Resting-State fMRI Data Analysis Toolkit Research Articles

Altered local gyrification index and corresponding resting-state functional connectivity in individuals with high test anxiety

Previous studies have reported that test anxiety is closely related to unreasonable cognitive patterns and maladaptive emotional responses. However, its underlying brain structural and functional basis has not been thoroughly studied. This study aimed to evaluate the potential difference in local gyration index (LGI) and corresponding resting-state functional connectivity (RSFC) in individuals with high test anxiety (HTA) compared with low test anxiety (LTA). Twenty-six individuals with HTA and 28 individuals with LTA underwent T1-weighted structural and resting-state functional magnetic resonance imaging scans. Using FreeSurfer software, we contrasted the LGI between the HTA and LTA groups using a surface-based general linear model to map group contrasts on a vertex-by-vertex basis. By selecting the cortical regions with significant differences in the LGI analysis as the regions of interest, the seed-based RSFC analysis was further carried out using the Resting-State fMRI Data Analysis Toolkit to examine the differences in the functional connectivity of these cortical regions with the whole brain between the two groups. The results showed that the LGI in several cortical regions of the executive control network (ECN) and the right lateral occipital gyrus was lower in the HTA group than in the LTA group. Furthermore, compared with the LTA group, the HTA group exhibited abnormal RSFC within the ECN, between the ECN and the visual network, and between the ECN and the sensorimotor network. Our findings might provide preliminary evidence for brain morphology and functional alterations in individuals with HTA and contribute to a better understanding of the pathophysiology of TA.

Altered Functional Connectivity of the Primary Visual Cortex in Patients With Iridocyclitis and Assessment of Its Predictive Value Using Machine Learning

PurposeTo explore the intrinsic functional connectivity (FC) alteration of the primary visual cortex (V1) between individuals with iridocyclitis and healthy controls (HCs) by the resting-state functional magnetic resonance imaging (fMRI) technique, and to investigate whether FC findings be used to differentiate patients with iridocyclitis from HCs.MethodsTwenty-six patients with iridocyclitis and twenty-eight well-matched HCs were recruited in our study and underwent resting-state fMRI examinations. The fMRI data were analyzed by Statistical Parametric Mapping (SPM12), Data Processing and Analysis for Brain Imaging (DPABI), and Resting State fMRI Data Analysis Toolkit (REST) software. Differences in FC signal values of the V1 between the individuals with iridocyclitis and HCs were compared using independent two-sample t-tests. Significant differences in FC between two groups were chosen as classification features for distinguishing individuals with iridocyclitis from HCs using a support vector machine (SVM) classifier that involved machine learning. Classifier performance was evaluated using permutation test analysis.ResultsCompared with HCs, patients with iridocyclitis displayed significantly increased FC between the left V1 and left cerebellum crus1, left cerebellum 10, bilateral inferior temporal gyrus, right hippocampus, and left superior occipital gyrus. Moreover, patients with iridocyclitis displayed significantly lower FC between the left V1 and both the bilateral calcarine and bilateral postcentral gyrus. Patients with iridocyclitis also exhibited significantly higher FC values between the right V1 and left cerebellum crus1, bilateral thalamus, and left middle temporal gyrus; while they displayed significantly lower FC between the right V1 and both the bilateral calcarine and bilateral postcentral gyrus (voxel-level P<0.01, Gaussian random field correction, cluster-level P<0.05). Our results showed that 63.46% of the participants were correctly classified using the leave-one-out cross-validation technique with an SVM classifier based on the FC of the left V1; and 67.31% of the participants were correctly classified based on the FC of the right V1 (P<0.001, non-parametric permutation test).ConclusionPatients with iridocyclitis displayed significantly disturbed FC between the V1 and various brain regions, including vision-related, somatosensory, and cognition-related regions. The FC variability could distinguish patients with iridocyclitis from HCs with substantial accuracy. These findings may aid in identifying the potential neurological mechanisms of impaired visual function in individuals with iridocyclitis.

Brain functional changes in perimenopausal women: an amplitude of low-frequency fluctuation study.

