Detecting functional connectivity in the resting brain: a comparison between ICA and CCA

Ma, Liangsuo; Wang, Binquan; Chen, Xiying; Xiong, Jinhu

doi:10.1016/j.mri.2006.09.032

Cited by 94 publications

(91 citation statements)

References 21 publications

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“…This findings is novel in that while a number of previous investigations have highlighted a convergence in the information provided by the 2 methods, [19][20][21][22][23][24] they have done so mainly on the basis of a qualitative judgment. To our knowledge, this study is the first to explore the issue in a quantitative manner on a whole dataset.…”

Section: Discussionmentioning

confidence: 85%

“…13,20,21,25 This effect suggests that the representation of the statistical properties of the dataset by ICA may be dependent on the number of components chosen, a finding in line with previous work that reported that the spatial and temporal discriminative ability of ICA is critically dependent on this parameter. 22,33 Indeed, 1 reason for partial correspondence between ICA and time course correlation analysis is that ICA can produce "fragmented" networks, whereby given networks of coherent activity, which would appear together in a single seed-based map, are scattered across multiple components. It has been shown that this effect is critically dependent on the choice of the number of components: As this is increased, the decomposition becomes less stable and some networks (such as the visual components) branch into clearly distinct subcomponents, whereas others apparently do not (such as the sensorimotor network).…”

Section: Discussionmentioning

confidence: 99%

“…A number of neuroimaging studies have shown that these 2 methods yield converging results [19][20][21][22][23][24] : For instance, the studies by Bluhm et al 20 and Long et al 21 have indicated that these 2 approaches identify the areas included in the DMN consistently, resulting in connectivity maps that are visually similar. Van Dijk et al 24 have examined the similarities between the 2 methods with a more quantitative analysis, albeit for 1 functional network only, showing that the correlation between ICA and the seed-based approach is moderate for the DMN (r ϭ 0.45).…”

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confidence: 99%

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Functional Connectivity during Resting-State Functional MR Imaging: Study of the Correspondence between Independent Component Analysis and Region-of-Interest−Based Methods

Rosazza¹,

Minati²,

Ghielmetti³

et al. 2011

AJNR Am J Neuroradiol

106

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Functional Connectivity during Resting-State Functional MR Imaging: Study of the Correspondence between Independent Component Analysis and Region-of-Interest−Based Methods

Rosazza¹,

Minati²,

Ghielmetti³

et al. 2011

AJNR Am J Neuroradiol

106

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show abstract

“…In addition, although these two methods (each with their own pros and cons; see ref. 20) often reveal similar rs-fcMRI findings in adults (25,26), these findings remain to be demonstrated in children.…”

Section: Discussionmentioning

confidence: 89%

“…Last, a recent publication by Fransson et al (24) using independent component analysis in sleeping preterm infants (studied at term equivalent) failed to find a complete default network, suggesting that spontaneous activity in the default network does, indeed, undergo developmental change. However, despite this consistency, it should be noted that independent component analysis (ICA) and correlation analysis do not always reveal identical results (25). In addition, although these two methods (each with their own pros and cons; see ref.…”

Section: Discussionmentioning

confidence: 99%

The maturing architecture of the brain's default network

Fair¹,

Cohen

Dosenbach

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

1,210

960

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In recent years, the brain's “default network,” a set of regions characterized by decreased neural activity during goal-oriented tasks, has generated a significant amount of interest, as well as controversy. Much of the discussion has focused on the relationship of these regions to a “default mode” of brain function. In early studies, investigators suggested that, the brain's default mode supports “self-referential” or “introspective” mental activity. Subsequently, regions of the default network have been more specifically related to the “internal narrative,” the “autobiographical self,” “stimulus independent thought,” “mentalizing,” and most recently “self-projection.” However, the extant literature on the function of the default network is limited to adults, i.e., after the system has reached maturity. We hypothesized that further insight into the network's functioning could be achieved by characterizing its development. In the current study, we used resting-state functional connectivity MRI (rs-fcMRI) to characterize the development of the brain's default network. We found that the default regions are only sparsely functionally connected at early school age (7–9 years old); over development, these regions integrate into a cohesive, interconnected network.

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Visual target modulation of functional connectivity networks revealed by self‐organizing group ICA

Ven¹,

Bledowski²,

Prvulovic³

et al. 2007

Human Brain Mapping

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We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study.

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Detecting functional connectivity in the resting brain: a comparison between ICA and CCA

Cited by 94 publications

References 21 publications

Functional Connectivity during Resting-State Functional MR Imaging: Study of the Correspondence between Independent Component Analysis and Region-of-Interest−Based Methods

Functional Connectivity during Resting-State Functional MR Imaging: Study of the Correspondence between Independent Component Analysis and Region-of-Interest−Based Methods

The maturing architecture of the brain's default network

Visual target modulation of functional connectivity networks revealed by self‐organizing group ICA

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