The Experts below are selected from a list of 25713 Experts worldwide ranked by ideXlab platform
Michael P. Milham - One of the best experts on this subject based on the ideXlab platform.
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computerized cognitive training for children with neurofibromatosis type 1 a pilot Resting State fMRI study
Psychiatry Research-neuroimaging, 2017Co-Authors: Yuliya N Yoncheva, Michael P. Milham, Kristina K Hardy, Daniel J Lurie, Krishna Somandepalli, Lanbo Yang, Gilbert Vezina, Nadja Kadom, Roger J PackerAbstract:In this pilot study, we examined training effects of a computerized working memory program on Resting State functional magnetic resonance imaging (fMRI) measures in children with neurofibromatosis type 1 (NF1). We contrasted pre- with post-training Resting State fMRI and cognitive measures from 16 participants (nine males; 11.1 ± 2.3 years) with NF1 and documented working memory difficulties. Using non-parametric permutation test inference, we found significant regionally specific differences (family-wise error corrected) in two of four voxel-wise Resting State measures: fractional amplitude of low frequency fluctuations (indexing peak-to-trough intensity of spontaneous oscillations) and regional homogeneity (indexing local intrinsic synchrony). Some cognitive task improvement was observed as well. These preliminary findings suggest that regionally specific changes in Resting State fMRI indices may be associated with treatment-related cognitive amelioration in NF1. Nevertheless, current results must be interpreted with caution pending independent controlled replication.
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Amplitude of low-frequency oscillations in schizophrenia: a Resting State fMRI study.
Schizophrenia research, 2009Co-Authors: Matthew J. Hoptman, Xi-nian Zuo, Pamela D. Butler, Daniel C. Javitt, Debra D'angelo, Cristina J. Mauro, Michael P. MilhamAbstract:Recently, a great deal of interest has arisen in Resting State fMRI as a measure of tonic brain function in clinical populations. Most studies have focused on the examination of temporal correlation between Resting State fMRI low-frequency oscillations (LFOs). Studies on the amplitudes of these low-frequency oscillations are rarely reported. Here, we used amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF; the relative amplitude that resides in the low frequencies) to examine the amplitude of LFO in schizophrenia. Twenty-six healthy controls and 29 patients with schizophrenia or schizoaffective disorder participated. Our findings show that patients showed reduced low-frequency amplitude in proportion to the total frequency band investigated (i.e., fALFF) in the lingual gyrus, left cuneus, left insula/superior temporal gyrus, and right caudate and increased fALFF in the medial prefrontal cortex and the right parahippocampal gyrus. ALFF was reduced in patients in the lingual gyrus, cuneus, and precuneus and increased in the left parahippocampal gyrus. These results suggest LFO abnormalities in schizophrenia. The implication of these abnormalities for schizophrenic symptomatology is further discussed.
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functional connectivity of the human amygdala using Resting State fMRI
NeuroImage, 2009Co-Authors: Amy Krain Roy, Bharat B Biswal, Zarrar Shehzad, Daniel S Margulies, A Clare M Kelly, Lucina Q Uddin, Kristin Gotimer, Xavier F Castellanos, Michael P. MilhamAbstract:The amygdala is composed of structurally and functionally distinct nuclei that contribute to the processing of emotion through interactions with other subcortical and cortical structures. While these circuits have been studied extensively in animals, human neuroimaging investigations of amygdala-based networks have typically considered the amygdala as a single structure, which likely masks contributions of individual amygdala subdivisions. The present study uses Resting State functional magnetic resonance imaging (fMRI) to test whether distinct functional connectivity patterns, like those observed in animal studies, can be detected across three amygdala subdivisions: laterobasal, centromedial, and superficial. In a sample of 65 healthy adults, voxelwise regression analyses demonstrated positively-predicted ventral and negatively-predicted dorsal networks associated with the total amygdala, consistent with previous animal and human studies. Investigation of individual amygdala subdivisions revealed distinct differences in connectivity patterns within the amygdala and throughout the brain. Spontaneous activity in the laterobasal subdivision predicted activity in temporal and frontal regions, while activity in the centromedial nuclei predicted activity primarily in striatum. Activity in the superficial subdivision positively predicted activity throughout the limbic lobe. These findings suggest that Resting State fMRI can be used to investigate human amygdala networks at a greater level of detail than previously appreciated, allowing for the further advancement of translational models.
