Subdivision of Broca's region based on individual-level functional connectivity
| Autor: | Rudolf Rübsamen, Daniel S. Margulies, Estrid Jakobsen, Michael Petrides, Pierre Bellec, Stefan Geyer, Joachim Böttger |
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| Rok vydání: | 2016 |
| Předmět: |
Adult
Male 050105 experimental psychology Young Adult 03 medical and health sciences 0302 clinical medicine Neuroimaging Parietal Lobe Neural Pathways Connectome Image Processing Computer-Assisted medicine Cluster Analysis Humans 0501 psychology and cognitive sciences Segmentation Cluster analysis Human Connectome Project medicine.diagnostic_test General Neuroscience 05 social sciences Parietal lobe Reproducibility of Results Magnetic Resonance Imaging Broca Area Broca's region Female Nerve Net Psychology Functional magnetic resonance imaging Neuroscience Cartography 030217 neurology & neurosurgery |
| Zdroj: | European Journal of Neuroscience. 43:561-571 |
| ISSN: | 0953-816X |
| DOI: | 10.1111/ejn.13140 |
| Popis: | Broca's region is composed of two adjacent cytoarchitectonic areas, 44 and 45, which have distinct connectivity to superior temporal and inferior parietal regions in both macaque monkeys and humans. The current study aimed to make use of prior knowledge of sulcal anatomy and resting-state functional connectivity, together with a novel visualization technique, to manually parcellate areas 44 and 45 in individual brains in vivo. One hundred and one resting-state functional magnetic resonance imaging datasets from the Human Connectome Project were used. Left-hemisphere surface-based correlation matrices were computed and visualized in brainGL. By observation of differences in the connectivity patterns of neighbouring nodes, areas 44 and 45 were manually parcellated in individual brains, and then compared at the group-level. Additionally, the manual labelling approach was compared with parcellation results based on several data-driven clustering techniques. Areas 44 and 45 could be clearly distinguished from each other in all individuals, and the manual segmentation method showed high test-retest reliability. Group-level probability maps of areas 44 and 45 showed spatial consistency across individuals, and corresponded well to cytoarchitectonic probability maps. Group-level connectivity maps were consistent with previous studies showing distinct connectivity patterns of areas 44 and 45. Data-driven parcellation techniques produced clusters with varying degrees of spatial overlap with the manual labels, indicating the need for further investigation and validation of machine learning cortical segmentation approaches. The current study provides a reliable method for individual-level cortical parcellation that could be applied to regions distinguishable by even the most subtle differences in patterns of functional connectivity. |
| Databáze: | OpenAIRE |
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