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# On the stratification of individuals' EEG connectivity in the context of cognitive disorders

Maxime Bedoin, Nesma Houmani, Bernadette Dorizzi, Jérôme Boudy, Kiyoka Kinugawa

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Maxime Bedoin, Nesma Houmani, Bernadette Dorizzi, Jérôme Boudy, Kiyoka Kinugawa. On the stratification of individuals' EEG connectivity in the context of cognitive disorders. The 13th European Conference on Clinical Neuroimaging (ECCN), Mar 2025, Madrid, Spain. [hal-05026742](https://hal.science/hal-05026742v1)

HAL Id: hal-05026742

https://hal.science/hal-05026742v1

Submitted on 9 Apr 2025

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# On the stratification of individuals' EEG connectivity in the context of cognitive disorders

## Introduction

Electroencephalography (EEG) analysis for dementia is mainly performed in the literature using statistical and supervised learning methods. Nevertheless, dementia diagnosis is still challenging due to heterogeneous phenotypes across patients. Therefore, we propose a data-driven approach with a clustering method to identify meaningful subgroups associated with EEG connectivity phenotypes.

## Methods

The study involves clinical data of 32 patients with Subjective Cognitive Impairment (SCI), 46 patients with Alzheimer's Disease (AD) and 17 patients with Vascular Dementia (VaD). We estimate the connectivity in 8 regions (prefrontal, frontal left, central, frontal right, temporal left, parietal, temporal right and occipital), using Dynamic Time Warping (DTW). Compared to Phase Lag Index, we observed that DTW is more powerful because it takes into account both amplitude and phase strength between signals. Then, we generate five clusters of the individuals, described in 8 dimensions, using a hierarchical clustering. The clusters are ranked by decreasing connectivity.

## Results

In all frequency bands, Cluster 5 (lowest connectivity) contains the majority of VaD patients (65%). Alpha 1 shows the highest Adjusted Rand Index indicating the relevance of the clustering in such band. Cluster 1 (highest connectivity) contains the majority of SCI (54%) and 33% of the AD patients. Clusters 2, 3 and 4 (three levels of moderate connectivity) contain mostly AD patients, with 40% of AD belonging to Cluster 2. Cluster 1 and Cluster 2 gather 82% of SCI patients.

## Conclusions

Our DTW-based approach allows to stratify the population into meaningful profiles according to the individuals' EEG connectivity. AD patients show more disparate profiles than SCI and VaD. This suggests that AD patients are more heterogeneous regarding their EEG connectivity. Our work indicates that EEG could be a useful neuroimaging technique for a refined characterization of individuals, necessary in precision medicine.