Sparse electrophysiological source imaging predicts aging-related gait speed slowing

TL;DR

This study develops sparse electrophysiological source imaging biomarkers from resting-state EEG to predict gait speed decline in aging, highlighting specific brain regions and improving prediction accuracy over traditional methods.

Contribution

Introduces novel sparse ESI models that outperform traditional methods and combines activation and connectivity features for better prediction of gait speed decline.

Findings

Sparse aESI models outperform LORETA methods.

Combining aESI and cESI improves prediction accuracy.

Biomarkers localized to orbitofrontal and temporal regions.

Abstract

Objective: We seek stable Electrophysiological Source Imaging (ESI) biomarkers associated with Gait Speed (GS) as a measure of functional decline. Towards this end we determine the predictive value of ESI activation and connectivity patterns of resting-state EEG Theta rhythm on physical performance decline measured by a slowing GS in aging individuals. Methods: As potential biomarkers related to GS changes, we estimate ESI using flexible sparse/smooth/non-negative models (NN-SLASSO), from which activation ESI (aESI) and connectivity ESI (cESI) features are selected using the Stable Sparse Classifier method. Results and Conclusions: Novel sparse aESI models outperformed traditional methods such as the LORETA family. The models combining aESI and cESI features improved the predictability of GS changes. Selected biomarkers from activation/connectivity patterns were localized to orbitofrontal and temporal cortical regions. Significance: The proposed methodology contributes to understanding the activation and connectivity of ESI complex patterns related to GS, providing potential biomarker features for GS slowing. Given the known relationship between GS decline and cognitive impairment, this preliminary work suggests it might be applied to other, more complex measures of healthy and pathological aging. Importantly, it might allow an ESI-based evaluation of rehabilitation programs.