Adeel Razi - One of the best experts on this subject based on the ideXlab platform.
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the effect of global signal regression on dcm estimates of noise and effective connectivity from Resting State fMRI
NeuroImage, 2020Co-Authors: Hannes Almgren, Adeel Razi, Karl J Friston, Frederik Van De Steen, Daniele MarinazzoAbstract:The influence of global BOLD fluctuations on Resting State functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between Resting State networks (RSNs) - as estimated with dynamic causal modelling (DCM) for Resting State fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we considered four extensive longitudinal Resting State fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of information gain with and without GSR. Our results showed negligible to small effects of GSR on effective connectivity within small (separately estimated) RSNs. However, although the effect sizes were small, there was substantial to conclusive evidence for an effect of GSR on connectivity parameters. For between-network connectivity, we found two important effects: the effect of GSR on between-network effective connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. In the cross-sectional (but not in the longitudinal) data, some connections showed substantial to conclusive evidence for an effect of GSR. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters characterising (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small Resting State networks the precision of posterior estimates was greater after GSR. In conclusion, GSR is a minor concern in DCM studies; however, quantitative interpretation of between-network connections (as opposed to average between-network connectivity) and noise parameters should be treated with some caution. The Kullback-Leibler divergence of the posterior from the prior (i.e., information gain) - together with the precision of posterior estimates - might offer a useful measure to assess the appropriateness of GSR in Resting State fMRI.
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the effect of global signal regression on dcm estimates of noise and effective connectivity from Resting State fMRI
bioRxiv, 2019Co-Authors: Hannes Almgren, Adeel Razi, Karl J Friston, Frederik Van De Steen, Daniele MarinazzoAbstract:Abstract The influence of the global BOLD signal on Resting State functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between Resting-State networks – as estimated with dynamic causal modelling (DCM) for Resting State fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal Resting State fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small Resting State networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in Resting State fMRI.
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hierarchical dynamic causal modeling of Resting State fMRI reveals longitudinal changes in effective connectivity in the motor system after thalamotomy for essential tremor
Frontiers in Neurology, 2017Co-Authors: Haejeong Park, Adeel Razi, Karl J Friston, Chongwon Pae, Changwon Jang, Peter Zeidman, Won Seok Chang, Jin Woo ChangAbstract:Thalamotomy at the ventralis intermedius nucleus for essential tremor is known to cause changes in motor circuitry, but how a focal lesion leads to progressive changes in connectivity is not clear. In order to understand the mechanisms by which thalamotomy exerts enduring effects on motor circuitry, a quantitative analysis of directed or effective connectivity among motor-related areas is required. We characterized changes in effective connectivity of the motor system following thalamotomy using (spectral) dynamic causal modelling (spDCM) for Resting State fMRI. To differentiate long-lasting treatment effects from transient effects, and to identify symptom-related changes in effective connectivity, we subject longitudinal Resting-State fMRI data to spDCM, acquired 1 day prior to, and 1 day, 7 days, and 3 months after thalamotomy using a non-cranium-opening MRI-guided focused ultrasound ablation technique. For the group-level (between subject) analysis of longitudinal (between session) effects, we introduce a multi-level parametric empirical Bayes analysis (PEB) for spDCM. We found remarkably selective and consistent changes in effective connectivity from the ventrolateral nuclei and the supplementary motor area to the contralateral dentate nucleus after thalamotomy, which may be mediated via a polysynaptic thalamic-cortical-cerebellar motor loop. Crucially, changes in effective connectivity predicted changes in clinical motor-symptom scores after thalamotomy. This study speaks to the efficacy of thalamotomy in regulating the dentate nucleus in the context of treating essential tremor. Furthermore, it illustrates the utility of PEB for group-level analysis of DCM in quantifying longitudinal changes in effective connectivity; i.e., measuring long-term plasticity in human subjects noninvasively.
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construct validation of a dcm for Resting State fMRI
NeuroImage, 2015Co-Authors: Joshua Kahan, Adeel Razi, Geraint Rees, Karl J FristonAbstract:Recently, there has been a lot of interest in characterising the connectivity of Resting State brain networks. Most of the literature uses functional connectivity to examine these intrinsic brain networks. Functional connectivity has well documented limitations because of its inherent inability to identify causal interactions. Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems — known as effective connectivity. This technical note addresses the validity of a recently proposed DCM for Resting State fMRI – as measured in terms of their complex cross spectral density – referred to as spectral DCM. Spectral DCM differs from (the alternative) stochastic DCM by parameterising neuronal fluctuations using scale free (i.e., power law) forms, rendering the stochastic model of neuronal activity deterministic. Spectral DCM not only furnishes an efficient estimation of model parameters but also enables the detection of group differences in effective connectivity, the form and amplitude of the neuronal fluctuations or both. We compare and contrast spectral and stochastic DCM models with endogenous fluctuations or State noise on hidden States. We used simulated data to first establish the face validity of both schemes and show that they can recover the model (and its parameters) that generated the data. We then used Monte Carlo simulations to assess the accuracy of both schemes in terms of their root mean square error. We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation using real Resting State fMRI data (from an open access resource) to study the functional integration within default mode network using spectral and stochastic DCMs.
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A DCM for Resting State fMRI.
NeuroImage, 2013Co-Authors: Karl J Friston, Joshua Kahan, BIBHUDATTA BISWAL, Adeel RaziAbstract:This technical note introduces a dynamic causal model (DCM) for Resting State fMRI time series based upon observed functional connectivity-as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal States that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.
Daniele Marinazzo - One of the best experts on this subject based on the ideXlab platform.
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the effect of global signal regression on dcm estimates of noise and effective connectivity from Resting State fMRI
NeuroImage, 2020Co-Authors: Hannes Almgren, Adeel Razi, Karl J Friston, Frederik Van De Steen, Daniele MarinazzoAbstract:The influence of global BOLD fluctuations on Resting State functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between Resting State networks (RSNs) - as estimated with dynamic causal modelling (DCM) for Resting State fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we considered four extensive longitudinal Resting State fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of information gain with and without GSR. Our results showed negligible to small effects of GSR on effective connectivity within small (separately estimated) RSNs. However, although the effect sizes were small, there was substantial to conclusive evidence for an effect of GSR on connectivity parameters. For between-network connectivity, we found two important effects: the effect of GSR on between-network effective connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. In the cross-sectional (but not in the longitudinal) data, some connections showed substantial to conclusive evidence for an effect of GSR. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters characterising (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small Resting State networks the precision of posterior estimates was greater after GSR. In conclusion, GSR is a minor concern in DCM studies; however, quantitative interpretation of between-network connections (as opposed to average between-network connectivity) and noise parameters should be treated with some caution. The Kullback-Leibler divergence of the posterior from the prior (i.e., information gain) - together with the precision of posterior estimates - might offer a useful measure to assess the appropriateness of GSR in Resting State fMRI.
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the effect of global signal regression on dcm estimates of noise and effective connectivity from Resting State fMRI
bioRxiv, 2019Co-Authors: Hannes Almgren, Adeel Razi, Karl J Friston, Frederik Van De Steen, Daniele MarinazzoAbstract:Abstract The influence of the global BOLD signal on Resting State functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between Resting-State networks – as estimated with dynamic causal modelling (DCM) for Resting State fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal Resting State fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small Resting State networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in Resting State fMRI.
