ArticleData in brief2023
A population-averaged structural connectomic brain atlas dataset from 422 HCP-aging subjects.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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0 citing papers in PubMed, 3 citations in OpenAlex.
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6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. The dataset described in this article contains a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates were made using multi-contrast group-wise registration based on 3T MRIs of 422 Human Connectome Project in Aging (HCP-A) subjects. To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures are provided. Finally, the subthalamic nucleus shown in the atlas is parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. The dataset is available on the OSF Repository: https://osf.io/p7syt.
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