# TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis

arXiv: https://arxiv.org/abs/2309.07947 · HTML: https://mlsanity.com/paper/2309.07947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 64 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 19 of 19)

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- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Brain Network Transformer](https://mlsanity.com/paper/2210.06681/) (2022)
- [Recent Advances in Recurrent Neural Networks](https://mlsanity.com/paper/1801.01078/) (2017)
- [BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks](https://mlsanity.com/paper/2204.07054/) (2022)
- [FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation](https://mlsanity.com/paper/2205.12465/) (2022)
- [Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis](https://mlsanity.com/paper/2207.00813/) (2022)
- [Explainable Classification of Brain Networks via Contrast Subgraphs](https://mlsanity.com/paper/2006.05176/) (2020)
- [Machine learning in resting-state fMRI analysis](https://mlsanity.com/paper/1812.11477/) (2018)
- [Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity](https://mlsanity.com/paper/1806.01764/) (2018)
- [Mining Density Contrast Subgraphs](https://mlsanity.com/paper/1802.06775/) (2018)
- [Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation](https://mlsanity.com/paper/2006.10520/) (2020)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)

## Abstract (arXiv)

In recent years, functional magnetic resonance imaging has emerged as a powerful tool for investigating the human brain's functional connectivity networks. Related studies demonstrate that functional connectivity networks in the human brain can help to improve the efficiency of diagnosing neurological disorders. However, there still exist two challenges that limit the progress of functional neuroimaging. Firstly, there exists an abundance of noise and redundant information in functional connectivity data, resulting in poor performance. Secondly, existing brain network models have tended to prioritize either classification performance or the interpretation of neuroscience findings behind the learned models. To deal with these challenges, this paper proposes a novel brain graph learning framework called Template-induced Brain Graph Learning (TiBGL), which has both discriminative and interpretable abilities. Motivated by the related medical findings on functional connectivites, TiBGL proposes template-induced brain graph learning to extract template brain graphs for all groups. The template graph can be regarded as an augmentation process on brain networks that removes noise information and highlights important connectivity patterns. To simultaneously support the tasks of discrimination and interpretation, TiBGL further develops template-induced convolutional neural network and template-induced brain interpretation analysis. Especially, the former fuses rich information from brain graphs and template brain graphs for brain disorder tasks, and the latter can provide insightful connectivity patterns related to brain disorders based on template brain graphs. Experimental results on three real-world datasets show that the proposed TiBGL can achieve superior performance compared with nine state-of-the-art methods and keep coherent with neuroscience findings in recent literatures.