# D-CoRP: Differentiable Connectivity Refinement for Functional Brain Networks

arXiv: https://arxiv.org/abs/2405.18658 · HTML: https://mlsanity.com/paper/2405.18658/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020)
- [Graph Information Bottleneck for Subgraph Recognition](https://mlsanity.com/paper/2010.05563/) (2020)
- [Graph Structure Learning with Variational Information Bottleneck](https://mlsanity.com/paper/2112.08903/) (2021)
- [Multi-modal learning for predicting the genotype of glioma](https://mlsanity.com/paper/2203.10852/) (2022)
- [BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification](https://mlsanity.com/paper/2103.08494/) (2021)
- [Generalizable Machine Learning in Neuroscience using Graph Neural Networks](https://mlsanity.com/paper/2010.08569/) (2020)

## Built on by (most cited first, 3 of 3)

- [Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma](https://mlsanity.com/paper/2508.09593/) (2025)
- [BrainSTR: Spatio-Temporal Contrastive Learning for Interpretable Dynamic Brain Network Modeling](https://mlsanity.com/paper/2603.09825/) (2026)
- [MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification](https://mlsanity.com/paper/2607.28681/) (2026)

## Abstract (arXiv)

Brain network is an important tool for understanding the brain, offering insights for scientific research and clinical diagnosis. Existing models for brain networks typically primarily focus on brain regions or overlook the complexity of brain connectivities. MRI-derived brain network data is commonly susceptible to connectivity noise, underscoring the necessity of incorporating connectivities into the modeling of brain networks. To address this gap, we introduce a differentiable module for refining brain connectivity. We develop the multivariate optimization based on information bottleneck theory to address the complexity of the brain network and filter noisy or redundant connections. Also, our method functions as a flexible plugin that is adaptable to most graph neural networks. Our extensive experimental results show that the proposed method can significantly improve the performance of various baseline models and outperform other state-of-the-art methods, indicating the effectiveness and generalizability of the proposed method in refining brain network connectivity. The code will be released for public availability.