Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies
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Abstract (arXiv)
Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 22 references that are in the map. First posted on arXiv 18 Jan 2024.
Builds on
7 of 22 references.
References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2020A Generalization of Transformer Networks to Graphscited by 661
- 2021Rethinking Graph Transformers with Spectral Attentioncited by 473
- 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 333
- 2021Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability predictioncited by 9
- 2022Latent Similarity Identifies Important Functional Connections for Phenotype Predictioncited by 5
- 2020Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studiescited by 3
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