mlsanityData to 7 Oct 2026

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

arXiv 2206.04486 ↗First posted 9 Jun 2022cs.LG q-bio.NCcited by 15

Not on the map. Why?

Abstract (arXiv)

Brain networks characterize complex connectivities among brain regions as graph structures, which provide a powerful means to study brain connectomes. In recent years, graph neural networks have emerged as a prevalent paradigm of learning with structured data. However, most brain network datasets are limited in sample sizes due to the relatively high cost of data acquisition, which hinders the deep learning models from sufficient training. Inspired by meta-learning that learns new concepts fast with limited training examples, this paper studies data-efficient training strategies for analyzing brain connectomes in a cross-dataset setting. Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets. In addition, we also explore two brain-network-oriented designs, including atlas transformation and adaptive task reweighing. Compared to other pre-training strategies, our meta-learning-based approach achieves higher and stabler performance, which demonstrates the effectiveness of our proposed solutions. The framework is also able to derive new insights regarding the similarities among datasets and diseases in a data-driven fashion.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 23 of its 41 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

23 of 41 references.

References · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  8. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
  9. 2019Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAMLcited by 359
  10. 2020How Neural Networks Extrapolate: From Feedforward to Graph Neural Networkscited by 218
  11. 2019Task2Vec: Task Embedding for Meta-Learningcited by 197
  12. 2020A Systematic Survey on Deep Generative Models for Graph Generationcited by 94
11 more
  1. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  2. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  3. 2020Meta-Learning with Adaptive Hyperparameterscited by 79
  4. 2022BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networkscited by 73
  5. 2022FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generationcited by 66
  6. 2021On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphscited by 49
  7. 2020Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measurescited by 31
  8. 2021Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosiscited by 20
  9. 2018Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Diseasecited by 19
  10. 2021BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysiscited by 13
  11. 2020A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settingscited by 8

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 4; 2024: 4; 2025: 3.

15 papers.

Sort:

  1. 2022Brain Network Transformercited by 92
  2. 2022Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysiscited by 58
  3. 2024Graph Neural Networks for Brain Graph Learning: A Surveycited by 13
  4. 2022Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networkscited by 12
  5. 2023PTGB: Pre-Train Graph Neural Networks for Brain Network Analysiscited by 10
  6. 2023Transformer-Based Hierarchical Clustering for Brain Network Analysiscited by 8
  7. 2023R-Mixup: Riemannian Mixup for Biological Networkscited by 8
  8. 2024BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signalscited by 6
  9. 2022Predicting Brain Age using Transferable coVariance Neural Networkscited by 4
  10. 2023Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Featurescited by 4
  11. 2024Towards a Foundation Model for Brain Age Prediction using coVariance Neural Networkscited by 4
  12. 2024BrainMAP: Learning Multiple Activation Pathways in Brain Networkscited by 2
3 more
  1. 2025A Survey of Cross-domain Graph Learning: Progress and Future Directionscited by 2
  2. 2025Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networkscited by 1
  3. 2025Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomescited by 0