Advanced Insights into Functional Brain Connectivity by Combining Tensor Decomposition and Partial Directed Coherence
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
Quantification of functional connectivity in physiological networks is frequently performed by means of time-variant partial directed coherence (tvPDC), based on time-variant multi-variate autoregressive models. The principle advantage of tvPDC lies in the combination of directionality, time variance and frequency selectivity simultaneously, offering a more differentiated view into complex brain networks. Yet the advantages specific to tvPDC also cause a large number of results, leading to serious problems in interpretability. To counter this issue, we propose the decomposition of multi-dimensional tvPDC results into a sum of rank-1 outer products. This leads to a data condensation which enables an advanced interpretation of results. Furthermore it is thereby possible to uncover inherent interaction patterns of induced neuronal subsystems by limiting the decomposition to several relevant channels, while retaining the global influence determined by the preceding multivariate AR estimation and tvPDC calculation of the entire scalp. Finally a comparison between several subjects is considerably easier, as individual tvPDC results are summarized within a comprehensive model equipped with subject-specific loading coefficients. A proof-of-principle of the approach is provided by means of simulated data; EEG data of an experiment concerning visual evoked potentials are used to demonstrate the applicability to real data.
Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 0129293 |
Seitenumfang | 18 |
Fachzeitschrift | PloS one |
Jahrgang | 10 |
Ausgabenummer | 6 |
Publikationsstatus | Veröffentlicht - 5 Juni 2015 |
Peer-Review-Status | Ja |
Extern publiziert | Ja |
Externe IDs
PubMed | 26046537 |
---|---|
WOS | 000355652200144 |
ORCID | /0000-0001-8264-2071/work/142254067 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- PRINCIPAL COMPONENT ANALYSIS, EVENT-RELATED-POTENTIALS, EEG DATA, MEMORY, EFFICIENT, NUMBER, FORM