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Yufeng Zang - One of the best experts on this subject based on the ideXlab platform.
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dparsf a matlab toolbox for pipeline data analysis of resting state fmri
Frontiers in Systems Neuroscience, 2010Co-Authors: Chaogan Yan, Yufeng ZangAbstract: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 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 (FC), regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF), and fractional ALFF (fALFF). 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.
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spontaneous brain activity in the default mode network is sensitive to different resting state conditions with limited cognitive load
PLOS ONE, 2009Co-Authors: Chaogan Yan, Xiangyu Long, Qihong Zou, Xinian Zuo, Dongqiang Liu, Chaozhe Zhu, Yufeng ZangAbstract:Background Recent functional MRI (fMRI) studies have demonstrated that there is an intrinsically organized default mode network (DMN) in the resting brain, primarily made up of the posterior cingulate cortex (PCC) and the medial prefrontal cortex (MPFC). Several previous studies have found that the DMN is minimally disturbed during different Resting-State conditions with limited cognitive demand. However, this conclusion was drawn from the visual inspection of the functional connectivity patterns within the DMN and no statistical comparison was performed. Methodology/Principal Findings Four Resting-State fMRI sessions were acquired: 1) eyes-closed (EC) (used to generate the DMN mask); 2) EC; 3) eyes-open with no fixation (EO); and 4) eyes-open with a fixation (EO-F). The 2–4 sessions were counterbalanced across participants (n = 20, 10 males). We examined the statistical differences in both functional connectivity and regional amplitude of low frequency fluctuation (ALFF) within the DMN among the 2–4 Resting-State conditions (i.e., EC, EO, and EO-F). Although the connectivity patterns of the DMN were visually similar across these three different conditions, we observed significantly higher functional connectivity and ALFF in both the EO and the EO-F conditions as compared to the EC condition. In addition, the first and second resting EC conditions showed significant differences within the DMN, suggesting an order effect on the DMN activity. Conclusions/Significance Our findings of the higher DMN connectivity and regional spontaneous activities in the resting state with the eyes open suggest that the participants might have more non-specific or non-goal-directed visual information gathering and evaluation, and mind wandering or daydreaming during the resting state with the eyes open as compared to that with the eyes closed, thus providing insights into the understanding of unconstrained mental activity within the DMN. Our results also suggest that it should be cautious when choosing the type of a resting condition and designating the order of the resting condition in multiple scanning sessions in experimental design.
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spontaneous brain activity in the default mode network is sensitive to different resting state conditions with limited cognitive load
PLOS ONE, 2009Co-Authors: Yongyong He, Xiangyu Long, Yufeng ZangAbstract:Background Recent functional MRI (fMRI) studies have demonstrated that there is an intrinsically organized default mode network (DMN) in the resting brain, primarily made up of the posterior cingulate cortex (PCC) and the medial prefrontal cortex (MPFC). Several previous studies have found that the DMN is minimally disturbed during different Resting-State conditions with limited cognitive demand. However, this conclusion was drawn from the visual inspection of the functional connectivity patterns within the DMN and no statistical comparison was performed. Methodology/Principal Findings Four Resting-State fMRI sessions were acquired: 1) eyes-closed (EC) (used to generate the DMN mask); 2) EC; 3) eyes-open with no fixation (EO); and 4) eyes-open with a fixation (EO-F). The 2–4 sessions were counterbalanced across participants (n = 20, 10 males). We examined the statistical differences in both functional connectivity and regional amplitude of low frequency fluctuation (ALFF) within the DMN among the 2–4 Resting-State conditions (i.e., EC, EO, and EO-F). Although the connectivity patterns of the DMN were visually similar across these three different conditions, we observed significantly higher functional connectivity and ALFF in both the EO and the EO-F conditions as compared to the EC condition. In addition, the first and second resting EC conditions showed significant differences within the DMN, suggesting an order effect on the DMN activity. Conclusions/Significance Our findings of the higher DMN connectivity and regional spontaneous activities in the resting state with the eyes open suggest that the participants might have more non-specific or non-goal-directed visual information gathering and evaluation, and mind wandering or daydreaming during the resting state with the eyes open as compared to that with the eyes closed, thus providing insights into the understanding of unconstrained mental activity within the DMN. Our results also suggest that it should be cautious when choosing the type of a resting condition and designating the order of the resting condition in multiple scanning sessions in experimental design.