Objective:To evaluate the effects of sex hormones on amplitude of low-frequency fluctuation (ALFF) in brain regions related to cognition in perimenopausal women.Methods:This cross-sectional study involved 25 perimenopausal women and 25 premenopausal women who underwent behavioral evaluations, sex hormone level measurements, and functional magnetic resonance imaging (fMRI). All data and ALFF analyses were preprocessed using the Data Processing Assistant for Resting-State fMRI. Statistical analyses were performed using the Resting-State fMRI Data Analysis Toolkit to explore the differences in ALFF between perimenopausal and premenopausal women. The gray matter volume (GMV) values extracted from brain regions (regions of interest) with significantly different ALFF values between the perimenopausal and premenopausal groups were compared. We analyzed the correlations of the ALFF and GMV values of these regions of interest with the results of behavioral evaluations and sex hormone levels in the two groups.Results:Compared with the premenopausal group, the perimenopausal group showed significant ALFF increase in the left gyrus rectus. Regions with decreased ALFF in the perimenopausal group included the left superior temporal gyrus, left inferior frontal gyrus, and left insula. The GMV values of the left gyrus rectus and left superior temporal gyrus were reduced in perimenopausal women. Furthermore, the estradiol level was negatively correlated with the ALFF value of the left gyrus rectus in perimenopausal women.Conclusions:The ALFF and GMV values of certain brain regions related to cognitive function were changed in perimenopausal women. Such functional brain alterations may provide more information regarding the mechanism of cognitive dysfunction in perimenopausal women.

Abnormal Regional Neural Activity and Reorganized Neural Network in Obesity: Evidence from Resting‐State fMRI

This study aimed to investigate regional neural activity and regulation of patterns inthe reorganized neural network of obesity and explore the correlation between brain activities and eating behavior. A total of 23 individuals with obesity and 23 controls with normal weight were enrolled. Functional magnetic resonance imaging (fMRI)data were acquired using 3.0-T MRI. Amplitude of low-frequency fluctuation and functional connectivity (FC) analyses were conducted using Data Processing Assistant for resting-state fMRI and Resting-State fMRI Data Analysis Toolkit (REST). The group with obesity showed increased amplitude of low-frequency values in left fusiform gyrus/amygdala, inferior temporal gyrus (ITG), hippocampus/parahippocampal gyrus, and bilateral caudate but decreased values in right superior temporal gyrus. The group with obesity showed increased FC between left caudate and right superior temporal gyrus, left fusiform gyrus/amygdala and left ITG, right caudate and left fusiform gyrus/amygdala, and right caudate and left hippocampus/parahippocampal gyrus. Dutch Eating Behavior Questionnaire-Emotional scores were positively correlated with FC between left hippocampus/parahippocampal gyrus and right caudate but negatively correlated with FC between left fusiform gyrus/amygdala and left ITG. The study indicated the reorganized neural network presented as a bilateral cross-regulation pattern across hemispheres between reward and various appetite-related functional processing, thus affecting emotional and external eating behavior. These results could provide further evidence for neuropsychological underpinnings of food intake and their neuromodulatory therapeutic potential in obesity.

The Metabolic Activity of Caudate and Prefrontal Cortex Negatively Correlates with the Severity of Idiopathic Parkinson's Disease.

Positron emission tomography (PET) scan with tracer [18F]-fluorodeoxy-glucose (18F-FDG) is widely used to measure the glucose metabolism in neurodegenerative disease such as Idiopathic Parkinson’s disease (IPD). Previous studies using 18F-FDG PET mainly focused on the motor or non-motor symptoms but not the severity of IPD. In this study, we aimed to determine the metabolic patterns of 18F-FDG in different stages of IPD defined by Hoehn and Yahr rating scale (H-Y rating scale) and to identify regions in the brain that play critical roles in disease progression. Fifty IPD patients were included in this study. They were 29 men and 21 women (mean±SD, age 57.7±11.1 years, disease duration 4.0±3.8 years, H-Y 2.2±1.1). Twenty healthy individuals were included as normal controls. Following 18F-FDG PET scan, image analysis was performed using Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST). The metabolic feature of IPD and regions-of-interests (ROIs) were determined. Correlation analysis between ROIs and H-Y stage was performed. SPM analysis demonstrated a significant hypometabolic activity in bilateral putamen, caudate and anterior cingulate as well as left parietal lobe, prefrontal cortex in IPD patients. In contrast, hypermetabolism was observed in the cerebellum and vermis. There was a negative correlation (p=0.007, r=-0.412) between H-Y stage and caudate metabolic activity. Moreover, the prefrontal area also showed a negative correlation with H-Y (P=0.033, r=-0.334). Thus, the uptake of FDG in caudate and prefrontal cortex can potentially be used as a surrogate marker to evaluate the severity of IPD.

