Subjects: Psychology >> Cognitive Psychology submitted time 2019-08-28
Abstract： The noninvasive functional magnetic resonance imaging (fMRI) technique has been a crucial method for brain imaging research. However, the divergence existing in the plethora of datasets acquired in the labs around the world limited the rate of progress of brain research. To address this issue, international brain scientists jointly proposed a framework named with the Brain Imaging Data Structure (BIDS) for organizing and describing neuroimaging data, and developed BIDS APPs for analyzing neuroimaging data organized in compliance with the BIDS. The present paper briefly introduced BIDS and the fMRI processing pipeline with BIDS APPs. Moreover, we discussed how to reasonably integrate the preprocessing pipeline with BIDS APPs (MRIQC and fmriprep) and the following statistical analysis. Specifically, we suggested that brain researchers should first organize their fMRI data in keeping with BIDS, and then use MRIQC APP to do automated quality control for both anatomical and functional MRI data organized in compliance with the BIDS. After eliminating the “bad data” (e.g., FD>0.2 mm), fmriprep can then be used to preprocess the “good data.” The fmriprep APP introduced two new preprocessing methods (ComCor and ICA-AROMA), which have been suggested to be effective in increasing sensitivity to group-level activation. However, it should be noted that the choice of fmriprep preprocessing methods determine which regressors should be included in the following individual general linear model (GLM). If only ComCor method was used, both the head motion related-noise (6 motion parameters) and cardiac and respiratory related-noise should be used as nuisance regressors in the following GLM analysis. If both ComCor and ICA-AROMA method were used, only regress cardiac and respiratory related-noise but not the head motion related-noise should be included in the following GLM analysis. This is because that ICA-AROMA method has removed the noise related to head motion in the preprocessed data, whereas ComCor did not. Therefore, regressing out motion-related variables in the GLM may reintroduce motion artifacts. We suggest that in the face of the emerging BIDS and BIDS APPs, brain researchers need to concern how to combine it with the traditional statistical software optimistically in order to attain a better statistical power.
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Cognitive Psychology submitted time 2019-05-24
Abstract： Cognitive automaticity is an inevitable road for human learning and improving. The traditional dichotomy view classifies cognitive processes intro controlled or automatic processes according to certain features of cognitive processes (e.g., unconsciousness). However, these features are not universal but up to the experimental paradigms used by researchers. New viewpoints based on the attentional resources theory were developed, taking the controlled-automatic process as a continuous dimension and regarding decreases of attentional resources as the central feature of the development of automaticity. Compared with dichotomy, the gradual view accords better with empirical findings. Further, the gradual view can be applied in both cognitive information processing and the stages model of skill acquisition theory, which confirmed the universality of gradual view.
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