Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis

Smolders, Alain; Martino, Federico De; Staeren, Noël; Scheunders, Paul; Sijbers, Jan; Goebel, Rainer; Formisano, Elia

doi:10.1016/j.mri.2007.02.018

Cited by 31 publications

(31 citation statements)

References 33 publications

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“…Fuzzyc-meanclusteringisadata-driven methodthatcompares the time courses of voxels, divides them into clusters, andlabels them with a value derivedfrom the distance from the clusters' centroids. This technique is named Bfuzzy^ because insteadof assigning everyelement to exactly one cluster, it produces for each voxel multiple cluster memberships with different probabilities [25]. Allthe centroids and memberships are constantlyupdated following the mathematical procedure described by Bezdek [26], which terminates when the interactions do not significantly change, establishedvia cluster algorithm distance measure.…”

Section: Clusteringcerebellum Activitymentioning

confidence: 99%

Cerebellar Clustering and Functional Connectivity During Pain Processing

et al. 2015

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The cerebellum has been traditionally considered a sensory-motor structure, butmore recently has been relatedto other cognitive andaffective functions. Previous research and meta-analytic studies suggested that it could be involved in pain processing. Our aim was to distinguish the functional networkssubservedbythecerebellumduringpainprocessing. Weusedfunctionalmagnetic resonanceimaging(fMRI)on12 subjects undergoing mechanical pain stimulation and resting state acquisition. For the analysis of data, we used fuzzy c-mean to cluster cerebellar activity of each participant during nociception. The mean time courses of the clusters were used as regressors in a general linear model (GLM)analysis to explore brain functional connectivity (FC) of the cerebellar clusters. We compared our results with the resting state FC of the same cluster and explored with meta-analysis the behavior profile ofthe FC networks.We identified three significantclusters: clusterV,involvingthe culmen and Turin,Turin, Italy 6 NationalInstituteofNeuroscience-Italy,Turin,Italy quadrangular lobules (vermis IV-V, hemispheres IV-V-VI); cluster VI, involving the posterior quadrangular lobule and superior semilunar lobule (hemisphere VI, crus 1, crus 2), andcluster VII, involvingthe inferior semilunar lobule (VIIb, crus1,crus2).ClusterVwas more connectedduringpainwith sensory-motor areas,clusterVIwithcognitive areas, andclusterVII with emotional areas. Our results indicate that during the application of mechanical punctate stimuli, the cerebellum is not only involved in sensory functions but also with areas typically associated with cognitive and affective functions. Cerebellum seems to be involved in various aspects of nociception, reflecting the multidimensionality of pain perception.

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Section: Clusteringcerebellum Activitymentioning

confidence: 99%

Cerebellar Clustering and Functional Connectivity During Pain Processing

et al. 2015

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“…Fuzzy clustering partitions a subset of n voxels in c "clusters" of activation (Zadeh, 1977;Smolders et al, 2007). The z-standardized signal time courses of all voxel are simultaneously considered, compared, and assigned to representative cluster time courses (cluster centroids).…”

Section: Functional Brain Connectivity (Fuzzy Clustering)mentioning

confidence: 99%

“…Settings derived from this analysis were then applied in all other subjects and runs. [Note that Smolders et al (2007) report a complete description of the fuzzy clustering algorithm we used. The same article includes a critical discussion on the influence of the algorithm settings and a comparison of the fuzzy clustering results with those obtained with spatial independent component analysis of the same data.]…”

