ArticleProceedings of machine learning research2022
FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
23 citing papers in PubMed.
- Hierarchical sparse spatiotemporal graph neural network for brain graph classification.iScience · 2026Article
- VarCoNet: A Variability-Aware Self-Supervised Framework for Functional Connectome Extraction From Resting-State fMRI.Human brain mapping · 2026Article
- A Dual-Stream Transformer with Self-Supervised Contrastive Training for fMRI-Based Autism Spectrum Disorder Classification.Brain sciences · 2026Article
- Core-Periphery Principle Guided State Space Model for Functional Connectome Classification.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- MMDD: A Multimodal Multitask Dynamic Disentanglement Framework for Robust Major Depressive Disorder Diagnosis Across Neuroimaging Sites.Diagnostics (Basel, Switzerland) · 2025Article
- MBrain sciences · 2025Article
- MAMVCL: Multi-Atlas Guided Multi-View Contrast Learning for Autism Spectrum Disorder Classification.Brain sciences · 2025Article
- Semantic locality-aware biclustering for brain functional network connectivity.Scientific reports · 2025Article
- Alzheimer's disease classification using mutual information generated graph convolutional network for functional MRI.Journal of Alzheimer's disease : JAD · 2025Article
- Innovative biomarker exploration in ASD: Combining Graph Neural Networks and permutation testing on fMRI data.Neuroimage. Reports · 2025Article
- DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks.Medical image analysis · 2025Article
- Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Brain networks and intelligence: A graph neural network based approach to resting state fMRI data.Medical image analysis · 2025Article
- mGNN-bw: Multi-Scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions.Brain sciences · 2024Review
- Article
- Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data.ArXiv · 2024Article
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- R-Mixup: Riemannian Mixup for Biological Networks.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep brain network generation. In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks. Along with the process, the key novel component is the graph generator which learns to transform raw time-series features into task-oriented brain networks. Our learnable graphs also provide unique interpretations by highlighting prediction-related brain regions. Comprehensive experiments on two datasets, i.e., the recently released and currently largest publicly available fMRI dataset Adolescent Brain Cognitive Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior effectiveness and interpretability of FBNETGEN. The implementation is available at https://github.com/Wayfear/FBNETGEN.
Indexed as
Identifiers
37377881PMC10296778What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.