Hierarchical clustering to measure connectivity in fMRI resting-state data

Cordes, Dietmar; Haughton, V M; Carew, John D.; Arfanakis, Konstantinos; Maravilla, K R

doi:10.1016/s0730-725x(02)00503-9

Cited by 335 publications

(253 citation statements)

References 23 publications

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“…Application of this type of clustering in fMRI analysis has so far focused on grouping voxels into small, functionally homogeneous regions (Cordes, Haughton, Carew, Arfanakis, & Maravilla, 2002;Filzmoser et al, 1999;Goutte et al, 1999;Thirion & Faugeras, 2004). In contrast, here we demonstrate the benefit of using clustering to construct a top-down model of global patterns of activation spanning the entire brain.…”

Section: Other Data-driven Approachesmentioning

confidence: 67%

Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems

Golland²,

et al. 2008

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Global organizational principles are critical for understanding cortical functionality. Recently we proposed a global sub-division of the posterior cortex into two large-scale systems. One system, labeled extrinsic, comprises the sensory-motor cortex, and is associated with the external environment. The second system, labeled intrinsic, overlaps substantially with the previously described "default-mode" network, and is likely associated with inner-oriented processing. This global partition of the cerebral cortex emerged from hemodynamic imaging data the analysis of which was constrained by pre-determined hypotheses. Here we applied a hypothesis-free, unsupervised two-class clustering algorithm (k-means) to a large set of fMRI data. The two clusters delineated by this unsupervised hypothesis-free procedure showed high anatomical consistency across individuals, and their cortical topography coincided largely with the previously determined extrinsic and intrinsic systems. These new clustering-based results confirm that the intrinsic-extrinsic subdivision constitutes a fundamental cortical divide.

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Section: Other Data-driven Approachesmentioning

confidence: 67%

Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems

Golland²,

et al. 2008

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“…To objectively separate the eta 2 profiles into groups, as done in Figure 3, hierarchical clustering analysis was performed on the eta 2 profiles to find any strong divisions among the set (Cordes et al, 2002;Dosenbach et al, 2007;Salvador et al, 2005). A '1-eta 2 ' calculation was used as a distance measure between the profiles.…”

Section: Resultsmentioning

confidence: 99%

Defining functional areas in individual human brains using resting functional connectivity MRI

et al. 2008

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The cerebral cortex is anatomically organized at many physical scales starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) studies often focus at the level of areas, networks, and systems. Except in restricted domains, (e.g. topographically-organized sensory regions), it is difficult to determine area boundaries in the human brain using fMRI. The ability to delineate functional areas non-invasively would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified functional areas. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity (rs-fcMRI), are especially promising as a way to accurately localize differences in patterns of correlated activity across large expanses of cortex. In the current report, we applied a novel set of image analysis tools to explore the utility of rs-fcMRI for defining wide-ranging functional area boundaries. We find that rs-fcMRI patterns show sharp transitions in correlation patterns and that these putative areal boundaries can be reliably detected in individual subjects as well as in group data. Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal boundaries across large expanses of cortex without the need for prior information about a region's function or topography. Our approach reliably produces maps of bounded regions appropriate in size and number for putative functional areas. These findings will hopefully stimulate further methodological refinements and validations.CORRESPONDING AUTHORS: Alexander

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“…This argument corresponds to functional theories that assume that only one global functional state occurs at any given moment in time (Baars, 2002a;Efron, 1970). Similar arguments are used to justify spatial clustering procedures in other brain imaging modalities such as fMRI (Cordes et al, 2002), in intracranial animal studies (Stopfer et al, 2003), and even in vitro (Wagenaar et al, 2006), embedding the application of the microstate methodology into an overarching framework used to explain particular functional features of brain activity (discussed in detail in Section 5).…”

Section: Basic Assumptions Of the Eeg Microstate Modelmentioning

confidence: 96%

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

2018

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SummaryThe present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of largescale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales.The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

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Hierarchical clustering to measure connectivity in fMRI resting-state data

Cited by 335 publications

References 23 publications

Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems

Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems

Defining functional areas in individual human brains using resting functional connectivity MRI

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

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