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Regional homogeneity (ReHo) quantifies spatially local synchronization of resting state fMRI signal and has been applied to lots of clinic studies. Accumulating evidences demonstrated that the synchronization between spatially distinct brain regions, i.e. functional connectivity, can be remarkably influenced if the global mean time course is regressed out, namely global signal regression (GSR). Very recently, it was reported GSR reduces the test-retest reliability of ReHo, and reduces the positive correlation between ReHo and head motion. In this study, we were interested in two questions: 1) how GSR affects the raw ReHo values and its spatial distribution over the brain; 2) how GSR affects the differences of ReHo between two resting states, eyes open (EO) and eyes closed (EC), in healthy individuals. We found that the ReHo values were reduced by GSR but the spatial distribution of ReHo was not changed remarkably. In addition, split-half reproducibility analysis showed reproducible ReHo difference between EO and EC in some areas (e.g., thalamus and caudate) only with GSR, but showed reproducible ReHo difference in some other area (right temporal pole) only without GSR. The effects of GSR were almost independent of regression of other nuisance covariates. Our results suggest that the influences of GSR on ReHo are remarkable, reliable and complex. For the between-condition comparison, the GSR effects are region specific. We suggest that, for application studies using ReHo approach, it would be helpful to report results both with and without GSR.

Opening and closing the eyes are fundamental behaviors for directing attention to the external versus internal world. However, it remains unclear whether the states of eyes-open (EO) relative to eyes-closed (EC) are associated with different topological organizations of functional neural networks for exteroceptive and interoceptive processing (processing the external world and internal state, respectively). Here, we used resting-state functional magnetic resonance imaging and neural network analysis to investigate the topological properties of functional networks of the human brain when the eyes were open versus closed. The brain networks exhibited higher cliquishness and local efficiency, but lower global efficiency during the EO state compared to the EC state. These properties suggest an increase in specialized information processing along with a decrease in integrated information processing in EO (vs. EC). More importantly, the "exteroceptive" network, including the attentional system (e.g., superior parietal gyrus and inferior parietal lobule), ocular motor system (e.g., precentral gyrus and superior frontal gyrus), and arousal system (e.g., insula and thalamus), showed higher regional nodal properties (nodal degree, efficiency and betweenness centrality) in EO relative to EC. In contrast, the "interoceptive" network, composed of visual system (e.g., lingual gyrus, fusiform gyrus and cuneus), auditory system (e.g., Heschl's gyurs), somatosensory system (e.g., postcentral gyrus), and part of the default mode network (e.g., angular gyrus and anterior cingulate gyrus), showed significantly higher regional properties in EC vs. EO. In addition, the connections across sensory modalities were altered by volitional eye opening. The synchronicity between the visual system and the motor, somatosensory and auditory systems, characteristic of EC, was attenuated in EO. Further, the connections between the visual system and the attention, arousal and subcortical systems were increased in EO. These results may indicate that EO leads to a suppression of sensory modalities (other than visual) to allocate resources to exteroceptive processing. Our findings suggest that the topological organization of human brain networks dynamically switches corresponding to the information processing modes as we open or close our eyes. (C) 2014 Published by Elsevier Inc.

A lot of functional magnetic resonance imaging (fMRI) studies have indicated that Granger causality analysis (GCA) is a suitable method to reveal causal effect among brain regions. Based on another MATLAB GUI toolkit, Resting State fMRI Data Analysis Toolkit (REST), we implemented GCA on MATLAB as a graphical user interface (GUI) toolkit. This toolkit, namely REST-GCA, could output both the residual-based F and the signed-path coefficient. REST-GCA also intergrates a programme that could transform the distribution of residual-based F to approximately normal distribution and then permit parametric statistical inference at group level. Using REST-GCA, we tested the causal effect of the right frontal-insular cortex (rFIC) onto each voxel in the whole brain, and vice versa, each voxel in the whole brain on the rFIC, in a voxel-wise way in a resting-state fMRI dataset from 30 healthy college students. Using Jarque-Bera goodness-of-fit test and the Lilliefors goodness-of-fit test, we found that the transformation from F to F' and the further standardization from F' to Z score substantially improved the normality. The results of one sample t-tests on Z score showed bi-directional positive causal effect between rFIC and the dorsal anterior cingulate cortex (dACC). One sample t-tests on the signed-path coefficients showed positive causal effect from rFIC to dACC but negative from dACC to rFIC. All these results indicate that REST-GCA may be useful toolkit for caudal analysis of fMRI data.

The multi-scan resting state fMRI (rs-fMRI) dataset was recently released; thus the test-retest (TRT) reliability of rs-fMRI measures can be assessed. However, because this dataset was acquired only from a single group under a single condition, we cannot directly evaluate whether the rs-fMRI measures can generate reproducible between-condition or between-group results. Because the modulation of resting state activity has gained increasing attention, it is important to know whether one rs-fMRI metric can reliably detect the alteration of the resting activity. Here, we shared a public Eyes-Open (EO)/Eyes-Closed (EC) dataset for evaluating the split-half reproducibility of the rs-fMRI measures in detecting changes of the resting state activity between EO and EC. As examples, we assessed the split-half reproducibility of three widely applied rs-fMRI metrics: amplitude of low frequency fluctuation, regional homogeneity, and seed-based correlation analysis. Our results demonstrated that reproducible patterns of EO-EC differences can be detected by all three measures, suggesting the feasibility of the EO/EC dataset for performing reproducibility assessment for other rs-fMRI measures.

Behavioral studies have suggested a low-frequency (0.05 Hz) fluctuation of sustained attention on the basis of the intra-individual variability of reaction-time. Conventional task designs for functional magnetic resonance imaging (fMRI) studies are not appropriate for frequency analysis. The present study aimed to propose a new paradigm, real-time finger force feedback (RT-FFF), to study the brain mechanisms of sustained attention and neurofeedback.We compared the low-frequency fluctuations in both behavioral and fMRI data from 38 healthy adults (19 males; mean age, 22.3 years). Two fMRI sessions, in RT-FFF and sham finger force feedback (S-FFF) states, were acquired (TR 2 s, Siemens Trio 3-Tesla scanner, 8 min each, counter-balanced). Behavioral data of finger force were obtained simultaneously at a sampling rate of 250 Hz.Frequency analysis of the behavioral data showed lower amplitude in the low-frequency band (0.004-0.104 Hz) but higher amplitude in the high-frequency band (27.02-125 Hz) in the RT-FFF than the S-FFF states. The mean finger force was not significantly different between the two states. fMRI data analysis showed higher fractional amplitude of low-frequency fluctuation (fALFF) in the S-FFF than in the RT-FFF state in the visual cortex, but higher fALFF in RT-FFF than S-FFF in the middle frontal gyrus, the superior frontal gyrus, and the default mode network.The behavioral results suggest that the proposed paradigm may provide a new approach to studies of sustained attention. The fMRI results suggest that a distributed network including visual, motor, attentional, and default mode networks may be involved in sustained attention and/or real-time feedback. This paradigm may be helpful for future studies on deficits of attention, such as attention deficit hyperactivity disorder and mild traumatic brain injury.

In this study, we explored different spontaneous functional connectivity patterns between the anterior prefrontal cortex and other brain regions in nonmedicated patients with obsessive-compulsive disorder in a resting state, and examined the relationship between the abnormal spontaneous functional connectivity patterns of the anterior prefrontal cortex and clinical symptoms in patients with obsessive-compulsive disorder. Twenty nonmedicated patients with obsessive-compulsive disorder and 20 sex-matched and age-matched healthy individuals underwent resting state functional MRI scanning. Compared with the healthy controls, significantly increased positive functional connectivity with the right anterior prefrontal cortex was observed in the right insula and the middle cingulate cortex in patients with obsessive-compulsive disorder. Our findings suggest that abnormal intrinsic or spontaneous functional connectivity in the cognitive control system in a resting state may underlie the pathophysiology of obsessive-compulsive disorder.

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.