Anticipating conflict: Neural correlates of a Bayesian belief and its motor consequence

Hu, Sien; Ide, Jaime S.; Zhang, Sheng; Li, Chiang‐Shan R.

doi:10.1016/j.neuroimage.2015.06.032

Cited by 53 publications

(97 citation statements)

References 143 publications

(164 reference statements)

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“…The results for the current cohort showed that, across all participants, GoRT was significantly correlated with p(Stop) (r ϭ 0.9665, p ϭ 0.0000, df ϭ 112), indicating a strong sequential effect, and the stop error rate was inversely correlated with p(Stop) (r ϭ Ϫ0.9080, p ϭ 0.0000, df ϭ 112), both of which were consistent with the predictions of the Bayesian model (see Fig. 1 in Hu et al, 2015).…”

Section: Behavioral Performancesupporting

confidence: 77%

“…Conflict anticipation was quantified by the probability of a stop trial computed from a Bayes optimal decision-making model, which assumes that participants choose whether to "go" on the basis of accumulating evidence. This model explained stopping behavior in the stop signal task and predicted increased stop error rate with increasing stop signal delay as well as faster stop error than correct go response time Hu et al, 2015). In combination with the imaging data, we reported that activations of righthemispheric SFG to conflict anticipation are negatively correlated with both SSRT and motor urgency.…”

Section: Introductionsupporting

confidence: 52%

“…All participants performed a stop signal task or SST (Li et al, 2009a;Hendrick et al, 2010;Hu and Li, 2012;Winkler et al, 2013), in which go and stop trials were randomly intermixed in presentation with an intertrial interval of 2 s. A fixation dot appeared on screen to signal the beginning of each trial. After an introductory period varying from 1 to 5 s (uniform distribution), the dot became a circle, the "go" signal, prompting participants to press a button quickly.…”

Section: Methodsmentioning

confidence: 99%

“…Our goal was to identify the neural correlates of conflict anticipation or the Bayesian estimate of a stop signal. We distinguished four trial outcomes, go success (GS), go error (GE), stop success (SS), and stop error (SE), and modeled BOLD signals by convolving the onsets of the fixation point (the beginning) of each trial with a canonical hemodynamic response function (HRF) and the temporal derivative of the canonical HRF (Friston et al, 1995b;Hu and Li, 2012). Realignment parameters in all six dimensions were entered in the model.…”

Section: Methodsmentioning

confidence: 99%

“…We included the following variables as parametric modulators in the model: p(Stop) of GS trials, SSD of SS trials, p(Stop) of SS trials, SSD of SE trials, and p(Stop) of SE trials, in that order. Inclusion of these variables as parametric modulators improved model fit (Büchel et al, 1996;Cohen, 1997;Büchel et al, 1998;Hu et al, 2015) and, specifically, the parametric modulator of p(Stop) allowed us to examine the neural correlates of conflict anticipation. Serial autocorrelation of the time series was corrected by a first degree autoregressive or AR(1) model (Friston et al, 2000;Della-Maggiore et al, 2002).…”

Section: Methodsmentioning

confidence: 99%

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The Right Superior Frontal Gyrus and Individual Variation in Proactive Control of Impulsive Response

et al. 2016

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A hallmark of cognitive control is the ability to rein in impulsive responses. Previously, we used a Bayesian model to describe trial-by-trial likelihood of the stop signal or p(Stop) and related regional activations to p(Stop) to response slowing in a stop signal task. Here, we characterized the regional processes of conflict anticipation in association with intersubject variation in impulse control in 114 young adults. We computed the stop signal reaction time (SSRT) and a measure of motor urgency, indexed by the reaction time (RT) difference between go and stop error trials or "GoRT Ϫ SERT," where GoRT is the go trial RT and SERT is the stop error RT. Motor urgency and SSRT were positively correlated across subjects. A linear regression identified regional activations to p(Stop), each in correlation with SSRT and motor urgency. We hypothesized that shared neural activities mediate the correlation between motor urgency and SSRT in proactive control of impulsivity. Activation of the ventromedial prefrontal cortex, posterior cingulate cortex and right superior frontal gyrus (SFG) during conflict anticipation correlated negatively with the SSRT. Activation of the right SFG also correlated negatively with GoRT Ϫ SERT. Therefore, activation of the right SFG was associated with more efficient response inhibition and less motor urgency. A mediation analysis showed that right SFG activation to conflict anticipation mediates the correlation between SSRT and motor urgency bidirectionally. The current results highlight a specific role of the right SFG in translating conflict anticipation to the control of impulsive response, which is consistent with earlier studies suggesting its function in action restraint.