Brain Activity Map Extraction of Neuromyelitis Optica Patients Using Resting-State fMRI Data Based on Amplitude of Low Frequency Fluctuations and Regional Homogeneity Analysis

Introduction: Neuromyelitis Optica (NMO) is a rare inflammatory disease of the central nervous system which generally affecting the spinal cord and optic nerve. Damage to the optic nerve can result in the patient's dim vision or even blindness, while the spinal cord damage may lead to sensory and motor paralysis and the weakness of the lower limbs in the patient. Magnetic Resonance Imaging (MRI) is one of the imaging methods for NMO detection. Recently, the fMRI scans in NMO disease is very common. fMRI is a non-invasive method which uses magnetic technology. fMRI images are related to environmental changes in the blood flow of any brain regions. This imaging method is designed to identify parts of the brain cortex activated for specific tasks, and it is mostly used to carry out neurological studies. Materials and Method: After the initial diagnosis by the neurologist, rest-fMRI imaging was performed on 25 healthy persons as the control group and 25 persons suffering from NMO as the test group. after the data were obtained, the data pre-processing was carried out which included the slice-timing, motion correction, and spatial normalization steps. In order to eliminate the noise and other artifact effects, a band-pass filter was used. Finally, the data were filtered using a Gaussian kernel for the smoothing purposes. The amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo) methods were used to investigate the activity of brain regions. For this purpose, at first, time series associated with each of the brain 3D volume voxels were extracted. After the selection of the brain regions between the two groups, registering the functional images on structural images and the standard atlas, the statistical analysis of the results was carried out. The present research was performed in MATLAB using the Resting-State fMRI Data Analysis Toolkit. Results: The statistical comparison of the images of the test and control groups which was obtained from the group analyses of the low frequency fluctuations mappings in the resting state pointed to a decrease in the activity of the left Anterior Cingulate Cortex, right precuneus, left Posterior Cingulate Cortex, medial frontal gyrus, and right middle temporal gyrus regions. On the other hand, there was observed an increase in the activity of right inferior frontal gyrus and left thalamus regions. Conclusion: The findings in the data associated with fMRI images in NMO patients, showed an abnormal brain activity in the regions associated with cognitive, optic, and motor tasks. This may be because of the consistency of the brain with functional disorder in NMO subjects as well as the compensation of this pathophysiological disorder by the other parts of the brain.

Altered regional and circuit resting-state activity in patients with occult spastic diplegic cerebral palsy

Very few studies have been made to investigate functional activity changes in occult spastic diplegic cerebral palsy (SDCP). The purpose of this study was to analyze whole-brain resting state regional brain activity and functional connectivity (FC) changes in patients with SDCP. We examined 12 occult SDCP and 14 healthy control subjects using resting-state functional magnetic resonance imaging. The data were analyzed using Resting-State fMRI Data Analysis Toolkit (REST) software. The regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and whole brain FC of the motor cortex and thalamus were analyzed and compared between the occult SDCP and control groups. Compared with the control group, the occult SDCP group showed decreased ReHo regions, including the bilateral frontal, parietal, and temporal lobes, the cerebellum, right cingulate gyrus, and right lenticular nucleus, whereas an increased ReHo value was observed in the left precuneus, calcarine, fusiform gyrus, and right precuneus. Compared with the control group, no significant differences in ALFF were noted in the occult SDCP group. With the motor cortex as the region of interest, the occult SDCP group showed decreased connectivity regions in the bilateral fusiform and lingual gyrus, but increased connectivity regions in the contralateral precentral and postcentral gyrus, supplementary motor area, and the ipsilateral postcentral gyrus. With the thalamus being regarded as the region of interest, the occult SDCP group showed decreased connectivity regions in the bilateral basal ganglia, cingulate, and prefrontal cortex, but increased connectivity regions in the bilateral precentral gyrus, the contralateral cerebellum, and inferior temporal gyrus. Resting-state regional brain activities and FC changes in the patients with occult SDCP exhibited a special distribution pattern, which is consistent with the pathology of the disease.

