Information flow between resting state networks
Ibai DíezAsier ErramuzpeIñaki EscuderoBeatriz MateosA CabreraDaniele MarinazzoErnesto Sanz‐ArigitaSebastiano StramagliaJesús M. Cortés
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The resting brain dynamics self-organizes into a finite number of correlated patterns known as resting state networks (RSNs). It is well known that techniques like independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting state magnetic resonance imaging. After haemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of Transfer Entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k = 1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k greater than one our method calculates the k-multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension-dependent, increasing from k =1 (i.e., the average voxels activity) up to a maximum occurring at k =5 to finally decay to zero for k greater than 10. This suggests that a small number of components (close to 5) is sufficient to describe the IF pattern between RSNs. Our method - addressing differences in IF between RSNs for any generic data - can be used for group comparison in health or disease. To illustrate this, we have calculated the interRSNs IF in a dataset of Alzheimer's Disease (AD) to find that the most significant differences between AD and controls occurred for k =2, in addition to AD showing increased IF w.r.t. controls.The concept of resting state functional magnetic resonance imaging (fMRI) is built onto an original finding in 1995 that brain hemispheres present synchronous signal fluctuations with distinct patterns. fMRI measurements rely on blood oxygenation changes that indirectly mirror neural activity. Therefore, the origin of certain functional connectivity patterns, resting state networks (RSNs), has been a widely debated research question and numerous contributing factors have been identified. According to current understanding the fluctuations reflect maintenance of the system integrity in addition to spontaneous thought and action processes in the resting state. A popular method to study the functional connectivity in resting state fMRI is spatial independent component analysis (ICA) that decomposes signal sources into statistically independent components. The dichotomy of functional specialization versus functional integration has a correspondence in fMRI studies where RSNs play the integrative viewpoint of brain function. Although canonical large-scale RSNs are broadly distributed they also express modularity that can be accomplished by ICA with a high number of estimated components. The characteristics of high ICA dimensionality are broadly investigated in the thesis. An enduring issue in resting state research has been the confounding noise sources like motion and cardiorespiratory processes which may hamper the analysis. In this thesis the ability of ICA to separate these noise sources from the default mode network, a major RSN, is studied. Additionally, the suitability of ICA for full frequency spectrum analysis, a relatively rare setting in biosignal analysis, is investigated. The results of the thesis support the viewpoint of ICA as a robust analysis method for functional connectivity analysis. Cardiorespiratory and motion induced noise did not confound the functional connectivity analyses with ICA. High dimensional ICA provided better signal source separation, revealed the modular structure of the RSNs and pinpointed the specific aberrations in the autism spectrum disorder population. ICA was also found applicable for fully explorative analysis in both the spatial and temporal domains and indicated functional connectivity changes induced by transcranial bright light stimulation.
Communication noise
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In this paper, we analyze the functional connectivity between the various parts of the brain using resting state fMRI(Functional Magnetic Resonance Imaging). Resting state fMRI scans are obtained when the subjects are relaxed and not involved in any task. During rest, various networks are active in the brain, named as resting state network (RSN) which includes Default Mode Network (DMN), Executives control network, salience, auditory, visual, dorsal attention and sensorimotor networks. We are using two methods to analyze the connectivity i.e. seed based method and Independent Component Analysis (ICA). In seed based method, the correlation of the seed is found with all other voxels. ICA aims at finding the various independent components based on the time series BOLD (Blood Oxygen Level Dependent) signals. Both of these methods are able to successfully identify various RSN. Seed based method is useful for the detailed analysis of a particular Region of Interest(ROI). On the other hand, ICA clearly identifies all the independent networks.
Salience (neuroscience)
Blood-oxygen-level dependent
Region of interest
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The parts of the human body affected by a disease do not only undergo structural changes but also demonstrate significant physiological (functional) abnormalities. An important parameter that reveals the functional state of tissue is the flow of blood per unit tissue volume or perfusion, which can be obtained using dynamic imaging methods. One mathematical approach widely used for estimating perfusion from dynamic imaging data is based on a convolutional tissue-flow model. In these approaches, deconvolution of the observed data is necessary to obtain the important physiological parameters within a voxel. Although several alternatives have been proposed for deconvolution, all of them treat neighboring voxels independently and do not exploit the spatial correlation between voxels or the temporal correlation within a voxel over time. These simplistic approaches result in a noisy perfusion map with poorly defined region boundaries. In this paper, we propose a novel perfusion estimation method which incorporates spatial as well as temporal correlation into the deconvolution process. Performance of our method is compared to standard methods using independent voxel processing. Both simulated and real data experiments illustrate the potential of our method.
Convolution (computer science)
Spatial correlation
Temporal resolution
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Group analysis
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Blind separation of mixture images which mutually independent has been solved efficiently by some independent component analysis(ICA) methods. But these methods often failed in case of the source images are statistically non-independent. A novel fixed-point FastICA algorithm based on complexity pursuit is presented in this paper and with the algorithm the mixed images which not mutually independent can be separated successfully. Experimental results demonstrate the efficiency of our proposed method.
FastICA
Component (thermodynamics)
Separation (statistics)
Component analysis
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Objective To examine the feasibility of functional localization in the human brain with resting-state (task-free) fMRI data using independent component analysis (ICA). Methods ICA was used to study the functional connectivity in resting-state in order to locate the functional regions. The resting-state fMRI data were collected using short TR,and the major impact of various physiological noises was eliminated after the data were low-pass filtered (cutoff frequency=0.08 Hz). ICA components were verified through reproducibility analysis,and only highly reproducible components were retained in the analysis of data. The results of ICA and the seed voxel method were then quantitatively compared for consistency. Results ICA was able to separate the functional connectivity maps for motor and primary visual systems without selecting the seed voxel. The results of ICA had high consistency with those of traditional seed voxel method. Furthermore,ICA simultaneously obtained the functional connectivity maps for the two systems within one dataset. Conclusion ICA overcame the subjectiveness in the seed voxel method,and was capable to obtain functional connectivity from resting-state fMRI data. This study supports the hypothesis that there is stronger functional connectivity within primary systems than between them. Moreover,the current study has demonstrated potential capability of ICA in clinical applications.
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Independent component analysis (ICA) is a new technique to statistically extract independent components from the observed multidimensional mixture of data. Many successful examples of ICA application in the filed of signal processing are reported recently. Independent component analysis (ICA) was originally developed to deal with problems that are closely related to cocktail- party problems.ICA is a powerful and useful statistical tool for extracting independent source given only observed data that are mixtures of the unknown sources.
Component (thermodynamics)
SIGNAL (programming language)
Source Separation
Separation (statistics)
Component analysis
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Blind separation of mixture images which mutually independent has been solved efficiently by some independent component analysis(ICA) methods. But these methods often failed in case of the source images are statistically non-independent. A novel fixed-point FastICA algorithm based on complexity pursuit is presented in this paper and with the algorithm the mixed images which not mutually independent can be separated successfully. Experimental results demonstrate the efficiency of our proposed method.
FastICA
Component (thermodynamics)
Separation (statistics)
Component analysis
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