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hemodynamic response function hrf variability confounds Resting State fMRI functional connectivity
Magnetic Resonance in Medicine, 2018Co-Authors: D Rangaprakash, Daniele Marinazzo, Gopikrishna DeshpandeAbstract:PURPOSE fMRI is the convolution of the hemodynamic response function (HRF) and unmeasured neural activity. HRF variability (HRFv) across the brain could, in principle, alter functional connectivity (FC) estimates from Resting-State fMRI (rs-fMRI). Given that HRFv is driven by both neural and non-neural factors, it is problematic when it confounds FC. However, this aspect has remained largely unexplored even though FC studies have grown exponentially. We hypothesized that HRFv confounds FC estimates in the brain's default-mode-network. METHODS We tested this hypothesis using both simulations (where the ground truth is known and modulated) as well as rs-fMRI data obtained in a 7T MRI scanner (N = 47, healthy). FC was obtained using 2 pipelines: data with hemodynamic deconvolution (DC) to estimate the HRF and minimize HRFv, and data with no deconvolution (NDC, HRFv-ignored). DC and NDC FC networks were compared, along with regional HRF differences, revealing potential false connectivities that resulted from HRFv. RESULTS We found evidence supporting our hypothesis using both simulations and experimental data. With simulations, we found that HRFv could cause a change of up to 50% in FC. With rs-fMRI, several potential false connectivities attributable to HRFv, with majority connections being between different lobes, were identified. We found a double exponential relationship between the magnitude of HRFv and its impact on FC, with a mean/median error of 30.5/11.5% caused in FC by HRF confounds. CONCLUSION HRFv, if ignored, could cause identification of false FC. FC findings from HRFv-ignored data should be interpreted cautiously. We suggest deconvolution to minimize HRFv.
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posterior cingulate cortex related co activation patterns a Resting State fMRI study in propofol induced loss of consciousness
PLOS ONE, 2014Co-Authors: Enrico Amico, Audrey Vanhaudenhuyse, Daniele Marinazzo, Francisco Gomez, Carol Di Perri, Damien Lesenfants, Pierre Boveroux, Vincent Bonhomme, Jeanfrancois Brichant, Steven LaureysAbstract:Background: Recent studies have been shown that functional connectivity of cerebral areas is not a static phenomenon, but exhibits spontaneous fluctuations over time. There is evidence that fluctuating connectivity is an intrinsic phenomenon of brain dynamics that persists during anesthesia. Lately, point process analysis applied on functional data has revealed that much of the information regarding brain connectivity is contained in a fraction of critical time points of a Resting State dataset. In the present study we want to extend this methodology for the investigation of Resting State fMRI spatial pattern changes during propofol-induced modulation of consciousness, with the aim of extracting new insights on brain networks consciousness-dependent fluctuations. Methods: Resting-State fMRI volumes on 18 healthy subjects were acquired in four clinical States during propofol injection: wakefulness, sedation, unconsciousness, and recovery. The dataset was reduced to a spatio-temporal point process by selecting time points in the Posterior Cingulate Cortex (PCC) at which the signal is higher than a given threshold (i.e., BOLD intensity above 1 standard deviation). Spatial clustering on the PCC time frames extracted was then performed (number of clusters=8), to obtain 8 different PCC co-activation patterns (CAPs) for each level of consciousness. Results: The current analysis shows that the core of the PCC-CAPs throughout consciousness modulation seems to be preserved. Nonetheless, this methodology enables to differentiate region-specific propofol-induced reductions in PCC-CAPs, some of them already present in the functional connectivity literature (e.g., disconnections of the prefrontal cortex, thalamus, auditory cortex), some others new (e.g., reduced co-activation in motor cortex and visual area). Conclusion: In conclusion, our results indicate that the employed methodology can help in improving and refining the characterization of local functional changes in the brain associated to propofol-induced modulation of consciousness. Citation: Amico E, Gomez F, Di Perri C, Vanhaudenhuyse A, Lesenfants D, et al. (2014) Posterior Cingulate Cortex-Related Co-Activation Patterns: A Resting State
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a blind deconvolution approach to recover effective connectivity brain networks from Resting State fMRI data
Medical Image Analysis, 2013Co-Authors: Wei Liao, Jurong Ding, Sebastiano Stramaglia, H Chen, Daniele MarinazzoAbstract:A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for Resting-State fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. In a recent study it has been proposed that relevant information in Resting-State fMRI can be obtained by inspecting the discrete events resulting in relatively large amplitude BOLD signal peaks. Following this idea, we consider Resting fMRI as ‘spontaneous event-related’, we individuate point processes corresponding to signal fluctuations with a given signature, extract a region-specific HRF and use it in deconvolution, after following an alignment procedure. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions.
Yufeng Zang - One of the best experts on this subject based on the ideXlab platform.
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influences of head motion regression on high frequency oscillation amplitudes of Resting State fMRI signals
Frontiers in Human Neuroscience, 2016Co-Authors: Binke Yuan, Yufeng Zang, Dongqiang LiuAbstract:High-frequency oscillations (HFOs, > 0.1 Hz) of Resting-State fMRI (rs-fMRI) signals have received much attention in recent years. Denoising is critical for HFO studies. Previous work indicated that head motion (HM) has remarkable influences on a variety of rs-fMRI metrics, but its influences on rs-fMRI HFOs are still unknown. In this study, we investigated the impacts of HM regression (HMR) on HFO results using a fast sampling rs-fMRI dataset. We demonstrated that apparent high-frequency (~0.2–0.4 Hz) components existed in the HM trajectories in majority of subjects. In addition, we found that individual-level HMR could robustly reveal more between-condition (eye-open vs. eye-closed) amplitude differences in high-frequency bands. Although regression of mean framewise displacement (FD) at the group level had little impact on the results, mean FD could significantly account for inter-subject variance of HFOs even after individual-level HMR. Our findings suggest that HM artifacts should not be ignored in HFO studies, and HMR is necessary for detecting HFO between-condition differences.
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Spatial vs. Temporal Features in ICA of Resting-State fMRI - A Quantitative and Qualitative Investigation in the Context of Response Inhibition
PLoS ONE, 2013Co-Authors: Lixia Tian, Gael Varoquaux, Yufeng Zang, Yazhuo Kong, Juejing Ren, Stephen SmithAbstract:Independent component analysis (ICA) can identify covarying functional networks in the Resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from Resting State fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of Resting-State fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each Resting State network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension, spatial smoothing and Z-transformation of the spatial maps, as well as the techniques for evaluating the temporal correlation between RSN timeseries. Overall, the temporal information acquired by ICA enabled us to investigate brain function from a complementary perspective to the information provided by spatial maps.
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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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an improved approach to detection of amplitude of low frequency fluctuation alff for Resting State fMRI fractional alff
Journal of Neuroscience Methods, 2008Co-Authors: Qihong Zou, Xi-nian Zuo, Yufeng Zang, Chaozhe Zhu, Yihong Yang, Xiangyu Long, Qingjiu Cao, Yufeng WangAbstract:Most of the Resting-State functional magnetic resonance imaging (fMRI) studies demonstrated the correlations between spatially distinct brain areas from the perspective of functional connectivity or functional integration. The functional connectivity approaches do not directly provide information of the amplitude of brain activity of each brain region within a network. Alternatively, an index named amplitude of low-frequency fluctuation (ALFF) of the Resting-State fMRI signal has been suggested to reflect the intensity of regional spontaneous brain activity. However, it has been indicated that the ALFF is also sensitive to the physiological noise. The current study proposed a fractional ALFF (fALFF) approach, i.e., the ratio of power spectrum of low-frequency (0.01-0.08 Hz) to that of the entire frequency range and this approach was tested in two groups of Resting-State fMRI data. The results showed that the brain areas within the default mode network including posterior cingulate cortex, precuneus, medial prefrontal cortex and bilateral inferior parietal lobule had significantly higher fALFF than the other brain areas. This pattern was consistent with previous neuroimaging results. The non-specific signal components in the cistern areas in Resting-State fMRI were significantly suppressed, indicating that the fALFF approach improved the sensitivity and specificity in detecting spontaneous brain activities. Its mechanism and sensitivity to abnormal brain activity should be evaluated in the future studies. (C) 2008 Elsevier B.V. All rights reserved.
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.