Section: Functional Brain Connectivity (Fuzzy Clustering)mentioning

confidence: 99%

Dynamic Premotor-to-Parietal Interactions during Spatial Imagery

Sack¹,

Jacobs²,

Martino³

et al. 2008

J. Neurosci.

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The neurobiological processes underlying mental imagery are a matter of debate and controversy among neuroscientists, cognitive psychologists, philosophers, and biologists. Recent neuroimaging studies demonstrated that the execution of mental imagery activates large frontoparietal and occipitotemporal networks in the human brain. These previous imaging studies, however, neglected the crucial interplay within and across the widely distributed cortical networks of activated brain regions. Here, we combined time-resolved eventrelated functional magnetic resonance imaging with analyses of interactions between brain regions (functional and effective brain connectivity) to unravel the premotor-parietal dynamics underlying spatial imagery. Participants had to sequentially construct and spatially transform a mental visual object based on either verbal or visual instructions. By concurrently accounting for the full spatiotemporal pattern of brain activity and network connectivity, we functionally segregated an early from a late premotor-parietal imagery network. Moreover, we revealed that the modality-specific information upcoming from sensory brain regions is first sent to the premotor cortex and then to the medial-dorsal parietal cortex, i.e., top-down from the motor to the perceptual pole during spatial imagery. Importantly, we demonstrate that the premotor cortex serves as the central relay station, projecting to parietal cortex at two functionally distinct stages during spatial imagery. Our approach enabled us to disentangle the multicomponential cognitive construct of mental imagery into its different cognitive subelements. We discuss and explicitly assign these mental subprocesses to each of the revealed effective brain connectivity networks and present an integrative neurobiological model of spatial imagery.

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“…PMC gray matter meshes were segmented from each subject morphological image and coregistered using the BrainVoyager QX high-resolution intersubject cortex alignment (see supplementary methods). Voxels belonging to this region were submitted to a voxelwise unsuper- vised fuzzy clustering technique as implemented in the BrainVoyager QX Fuzzy clustering plugin [56]. Fuzzy clustering partitions a subset of n voxels in c ''clusters'' of activation [56,57].…”

Section: Voxelwise Parcellationmentioning

confidence: 99%

“…Voxels belonging to this region were submitted to a voxelwise unsuper- vised fuzzy clustering technique as implemented in the BrainVoyager QX Fuzzy clustering plugin [56]. Fuzzy clustering partitions a subset of n voxels in c ''clusters'' of activation [56,57]. The z-standardized signal time courses of all voxels are simultaneously considered, compared, and assigned to representative cluster time courses (cluster centroids).…”

Section: Voxelwise Parcellationmentioning

confidence: 99%

022 Functional connectivity of the posteromedial cortex

Cavanna¹,

Geminiani²,

D’Agata³

et al. 2010

Journal of Neurology, Neurosurgery & Psychiatry

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As different areas within the PMC have different connectivity patterns with various cortical and subcortical regions, we hypothesized that distinct functional modules may be present within the PMC. Because the PMC appears to be the most active region during resting state, it has been postulated to play a fundamental role in the control of baseline brain functioning within the default mode network (DMN). Therefore one goal of this study was to explore which components of the PMC are specifically involved in the DMN. In a sample of seventeen healthy volunteers, we performed an unsupervised voxelwise ROI-based clustering based on resting state functional connectivity. Our results showed four clusters with different network connectivity. Each cluster showed positive and negative correlations with cortical regions involved in the DMN. Progressive shifts in PMC functional connectivity emerged from anterior to posterior and from dorsal to ventral ROIs. Ventral posterior portions of PMC were found to be part of a network implicated in the visuo-spatial guidance of movements, whereas dorsal anterior portions of PMC were interlinked with areas involved in attentional control. Ventral retrosplenial PMC selectively correlated with a network showing considerable overlap with the DMN, indicating that it makes essential contributions in self-referential processing, including autobiographical memory processing. Finally, ventral posterior PMC was shown to be functionally connected with a visual network. The paper represents the first attempt to provide a systematic, unsupervised, voxelwise clustering of the human posteromedial cortex (PMC), using resting-state functional connectivity data. Moreover, a ROI-based parcellation was used to confirm the results.

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Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis

Cited by 31 publications

References 33 publications

Cerebellar Clustering and Functional Connectivity During Pain Processing

Cerebellar Clustering and Functional Connectivity During Pain Processing

Dynamic Premotor-to-Parietal Interactions during Spatial Imagery

022 Functional connectivity of the posteromedial cortex

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