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

Last synced: about 1 year ago
JSON representation

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 (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2022-07-14T03:14:13.000Z (almost 4 years ago)
• Last Synced: 2025-04-11T12:11:40.647Z (about 1 year 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: 2
• 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

Awesome Lists containing this project

README

          
# Histogram-weighted Networks (hiwenet)

[![status](http://joss.theoj.org/papers/df10a3a527fe169447a64c0cc810ff3c/status.svg)](http://joss.theoj.org/papers/df10a3a527fe169447a64c0cc810ff3c)

[![travis](https://travis-ci.org/raamana/hiwenet.svg?branch=master)](https://travis-ci.org/raamana/hiwenet.svg?branch=master)

[![Code Health](https://landscape.io/github/raamana/hiwenet/master/landscape.svg?style=flat)](https://landscape.io/github/raamana/hiwenet/master)

[![codecov](https://codecov.io/gh/raamana/hiwenet/branch/master/graph/badge.svg)](https://codecov.io/gh/raamana/hiwenet)

[![PyPI version](https://badge.fury.io/py/hiwenet.svg)](https://badge.fury.io/py/hiwenet)

[![Python versions](https://img.shields.io/badge/python-2.7%2C%203.5%2C%203.6-blue.svg)]

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

|||

|--:|---|

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