# Effective and Interpretable fMRI Analysis via Functional Brain Network Generation

arXiv: https://arxiv.org/abs/2107.11247 · HTML: https://mlsanity.com/paper/2107.11247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Learning Discrete Structures for Graph Neural Networks](https://mlsanity.com/paper/1903.11960/) (2019)
- [Discrete Graph Structure Learning for Forecasting Multiple Time Series](https://mlsanity.com/paper/2101.06861/) (2021)
- [Graph Neural Network for Interpreting Task-fMRI Biomarkers](https://mlsanity.com/paper/1907.01661/) (2019)
- [Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification](https://mlsanity.com/paper/1704.07487/) (2017)
- [Analyzing complex functional brain networks: fusing statistics and network science to understand the brain](https://mlsanity.com/paper/1302.5721/) (2013)
- [A hierarchical independent component analysis model for longitudinal Neuroimaging studies](https://mlsanity.com/paper/1808.01557/) (2018)
- [Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression](https://mlsanity.com/paper/2012.12626/) (2020)

## Built on by (most cited first, 2 of 2)

- [Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help](https://mlsanity.com/paper/2501.17207/) (2025)
- [Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction](https://mlsanity.com/paper/2211.08378/) (2022)

## Abstract (arXiv)

Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing functional brain networks are noisy and unaware of downstream prediction tasks, while also incompatible with recent powerful machine learning models of GNNs. In this work, we develop an end-to-end trainable pipeline to extract prominent fMRI features, generate brain networks, and make predictions with GNNs, all under the guidance of downstream prediction tasks. Preliminary experiments on the PNC fMRI data show the superior effectiveness and unique interpretability of our framework.