Quantitative EEG connectivity provides an encephalopathy-proximal readout that can detect poststroke delirium with AUC=0.892 and may characterize delirium-related endotypes beyond the intermittently observed clinical phenotype.
Poststroke delirium occurred in 32% of acute ischemic stroke or TIA patients assessed within 48 hours.
A multivariable qEEG model discriminated poststroke delirium with high accuracy.
PSD was characterized by spectral slowing and altered amplitude envelope correlation (AECc) in the qEEG.
Theta-band AECc showed inverse correlations with neuroinflammatory biomarkers VILIP-1 and CX3CL1 in an exploratory subcohort.
Neuroinflammatory and systemic biomarkers were less consistently associated with the binary clinical delirium phenotype than with qEEG metrics.
The authors conceptualize delirium as a severity level on a continuum of delirium-related encephalopathy rather than a binary phenotype.
The authors identified the need for validation of these findings in larger, multicenter cohorts.
This research suggests that brain wave measurements (EEG) taken shortly after a stroke can reliably detect delirium — a state of acute confusion that is common after stroke but often missed by standard clinical checks. In a study of 87 stroke patients, about one-third developed delirium, and a computer-based analysis of EEG patterns was able to identify these patients with about 89% accuracy overall. The EEG patterns in delirious patients showed slower brain activity and disrupted communication between brain regions, which the researchers argue reflects a more direct window into what is happening in the brain than periodic bedside assessments alone. The study also explored whether blood markers of inflammation were connected to these abnormal brain wave patterns. In a smaller group of 31 patients, two inflammatory signaling molecules — VILIP-1 and CX3CL1 — were inversely linked to disrupted brain network connectivity as measured by EEG, meaning higher levels of these molecules corresponded to more disrupted brain communication. Interestingly, these same inflammatory markers did not consistently predict whether a patient was clinically classified as delirious, suggesting that EEG may capture brain dysfunction in a more nuanced way than a simple yes/no delirium diagnosis. This research suggests that quantitative EEG could serve as a useful tool for earlier and more objective detection of post-stroke delirium, and could help researchers better understand the biological mechanisms driving it. However, the study was conducted at a single center with a relatively small number of patients, and the inflammation findings were exploratory, so larger studies across multiple hospitals are needed to confirm these results before they could be applied in routine clinical care.
Blücher M, Armagan N, Osswald A, Mengel A, Vogelgesang A, Ruhnau J, et al.. (2026). Quantitative EEG-Based Detection of Poststroke Delirium Endotypes and Their Association With Biomarkers of Inflammation.. Brain and behavior. https://doi.org/10.1002/brb3.71678