Novel advances in the characterization of dementia subtypes: from multi‐feature classification to whole brain computational models

Background Brain functional connectivity analyses derived from electroencephalography (EEG) provides relevant information for classification of dementia subtypes. The predictive strength of classification tools can be benefit from integrative, multi‐feature analysis of EEG which result in composite metric of functional connectivity. Additionally, significant improvement can be obtained when connectivity analyses consider the dependence between groups of regions as a whole system (high order interactions), instead of conceiving the network as a collection of pairwise interactions. Method Five minutes, resting‐state EEG (rsEEG) was recorded from healthy controls (HC, n=42), and participants diagnosed with either behavioral variant frontotemporal dementia (bvFTD, n=19) or Alzheimer Disease (AD, n=33). EEG source localization analyses were conducted. Whole brain functional connectivity (82 anatomic compartments, AAL atlas) was analyzed using 17 metrics to capture different statistical dependence between source space rsEEG time series, in both time‐, and frequency‐domains. Furthermore, high‐order, non‐lineal statistical interdependencies between group of brain regions were assessed using multivariate information theory. Gradient boosting classifiers with Bayesian optimization, harmonization, and feature elimination were implemented. Result Classification of dementia subtypes, using a particular frequency‐domain connectivity metric, displayed better performances as features for classification comprised information from multiple EEG frequency bands. Likewise, classification systems including information from several connectivity metrics outperformed those achieved with individual metrics. Dementia classification conducted with connectivity metrics derived from mutual information significantly improved as order of interaction (number of brain regions consider as a network) increased. This latter approach boosted multimodal (fMRI‐EEG) classification of dementias based on brain functional connectivity, and served as input for whole‐brain computational models describing the pathophysiology of neurodegenerative diseases. Conclusion Integrative, composite metrics of connectivity, and connectivity analyses of source localized rsEEG based in high order interactions capture relevant information to discriminate dementia subtypes, and provide a reliable and interpretable description of brain functional connectivity and neurodegeneration.