Correlation between the clinical symptoms and the activation pattern of brain neurons in patients with obsessive-compulsive disorder

Objective: To explore the correlation between clinical symptoms of patients with obsessive-compulsive disorder (OCD) and the activation pattern of brain neurons at resting. Methods: A total of 26 patients diagnosed with fist-episode OCD were selected and underwent functional magnetic resonance imaging (fMRI) scanning at resting state. The fMRI data were processed by statistical parametric mapping (SPM8), Data Processing Assistant for Resting-State fMRI Analysis Toolkit (DPARSFA), and Resting State fMRI Data Analysis Toolkit (REST) software. With age as concomitant variable, Pearson Correlation Analysis was adopted to study the correlation between clinical symptoms (obsessive thoughts and compulsive behavior) of patients and their regional homogeneity ( ReHo) values of whole brain. Results: The positive correlation between obsessive thoughts score of patients with OCD and ReHo value lied in insular_L, insular_R, cerebellum_L, cerebellum_R, calcarine_L, cuneus_L. The negative correlation between obsessive thoughts score of patients with OCD and ReHo value lied in thalamus_L, precuneus_L, caudate_L, cingulum_R, frontal_R. The positive correlation between compulsive behavior score of patients with OCD and ReHo value lied in temporal_L, frontal_inf_orb_l, frontal_inf_orb_R, frontal_mid_L, precentral_R. The negative correlation between compulsive behavior score of patients with OCD and ReHo value lied in cingulum-L, cingulum-R, caudate_L. Conclusion: Different obsessive-compulsive symptoms has different image foundation, and the dysfunction of cingulate has a major impact on the incidence of both obsessive thoughts and compulsive behavior of OCD patients.

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI.

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

Change in brain network connectivity during PACAP38-induced migraine attacks: A resting-state functional MRI study.

To investigate resting-state functional connectivity in the salience network (SN), the sensorimotor network (SMN), and the default mode network (DMN) during migraine attacks induced by pituitary adenylate cyclase-activating polypeptide-38 (PACAP38). In a double-blind, randomized study, 24 female migraine patients without aura received IV PACAP38 or vasoactive intestinal polypeptide (VIP) over 20 minutes. Both peptides are closely related and cause vasodilation, but only PACAP38 induces migraine attacks. VIP was therefore used as active placebo. Resting-state functional MRI was recorded before and during PACAP38-induced migraine attacks and before and after VIP infusion. We analyzed data by Statistical Parametric Mapping 8 and the Resting-State fMRI Data Analysis Toolkit for Matlab in a seed-based fashion. PACAP38 (n = 16) induced migraine attacks and increased connectivity with the bilateral opercular part of the inferior frontal gyrus in the SN. In SMN, there was increased connectivity with the right premotor cortex and decreased connectivity with the left visual cortex. Several areas showed increased (left primary auditory, secondary somatosensory, premotor, and visual cortices) and decreased (right cerebellum and left frontal lobe) connectivity with DMN. We found no resting-state network changes after VIP (n = 15). PACAP38-induced migraine attack is associated with altered connectivity of several large-scale functional networks of the brain.

Brief mindfulness training alters causal brain connections in mTBI

Traumatic brain injury (TBI) is a significant cause of disability in the United States and the mild TBI (mTBI) is the most prevalent. Previous research indicates the positive effect of mindfulness training on symptoms of chronic mTBI such as cognitive functioning and emotion [1]. However it remains unclear which brain regions play a crucial role in mTBI recovery and the causal relationship among these regions. Here we apply a dynamic causal modeling (DCM) in resting state fMRI [2] to demonstrate the causal relationships among the core regions involved in mTBI. Fourteen veteran students with mTBI were recruited through campus advertisements. We used 2 weeks of integrative body-mind training as mindfulness intervention, previously reported in our series of randomized studies [3,4]. All data were collected using a 3-Telsa Siemens Skyra scanner. Functional data were processed using the Data Processing Assistant for Resting-State fMRI, which is based on SPM and Resting-State fMRI Data Analysis Toolkit. Based on literature, we specified four regions of interest within default mode network (DMN) - medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and bilateral inferior parietal lobule (Left IPL and Right IPL), same regions and coordinates as in previous sDCM studies [2,5]. Based on SPM12, we estimated and specified the DCM for each subject and later compared the differences of effective connectivity before and after mindfulness. Our results suggest the different causal relationships following mindfulness training. Specifically, after training there was a significant decrease in the strength of excitatory input from mPFC to PCC, and a significant increase in the strength of inhibitory input from mPFC to LIPL (all p<0.05).

