2011
DOI: 10.3389/fninf.2011.00018
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Hierarchical information-based clustering for connectivity-based cortex parcellation

Abstract: One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (“the functional fingerprint”) is closely related to its anatomical connections (“the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each… Show more

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Cited by 37 publications
(42 citation statements)
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“…The main issues with these methods are the definition of the number of clusters a priori and the reliance on initial random sampling, as it has been shown that iterative repetitions of the same method may lead to different results [9]. To overcome these limitations, and assuming that brain networks have hierarchical properties [10,11], several hierarchical 25 clustering methods that compute a parcellation at each level in the hierarchy have been proposed [12,13,14,15]. These methods obtain brain parcellations at multiple granularities without the need to define the number of clusters.…”
Section: Introductionmentioning
confidence: 99%
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“…The main issues with these methods are the definition of the number of clusters a priori and the reliance on initial random sampling, as it has been shown that iterative repetitions of the same method may lead to different results [9]. To overcome these limitations, and assuming that brain networks have hierarchical properties [10,11], several hierarchical 25 clustering methods that compute a parcellation at each level in the hierarchy have been proposed [12,13,14,15]. These methods obtain brain parcellations at multiple granularities without the need to define the number of clusters.…”
Section: Introductionmentioning
confidence: 99%
“…Gorbach et al [13] proposed a hierarchical method 30 that clusters voxels using the mutual information between tractograms, and obtained promising results for specific regions of the brain. The use of mutual information as a similarity measure is, therefore, an effective solution to group voxels.…”
Section: Introductionmentioning
confidence: 99%
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“…This Research Topic of Frontiers in Neuroinformatics, dedicated to the memory of Rolf Kötter (1961Kötter ( -2010 and his pioneering work in the field of brain connectomics, comprises contributions that elucidate different levels of connectivity analysis (from MRIbased methods, through axonal tracing techniques, to mapping of functional connectivity in relation to detailed 3-D reconstructions Gorbach et al, 2011;Yendiki et al, 2011] to ex vivo mapping of detailed fiber architectures [(C,e) Axer et al, 2011b; (e,F) Annese, 2012]. (g-H) Novel experimental methods in animal models include combined optogenetic and functional MRI mapping of specific connections (g) (Lee, 2011) (Borisyuk et al, 2011).…”
Section: Background and Scopementioning
confidence: 99%
“…Knowledge about the specific hodological organization of different brain regions may thus predict the various functional properties of such regions. Gorbach et al (2011) explore relationships between the functional and connectional "fingerprints" of cerebrocortical areas in the human brain, by using hierarchical information-based clustering of MRI-based connectivity measures. They propose an automated hierarchical parcelation approach to identify cortical subunits that are consistent with cytoarchitectonic maps and previous connectivity-based parcelation schemes ( Figure 1K).…”
Section: Integrative Efforts: Assembling Connectomesmentioning
confidence: 99%