https://github.com/raamana/hiwenet

Histogram-weighted Networks for Connectivity & Advanced Analysis in Neuroscience
https://github.com/raamana/hiwenet

biomarkers connectivity feature-extraction graph histogram-weighted-networks machine-learning neuroimaging neuroscience

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Histogram-weighted Networks for Connectivity & Advanced Analysis in Neuroscience

• Host: GitHub
• URL: https://github.com/raamana/hiwenet
• Owner: raamana
• License: mit
• Created: 2017-07-24T10:41:30.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-07-14T03:14:13.000Z (over 2 years ago)
• Last Synced: 2024-10-07T15:15:40.942Z (3 months ago)
• Topics: biomarkers, connectivity, feature-extraction, graph, histogram-weighted-networks, machine-learning, neuroimaging, neuroscience
• Language: Python
• Homepage: http://hiwenet.readthedocs.io
• Size: 1.08 MB
• Stars: 8
• Watchers: 3
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# Histogram-weighted Networks (hiwenet)

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Histogram-weighted Networks for Feature Extraction and Advanced Analysis in Neuroscience

Network-level analysis of various features, esp. if it can be individualized for a single-subject,

 is proving to be a valuable tool in many applications. Ability to extract the networks for a given subject individually on its own, would allow for feature extraction conducive to predictive modeling, unlike group-wise networks which can only be used for descriptive and explanatory purposes. This package extracts single-subject (individualized, or intrinsic) networks from node-wise data by computing the edge weights based on histogram distance between the distributions of values within each node. Individual nodes could be an ROI or a patch or a cube, or any other unit of relevance in your application. This is a great way to take advantage of the full distribution of values available within each node, relative to the simpler use of averages (or another summary statistic) to compare two nodes/ROIs within a given subject.

Rough scheme of computation is shown below:

![illustration](docs/illustration.png)

## Installation

`pip install -U hiwenet`

## Documentation

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| Docs: |  http://hiwenet.readthedocs.io |