Investigations 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: 7.06). 06/2005; 360(1457):1001-13. DOI: 10.1098/rstb.2005.1634
Source: PubMed


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; DOI:10.1002/hbm.22933 · 5.97 Impact Factor
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    • "kmann et al . , 2005 ; Khalili - Mahani et al . , 2014 ) . To account for noise , even after FIX , a white matter , and a cerebrospinal fluid template were included in the analysis ( Fox et al . , 2005 ; Birn , 2012 ) . In the dual regression , individual time series were first extracted for each template , using the eight resting state networks ( Beckmann et al . , 2005 ) and the two additional white matter and cerebrospinal fluid maps ( Fox et al . , 2005 ; Birn , 2012 ) , in a spatial regression against the individual fMRI data set ( regression 1 ) ."
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    ABSTRACT: Introduction: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. Early differentiation between both types of dementia may be challenging due to heterogeneity and overlap of symptoms. Here, we apply resting state functional magnetic resonance imaging (fMRI) to study functional brain connectivity differences between AD and bvFTD. Methods: We used resting state fMRI data of 31 AD patients, 25 bvFTD patients, and 29 controls from two centers specialized in dementia. We studied functional connectivity throughout the entire brain, applying two different analysis techniques, studying network-to-region and region-to-region connectivity. A general linear model approach was used to study group differences, while controlling for physiological noise, age, gender, study center, and regional gray matter volume. Results: Given gray matter differences, we observed decreased network-to-region connectivity in bvFTD between (a) lateral visual cortical network and lateral occipital and cuneal cortex, and (b) auditory system network and angular gyrus. In AD, we found decreased network-to-region connectivity between the dorsal visual stream network and lateral occipital and parietal opercular cortex. Region-to-region connectivity was decreased in bvFTD between superior temporal gyrus and cuneal, supracalcarine, intracalcarine cortex, and lingual gyrus. Conclusion: We showed that the pathophysiology of functional brain connectivity is different between AD and bvFTD. Our findings support the hypothesis that resting state fMRI shows disease-specific functional connectivity differences and is useful to elucidate the pathophysiology of AD and bvFTD. However, the group differences in functional connectivity are less abundant than has been shown in previous studies.
    Frontiers in Human Neuroscience 10/2015; 9:474. DOI:10.3389/fnhum.2015.00474 · 3.63 Impact Factor
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    • "However, little research has examined the relation between AVG experience and the integration of attentional and working memory networks. Research of resting-state functional connectivity (FC, a dynamic coordinated activity for communicating information on connected brain regions [22] [23]) reveals two separate functional networks for attention and working memory, respectively [24] [25] [26] [27] [28] [29]. The Salience Network (SN), typically including anterior cingulate cortex (ACC) and anterior insula, supports the detection of salient events. "

    Neural Plasticity 10/2015; · 3.58 Impact Factor
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