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Section: Behavioral Performancesupporting

confidence: 77%

Section: Introductionsupporting

confidence: 52%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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The Right Superior Frontal Gyrus and Individual Variation in Proactive Control of Impulsive Response

et al. 2016

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The independent influences of age and education on functional brain networks and cognition in healthy older adults

Perry

Wang

Kochan

et al. 2017

Human Brain Mapping

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Healthy aging is accompanied by a constellation of changes in cognitive processes and alterations in functional brain networks. The relationships between brain networks and cognition during aging in later life are moderated by demographic and environmental factors, such as prior education, in a poorly understood manner. Using multivariate analyses, we identified three latent patterns (or modes) linking resting-state functional connectivity to demographic and cognitive measures in 101 cognitively normal elders. The first mode (P = 0.00043) captures an opposing association between age and core cognitive processes such as attention and processing speed on functional connectivity patterns. The functional subnetwork expressed by this mode links bilateral sensorimotor and visual regions through key areas such as the parietal operculum. A strong, independent association between years of education and functional connectivity loads onto a second mode (P = 0.012), characterized by the involvement of key hub regions. A third mode (P = 0.041) captures weak, residual brain-behavior relations. Our findings suggest that circuits supporting lower level cognitive processes are most sensitive to the influence of age in healthy older adults. Education, and to a lesser extent, executive functions, load independently onto functional networks-suggesting that the moderating effect of education acts upon networks distinct from those vulnerable with aging. This has important implications in understanding the contribution of education to cognitive reserve during healthy aging. Hum Brain Mapp 38:5094-5114, 2017. © 2017 Wiley Periodicals, Inc.

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Machine learning approaches linking brain function to behavior in the ABCD STOP task

Yuan

Hahn

Allgaier

et al. 2022

Human Brain Mapping

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The stop‐signal task (SST) is one of the most common fMRI tasks of response inhibition, and its performance measure, the stop‐signal reaction‐time (SSRT), is broadly used as a measure of cognitive control processes. The neurobiology underlying individual or clinical differences in response inhibition remain unclear, consistent with the general pattern of quite modest brain–behavior associations that have been recently reported in well‐powered large‐sample studies. Here, we investigated the potential of multivariate, machine learning (ML) methods to improve the estimation of individual differences in SSRT with multimodal structural and functional region of interest‐level neuroimaging data from 9‐ to 11‐year‐olds children in the ABCD Study. Six ML algorithms were assessed across modalities and fMRI tasks. We verified that SST activation performed best in predicting SSRT among multiple modalities including morphological MRI (cortical surface area/thickness), diffusion tensor imaging, and fMRI task activations, and then showed that SST activation explained 12% of the variance in SSRT using cross‐validation and out‐of‐sample lockbox data sets ( n = 7298). Brain regions that were more active during the task and that showed more interindividual variation in activation were better at capturing individual differences in performance on the task, but this was only true for activations when successfully inhibiting. Cortical regions outperformed subcortical areas in explaining individual differences but the two hemispheres performed equally well. These results demonstrate that the detection of reproducible links between brain function and performance can be improved with multivariate approaches and give insight into a number of brain systems contributing to individual differences in this fundamental cognitive control process.

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Anticipating conflict: Neural correlates of a Bayesian belief and its motor consequence

Cited by 53 publications

References 143 publications

The Right Superior Frontal Gyrus and Individual Variation in Proactive Control of Impulsive Response

The Right Superior Frontal Gyrus and Individual Variation in Proactive Control of Impulsive Response

The independent influences of age and education on functional brain networks and cognition in healthy older adults

Machine learning approaches linking brain function to behavior in the ABCD STOP task

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