# GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis

arXiv: https://arxiv.org/abs/2203.09034 · HTML: https://mlsanity.com/paper/2203.09034/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 64 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 30 of 32)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [A Theoretical Analysis of Contrastive Unsupervised Representation Learning](https://mlsanity.com/paper/1902.09229/) (2019)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [Large-Scale Representation Learning on Graphs via Bootstrapping](https://mlsanity.com/paper/2102.06514/) (2021)
- [Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data](https://mlsanity.com/paper/2010.03622/) (2020)
- [Predicting What You Already Know Helps: Provable Self-Supervised Learning](https://mlsanity.com/paper/2008.01064/) (2020)
- [From Canonical Correlation Analysis to Self-supervised Graph Neural Networks](https://mlsanity.com/paper/2106.12484/) (2021)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Self-training Avoids Using Spurious Features Under Domain Shift](https://mlsanity.com/paper/2006.10032/) (2020)
- [Revisiting Over-smoothing in Deep GCNs](https://mlsanity.com/paper/2003.13663/) (2020)
- [Label Efficient Learning of Transferable Representations across Domains and Tasks](https://mlsanity.com/paper/1712.00123/) (2017)
- [InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction](https://mlsanity.com/paper/1903.04233/) (2019)
- [BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks](https://mlsanity.com/paper/1802.04692/) (2018)
- [KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey](https://mlsanity.com/paper/2110.01858/) (2021)
- [RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data](https://mlsanity.com/paper/2103.00221/) (2021)
- [VGAER: Graph Neural Network Reconstruction based Community Detection](https://mlsanity.com/paper/2201.04066/) (2022)
- [Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI](https://mlsanity.com/paper/1910.06950/) (2019)
- [Context Matters: Graph-based Self-supervised Representation Learning for Medical Images](https://mlsanity.com/paper/2012.06457/) (2020)

## Built on by (most cited first, 19 of 19)

- [LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching](https://mlsanity.com/paper/2306.11925/) (2023)
- [MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction](https://mlsanity.com/paper/2406.14455/) (2024)
- [Benchmarking Graph Neural Networks for FMRI analysis](https://mlsanity.com/paper/2211.08927/) (2022)
- [CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders](https://mlsanity.com/paper/2503.07852/) (2025)
- [Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling](https://mlsanity.com/paper/2505.15135/) (2025)
- [Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis](https://mlsanity.com/paper/2502.20769/) (2025)
- [Parkinson's Disease Classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features](https://mlsanity.com/paper/2311.14902/) (2023)
- [Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review](https://mlsanity.com/paper/2405.00577/) (2024)
- [MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder](https://mlsanity.com/paper/2409.06163/) (2024)
- [Identity Preserving Latent Diffusion for Brain Aging Modeling](https://mlsanity.com/paper/2503.09634/) (2025)
- [VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI](https://mlsanity.com/paper/2510.02120/) (2025)
- [Fine-Grained Traceability for Transparent ML Pipelines](https://mlsanity.com/paper/2601.14971/) (2026)
- [Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning](https://mlsanity.com/paper/2409.13887/) (2024)
- [HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks](https://mlsanity.com/paper/2410.02087/) (2024)
- [scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2502.02630/) (2025)
- [Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks](https://mlsanity.com/paper/2509.10524/) (2025)
- [Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning](https://mlsanity.com/paper/2605.14048/) (2026)
- [HyperAMS-Net: Adaptive Multi-Scale Spatial Hypergraph Network for Brain Disorder Classification](https://mlsanity.com/paper/2609.19755/) (2026)
- [Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification](https://mlsanity.com/paper/2609.25650/) (2026)

## Abstract (arXiv)

In this work, we focus on the challenging task, neuro-disease classification, using functional magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph convolutional neural networks (GCNs) have achieved remarkable success. However, these achievements are inseparable from abundant labeled data and sensitive to spurious signals. To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE. Concretely, it is demanding to design a suitable and effective SSL strategy to extract formation and robust features for fMRI. To this end, we investigate several new graph augmentation strategies from fMRI dynamic functional connectives (FC) for SSL training. Further, we leverage canonical-correlation analysis (CCA) on different temporal embeddings and present the theoretical implications. Consequently, this yields a novel two-step GCN learning procedure comprised of (i) SSL on an unlabeled fMRI population graph and (ii) fine-tuning on a small labeled fMRI dataset for a classification task. Our method is tested on two independent fMRI datasets, demonstrating superior performance on autism and dementia diagnosis.