Degree centrality and fractional amplitude of low-frequency oscillations associated with Stroop interference

Abstract

Stroop paradigms are commonly used as an index of attention deficits and a tool for investigating functions of the frontal lobes and other associated structures. Here we investigated the correlation between resting-state functional magnetic imaging (fMRI) measures [degree centrality (DC)/fractional amplitude of low frequency fluctuations (fALFFs)] and Stroop interference. We examined this relationship in the brains of 958 healthy young adults. DC reflects the number of instantaneous functional connections between a region and the rest of the brain within the entire connectivity matrix of the brain (connectome), and thus how much of the node influences the entire brain areas, while fALFF is an indicator of the intensity of regional brain spontaneous activity. Reduced Stroop interference was associated with larger DC in the left lateral prefrontal cortex, left IFJ, and left inferior parietal lobule as well as larger fALFF in the areas of the dorsal attention network and the precuneus. These findings suggest that Stroop performance is reflected in resting state functional properties of these areas and the network. In addition, default brain activity of the dorsal attention network and precuneus as well as higher cognitive processes represented there, and default stronger global influence of the areas critical in executive functioning underlie better Stroop performance.

A Comprehensive Analysis of the Correlations between Resting-State Oscillations in Multiple-Frequency Bands and Big Five Traits

“…In this study, we performed the same preprocessing as previously described (Takeuchi et al, 2015). For the preprocessing of imaging data, we used SPM8 (Wellcome Department of Cognitive Neurology, London, UK 1 ) implemented in Matlab and SPM8’s extension software Data Processing Assistant for Resting-state fMRI (DPARSF; Chao-Gan and Yu-Feng, 2010).…”

“…These methods modified the diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL; Ashburner, 2007) to give functional images with 3.75 × 3.75 × 3.75 mm 3 voxels. Our previously published Supplemental Methods provide detailed information on the aforementioned procedures (Takeuchi et al, 2015). …”

Decreased Subcortical and Increased Cortical Degree Centrality in a Nonclinical College Student Sample with Subclinical Depressive Symptoms: A Resting-State fMRI Study

“…As a result, we obtained an n × n matrix of Pearson correlation coefficients between any pair of voxels to construct the whole-brain FC matrix for each participant. Then, individual correlation matrices were transformed into a Z -score matrix using Fisher’s r-to-z transformation to improve normality (Takeuchi et al, 2015). The weighted DC strength of a voxel as the sum of the connections ( Z -values) between a given brain voxel and all other voxels was then computed.…”

“…As previously described (Li et al, 2016), to eliminate possible spurious connectivity, we used thresholded the Pearson correlation coefficient at r > 0.25 by thresholding each correlation at P ≤ 0.001. Furthermore, standardized weighted DC maps were acquired by subtracting the mean value, and then dividing by the standard deviation within the whole gray matter mask (Zuo et al, 2012; Takeuchi et al, 2015). Finally, the resulting DC maps were spatially smoothed with a 6-mm full width at half maximum (FWHM) Gaussian kernel.…”

Resting-State fMRI Associated with Stop-Signal Task Performance in Healthy Middle-Aged and Elderly People

“…Understanding these relationships may have practical clinical applications, especially to infer how well an individual will perform a task if their on-task brain activity is somehow difficult to acquire. Thus, recent studies have investigated the relationship between RS-fMRI and task performance using various cognitive control tasks, including the N-back working memory test (Evers et al, 2012; Sala-Llonch et al, 2012), Stroop task (Evers et al, 2012; Takeuchi et al, 2015), Eriksen flanker task (Mennes et al, 2013), and California verbal learning test (Ystad et al, 2010). In addition, RS-fMRI has also been shown to be correlated to stopping ability (Tian et al, 2012; Hu et al, 2014), which is of main interest in this study.…”