Imagine your brain as a sprawling network of interconnected highways. When a stroke occurs, these highways may get damaged, leading to cognitive impairment. But what if we could predict the future of those cognitive abilities? That’s exactly what a group of scientists set out to do using machine learning and electroencephalography (EEG). By analyzing brain network properties derived from EEG data, they developed a model that could accurately predict post-stroke cognitive function. It turns out that certain network attributes in the theta band were particularly linked to cognitive scores. Features in the frontal lobe played a key role in predicting cognitive outcomes. The model had an impressive accuracy of 97% in predicting lesion laterality and achieved respectable R-squared values of 0.76 and 0.65 for the left and right stroke groups, respectively. This groundbreaking research opens up new possibilities for better understanding and managing post-stroke cognitive impairment. If you’re curious to learn more about how EEG can unlock insights into the mind, check out the full article!

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.

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