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Gary H. Glover - One of the best experts on this subject based on the ideXlab platform.
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time frequency dynamics of resting state brain connectivity measured with fmri
NeuroImage, 2010Co-Authors: Catie Chang, Gary H. GloverAbstract:Abstract Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both Task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time–frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode Network, examining its relationship with both the “anticorrelated” (“Task-Positive”) Network as well as other nodes of the default-mode Network. It was observed that coherence and phase between the PCC and the anticorrelated Network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state Networks.
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effects of model based physiological noise correction on default mode Network anti correlations and correlations
NeuroImage, 2009Co-Authors: Catie Chang, Gary H. GloverAbstract:Abstract Previous studies have reported that the spontaneous, resting-state time course of the default-mode Network is negatively correlated with that of the “Task-Positive Network”, a collection of regions commonly recruited in demanding cognitive Tasks. However, all studies of negative correlations between the default-mode and Task-Positive Networks have employed some form of normalization or regression of the whole-brain average signal (“global signal”); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode Network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the Task-Positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and Positive correlations with the default-mode Network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode Network and regions of the Task-Positive Network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within Task-Positive regions at the group-level (p
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effects of model based physiological noise correction on default mode Network anti correlations and correlations
NeuroImage, 2009Co-Authors: Catie Chang, Gary H. GloverAbstract:Previous studies have reported that the spontaneous, resting-state time course of the default-mode Network is negatively correlated with that of the "Task-Positive Network", a collection of regions commonly recruited in demanding cognitive Tasks. However, all studies of negative correlations between the default-mode and Task-Positive Networks have employed some form of normalization or regression of the whole-brain average signal ("global signal"); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode Network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the Task-Positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and Positive correlations with the default-mode Network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162-167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode Network and regions of the Task-Positive Network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within Task-Positive regions at the group-level (p<0.05, uncorrected; no regions at the group level were significant at FDR=0.05). Furthermore, physiological noise correction caused region-specific decreases in Positive correlations within the default-mode Network, reducing apparent false Positives. It was observed that the low-frequency respiratory volume and cardiac rate regressors used within the physiological noise correction algorithm displayed significant (but not total) shared variance with the global signal, and constitute a model-based alternative to correcting for non-neural global noise.
Catie Chang - One of the best experts on this subject based on the ideXlab platform.
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default mode and Task Positive Network activity in major depressive disorder implications for adaptive and maladaptive rumination
Biological Psychiatry, 2011Co-Authors: Paul J Hamilton, Catie Chang, Daniella J Furman, Moriah E Thomason, Emily L Dennis, Ian H GotlibAbstract:Background Major depressive disorder (MDD) has been associated reliably with ruminative responding; this kind of responding is composed of both maladaptive and adaptive components. Levels of activity in the default-mode Network (DMN) relative to the Task-Positive Network (TPN), as well as activity in structures that influence DMN and TPN functioning, may represent important neural substrates of maladaptive and adaptive rumination in MDD. Methods We used a unique metric to estimate DMN dominance over TPN from blood oxygenation level-dependent data collected during eyes-closed rest in 17 currently depressed and 17 never-disordered adults. We calculated correlations between this metric of DMN dominance over TPN and the depressive, brooding, and reflective subscales of the Ruminative Responses Scale, correcting for associations between these measures both with one another and with severity of depression. Finally, we estimated and compared across groups right fronto-insular cortex (RFIC) response during initiations of ascent in DMN and in TPN activity. Results In the MDD participants, increasing levels of DMN dominance were associated with higher levels of maladaptive, depressive rumination and lower levels of adaptive, reflective rumination. Moreover, our RFIC state-change analysis showed increased RFIC activation in the MDD participants at the onset of increases in TPN activity; conversely, healthy control participants exhibited increased RFIC response at the onset of increases in DMN activity. Conclusions These findings support a formulation in which the DMN undergirds representation of negative, self-referential information in depression, and the RFIC, when prompted by increased levels of DMN activity, initiates an adaptive engagement of the TPN.