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default mode network as revealed with multiple methods for resting state functional mri analysis
Journal of Neuroscience Methods, 2008Co-Authors: Xiangyu Long, Kuncheng Li, Vesa Kiviniemi, Tianzi Jiang, Qiyong Gong, Yihong Yang, Hong Yang, Liang Wang, Yufeng ZangAbstract:Recently, human brain activity during a Resting-State has attracted increasing attention. Several studies have found that there are two networks: the default mode network and its anti-correlation network. Some studies have subsequently showed that the functions of brain areas within the default mode network are crucial in human mental activity. To further discern the brain default mode network as well as its anti-correlation network during Resting-State, we used three methods to analyze Resting-State functional magnetic resonance imaging (fMRI) data; regional homogeneity analysis, linear correlation and independent component analysis, on four groups of dataset. Our results showed the existence of these two networks prominently and consistently during a resting- and conscious-state across the three methods. This consistency was exhibited in four independent groups of normal adults. Moreover, the current results provided evidences that the brain areas within the two anti-correlated networks are highly integrated at both the intra- and inter-regional level. 2008 Elsevier B.V. All rights reserved.
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Altered Resting State Functional Connectivity Patterns of Anterior Cingulate Cortex in Adolescents with Attention Deficit Hyperactivity Disorder
Neuroscience Letters, 2006Co-Authors: Lixia Tian, Tianzi Jiang, Yufeng Zang, Meng Liang, Yufeng Wang, Manqiu Sui, Qingjiu Cao, Miao Peng, Yan ZhuoAbstract:Dorsal anterior cingulate cortex (dACC) has been found to function abnormally in attention deficit hyperactivity disorder (ADHD) patients in several former functional MRI (fMRI) studies. Resting-State low-frequency fluctuations (LFFs) of blood oxygen level-dependent (BOLD) fMRI signals have been proved to be quite informative. This study used Resting-State LFFs to investigate the Resting-State functional connectivity pattern differences of dACC in adolescents with and without ADHD. As compared to the controls, the ADHD patients exhibited more significant Resting-State functional connectivities with the dACC in bilateral dACC, bilateral thalamus, bilateral cerebellum, bilateral insula and bilateral brainstem (pons). No brain region in the controls was found to exhibit more significant Resting-State functional connectivity with the dACC. We suggest these abnormally more significant functional connectivities in the ADHD patients may indicate the abnormality of autonomic control functions in them.
Stefan Sunaert - One of the best experts on this subject based on the ideXlab platform.
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resting state functional magnetic resonance imaging for language preoperative planning
Frontiers in Human Neuroscience, 2016Co-Authors: Paulo Branco, Daniela Seixas, Sabine Deprez, Silvia Kovacs, Ronald Peeters, Sao Luis Castro, Stefan SunaertAbstract:Functional magnetic resonance imaging, fMRI, is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artefacts can be critical limitations in clinical settings. Recent advances in Resting-State protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using Resting-State fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-State networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and resting state language maps, particularly within the language regions of interest. Furthermore, Resting-State language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that Resting-State protocols may be suitable to map language networks in a quick and clinically efficient way.
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resting state functional magnetic resonance imaging for language preoperative planning
Frontiers in Human Neuroscience, 2016Co-Authors: Paulo Branco, Daniela Seixas, Sabine Deprez, Silvia Kovacs, Ronald Peeters, Sao Luis Castro, Stefan SunaertAbstract:Functional magnetic resonance imaging (fMRI) is a well-known non-invasive technique for the study of brain function. One of its most common clinical applications is preoperative language mapping, essential for the preservation of function in neurosurgical patients. Typically, fMRI is used to track task-related activity, but poor task performance and movement artifacts can be critical limitations in clinical settings. Recent advances in Resting-State protocols open new possibilities for pre-surgical mapping of language potentially overcoming these limitations. To test the feasibility of using Resting-State fMRI instead of conventional active task-based protocols, we compared results from fifteen patients with brain lesions while performing a verb-to-noun generation task and while at rest. Task-activity was measured using a general linear model analysis and independent component analysis (ICA). Resting-State networks were extracted using ICA and further classified in two ways: manually by an expert and by using an automated template matching procedure. The results revealed that the automated classification procedure correctly identified language networks as compared to the expert manual classification. We found a good overlay between task-related activity and Resting-State language maps, particularly within the language regions of interest. Furthermore, Resting-State language maps were as sensitive as task-related maps, and had higher specificity. Our findings suggest that Resting-State protocols may be suitable to map language networks in a quick and clinically efficient way.
