Dataset Information

BOLD data representing activation and connectivity for rare no-go versus frequent go cues.

ABSTRACT: The neural circuitry underlying response control is often studied using go/no-go tasks, in which participants are required to respond as fast as possible to go cues and withhold from responding to no-go stimuli. In the current task, response control was studied using a fully counterbalanced design in which blocks with a low frequency of no-go cues (75% go, 25% no-go) were alternated with blocks with a low frequency of go cues (25% go, 75% no-go); see also "Segregating attention from response control when performing a motor inhibition task: Segregating attention from response control" [1]. We applied a whole brain corrected, paired t-test to the data assessing for regions differentially activated by low frequency no-go cues relative to high frequency go cues. In addition, we conducted a generalized psychophysiological interaction analysis on the data using a right inferior frontal gyrus seed region. This region was identified through the BOLD response t-test and was chosen because right inferior gyrus is highly implicated in response inhibition.

SUBMITTER: Meffert H

PROVIDER: S-EPMC4761693 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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BOLD data representing activation and connectivity for rare no-go versus frequent go cues.

Meffert Harma H Hwang Soonjo S Nolan Zachary T ZT Chen Gang G Blair James R JR

Data in brief 20160209

The neural circuitry underlying response control is often studied using go/no-go tasks, in which participants are required to respond as fast as possible to go cues and withhold from responding to no-go stimuli. In the current task, response control was studied using a fully counterbalanced design in which blocks with a low frequency of no-go cues (75% go, 25% no-go) were alternated with blocks with a low frequency of go cues (25% go, 75% no-go); see also "Segregating attention from response con ...[more]

PMID: 26955650

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Project description:Functional connectivity MRI, based on Blood-Oxygenation-Level-Dependent (BOLD) signals, is typically performed while the subject is at rest. On the other hand, BOLD is also widely used in physiological imaging such as cerebrovascular reactivity (CVR) mapping using hypercapnia (HC) as a modulator. We therefore hypothesize that hypercapnia BOLD data can be used to extract FC metrics after factoring out the effects of the physiological modulation, which will allow simultaneous assessment of neural and vascular function and may be particularly important in populations such as aging and cerebrovascular diseases. The present work aims to systematically examine the feasibility of hypercapnia BOLD-based FC mapping using three commonly applied analysis methods, specifically dual-regression Independent Component Analysis (ICA), region-based FC matrix analysis, and graph-theory based network analysis, in a large cohort of 170 healthy subjects ranging from 20 to 88 years old. To validate the hypercapnia BOLD results, we also compared these FC metrics with those obtained from conventional resting-state data. ICA analysis of the hypercapnia BOLD data revealed FC maps that strongly resembled those reported in the literature. FC matrix using region-based analysis showed a correlation of 0.97 on the group-level and 0.54?±?0.10 on the individual-level, when comparing between hypercapnia and resting-state results. Although the correspondence on the individual-level was moderate, this was primarily attributed to variations intrinsic to FC mapping, because a corresponding resting-vs-resting comparison in a sub-cohort (N?=?39) revealed a similar correlation of 0.57?±?0.09. Graph-theory computations were also feasible in hypercapnia BOLD data and indices of global efficiency, clustering coefficient, modularity, and segregation were successfully derived. Hypercapnia FC results revealed age-dependent differences in which within-network connections generally exhibited an age-dependent decrease while between-network connections showed an age-dependent increase.

Dataset Information

BOLD data representing activation and connectivity for rare no-go versus frequent go cues.

Publications

BOLD data representing activation and connectivity for rare no-go versus frequent go cues.

Similar Datasets

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