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hemidentification

https://github.com/trevorkmday/hemidentification

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 6 DOI reference(s) in README
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    Academic publication links
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    Institutional organization owner
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (4.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: TrevorKMDay
  • Language: R
  • Default Branch: main
  • Size: 4.88 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 1 year ago · Last pushed 8 months ago
Metadata Files
Readme Citation

README.md

hemidentification

This is supporting code for a project designed to identify whether a partial within-hemisphere connectome belongs to a left or a right hemisphere using Human Connectome Project-Young Adult (HCP-YA) data (Van Essen et al., 2013).

The project especially relies on results and code from Hannum et al. (2023).

Description of code

code/fake_hemiconnectome/: Code to generate a schematic (hemi-)connectome with four symmetric ROIs per hemisphere, a la Glasser parcellation.

code/fold_testing/: Code to test the creation of k-folds for model evaluation, using HCP-YA demographic data.

code/formula: TeX code to generate the formula for Matthew's correlation coefficient, because the Google Docs equation editor is inadequate.

code/hypotheses/: Code to generate the hypothesis figures from the registered report.

References

  • Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), Article 7615. https://doi.org/10.1038/nature18933

  • Hannum, A., Lopez, M. A., Blanco, S. A., & Betzel, R. F. (2023). High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding. Human Brain Mapping, 44(16), 5294–5308. https://doi.org/10.1002/hbm.26423

  • Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041

Owner

  • Name: Trevor K. M. Day
  • Login: TrevorKMDay
  • Kind: user
  • Location: Minneapolis, MN
  • Company: Institute of Child Development, UMN

PhD student Institute of Child Development University of Minnesota, Twin Cities

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: hemidentification
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Trevor K M
    family-names: Day
    email: trevor.day@georgetown.edu
    affiliation: Georgetown University
    orcid: 'https://orcid.org/0000-0003-2911-8312'
license: CC-BY-4.0
date-released: 2025-01-29

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