# Identification of multi-scale hierarchical brain functional networks using deep matrix factorization

arXiv: https://arxiv.org/abs/1809.05557 · HTML: https://mlsanity.com/paper/1809.05557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Extraction of Hierarchical Functional Connectivity Components in human brain using Adversarial Learning](https://mlsanity.com/paper/2104.10255/) (2021)

## Abstract (arXiv)

We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organization from resting state fMRI data. Our method is built upon a deep semi-nonnegative matrix factorization framework to jointly detect the FNs at multiple scales with a hierarchical organization, enhanced by group sparsity regularization that helps identify subject-specific FNs without loss of inter-subject comparability. The proposed method has been validated for predicting subject-specific functional activations based on functional connectivity measures of the hierarchical multi-scale FNs of the same subjects. Experimental results have demonstrated that our method could obtain subject-specific multi-scale hierarchical FNs and their functional connectivity measures across different scales could better predict subject-specific functional activations than those obtained by alternative techniques.