Investigation into resting-state connectivity using independent component analysis

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
Philosophical Transactions of The Royal Society B Biological Sciences (Impact Factor: 6.31). 06/2005; 360(1457):1001-13. DOI: 10.1098/rstb.2005.1634
Source: PubMed

ABSTRACT Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.

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    • "Frequently used hard brain nodes include cytoarchitecture (Brodmann, 1909) and AAL (Tzourio-Mazoyer et al., 2002), while frequently used soft brain nodes include the probabilistic atlases from Jülich (microanatomical) and from Harvard-Oxford (macroanatomical). As a more data-driven variant of whole-brain parcellation, sets of coherent functional nodes can be obtained by (spatial) independent component analysis (ICA; Beckmann et al., 2005; Malherbe et al., 2014; cf. below). "
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    ABSTRACT: Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies.
    Human Brain Mapping 01/2016; · 6.92 Impact Factor
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    • "Independent component analysis ( ICA ) is another way of extracting multiple RSNs of the brain by statistically decomposing the acquired signal in a set of separate intrinsic components ( Beckmann et al . , 2005 ; Damoiseaux et al . , 2006 ) . By doing so , ICA does not require any a priori assumption"
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    ABSTRACT: In the past decade, the fusion between diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) has opened the way for exploring structure-function relationships in vivo. As it stands, the common approach usually consists of analysing fMRI and dMRI datasets separately or using one to inform the other, such as using fMRI activation sites to reconstruct dMRI streamlines that interconnect them. Moreover, given the large inter-individual variability of the healthy human brain, it is possible that valuable information is lost when a fixed set of dMRI/fMRI analysis parameters such as threshold values are assumed constant across subjects. By allowing one to modify such parameters while viewing the results in real-time, one can begin to fully explore the sensitivity of structure-function relations and how they differ across brain areas and individuals. This is especially important when interpreting how structure-function relationships are altered in patients with neurological disorders, such as the presence of a tumor. In this study, we present and validate a novel approach to achieve this: First, we present an interactive method to generate and visualize tractography-driven resting-state functional connectivity, which reduces the bias introduced by seed size, shape and position. Next, we demonstrate that structural and functional reconstruction parameters explain a significant portion of intra-and inter-subject variability. Finally, we demonstrate how our proposed approach can be used in a neurosurgical planning context. We believe this approach will promote the exploration of structure-function relationships in a subject-specific aspect and will open new opportunities for connectomics.
    Frontiers in Neuroscience 08/2015; 9(275). DOI:10.3389/fnins.2015.00275 · 3.70 Impact Factor
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    • "Each extracted component was described as a spatial z-map (zIC) built to quantify each voxel membership to the specific IC. These z-values represent the correlation between the voxel time series and IC time series divided by the standard error of the residual noise (Beckmann et al., 2005). "
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    ABSTRACT: Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has an homologue in the other hemisphere. We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1- homogeneity of its constituting voxels intrinsic activity, and 2- a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 grey nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization. Copyright © 2015. Published by Elsevier B.V.
    Journal of neuroscience methods 07/2015; DOI:10.1016/j.jneumeth.2015.07.013 · 1.96 Impact Factor
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