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Independent component analysis of functional networks for response inhibition: Inter-subject variation in stop signal reaction time.

ABSTRACT: Cognitive control is a critical executive function. Many studies have combined general linear modeling and the stop signal task (SST) to delineate the component processes of cognitive control. For instance, by contrasting stop success (SS) and stop error (SE) trials in the SST, investigators examined regional responses to stop signal inhibition. In contrast to this parameterized approach, independent component analysis (ICA) elucidates brain networks subserving cognitive control. In our earlier work of 59 adults performing the SST during fMRI, we characterized six independent components (ICs). However, none of these ICs correlated with stop signal performance, raising questions about their behavioral validity. Here, in a larger sample (n = 100), we identified and explored 23 ICs for correlation with the stop signal reaction time (SSRT), a measure of the efficiency of response inhibition. At a corrected threshold (P < 0.0005), a paracentral lobule-midcingulate network and a left inferior parietal-supplementary motor-somatomotor network showed a positive correlation between SE beta weight and SSRT. In contrast, a midline cerebellum-thalamus-pallidum network showed a negative correlation between SE beta weight and SSRT. These findings suggest that motor preparation and execution prolongs the SSRT, likely via an interaction between the go and stop processes as suggested by the race model. Behaviorally, consistent with this hypothesis, the difference in G and SE reaction times is positively correlated with SSRT across subjects. These new results highlight the importance of cognitive motor regions in response inhibition and support the utility of ICA in uncovering functional networks for cognitive control in the SST.

SUBMITTER: Zhang S

PROVIDER: S-EPMC4545723 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The stop signal task has been used to quantify the human inhibitory control. The inter-subject and intra-subject variability was investigated under the inhibition of human response with a realistic environmental scenario. In present study, we used a battleground scenario where a sniper-scope picture was the background, a target picture was a go signal, and a nontarget picture was a stop signal. The task instructions were to respond on the target image and inhibit the response if a nontarget image appeared. This scenario produced a threatening situation and endorsed the evaluation of how subject's response inhibition manifests in a real situation. In this study, 32 channels of electroencephalography (EEG) signals were collected from 20 participants during successful stop (response inhibition) and failed stop (response) trials. These EEG signals were used to predict two possible outcomes: successful stop or failed stop. The inter-subject variability (between-subjects) and intra-subject variability (within-subjects) affect the performance of participants in the classification system. The EEG signals of successful stop versus failed stop trials were classified using quadratic discriminant analysis (QDA) and linear discriminant analysis (LDA) (i.e., parametric) and K-nearest neighbor classifier (KNNC) and Parzen density-based (PARZEN) (i.e., nonparametric) under inter- and intra-subject variability. The EEG activities were found to increase during response inhibition in the frontal cortex (F3 and F4), presupplementary motor area (C3 and C4), parietal lobe (P3 and P4), and occipital (O1 and O2) lobe. Therefore, power spectral density (PSD) of EEG signals (1-50Hz) in F3, F4, C3, C4, P3, P4, O1, and O2 electrodes were measured in successful stop and failed stop trials. The PSD of the EEG signals was used as the feature input for the classifiers. Our proposed method shows an intra-subject classification accuracy of 97.61% for subject 15 with QDA classifier in C3 (left motor cortex) and an overall inter-subject classification accuracy of 71.66% ± 9.81% with the KNNC classifier in F3 (left frontal lobe). These results display how inter-subject and intra-subject variability affects the performance of the classification system. These findings can be used effectively to improve the psychopathology of attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), schizophrenia, and suicidality.

Project description:BACKGROUND:The P3 component of the event-related potential (ERP) has been particularly useful in alcohol research for identifying endophenotypes of alcohol-use disorder (AUD) risk in sober subjects. However, practice and/or fatigue reduce P3 amplitude, limiting the ability to ascertain acute and adaptive effects of alcohol exposure. Here, we report acute alcohol effects on P3 amplitude and latency using an adaptive stop signal task (aSST). METHODS:One hundred forty-eight non-dependent moderate to heavy social drinkers, ages 21 to 27, participated in two single-blind, alcohol or placebo, counterbalanced sessions approximately 1 week apart. During each session, subjects performed an adaptive stop signal task (aSST) at 1) baseline, 2) upon reaching the target 60 mg/dL breath alcohol concentration or at the equivalent time during the placebo session, and 3) approximately 135 min later while the breath alcohol concentration was clamped. Here, we report on differences between baseline and first subsequent measurements across the experimental sessions. During each aSST run, the stop signal delay (SSD, the time between stop and go signals) adjusted trial-by-trial, based on the subject's performance. RESULTS:The aSST reliably generated a STOP P3 component that did not change significantly with repeated task performance. The pre-infusion SSD distribution was bimodal, with mean values several hundred msec apart (FAST: 153 msec and SLOW: 390 msec). This suggested different response strategies: FAST SSD favoring "going" over "stopping", and SLOW SSD favoring "stopping" over "going". Exposure to alcohol at 60 mg/dL differentially affected the amplitude and latency of the STOP P3 according to SSD group. Alcohol significantly reduced P3 amplitude in the SLOW SSD compared to the FAST SSD group, but significantly increased P3 latency in the FAST SSD compared to the SLOW SSD group. CONCLUSIONS:The aSST is a robust and sensitive task for detecting alcohol-induced changes in inhibition behavior as measured by the P3 component in a within-subject design. Alcohol was associated with P3 component changes, which varied by SSD group, suggesting a differential effect as a function of task strategy. Overall, the data support the potential utility of the aSST in the detection of alcohol response-related AUD risk.

Project description:Previous studies have delineated the neural processes of motor response inhibition during a stop signal task, with most reports focusing on the cortical mechanisms. A recent study highlighted the importance of subcortical processes during stop signal inhibition in 13 individuals and suggested that the subthalamic nucleus (STN) may play a role in blocking response execution (Aron and Poldrack, 2006. Cortical and subcortical contributions to Stop signal response inhibition: role of the subthalamic nucleus. J Neurosci 26, 2424-2433). Here in a functional magnetic resonance imaging (fMRI) study we replicated the finding of greater activation in the STN during stop (success or error) trials, compared to go trials, in a larger sample of subjects (n=30). However, since a contrast between stop and go trials involved processes that could be distinguished from response inhibition, the role of subthalamic activity during stop signal inhibition remained to be specified. To this end we followed an alternative strategy to isolate the neural correlates of response inhibition (Li et al., 2006a. Imaging response inhibition in a stop signal task--neural correlates independent of signal monitoring and post-response processing. J Neurosci 26, 186-192). We compared individuals with short and long stop signal reaction time (SSRT) as computed by the horse race model. The two groups of subjects did not differ in any other aspects of stop signal performance. We showed greater activity in the short than the long SSRT group in the caudate head during stop successes, as compared to stop errors. Caudate activity was positively correlated with medial prefrontal activity previously shown to mediate stop signal inhibition. Conversely, bilateral thalamic nuclei and other parts of the basal ganglia, including the STN, showed greater activation in subjects with long than short SSRT. Thus, fMRI delineated contrasting roles of the prefrontal-caudate and striato-thalamic activities in mediating motor response inhibition.

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Independent component analysis of functional networks for response inhibition: Inter-subject variation in stop signal reaction time.

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