| Objective:We tested the correlation relationships between functional connectivity and trait approach/avoidance motivation, behavior performance on healthy young adults.Subjects and Methods:Based on BAS/BIS questionnaire,45out of618freshmen were of significant motivation traits, and27subjects of them were included in our study at last. The participants were arranged to complete a flanker cognitive test2hours before or after the image colletion, and their performance which described as congruent condition reaction time and incongurent condition reaction time were recorded. Resting-state fMRI scan and sagittal structural images were all collected using a GE3.0T Signa HDX scanner. During the scanning, all subjects were explicitly instructed to keep their eyes closed, relax, as motionless as possible, and think of nothing. All preprocessing steps were carried out using the DPARSF software based on Matlab. The preprocessing of rs-fMRI data including slice timing, realignment, spatial normalization to the MNI space, resampling to to3×3×3mm3cubic voxels, smoothed with a6mm full width at half maximum, a band-pass frequency filter (0.01-0.08Hz, to reduce low-frequency drift and high-frequency noise). We choosed three brain networks, including the default mode network, the dorsal attention network and the frontoparietal control network, they were all widely used, and closely related to most tasks. The functional connectivity analysis method was used to replicate the three networks. Six seed ROIs were defined and used to produce the three networks:for the default mode network, MPFC(-1,47,-4) and PCC(-5,-49,40), the dosal attention network, MT+(-48,-70,0) and SPL(-27,-52,57), and the frontoparietal control network, RLPFC(-36,57,9) and aIPL(-52,-49,47). For each participant, the mean BOLD signal time course was extracted from each of the six spherical ROIs, which centered on the foregoing coordinated, with a radius of8mm. Then calculated the average time series. In the removal of the covariates(including head movement, the whole brain mean value, white matter, cerebrosphinal fluid signal), each of these time courses was correlated with the time couses for every voxel in the brain, the correlation coefficients were then converted to z-values using Fisher’s r-to-z transformation. Whole-brain voxel-wise z-maps were then subjected to one-sample t test to assess statistical significance across participants at the group level (p<0.05, FWE correction). We then derived conjunction maps for each network where only those voxels that were significant in both t-maps. The obtained three networks were again set ROIs, extracted their time couses for each voxel, and calculated the average time series, then correlation analysis were done between any two of the three ROIs. We finally got the z value. Stastical analyzes were performed using SPSS16.0software, the correlation relationships between functional connectivity and trait approach/avoidance motivation, behavior performance were tested.Results:After the affected factors (head motion, behavioral performance and machine noise) were ruled out,27subjects were ultimately included. The three networks that included the default mode network, the dorsal attention network, and the frantoparietal control network, which we replicated through seed functional correlation method were highly consistent with other reports. Using ROI-wise functional connection analysis, we got the strength of the connection between the networks.We found that the strength of the connection between DAN-FPN was significantly correlated with task performance. BAS score was significantly correlated with the strength between DMN-FPN, and DAN-FPN, BIS score was significantly correlated with DMN-FPN.Conclusion:1. The strength of connection between DAN-FPN is significantly correlated with task performance, and not affected by motivation traits.2. Trait approach motivation is significantly correlated with DMN-FPN and DAN-FPN, trait avoidance motivation is significantly correlated with DMN-FPN. |
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