3 research outputs found
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
Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning
Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10–20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox