Motes, M. | bioRxiv Science

bioRxiv2020

THE INFLUENCE OF PRE-SUPPLEMENTARY MOTOR AREA TARGETED HIGH-DEFINITION TRANSCRANIAL DIRECT CURRENT STIMULATION ON INHIBITORY CONTROL

AO_SCPLOWBSTRACTC_SCPLOWThe neural underpinnings of inhibitory control, an executive cognitive control function, has been a topic of interest for several decades due to both its clinical significance and the maturation of cognitive science disciplines. Behavioral, imaging, and electrophysiological studies suggest that the pre-supplementary motor area (preSMA) serves as a primary hub in a network of regions engaged in inhibition. High-definition transcranial direct current stimulation (HD-tDCS) allows us to modulate neural function to assess cortical contribution to cognitive functioning. The present study targeted HD-tDCS modulation of preSMA to affect inhibition. Participants were randomly assigned to receive 20 min of Sham, Anodal, or Cathodal stimulation prior to completing a semantically cued go/nogo task while electroencephalography (EEG) data were recorded. Both anodal and cathodal stimulation improved inhibitory performance as measured by faster reaction times and increased (greater negative) N2 event-related potentials (ERPs). In contrast, the Sham group did not show such changes. We did not find support for the anodal/cathodal dichotomy for HD neural stimulation. These findings constitute an early investigation into role of the preSMA in inhibitory control and in exploring application of HD-tDCS to the preSMA in order to improve inhibitory control.

neuroscience↗

bioRxiv2019

Identification of selection and inhibition components in a go/nogo task from EEG spectra using a machine learning model

AO_SCPLOWBSTRACTC_SCPLOWPrior Go/NoGo studies have localized specific regions and EEG spectra for which traditional approaches have distinguished between Go and NoGo conditions. A more detailed characterization of the spatial distribution and timing of the synchronization of frequency bands would contribute substantially to the clarification of neural mechanisms that underlie performance of the Go/NoGo task. The present study used a machine learning approach to learn the features that distinguish between ERSPs involved in selection and inhibition in a Go/NoGo task. A neural network classifier was used to predict task conditions for each subject to characterize ERSPs associated with Go versus NoGo trials. The final model accurately identified individual task conditions at an overall rate of 92%, estimated by 5-fold cross-validation. The detailed accounting of EEG time-frequency patterns localized to brain sources (i.e., thalamus, preSMA, orbitofrontal cortex, and superior parietal cortex) provides elaboration on previous findings from fMRI and EEG studies and more information about EEG power changes in multiple frequency bands (i.e., primarily theta power increase, alpha decreases, and beta increases and decreases) within these regions underlying the selection and inhibition processes engaged in the Go and NoGo trials. This extends previous findings, providing more information about neural mechanisms underlying selection and inhibition processes engaged in the Go and NoGo trials, respectively, and may offer insight into therapeutic uses of neuromodulation in neural dysfunction.

neuroscience↗