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A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.04.22.440936Source: DataRank Database

A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2021). On theSindex it has a DataRank of 0. It has been cited 2 times.

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Open Access2 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)OA: greenTopics (22)IDs (PubMed, OpenAlex)

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Authors (2)

Vince D. Calhoun ORCID,Behnam Kazemivash ORCID

Live enrichment

PubMed:19162232 OpenAlex:https://openalex.org/W3157563165

National Institutes of Health

Grant: 3R01MH118695-03S1

Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood d

National Institutes of Health

Grant: 3R01EB020407-02S1

Unified multivariate data-driven solutions for static and dynamic brain connectivity

greenLicense: cc-by-nc

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2021/04/23/2021.04.22.440936.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2021.04.22.440936

repositoryhttps://doi.org/10.1101/2021.04.22.440936

https://doi.org/10.1016/j.jneumeth.2022.109478

Fields of Study

Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications03 medical and health sciences0302 clinical medicine

Keywords

Computer scienceArtificial intelligenceNeuroimagingContext (archaeology)VoxelResidualPattern recognition (psychology)Machine learningNeurosciencePsychologyGeographyBrain MappingRestConnectomeBrainHumansMagnetic Resonance Imaging