Current Location:home > Browse
Your conditions: Chao-Gan Yan(3)

1. chinaXiv:201711.00276 [pdf]

DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging

Chao-Gan Yan; Xin-Di Wang; Xi-Nian Zuo; Yu-Feng Zang
Subjects: Psychology >> Applied Psychology

Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.

submitted time 2017-11-06 Hits8151Downloads2342 Comment 0

2. chinaXiv:201711.00277 [pdf]

Linear trend of resting-state fMRI time series

Xin-Di Wang; Chao-Gan Yan; Yu-Feng Zang
Subjects: Psychology >> Applied Psychology

Although爈inear trend removing has often been implemented as a routine preprocessing step in resting-state functional magnetic resonance imaging (RS-fMRI) data analysis, the爏patial distribution爋f the magnitude of linear trend is still unclear. Further, it is interesting whether there will be any difference of the linear trend magnitude between different resting-states. For the first aim, we analyzed 5 RS-fMRI datasets from 5 different scanners (namely Beijing-Simens-3T, Cambridge-Siemens-3T, CCBD-GE750-3T, Milwaukee-GE-3T, and Oulu-GE-1.5T). One-sample t-tests on the regression coefficient (i.e., the magnitude of linear trend) were performed for each datasets. For the second aim, we used 2 datasets in each of which different states were compared, one containing eyes-open resting-state (EO-RS) vs. eyes-closed resting-state (EC-RS) and the other containing two steady-state tasks, i.e.,爎eal-time finger force feedback?RT-FFF) and sham finger force feedback (S-FFF) tasks. Paired t-tests were performed between EO-RS and EC-RS, and between RT-FFF and S-FFF. One-sample t-tests showed that the spatial pattern of linear trend of RS-fMRI time series were quite different between different manufactures. The 3T SIEMENS scanners showed positive linear trend in almost all part of the brain, while GE scanners showed primarily negative linear trend in most part of the brain. Paired t-tests showed some differences between paired conditions; differences between EO-RS and EC-RS were mainly in cuneus and eyeballs, and differences between RT-FFF and S-FFF were found in the thalamus, anterior cingulate gyrus, and right sensorimotor cortex. The current study indicated that, while the manufacturer-dependent linear trend of RS-fMRI time series were mostly scanner-related noise, the linear trend may also be physiological noise (eyeballs) or even physiologically meaningful, especially during steady-state tasks.

submitted time 2017-11-06 Hits6026Downloads1309 Comment 0

3. chinaXiv:201711.00274 [pdf]

Concordance Among Indices of Intrinsic Brain Function:Inter-Individual Variation and Temporal Dynamics Perspectives

Chao-Gan Yan; Zhen Yang; Stanley J. Colcombe; Xi-Nian Zuo; Michael P. Milham
Subjects: Psychology >> Experimental Psychology

Various resting-state fMRI (R-fMRI) measures have been developed to characterize intrinsic brain activity. While each of these measures has gained a growing presence in the literature, questions remain regarding the common and unique aspects these indices capture. The present work provided a comprehensive examination of inter-individual variation and intra-individual temporal variation for commonly used measures, including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, network centrality and global signal correlation. Regardless of whether examining intra-individual or inter-individual variation, we found that these definitionally distinct R-fMRI indices tend to exhibit a relatively high degree of covariation. When taken as a measure of intrinsic brain function, inter-individual differences in concordance for R-fMRI indices appeared to be stable, and negatively related to age (i.e., functional concordance among indices decreases with age). To understand the functional significance of concordance, we noted that higher concordance was generally associated with higher strengths of R-fMRI indices, regardless of whether looking through the lens of inter-individual (i.e., high vs. low concordance participants) or intra-individual (i.e., high vs. low concordance states identified via temporal dynamic analyses) differences. Finally, temporal dynamics analyses also revealed that high concordance states are characterized by increased within- and between-network functional connectivity, suggesting more general variations in network integration and segregation. The current study draws attention to questions regarding how to select an R-fMRI index for usage in a given study, as well as how to compare findings across studies that examine inter-individual or group differences using different indices. Additionally, our work suggests global neural signals exist in the brain, and their spontaneous variations over time result in fluctuations in the connectedness of brain regions.

submitted time 2017-11-06 Hits4635Downloads1462 Comment 0

  [1 Pages/ 3 Totals]