Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation.
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ABSTRACT:
SUBMITTER: Tian X
PROVIDER: S-EPMC5561846 | biostudies-other | 2013 Jun
REPOSITORIES: biostudies-other
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Project description: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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Project description:Regional differences in large-scale connectivity have been proposed to underlie functional specialization along the anterior-posterior axis of the medial temporal lobe (MTL), including the hippocampus (HC) and the parahippocampal gyrus (PHG). However, it is unknown whether functional connectivity (FC) can be used reliably to parcellate the human MTL. The current study aimed to differentiate subregions of the HC and the PHG based on patterns of whole-brain intrinsic FC. FC maps were calculated for each slice along the longitudinal axis of the PHG and the HC. A hierarchical clustering algorithm was then applied to these data in order to group slices according to the similarity of their connectivity patterns. Surprisingly, three discrete clusters were identified in the PHG. Two clusters corresponded to the parahippocampal cortex (PHC) and the perirhinal cortex (PRC), and these regions showed preferential connectivity with previously described posterior-medial and anterior-temporal networks, respectively. The third cluster corresponded to an anterior PRC region previously described as area 36d, and this region exhibited preferential connectivity with auditory cortical areas and with a network involved in visceral processing. The three PHG clusters showed different profiles of activation during a memory-encoding task, demonstrating that the FC-based parcellation identified functionally dissociable sub-regions of the PHG. In the hippocampus, no sub-regions were identified via the parcellation procedure. These results indicate that connectivity-based methods can be used to parcellate functional regions within the MTL, and they suggest that studies of memory and high-level cognition need to differentiate between PHC, posterior PRC, and anterior PRC.
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Project description:IntroductionHuman cingulate cortex (CC) has been implicated in many functions, which is highly suggestive of the existence of functional subregions.MethodsIn this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) to parcellate the human cingulate cortex (CC) based on resting-state functional connectivity (rsFC) patterns and anatomical connectivity (AC) patterns, to analyze the rsFC patterns and the AC patterns of different subregions, and to recognize whether the parcellation results obtained by the two different methods were consistent.ResultsThe CC was divided into six functional subregions, including the anterior cingulate cortex, dorsal anterior midcingulate cortex, ventral anterior midcingulate cortex, posterior midcingulate cortex, dorsal posterior cingulate cortex, and ventral posterior cingulate cortex. The CC was also divided into ten anatomical subregions, termed Subregion 1 (S1) to Subregion 10 (S10). Each subregion showed specific connectivity patterns, although the functional subregions and the anatomical subregions were internally consistent.ConclusionsUsing different model MRI images, we established a parcellation scheme, which is internally consistent for the human CC, which may provide an in vivo guide for subregion-level studies and improve our understanding of this brain area at subregional levels.
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Project description:PurposeFunctional changes have been observed between diseased and healthy subjects, and functional brain atlases derived from healthy populations may fail to reflect functional characteristic of the diseased brain. Therefore the aim of this study was to generate a visual atlas based on functional connectivity from primary open-angle glaucoma (POAG) patients and to prove the applicability of the visual atlas in functional connectivity and network analysis.MethodsFunctional magnetic resonance images were acquired from 36 POAG patients and 20 healthy controls. Two data-driven approaches, K-means and Ward clustering algorithms, were adopted for visual cortices parcellation. Dice coefficient and adjusted Rand index were used to assess reproducibility of the two approaches. Homogeneity index, silhouette coefficient, and network properties were adopted to assess functional validity for the data-driven approaches and frequently used brain atlas. Graph theoretical analysis was adopted to investigate altered network patterns in POAG patients based on data-driven visual atlas.ResultsParcellation results demonstrated asymmetric patterns between left and right hemispheres in POAG patients compared with healthy controls. In terms of evaluating metrics, K-means performed better than Ward clustering in reproducibility. Data-driven parcellations outperformed frequently used brain atlases in terms of functional homogeneity and network properties. Graph theoretical analysis revealed that atlases generated by data-driven approaches were more conducive in detecting network alterations between POAG patients and healthy controls.ConclusionsOur findings suggested that POAG patients experienced functional alterations in the visual cortices. Results also highlighted the necessity of data-driven atlases for functional connectivity and functional network analysis of POAG brain.
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Project description:The cerebellum is traditionally well known for its role in motor learning and coordination. Recently, it is recognized that the function of the cerebellum is highly diverse and extends to non-motor domains, such as working memory, emotion and language. The diversity of the cerebellum can be appreciated by examining its extensive connectivity to the cerebral regions selective for both motor and cognitive functions. Importantly, the pattern of cerebro-cerebellar connectivity is specific and distinct to different cerebellar subregions. Therefore, to understand the cerebellum and the various functions it involves, it is essential to identify and differentiate its subdivisions. However, most studies are still referring the cerebellum as one brain structure or by its gross anatomical subdivisions, which does not necessarily reflect the functional mapping of the cerebellum. We here employed a data-driven method to generate a functional connectivity-based parcellation of the cerebellum. Our results demonstrated that functional connectivity-based atlas is superior to existing atlases in regards to cluster homogeneity, accuracy of functional connectivity representation and individual identification. Furthermore, our functional atlas improves statistical results of task fMRI analyses, as compared to the standard voxel-based approach and existing atlases. Our detailed functional parcellation provides a valuable tool for elucidating the functional diversity and connectivity of the cerebellum as well as its network relationships with the whole brain.
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