Mapping the smoking addiction using dynamic causal modelling at rest

Tobacco use is the leading preventable cause of death. Previous research show that brain areas including medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC) and others involved in smoking addiction [1]. However it remains unclear which brain regions play a crucial role in smoking addiction and the relationship among these regions. Since functional connectivity does not support inferences about causal brain connections, the changes in information flow in these distributed systems involved in smoking remain largely unknown. Here we apply a dynamic causal modeling (DCM) in resting state fMRI [2] to demonstrate the causal relationships among the core regions in smoking addiction. Healthy college students were recruited through campus advertisements. Among those who responded, there were 14 cigarette smokers and 14 nonsmokers. All data were collected using a 3-Telsa Siemens Skyra scanner. Functional data were processed using the Data Processing Assistant for Resting-State fMRI, which is based on SPM (www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit. Based on literature, we specified four regions of interest (ROIs) within the default mode network (DMN), which are medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and bilateral inferior parietal lobule ( Left IPL and Right IPL), same regions and coordinates as in previous studies that use sDCM analysis for resting state [2,3]. Based on SPM12, we estimated and specified the DCM for each subject and later compared the differences of effective connectivity between two groups by using two-sample test. Our results suggest the different causal relationships between nonsmokers and smokers. Specifically, there was a lower strength of excitatory input from PCC to mPFC for smokers than nonsmokers and a lower strength of excitatory input from RIPL to mPFC for smokers than nonsmokers (all p<0.05).

Abnormal amplitude of low frequency fluctuation during the resting state in adolescents with online game addiction

Objective To investigate the characteristic of regional amplitude of low frequency fluctuation (ALFF) in the brain of adolescents with online game addiction during the resting state. Methods The ALFF method was employed to explore the local features of spontaneous brain activity in adolescents with online game addiction (n=26) and healthy controls (n=23) during resting-state by Resting-State fMRI Data Analysis Toolkit. Results Compared with healthy controls, adolescents with online game addiction showed increased ALFF values in the anterior cingulate cortex (ACC), left middle cingulate cortex (MCC), bilateral caudate, right pallidum, bilateral parahippocampal gyrus, and right insula. It was noteworthy that the ALFF values of the right caudate (r=0.4437, P=0.0232) and right parahippocampal gyrus (r=0.5227, P=0.0062) were positively correlated with the duration of online game addiction. Conclusion The abnormal spontaneous neuronal activity properties during the resting state were revealed in the brain regions of adolescents with online game addiction. And the abnormalities may become more significant as the disease progresses. Key words: Online game addiction; Resting state; Amplitude of low frequency fluctuation

Spontaneous brain activity changes in children with primary monosymptomatic nocturnal enuresis: A resting‐state fMRI study

Primary monosymptomatic nocturnal enuresis (PMNE) is a common disorder in school-aged children. However, little is known about resting-state neural function in individuals with PMNE. In this work, resting-state functional magnetic resonance imaging (fMRI) was used to investigate changes in spontaneous brain activity in children with PMNE. We analyzed resting-state fMRI data using statistical parametric mapping (SPM) and a resting-state fMRI data analysis toolkit (REST). Regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) values were calculated to analyze spontaneous brain activity in 16 children with PMNE and 16 healthy controls. Children with PMNE exhibited significant differences in ALFF or ReHo in the left inferior frontal gyrus, medial frontal gyrus (Brodmann area, BA 10), and left midbrain. Abnormalities in BA 10 and the inferior frontal gyrus may affect children's decision-making with regard to voiding; abnormalities in the midbrain in PMNE children may influence the internal signal transmission in their bladder control network. Our data indicate that, in children with PMNE, several brain areas related to the micturate control network undergo developmental delay.

Granger causality analysis implementation on MATLAB: A graphic user interface toolkit for fMRI data processing

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.

REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing

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.

DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for “pipeline” data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for “pipeline” data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.