| In 1935, the Stroop effect was found by Stroop, an American psychologist. In the color-naming experiment, the responses to incongruent stimuli (I, e.g., the word RED printed in blue) are slower and less accurate in comparison to neutral stimuli (C, e.g., the string XXXX printed in red). This decline in performance is termed the Stroop effect. It has been widely used to explore the nature of attention in cognitive psychology and cognitive neuroscience. There are large individual differences in Stroop effect. These differences have mainly been found to be embodied in gender, age, hemispheric differences and language difference.Recent applications of resting state functional magnetic resonance imaging (RS-fMRI) indicate that individual differences in "intrinsic" functional organization of human brain can effectively index the individual differences in cognitive activity. According to prevailing theories of brain function, the connectivity, as well as the modularity, is very important when we explore the neural basis of cognitive activity (Mesulam,1998). ReHo may be used to explore the modularity which describes specialized processing within local brain regions. ReHo was performed on a voxel-by-voxel basis by calculating Kendall’s coefficient of concordance of the time series of a given cluster of neighboring voxels. Here, cubic clusters of 27 voxels (corner connection) were used and the ReHo value of every cubic cluster was assigned to the central voxel. The conventional functional connectivity can be used to explore the connectivity which refers to contemporaneous information flow across large-scale distributed networks. Resting state functional connectivity (RSFC) is used to detect synchronization between brain regions across time in task-free or "rest" setting.In the current study, we would investigate the special local brain regions and specific functional circuits (both regional homogeneity and functional connectivity patterns) at rest that can predict individual difference in Stroop effect.In the present study, Forty-four healthy, right-handed college students (34 females; mean age=18.9 years, SD=0.8) from Southwest University, were recruited for this study as paid participants and their RS-fMRI data and behavioral data were collected. We performed a correlation analysis between participants’ performance Stroop effect and their ReHos to uncover potential core regions that could account for individual variation in cognitive control ability; then we defined such core regions as seeds and calculated the RSFC strength between each seed and other voxels to construct the brain network associated with the core regions, and then we assessed the predictive power of these connectivity strengths for the Stroop effect of each subject.ReHo-behavior analysis showed significantly positive corrections in the left inferior frontal gyrus (LIFG), the left insula and the ventral anterior cingulate cortex (vACC) and significantly negative correction in the left precentral gyrus. Results from RSFC-behavior correlation analysis showed significant correction between Stroop effect and the FC strength in vACC/left Precuneus, left Insula/right putmen, left Insula/ACC, left precentral gryrus/left middle frontal gyrus (LMFG), left precentral gryrus/left precentralgyrus, left precentral gyrus/supplementary motor areas (SMA), LIFG/right middle occipital gyrus (RMOG) and LIFG/cuneus. The ReHo and function connectivity strengths of the four observed regions together accounted for 77.5% of individual difference in Stroop effect.The present findings support that LIFG is critical in resolving competition among incompatible characterizations by adjusting the early stage of information processing and the regions associated with response inhibition also contribute to Stroop performance. It also confirms the notion that the DMN is important in goal-directed behavior and/or mental effort during cognitive tasks. Furthermore, the RS-fMRI signals could be used to predicte the individual difference in Stroop effect. |
PDF Full Text Request