Investigating online effects of transcranial direct current stimulation from NIRS-EEG joint-imaging using Kalman Filter based online parameter estimation of an autoregressive model Investigating online effects of transcranial direct current stimulation fr


Although, there has been a significant improvement in neuroimaging technologies and analysis methods, the fundamental relationship between local changes in cerebral hemodynamics and the underlying neural activity remains largely unknown. The focus of this computational work was to understand the relation between electrophysiological and hemodynamic data acquired simultaneously from the human cortex during non-invasive brain stimulation (NIBS). This computational work was performed using preliminary data collected at Prof. Perrey’s Group - Neuroplasticity and Rehabilitation at the Movement to Health Laboratory, EuroMov, University of Montpellier. Prof. Perrey’s Group has shown the feasibility of using a combined multi-electrode tDCS-multi channel functional NIRS setup to determine the effects of different tDCS protocols on bilateral sensorimotor cortex activation. The respective neural activity, assessed with electroencephalogram (EEG), is postulated to be closely related, spatially and temporally, to cerebral blood flow (CBF) that supplies glucose via neurovascular coupling. This hemodynamic response to neural activity can be captured by near-infrared spectroscopy (NIRS), which enables continuous monitoring of cerebral oxygenation. In this study, the NIRS-EEG joint imaging data was processed to investigate the relation between alterations in EEG band power (specifically, <12Hz based on a prior work) and oxy-hemoglobin concentration (O2Hb) in the slow frequency regime (around 0.1 Hz). Here, a computational autoregressive (ARX) model was investigated for understanding the relationship between simultaneously acquired electroencephalographic (EEG band-power <12Hz) and near infrared spectroscopic (O2Hb) data during anodal tDCS. The online parameter estimation of the ARX model was performed with a Kalman filter and this online parameter estimation technique was shown to be sensitive towards transient changes in the cross- correlation between EEG band-power and O2Hb. The computation model for online tracking of the relation between EEG band-power and O2Hb was developed which needs to be tested on a larger subject pool in the future studies. This may allow quantitative assessment of the existence of a coupling relationship between electrophysiological and hemodynamic response to NIBS in health and disease.