Neural oscillatory responses to performance monitoring differ between high- and low-impulsive individuals, but are unaffected by TMS

Neural indices of heritable impulsivity: Impact of the COMT Val158Met polymorphism on frontal beta power during early motor preparation

Studies of COMT Val158Met suggest that the neural circuitry subserving inhibitory control may be modulated by this functional polymorphism altering cortical dopamine availability, thus giving rise to heritable differences in behaviors. Using an anatomically-constrained magnetoencephalography method and stratifying the sample by COMT genotype, from a larger sample of 153 subjects, we examined the spatial and temporal dynamics of beta oscillations during motor execution and inhibition in 21 healthy Met158/Met158 (high dopamine) or 21 Val158/Val158 (low dopamine) genotype individuals during a Go/NoGo paradigm. While task performance was unaffected, Met158 homozygotes demonstrated an overall increase in beta power across regions essential for inhibitory control during early motor preparation (∼100 ms latency), suggestive of a global motor "pause" on behavior. This increase was especially evident on Go trials with slow response speed and was absent during inhibition failures. Such a pause could underlie the tendency of Met158 allele carriers to be more cautious and inhibited. In contrast, Val158 homozygotes exhibited a beta drop during early motor preparation, indicative of high response readiness. This decrease was associated with measures of behavioral disinhibition and consistent with greater extraversion and impulsivity observed in Val homozygotes. These results provide mechanistic insight into genetically-determined interindividual differences of inhibitory control with higher cortical dopamine associated with momentary response hesitation, and lower dopamine leading to motor impulsivity.

Impulsivity Classification Using EEG Power and Explainable Machine Learning

Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified [Formula: see text]-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power ([Formula: see text]-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model's output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.

Neural Encoding of the Reliability of Directional Information During the Preparation of Targeted Movements

Visual information about the location of an upcoming target can be used to prepare an appropriate motor response and reduce its reaction time. Here, we investigated the brain mechanisms associated with the reliability of directional information used for motor preparation. We recorded brain activity using magnetoencephalography (MEG) during a delayed reaching task in which a visual cue provided valid information about the location of the upcoming target with 50, 75, or 100% reliability. We found that reaction time increased as cue reliability decreased and that trials with invalid cues had longer reaction times than trials with valid cues. MEG channel analysis showed that during the late cue period the power of the beta-band from left mid-anterior channels, contralateral to the responding hand, correlated with the reliability of the cue. This effect was source localized over a large motor-related cortical and subcortical network. In addition, during invalid-cue trials there was a phasic increase of theta-band power following target onset from left posterior channels, localized to the left occipito-parietal cortex. Furthermore, the theta-beta cross-frequency coupling between left mid-occipital and motor cortex transiently increased before responses to invalid-cue trials. In conclusion, beta-band power in motor-related areas reflected the reliability of directional information used during motor preparation, whereas phasic theta-band activity may have signaled whether the target was at the expected location or not. These results elucidate mechanisms of interaction between attentional and motor processes.

Neural oscillatory responses to performance monitoring differ between high- and low-impulsive individuals, but are unaffected by TMS

Higher impulsivity may arise from neurophysiological deficits of cognitive control in the prefrontal cortex. Cognitive control can be assessed by time‐frequency decompositions of electrophysiological data. We aimed to clarify neuroelectric mechanisms of performance monitoring in connection with impulsiveness during a modified Eriksen flanker task in high‐ (n = 24) and low‐impulsive subjects (n = 21) and whether these are modulated by double‐blind, sham‐controlled intermittent theta burst stimulation (iTBS). We found a larger error‐specific peri‐response beta power decrease over fronto‐central sites in high‐impulsive compared to low‐impulsive participants, presumably indexing less effective motor execution processes. Lower parieto‐occipital theta intertrial phase coherence (ITPC) preceding correct responses predicted higher reaction time (RT) and higher RT variability, potentially reflecting efficacy of cognitive control or general attention. Single‐trial preresponse theta phase clustering was coupled to RT in correct trials (weighted ITPC), reflecting oscillatory dynamics that predict trial‐specific behavior. iTBS did not modulate behavior or EEG time‐frequency power. Performance monitoring was associated with time‐frequency patterns reflecting cognitive control (parieto‐occipital theta ITPC, theta weighted ITPC) as well as differential action planning/execution processes linked to trait impulsivity (frontal low beta power). Beyond that, results suggest no stimulation effect related to response‐locked time‐frequency dynamics with the current stimulation protocol. Neural oscillatory responses to performance monitoring differ between high‐ and low‐impulsive individuals, but are unaffected by iTBS.