Parcellation of the primary cerebral cortices based on local connectivity profiles

Li, Qiaojun; Song, Ming; Fan, Lingzhong; Liu, Yong; Jiang, Tianzi

doi:10.3389/fnana.2015.00050

Cited by 9 publications

(16 citation statements)

References 57 publications

(87 reference statements)

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“…The ideal way to split the V1 ROI into anterior and posterior portions would have been to measure each voxel's BOLD modulation to stimulation at varying locations in the visual field in a per‐subject fashion [Wandell et al, ], but as we did not have this data available we opted to simply split the region halfway along the anterior/posterior axis. Based on previous neuroanatomic research [Li et al ; Yu et al, ], we believe this approach would capture the majority of an effect. We then applied the same method as we had previously to measure the functional connectivity and streamline density between left and right anterior V1 ROIs, and left and right posterior V1 ROIs.…”

Section: Methodsmentioning

confidence: 99%

Altered whole-brain connectivity in albinism

et al. 2016

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Albinism is a group of congenital disorders of the melanin synthesis pathway.Multiple ocular, white matter and cortical abnormalities occur in albinism, including a greater decussation of nerve fibres at the optic chiasm, foveal hypoplasia and nystagmus. Despite this, visual perception is largely preserved. We proposed that this may be attributable to reorganisation among cerebral networks, including an increased interhemispheric connectivity of the primary visual areas.We applied a graph-theoretic model to explore brain connectivity networks derived from resting-state functional and diffusion-tensor magnetic resonance imaging data in 23 people with albinism and 20 controls. We tested for group differences in connectivity between primary visual areas and in summary network organisation descriptors. We supplemented our main findings with analyses of control regions, brain volumes and white matter microstructure.We found significant functional interhemispheric hyperconnectivity of the primary visual areas in the albinism group (p=0.012). Tests of interhemispheric connectivity based on the diffusion-tensor data showed no significant group difference (p=0.713).Second, we found that a range of functional whole-brain network metrics were abnormal in people with albinism, including the clustering coefficient (p=0.005), although this may have been driven partly by overall differences in connectivity, rather than reorganisation.Based on our results, we suggest that changes occur in albinism at the whole-brain level, and not just within the visual processing pathways. We propose that our findings may reflect compensatory adaptations to increased chiasmic decussation, foveal hypoplasia and nystagmus.

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Section: Methodsmentioning

confidence: 99%

Altered whole-brain connectivity in albinism

et al. 2016

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“…V1 and V2 masks of the HCP data were specified by the JuBrain atlas (Amunts, Malikovic, Mohlberg, Schormann, & Zilles, ) that was registered onto the MNI standard space (Supporting Information, Figure S1), and those of our local data were defined by retinotopic mapping (Engel et al, ). There are mainly three types of connectivity profiles that can be used for parcellation analyses—global, short‐range, and long‐range connectivity profiles (Li et al, ). The global connectivity profile is computed with connectivity information between a given voxel and all other voxels in the brain, whereas the short‐range connectivity profile is computed using only the connectivity information of a given voxel and other voxels within the same region of interest (ROI).…”

Section: Methodsmentioning

confidence: 99%

“…Structural or functional connectivity information that reflects inter‐regional dependencies can be used for cortical parcellation (Behrens et al, ; Johansen‐Berg et al, ; Kim et al, ; Li, Song, Fan, Liu, & Jiang, ). This kind of parcellation based on connectivity patterns is referred to as connectivity‐based parcellation (CBP), and CBP has been successfully applied to many cortical areas.…”

Section: Introductionmentioning

confidence: 99%

“…CBP using the neuronal fiber information derived from diffusion tensor imaging (DTI) was adopted to parcellate human thalamic regions into subregions (Behrens et al, ) and the medial frontal cortex (MFC) into supplementary motor area (SMA) and pre‐SMA (Johansen‐Berg et al, ). While many studies have focused on structural connectivity information (Behrens et al, ; Johansen‐Berg et al, ), some recent parcellation studies have used patterns of functional connectivity which measure the degree of synchronous temporal fluctuations reflecting functional coupling among different voxels or regions in the brain (Kim et al, ; Li et al, ). Using functional connectivity information which was computed by temporal correlations among voxel pairs during resting‐state functional MRI (rs‐fMRI), CBP was applied to parcellate the MFC into SMA and pre‐SMA (Kim et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Using functional connectivity information which was computed by temporal correlations among voxel pairs during resting‐state functional MRI (rs‐fMRI), CBP was applied to parcellate the MFC into SMA and pre‐SMA (Kim et al, ). In another study, both the fiber information from DTI and functional connectivity information from rs‐fMRI were successfully adopted to divide the primary sensory cortices into subregions (Li et al, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Functional connectivity based parcellation of early visual cortices

Park

Tark

Shim

et al. 2017

Human Brain Mapping

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Human brain can be divided into multiple brain regions based on anatomical and functional properties. Recent studies showed that resting-state connectivity can be utilized for parcellating brain regions and identifying their distinctive roles. In this study, we aimed to parcellate the primary and secondary visual cortices (V1 and V2) into several subregions based on functional connectivity and to examine the functional characteristics of each subregion. We used resting-state data from a research database and also acquired resting-state data with retinotopy results from a local site. The long-range connectivity profile and three different algorithms (i.e., K-means, Gaussian mixture model distribution, and Ward's clustering algorithms) were adopted for the parcellation. We compared the parcellation results within V1 and V2 with the eccentric map in retinotopy. We found that the boundaries between subregions within V1 and V2 were located in the parafovea, indicating that the anterior and posterior subregions within V1 and V2 corresponded to peripheral and central visual field representations, respectively. Next, we computed correlations between each subregion within V1 and V2 and intermediate and high-order regions in ventral and dorsal visual pathways. We found that the anterior subregions of V1 and V2 were strongly associated with regions in the dorsal stream (V3A and inferior parietal gyrus), whereas the posterior subregions of V1 and V2 were highly related to regions in the ventral stream (V4v and inferior temporal gyrus). Our findings suggest that the anterior and posterior subregions of V1 and V2, parcellated based on functional connectivity, may have distinct functional properties.

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Whole‐brain functional connectivity correlates of obesity phenotypes

Park

Byeon

Lee

et al. 2020

Human Brain Mapping

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Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole‐brain functional connectivity remains poorly understood. Here, we identified a neuroimaging‐based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large‐scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole‐brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole‐brain functional connectivity and have important implications for the obesity neuroimaging community.

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Parcellation of the primary cerebral cortices based on local connectivity profiles

Cited by 9 publications

References 57 publications

Altered whole-brain connectivity in albinism

Altered whole-brain connectivity in albinism

Functional connectivity based parcellation of early visual cortices

Whole‐brain functional connectivity correlates of obesity phenotypes

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