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time frequency dynamics of resting state brain connectivity measured with fmri
NeuroImage, 2010Co-Authors: Catie Chang, Gary H. GloverAbstract:Abstract Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both Task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time–frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode Network, examining its relationship with both the “anticorrelated” (“Task-Positive”) Network as well as other nodes of the default-mode Network. It was observed that coherence and phase between the PCC and the anticorrelated Network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state Networks.
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effects of model based physiological noise correction on default mode Network anti correlations and correlations
NeuroImage, 2009Co-Authors: Catie Chang, Gary H. GloverAbstract:Abstract Previous studies have reported that the spontaneous, resting-state time course of the default-mode Network is negatively correlated with that of the “Task-Positive Network”, a collection of regions commonly recruited in demanding cognitive Tasks. However, all studies of negative correlations between the default-mode and Task-Positive Networks have employed some form of normalization or regression of the whole-brain average signal (“global signal”); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode Network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the Task-Positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and Positive correlations with the default-mode Network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode Network and regions of the Task-Positive Network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within Task-Positive regions at the group-level (p
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effects of model based physiological noise correction on default mode Network anti correlations and correlations
NeuroImage, 2009Co-Authors: Catie Chang, Gary H. GloverAbstract:Previous studies have reported that the spontaneous, resting-state time course of the default-mode Network is negatively correlated with that of the "Task-Positive Network", a collection of regions commonly recruited in demanding cognitive Tasks. However, all studies of negative correlations between the default-mode and Task-Positive Networks have employed some form of normalization or regression of the whole-brain average signal ("global signal"); these processing steps alter the time series of voxels in an uninterpretable manner as well as introduce spurious negative correlations. Thus, the extent of negative correlations with the default mode Network without global signal removal has not been well characterized, and it is has recently been hypothesized that the apparent negative correlations in many of the Task-Positive regions could be artifactually induced by global signal pre-processing. The present study aimed to examine negative and Positive correlations with the default-mode Network when model-based corrections for respiratory and cardiac noise are applied in lieu of global signal removal. Physiological noise correction consisted of (1) removal of time-locked cardiac and respiratory artifacts using RETROICOR (Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162-167), and (2) removal of low-frequency respiratory and heart rate variations by convolving these waveforms with pre-determined transfer functions (Birn et al., 2008; Chang et al., 2009) and projecting the resulting two signals out of the data. It is demonstrated that negative correlations between the default-mode Network and regions of the Task-Positive Network are present in the majority of individual subjects both with and without physiological noise correction. Physiological noise correction increased the spatial extent and magnitude of negative correlations, yielding negative correlations within Task-Positive regions at the group-level (p<0.05, uncorrected; no regions at the group level were significant at FDR=0.05). Furthermore, physiological noise correction caused region-specific decreases in Positive correlations within the default-mode Network, reducing apparent false Positives. It was observed that the low-frequency respiratory volume and cardiac rate regressors used within the physiological noise correction algorithm displayed significant (but not total) shared variance with the global signal, and constitute a model-based alternative to correcting for non-neural global noise.
Bharat B. Biswal - One of the best experts on this subject based on the ideXlab platform.
