Perspective: Disentangling the effects of tES on neurovascular unit

Abstract

Transcranial electrical stimulation (tES) can modulate the neurovascular unit, including the perivascular space morphology, but the mechanisms are unclear. In this perspective article, we used an open-source “rsHRF toolbox” and an open-source functional magnetic resonance imaging (fMRI) transcranial direct current stimulation (tDCS) data set to show the effects of tDCS on the temporal profile of the haemodynamic response function (HRF). We investigated the effects of tDCS in the gray matter and at three regions of interest in the gray matter, namely, the anodal electrode (FC5), cathodal electrode (FP2), and an independent site remote from the electrodes (PZ). A “canonical HRF” with time and dispersion derivatives and a finite impulse response (FIR) model with three parameters captured the effects of anodal tDCS on the temporal profile of the HRF. The FIR model showed tDCS onset effects on the temporal profile of HRF for verum and sham tDCS conditions that were different from the no tDCS condition, which questions the validity of the sham tDCS (placebo). Here, we postulated that the effects of tDCS onset on the temporal profile of HRF are subserved by the effects on neurovascular coupling. We provide our perspective based on previous work on tES effects on the neurovascular unit, including mechanistic grey-box modeling of the effects of tES on the vasculature that can facilitate model predictive control (MPC). Future studies need to investigate grey-box modeling of online effects of tES on the neurovascular unit, including perivascular space, neurometabolic coupling, and neurovascular coupling, that can facilitate MPC of the tES dose-response to address the momentary (“state”) and phenotypic (“trait”) factors.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task

“…The region 6vr+ has greatest connectivity between the extent (as percentage) of the cerebral surface area and the cerebellar gray matter volume (Buckner et al, 2011 ), which needs future investigation based on fNIRS directed functional connectivity [specifically, subserved by ventral superior longitudinal fascicle–SLF III (Kamat et al, 2022 )] vis-à-vis error-related perception action coupling. Also, tDCS effects on the neurovascular unit (Arora and Dutta, 2023 ) needs investigation where a POS-PRE decrease in the left prefrontal scalp EEG activity (within 1Hz−40Hz and 1 s−5 s at the start of the FLS task) was found to be related to an increase in the left prefrontal HbO cortical activation (from 5 s to 15 s) – see Figures 6F , 7A . Notably, the left prefrontal scalp ERSP changes were partly driven by the EEG frequencies below 8Hz [e.g., theta activity as mechanism of cognitive control (Cavanagh and Frank, 2014 )] as seen in the Figures 6E , G .…”

Operational Modal Analysis of Near-Infrared Spectroscopy Measure of 2-Month Exercise Intervention Effects in Sedentary Older Adults with Diabetes and Cognitive Impairment

“…Then, the pre-processing was followed by conversion to oxyHb and deoxyHb concentration with partial path length factor (function: hmrR_OD2Conc) of 1.0. The last is computation of the hemodynamic response function (HRF) where we aimed at investigating the shape of the HRF without imposing canonical modeling constraints [103]. In prior work, Jahani and colleagues [89] used the time range of 25 to 50 s (i.e., less than 60 s) because in this time range the hemodynamic signal reached steady state.…”

“…Since we did not have subject-specific magnetic resonance imaging to create an individual head model, we used the "Colin27" digital brain atlas (accessed on 12 June 2023 http://mcx.space/wiki/index.cgi?action=browse&id=MMC/Colin27At-lasMesh&oldid=MMC/CollinsAtlasMesh) for the reconstruction of brain activation images with the default regularization scaling parameter = 0.01. Finally, we used the NIRS Brain AnalyzIR Toolbox [104] for statistical analysis that provided a finite impulse response (FIR) model which allowed an unconstrained deconvolution and estimation of the full hemodynamic response [103]. We used default pipeline (nirs.modules.default_modules.single_subject with resampling at 0.5 Hz to reduce computational load) on the raw data, including AR-IRLS for correcting motion and serially correlated errors [105], for the statistical analysis using FIR basis function (nirs.design.basis.FIR with binwidth = 1, nbins = 30, isIRF = false)-the statistical analysis is described in further details in the Statistical analysis subsection.…”

“…The stabilization diagram for modal analysis ('modalsd' in Matlab) allowed selection of stable modes (frequency, damping) that do not change substantially with the change in the size of the model (i.e., estimated number of modes). From our prior computational work on EMA of NVC [18,19,103], a maximum of 30 modes were estimated in the current study using OMA. For OMA, we were interested in the physiological modes based on our prior computational work on EMA of NVC [18,19,103] with frequencies less than 0.2 Hz that were found relevant for fNIRS including LFO (0.05-0.2 Hz) and VLFO (<0.05 Hz) [16,17].…”

“…From our prior computational work on EMA of NVC [18,19,103], a maximum of 30 modes were estimated in the current study using OMA. For OMA, we were interested in the physiological modes based on our prior computational work on EMA of NVC [18,19,103] with frequencies less than 0.2 Hz that were found relevant for fNIRS including LFO (0.05-0.2 Hz) and VLFO (<0.05 Hz) [16,17]. Finally, we applied a multi-stage clustering for automated OMA built on the definitions of the pole distance and modal assurance criterion [108] using the Modal Toolkit (accessed on 12 June 2023 https://code.vt.edu/vibes-lab/modal-analysis).…”