Stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability where the impairment of motor control has been mentioned most frequently as the most important disability. Therefore, innovative methodologies for stroke neurorehabilitation are urgently required to reduce long-term disability.
Neuroplasticity is the ability of the central nervous system to respond to intrinsic or extrinsic stimuli by reorganizing its structure, function and connections. Neuroplasticity is involved in post-stroke restorative rehabilitation of upper-limb function, but also can cause maladaptive functional outcomes, which can compromise re-gain of function via implementation of sub-optimal compensatory movement strategies. Such neuroplastic changes can be facilitated with noninvasive brain stimulation (NIBS) techniques, such as transcranial direct current stimulation (tDCS). tDCS - an electrically based intervention directed at the central nervous system level - is a promising tool to facilitate neuroplasticity in stroke rehabilitation. In this work, we investigate on a physiological signal changes appearing on functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) neuroimaging systems to objectively quantify the progress of a chosen tDCS treatment regime, correlating outcome with brain activation patterns as a marker of the underlying neuronal plasticity. Here, it was postulated that tDCS (Dietzel and Heinemann 1986) that perturbs hemodynamic (fNIRS) and electrophysiological (EEG) responses where the interactions between the hemodynamic and electrophysiological responses, captured with NIRS-EEG joint modeling, may provide an assessment of neurovascular coupling. Such an approach is novel since it introduces neuroimaging in a field so far not accessible by existing technology. We use adaptive identification techniques to correspond to the time-variances as previously applied to other application for muscular response changes.
Title: Computational modeling for brain dynamics time-variance evoked by noninvasive brain stimulation. Keywords: Computational modeling, Neuroplasticity, Adaptive tracking, Brain dynamics, Signal processing