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regional homogeneity of resting state fmri contributes to both neurovascular and Task activation variations
Magnetic Resonance Imaging, 2013Co-Authors: Rui Yuan, Bharat B. Biswal, Eun H Kim, Sabrina Barik, Bart RypmaAbstract:article i nfo The Task induced blood oxygenation level dependent signal changes observed using functional magnetic resonance imaging (fMRI) are critically dependent on the relationship between neuronal activity and hemodynamic response. Therefore, understanding the nature of neurovascular coupling is important when interpreting fMRI signal changes evoked via Task. In this study, we used regional homogeneity (ReHo), a measure of local synchronization of the BOLD time series, to investigate whether the similarities of one voxel with the surrounding voxels are a property of neurovascular coupling. FMRI scans were obtained from fourteen subjects during bilateral finger tapping (FTAP), digit-symbol substitution (DSST) and periodic breath holding (BH) paradigm. A resting-state scan was also obtained for each of the subjects for 4 min using identical imaging parameters. Inter-voxel correlation analyses were conducted between the resting-state ReHo, resting-state amplitude of low frequency fluctuations (ALFF), BH responses and Task activations within the masks related to Task activations. There was a reliable mean voxel-wise spatial correlation between ReHo and other neurovascular variables (BH responses and ALFF). We observed a moderate correlation between ReHo and Task activations (FTAP: r = 0.32; DSST: r = 0.22) within the Task Positive Network and a small yet reliable correlation within the default mode Network (DSST: r = −0.08). Subsequently, a linear regression was used to estimate the contribution of ReHo, ALFF and BH responses to the Task activated voxels. The unique contribution of ReHo was minimal. The results suggest that regional synchrony of the BOLD activity is a property that can explain the variance of neurovascular coupling and Task activations; but its contribution to Task activations can be accounted for by other neurovascular factors such as the ALFF.
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inter individual differences in resting state functional connectivity predict Task induced bold activity
NeuroImage, 2010Co-Authors: Maarten Mennes, Bharat B. Biswal, Clare Kelly, Adriana Di Martino, Xavier F Castellanos, Michael P MilhamAbstract:Abstract The resting brain exhibits coherent patterns of spontaneous low-frequency BOLD fluctuations. These so-called resting-state functional connectivity (RSFC) Networks are posited to reflect intrinsic representations of functional systems commonly implicated in cognitive function. Yet, the direct relationship between RSFC and the BOLD response induced by Task performance remains unclear. Here we examine the relationship between a region's pattern of RSFC across participants and that same region's level of BOLD activation during an Eriksen Flanker Task. To achieve this goal we employed a voxel-matched regression method, which assessed whether the magnitude of Task-induced activity at each brain voxel could be predicted by measures of RSFC strength for the same voxel, across 26 healthy adults. We examined relationships between Task-induced activation and RSFC strength for six different seed regions [Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional Networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673–9678.], as well as the “default mode” and “Task-Positive” resting-state Networks in their entirety. Our results indicate that, for a number of brain regions, inter-individual differences in Task-induced BOLD activity were predicted by one of two resting-state properties: (1) the region's Positive connectivity strength with the Task-Positive Network, or (2) its negative connectivity with the default mode Network. Strikingly, most of the regions exhibiting a significant relationship between their RSFC properties and Task-induced BOLD activity were located in transition zones between the default mode and Task-Positive Networks. These results suggest that a common mechanism governs many brain regions' neural activity during rest and its neural activity during Task performance.
Chandan J Vaidya - One of the best experts on this subject based on the ideXlab platform.
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working memory related changes in functional connectivity persist beyond Task disengagement
Human Brain Mapping, 2014Co-Authors: Evan M Gordon, Andrew L Breeden, Stephanie E Bean, Chandan J VaidyaAbstract:We examined whether altered connectivity in functional Networks during working memory performance persists following conclusion of that performance, into a subsequent resting state. We conducted functional magnetic resonance imaging (fMRI) in 50 young adults during an initial resting state, followed by an N-back working memory Task and a subsequent resting state, in order to examine changes in functional connectivity within and between the default-mode Network (DMN) and the Task-Positive Network (TPN) across the three states. We found that alterations in connectivity observed during the N-back Task persisted into the subsequent resting state within the TPN and between the DMN and TPN, but not within the DMN. Further, speed of working memory performance and TPN connectivity strength during the N-back Task predicted connectivity strength in the subsequent resting state. Finally, DMN connectivity measured before and during the N-back Task predicted individual differences in self-reported inattentiveness, but this association was not found during the post-Task resting state. Together, these findings have important implications for models of how the brain recovers following effortful cognition, as well as for experimental designs using resting and Task scans.
Tianzi Jiang - One of the best experts on this subject based on the ideXlab platform.
