Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI
Salla‐Maarit KokkonenJuha NikkinenJukka RemesJussi KantolaTuomo StarckMarianne HaapeaJuho TuominenOsmo TervonenVesa Kiviniemi
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Keywords:
Finger tapping
Brain mapping
Sensorimotor cortex
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.
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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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Finger tapping
Brain mapping
Sensorimotor cortex
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Measuring whole-brain functional connectivity patterns based on task-free ('resting-state') spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisition is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns (commonly termed 'resting state networks'). We employed a 'steady-states' paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing us to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis) using standard resting-state functional connectivity (FC) analysis, we show that the whole-brain network architecture characteristic of the resting-state is comparable across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Changes in functional connectivity were detected locally, within the active networks. But to identify these local changes, the contributions of different FC networks to the global intrinsic connectivity pattern had to be isolated. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.
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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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The past decade has seen astounding discoveries about resting-state brain activity patterns in normal brain as well as their alterations in brain diseases. While the vast majority of resting-state studies are based on the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI), arterial spin labeling (ASL) perfusion fMRI can simultaneously capture BOLD and cerebral blood flow (CBF) signals, providing a unique opportunity for assessing resting brain functions with concurrent BOLD (ccBOLD) and CBF signals. Before taking that benefit, it is necessary to validate the utility of ccBOLD signal for resting-state analysis using conventional BOLD (cvBOLD) signal acquired without ASL modulations. To address this technical issue, resting cvBOLD and ASL perfusion MRI were acquired from a large cohort (n = 89) of healthy subjects. Four widely used resting-state brain function analyses were conducted and compared between the two types of BOLD signal, including the posterior cingulate cortex (PCC) seed-based functional connectivity (FC) analysis, independent component analysis (ICA), analysis of amplitude of low frequency fluctuation (ALFF), and analysis of regional homogeneity (ReHo). Consistent default mode network (DMN) as well as other resting-state networks (RSNs) were observed from cvBOLD and ccBOLD using PCC-FC analysis and ICA. ALFF from both modalities were the same for most of brain regions but were different in peripheral regions suffering from the susceptibility gradients induced signal drop. ReHo showed difference in many brain regions, likely reflecting the SNR and resolution differences between the two BOLD modalities. The DMN and auditory networks showed highest CBF values among all RSNs. These results demonstrated the feasibility of ASL perfusion MRI for assessing resting brain functions using its concurrent BOLD in addition to CBF signal, which provides a potentially useful way to maximize the utility of ASL perfusion MRI.
Posterior cingulate
Blood-oxygen-level dependent
Brain mapping
Human brain
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Brain mapping
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Purpose: The objective of this study was to separate multiple signal components present in functional MRI (fMRI) data sets. Blind source separation techniques were applied to the analysis of fMRI data to determine multiple physiologically relevant independent signal sources. Method: Computer simulations were performed to test the reliability and robustness of the independent component analysis (ICA). Four subjects (3 males and 1 female between 14 and 29 years old) were scanned under various stimulus conditions: (1) rest while breathing room air, (2) bilateral finger tapping while breathing room air, and (3) hypercapnia during bilateral finger tapping. Results: Simulations performed on synthetic data sets demonstrated that not only could the algorithm reliably detect the shapes of each of the source signals, but it also preserved their relative amplitudes. The algorithm also performed robustly in the presence of noise. With use of fMRI time series data sets from bilateral finger tapping during hypercapnia, distinct physiologically relevant independent sources were reliably estimated. One independent component corresponded to the hypercapnic cerebrovascular response, and another independent component corresponded to cortical activation from bilateral finger tapping. In three of the four subjects, the underlying fluctuations in signal related to baseline respiratory rate were identified in the third independent component. Principal component analysis (PCA) could not separate these two independent physiological components. Conclusion: With use of ICA, signals originating from independent sources could be separated from a linear mixture of observed data. Limitations of PCA were also demonstrated.
Finger tapping
Source Separation
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