LGNEIVNCMay 25, 2022

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation

arXiv:2205.12465v2142 citationsh-index: 40Has Code
Originality Highly original
AI Analysis

This work addresses the challenge of improving clinical predictions from fMRI data for neuroscience and medical applications, representing a novel method rather than an incremental improvement.

The authors tackled the problem of noisy and task-unaware functional brain networks in fMRI analysis by developing FBNETGEN, an end-to-end framework that generates task-oriented networks using graph neural networks, achieving superior effectiveness and interpretability on two datasets including the large ABCD dataset.

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.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes