Transcranial electrical stimulation effects on neurovascular coupling

Abstract

Abstract Transcranial electrical stimulation (tES) can exert cerebrovascular effects, but the mechanisms are unclear. Long-term (≥3min) transcranial direct current stimulation (tDCS) can also change the extracellular ion concentrations that can modulate neuronal excitability. An increase in interstitial K+ can modulate the neurovascular system's sensitivity via Kir channels in the astrocytes and the mural cells.To gain a mechanistic understanding of the tES effects on neurovascular coupling (NVC), we used a physiologically constrained multi-compartmental model (having nested pathways) for modal analysis to study the dynamic properties of the system for the output changes in blood vessel circumference and their natural frequencies in the frequency domain. Then, we used the open-source rsHRF toolbox and the functional magnetic resonance imaging (fMRI)-tDCS dataset to show the effects of anodal tDCS on hemodynamic response function (HRF) in the grey matter and at three regions of interest in the grey matter underlying anodal electrode (FC5), cathodal electrode (FP2), and an independent site remote from the electrodes (PZ). A canonical HRF model and a Finite Impulse Response (FIR) model captured the anodal tDCS effects on the temporal profile of the HRF. The modal analysis characterized the tES's vascular effects through neuronal and non-neuronal pathways, where stable modes were found for the smooth muscle cell compartment in the 0Hz-0.05Hz range.Our study showed tDCS onset effects on the neurovascular coupling (and HRF) for verum and sham tDCS conditions that were different from the no tDCS condition, which questions the validity of the placebo. Therefore, it is crucial to avoid fitting a common HRF for the whole brain, and disentangling the tES effect on the HRF is critical to the fMRI-tES studies. Future studies also need to address the trade-off between bias (in canonical HRF) and variance (in FIR HRF) that can be achieved by applying a mechanistic grey-box model.