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resting state functional connectivity of the vermal and hemispheric subregions of the cerebellum with both the cerebral cortical Networks and subcortical structures
NeuroImage, 2012Co-Authors: Li Sang, Wen Qin, Yong Liu, Wei Han, Yunting Zhang, Tianzi JiangAbstract:The human cerebellum is a heterogeneous structure, and the pattern of resting-state functional connectivity (rsFC) of each subregion has not yet been fully characterized. We aimed to systematically investigate rsFC pattern of each cerebellar subregion in 228 healthy young adults. Voxel-based analysis revealed that several subregions showed similar rsFC patterns, reflecting functional integration; however, different subregions displayed distinct rsFC patterns, representing functional segregation. The same vermal and hemispheric subregions showed either different patterns or different strengths of rsFCs with the cerebrum, and different subregions of lobules VII and VIII displayed different rsFC patterns. Region of interest (ROI)-based analyses also confirmed these findings. Specifically, strong rsFCs were found: between lobules I-VI and vermal VIIb-IX and the visual Network; between hemispheric VI, VIIb, VIIIa and the auditory Network; between lobules I-VI, VIII and the sensorimotor Network; between lobule IX, vermal VIIIb and the default-mode Network; between lobule Crus I, hemispheric Crus II and the fronto-parietal Network; between hemispheric VIIb, VIII and the Task-Positive Network; between hemispheric VI, VIIb, VIII and the salience Network; between most cerebellar subregions and the thalamus; between lobules V, VIIb and the midbrain red nucleus; between hemispheric Crus I, Crus II, vermal VIIIb, IX and the caudate nucleus; between lobules V, VI, VIIb, VIIIa and the pallidum and putamen; and between lobules I-V, hemispheric VIII, IX and the hippocampus and amygdala. These results confirm the existence of both functional integration and segregation among cerebellar subregions and largely improve our understanding of the functional organization of the human cerebellum. (C) 2012 Elsevier Inc. All rights reserved.
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schizophrenic patients and their unaffected siblings share increased resting state connectivity in the Task negative Network but not its anticorrelated Task Positive Network
Schizophrenia Bulletin, 2012Co-Authors: Yoshio Kaneko, Xuan Ouyang, Li Li, Eric Y H Chen, Tianzi Jiang, Yuan ZhouAbstract:Abnormal connectivity of the anticorrelated intrinsic Networks, the Task-negative Network (TNN), and the Task-Positive Network (TPN) is implicated in schizophrenia. Comparisons between schizophrenic patients and their unaffected siblings enable further understanding of illness susceptibility and pathophysiology. We examined the resting-state connectivity differences in the intrinsic Networks between schizophrenic patients, their unaffected siblings, and healthy controls.
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functional disintegration in paranoid schizophrenia using resting state fmri
Schizophrenia Research, 2007Co-Authors: Yuan Zhou, Meng Liang, Lixia Tian, Kun Wang, Tianzi JiangAbstract:Functional disintegration has been observed in schizophrenia during Task performance. We sought to investigate functional disintegration during rest because an intrinsic functional brain organization, including both “Task-negative” (i.e., “default mode” )a nd “Task-Positive” Networks, has been suggested to play an important role in integrating ongoing information processing. Additionally, the brain regions that are involved in the intrinsic organization are believed to be abnormal in schizophrenia. Patients with paranoid schizophrenia(N=18)andhealthyvolunteers(N=18)underwentaresting-statefMRIscan.Functionalconnectivityanalysiswasusedto identify the connectivity between each pair of brain regions within this intrinsic organization, and differences were examined in patients versus healthy volunteers. Compared to healthy volunteers, patients showed significant differences in connectivity within Networks and between Networks, most notably in the connectivities associated with the bilateral dorsal medial prefrontal cortex, the lateral parietal region, the inferior temporal gyrus of the “Task-negative” Network and with the right dorsolateral prefrontal cortex and the right dorsal premotor cortex of the “Task-Positive” Network. These results suggested that the interregional functional connectivities in the intrinsic organization are altered in patients with paranoid schizophrenia. These abnormalities could be the source of abnormalities in the coordination of and competition between information processing activities in the resting brain of paranoid patients. © 2007 Elsevier B.V. All rights reserved.
