  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.
