 4:30 pm PDT, Fairview/Kitsilano Ballroom

Yessica Martínez Serrato^1,2 (), Thomas Jackson³, Ali Mazaheri^1,2; ¹University of Birmingham| School of Psychology, ²Centre for Human Brain Health, ³University of Birmingham| Inflammation and Ageing

Elective surgeries are common in older adults due to age-related health conditions, yet these procedures often lead to cognitive decline and reduced quality of life. Disruptions in attention networks, particularly the alerting network, have been linked to post-surgical cognitive decline, including delirium and memory impairments. This study examined how task-related EEG changes, specifically alpha modulation during the Attention Network Task (ANT), relate to post-operative cognitive outcomes, alongside inflammatory profiles that may contribute to vulnerability. Pre-surgical EEG recordings were collected during the ANT, which measures alerting, orienting, and executive control. The analysis focused on alpha suppression within the alerting network, as this EEG signature has been shown to predict post-operative cognitive outcomes. Inflammatory markers were assessed to explore their relationship with attention network function and clinical outcomes. We observed that greater alpha suppression in the alerting network was associated with better cognitive resilience following surgery, whereas reduced suppression corresponded with higher incidence of cognitive decline, including delirium and memory deficits. Elevated inflammatory markers were also linked to disrupted alpha suppression, highlighting a potential interaction between inflammation and attention networks in shaping cognitive outcomes. These findings confirm the predictive value of alpha suppression in the alerting network and underscore the role of inflammatory profiles in modulating vulnerability. By integrating EEG-based task measures with inflammatory markers and clinical data, this work demonstrates a comprehensive approach to identifying older patients at risk of cognitive impairment and supporting targeted strategies to improve recovery.

Topic Area: ATTENTION: Development & aging