Predicting aging-related decline in physical performance with sparse electrophysiological source imaging

Objective: We introduce a methodology for selecting biomarkers from activation and connectivity derived from Electrophysiological Source Imaging (ESI). Specifically, we pursue the selection of stable biomarkers associated with cognitive decline based on source activation and connectivity patterns of resting-state EEG theta rhythm, used as predictors of physical performance decline in aging individuals measured by a Gait Speed (GS) slowing. Methods: Our two-step methodology involves estimating ESI using flexible sparse-smooth-nonnegative models, from which activation ESI (aESI) and connectivity ESI (cESI) features are derived. The Stable Sparse Classifier method then selects potential biomarkers related to GS changes. Results and Conclusions: Our predictive models using aESI outperform traditional methods such as the LORETA family. The models combining aESI and cESI features provide the best prediction of GS changes. Potential biomarkers from activation/connectivity patterns involve orbitofrontal and temporal cortical regions. Significance: The proposed methodology contributes to the understanding of activation and connectivity of GS-related ESI and provides features that are potential biomarkers of GS slowing. Given the known relationship between GS decline and cognitive impairment, this preliminary work opens novel paths to predict the progression of healthy and pathological aging and might allow an ESI-based evaluation of rehabilitation programs

ERP Source Analysis Guided by fMRI During Familiar Face Processing

Event related potentials (ERPs) provide precise temporal information about cognitive processing, but with poor spatial resolution, while functional magnetic resonance imaging (fMRI) reliably identifies brain areas involved, but with poor temporal resolution. Here we use fMRI to guide source localization of the ERPs at different times for studying the temporal dynamics of the neural system for recognizing familiar faces. fMRI activation areas were defined in a previous experiment applying the same paradigm used for ERPs. The Bayesian model averaging (BMA) method was used to estimate the generators of the ERPs to unfamiliar, visually familiar, and personally-familiar faces constraining the model by fMRI activation results. For this, higher prior probabilities in the solution space were assigned to the fMRI-defined regions, which included face-selective areas and other areas related to “person knowledge” retrieval. Source analysis was carried out in three-time windows: early (150–210 ms), middle (300–380 ms) and late (460–580 ms). The early and middle responses were generated in fMRI-defined areas for all face categories, while these areas do not contribute to the late response. Different areas contributed to the generation of the early and middle ERPs elicited by unfamiliar faces: fusiform (Fus), inferior occipital, superior temporal sulcus and the posterior cingulate (PC) cortices. For familiar faces, the contributing areas were Fus, PC and anterior temporal areas for visually familiar faces, with the addition of the medial orbitofrontal areas and other frontal structures for personally-significant faces. For both unfamiliar and familiar faces, more extended and reliable involvement of contributing areas were obtained for the middle compare with early time window. Our fMRI guide ERP source analysis suggested the recruitment of person-knowledge processing areas as early as 150–210 ms after stimulus onset during recognition of personally-familiar faces. We concluded that fMRI-constrained BMA source analysis provide information regarding the temporal-dynamics in the neural system for cognitive processsing