R. Todd Constable - One of the best experts on this subject based on the ideXlab platform.
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graph theory based parcellation of functional subunits in the brain from resting state fmri data
NeuroImage, 2010Co-Authors: Xilin Shen, Xenophon Papademetris, R. Todd ConstableAbstract:Resting-State fMRI provides a method to examine the functional network of the brain under spontaneous fluctuations. A number of studies have proposed using Resting-State BOLD data to parcellate the brain into functional subunits. In this work, we present two state-of-the-art graph-based partitioning approaches, and investigate their application to the problem of brain network segmentation using Resting-State fMRI. The two approaches, the normalized cut (Ncut) and the modularity detection algorithm, are also compared to the the Gaussian mixture model (GMM) approach. We show that the Ncut approach performs consistently better than the modularity detection approach, and it also outperforms the GMM approach for in vivo fMRI data. Resting-State fMRI data were acquired from 43 healthy subjects, and the Ncut algorithm was used to parcellate several different cortical regions of interest. The group-wise delineation of the functional subunits based on Resting-State fMRI was highly consistent with the parcellation results from two task-based fMRI studies (one with 18 subjects and the other with 20 subjects). The findings suggest that whole-brain parcellation of the cortex using Resting-State fMRI is feasible, and that the Ncut algorithm provides the appropriate technique for this task.
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graph theory based parcellation of functional subunits in the brain from resting state fmri data
NeuroImage, 2010Co-Authors: Xilin Shen, Xenophon Papademetris, R. Todd ConstableAbstract:Resting-State fMRI provides a method to examine the functional network of the brain under spontaneous fluctuations. A number of studies have proposed using Resting-State BOLD data to parcellate the brain into functional subunits. In this work, we present two state-of-the-art graph-based partitioning approaches, and investigate their application to the problem of brain network segmentation using Resting-State fMRI. The two approaches, the normalized cut (Ncut) and the modularity detection algorithm, are also compared to the Gaussian mixture model (GMM) approach. We show that the Ncut approach performs consistently better than the modularity detection approach, and it also outperforms the GMM approach for in vivo fMRI data. Resting-State fMRI data were acquired from 43 healthy subjects, and the Ncut algorithm was used to parcellate several different cortical regions of interest. The group-wise delineation of the functional subunits based on Resting-State fMRI was highly consistent with the parcellation results from two task-based fMRI studies (one with 18 subjects and the other with 20 subjects). The findings suggest that whole-brain parcellation of the cortex using Resting-State fMRI is feasible, and that the Ncut algorithm provides the appropriate technique for this task.
Tianzi Jiang - One of the best experts on this subject based on the ideXlab platform.
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default mode network as revealed with multiple methods for resting state functional mri analysis
Journal of Neuroscience Methods, 2008Co-Authors: Xiangyu Long, Kuncheng Li, Vesa Kiviniemi, Tianzi Jiang, Qiyong Gong, Yihong Yang, Hong Yang, Liang Wang, Yufeng ZangAbstract:Recently, human brain activity during a Resting-State has attracted increasing attention. Several studies have found that there are two networks: the default mode network and its anti-correlation network. Some studies have subsequently showed that the functions of brain areas within the default mode network are crucial in human mental activity. To further discern the brain default mode network as well as its anti-correlation network during Resting-State, we used three methods to analyze Resting-State functional magnetic resonance imaging (fMRI) data; regional homogeneity analysis, linear correlation and independent component analysis, on four groups of dataset. Our results showed the existence of these two networks prominently and consistently during a resting- and conscious-state across the three methods. This consistency was exhibited in four independent groups of normal adults. Moreover, the current results provided evidences that the brain areas within the two anti-correlated networks are highly integrated at both the intra- and inter-regional level. 2008 Elsevier B.V. All rights reserved.
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decreased regional homogeneity in schizophrenia a resting state functional magnetic resonance imaging study
Neuroreport, 2006Co-Authors: Haihong Liu, Zhening Liu, Meng Liang, Yihui Hao, Lihua Tan, Fan Kuang, Tianzi JiangAbstract:We used a newly reported regional homogeneity approach to measure the temporal homogeneity of blood oxygen level-dependent signal for exploring the brain activity of schizophrenia in a resting state. The results showed decreased regional homogeneity in schizophrenia, which distributed over the bilateral frontal, temporal, occipital, cerebellar posterior, right parietal and left limbic lobes, similar to the findings reported in previous resting state functional studies. The brain regions that showed decreased regional homogeneity are believed to be involved in the psychopathology and pathophysiology of schizophrenia. Our results indicate that abnormal brain activity of schizophrenia may exist in a resting state and the regional homogeneity may be potentially helpful in understanding the resting state of schizophrenia.
