CNR ExploRA

Articolo in rivista, 2022, ENG, 10.3390/brainsci12091179

Functional Source Separation-Identified Epileptic Network: Analysis Pipeline

Olejarczyk, Elzbieta; Zappasodi, Filippo; Ricci, Lorenzo; Pascarella, Annalisa; Pellegrino, Giovanni; Paulon, Luca; Assenza, Giovanni; Tecchio, Franca

Università Telematica Internazionale UNINETTUNO; Ospedale San Camillo; Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences; Consiglio Nazionale delle Ricerche; Istituto Di Scienze E Tecnologie Della Cognizione, Rome; Università Campus Bio-Medico di Roma; University of G. d'Annunzio Chieti and Pescara

This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.

Brain sciences 12 (9)