Functional parcellation of the hippocampus from resting-state dynamic functional connectivity
•A two-stage spectral clustering of FC is used to identify the hippocampus subdivisions.•Finer co-variance structures are found in the dFC based parcellation compared with sFC-based parcellation.•dFC-based functional subdivisions reorganize to state-dependent segmentation. The hippocampus consists o...
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Veröffentlicht in: | Brain research 2019-07, Vol.1715, p.165-175 |
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Sprache: | eng |
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Zusammenfassung: | •A two-stage spectral clustering of FC is used to identify the hippocampus subdivisions.•Finer co-variance structures are found in the dFC based parcellation compared with sFC-based parcellation.•dFC-based functional subdivisions reorganize to state-dependent segmentation.
The hippocampus consists of functionally and structurally heterogeneous regions that are involved in multiple functions such as learning and memory. Previous studies on connectivity-based functional subdivisions of the hippocampus, however, overlooked the dynamic nature of resting-state functional connectivity (FC). In this study, we selected 50 subjects with the lowest head motion from the Human Connectome Project dataset and performed a two-stage spectral clustering technique to windowed FC correlations for identifying the potential covariant structures during the spontaneous fluctuation of hippocampal-cortical FC. The obtained covariant structures were believed to be functionally homogeneous by coupling with whole-brain regions in all transient connectivity states and consequently subdivided the left and right hippocampus into six and five functional subregions, respectively. Further, we demonstrated that this dynamic-FC-derived hippocampal parcellation exhibited significantly improved reproducibility of segmented subregions across subjects compared with static FC analysis. The findings extend our understanding to the functional organization within the hippocampus and provide a more comprehensive view of the functional flexibility of the hippocampus over time. |
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ISSN: | 0006-8993 1872-6240 |
DOI: | 10.1016/j.brainres.2019.03.023 |