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Altered Resting State Functional Connectivity Patterns of Anterior Cingulate Cortex in Adolescents with Attention Deficit Hyperactivity Disorder
Neuroscience Letters, 2006Co-Authors: Lixia Tian, Tianzi Jiang, Yufeng Zang, Meng Liang, Yufeng Wang, Manqiu Sui, Qingjiu Cao, Miao Peng, Yan ZhuoAbstract:Dorsal anterior cingulate cortex (dACC) has been found to function abnormally in attention deficit hyperactivity disorder (ADHD) patients in several former functional MRI (fMRI) studies. Resting-State low-frequency fluctuations (LFFs) of blood oxygen level-dependent (BOLD) fMRI signals have been proved to be quite informative. This study used Resting-State LFFs to investigate the Resting-State functional connectivity pattern differences of dACC in adolescents with and without ADHD. As compared to the controls, the ADHD patients exhibited more significant Resting-State functional connectivities with the dACC in bilateral dACC, bilateral thalamus, bilateral cerebellum, bilateral insula and bilateral brainstem (pons). No brain region in the controls was found to exhibit more significant Resting-State functional connectivity with the dACC. We suggest these abnormally more significant functional connectivities in the ADHD patients may indicate the abnormality of autonomic control functions in them.
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modulation of functional connectivity during the resting state and the motor task
Human Brain Mapping, 2004Co-Authors: Tianzi Jiang, Yufeng Zang, Xuchu WengAbstract:Quite a few studies in functional magnetic resonance imaging (fMRI) have tested that, even in a resting state, motor cortices constitute a network. It has never been investigated how the network modulates from the resting state to the motor task state. In this report, by a newly developed approach taking into account n-to-1 connectivity using 1-to-1 connectivity measures instead of conventional pairwise connectivity, we show the existence of a large organized functional connectivity network related to motor function in the resting brain with fMRI. More importantly, we found that such a network can be modulated from a conscious resting state to planning, initiation, coordination, guidance, and termination of voluntary movement state, exhibited by significant changes of functional connectivity of some brain regions in different brain activity. Moreover, a quantitative description of such a functional modulation has also been presented.
Alvaro Pascualleone - One of the best experts on this subject based on the ideXlab platform.
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measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging fcmri and transcranial magnetic stimulation tms
NeuroImage, 2012Co-Authors: Michael D Fox, Mark A Halko, Mark C Eldaief, Alvaro PascualleoneAbstract:Both resting state functional magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS) are increasingly popular techniques that can be used to non-invasively measure brain connectivity in human subjects. TMS shows additional promise as a method to manipulate brain connectivity. In this review we discuss how these two complimentary tools can be combined to optimally study brain connectivity and manipulate distributed brain networks. Important clinical applications include using resting state fcMRI to guide target selection for TMS and using TMS to modulate pathological network interactions identified with resting state fcMRI. The combination of TMS and resting state fcMRI has the potential to accelerate the translation of both techniques into the clinical realm and promises a new approach to the diagnosis and treatment of neurological and psychiatric diseases that demonstrate network pathology.
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longitudinal changes of resting state functional connectivity during motor recovery after stroke
Stroke, 2011Co-Authors: Changhyun Park, Won Hyuk Chang, Oh Young Bang, Alvaro PascualleoneAbstract:Background and Purpose—Functional MRI (fMRI) studies could provide crucial information on the neural mechanisms of motor recovery in patients with stroke. Resting-State fMRI is applicable to patients with stroke who are not capable of proper performance of the motor task. In this study, we explored neural correlates of motor recovery in patients with stroke by investigating longitudinal changes in Resting-State functional connectivity of the ipsilesional primary motor cortex (M1). Methods—A longitudinal observational study using repeated fMRI experiments was conducted in 12 patients with stroke. Resting-State fMRI data were acquired 4 times over a period of 6 months. Patients participated in the first session of fMRI shortly after onset and thereafter in subsequent sessions at 1, 3, and 6 months after onset. Resting-State functional connectivity of the ipsilesional M1 was assessed and compared with that of healthy subjects. Results—Compared with healthy subjects, patients demonstrated higher functional co...
