mlsanityData to 14 Oct 2026

Learning to Model the Relationship Between Brain Structural and Functional Connectomes

arXiv 2112.09906 ↗First posted 18 Dec 2021eess.SP cs.LGcited by 5

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Abstract (arXiv)

Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system, where the SC is used as input to predict empirical FC. A trainable graph convolutional encoder captures direct and indirect interactions between brain regions-of-interest that mimic actual neural communications, as well as to integrate information from both the structural network topology and nodal (i.e., region-specific) attributes. The encoder learns node-level SC embeddings which are combined to generate (whole brain) graph-level representations for reconstructing empirical FC networks. The proposed end-to-end model utilizes a multi-objective loss function to jointly reconstruct FC networks and learn discriminative graph representations of the SC-to-FC mapping for downstream subject (i.e., graph-level) classification. Comprehensive experiments demonstrate that the learnt representations of said relationship capture valuable information from the intrinsic properties of the subject's brain networks and lead to improved accuracy in classifying a large population of heavy drinkers and non-drinkers from the Human Connectome Project. Our work offers new insights on the relationship between brain networks that support the promising prospect of using graph representation learning to discover more about human brain activity and function.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 76 references that are in the map. First posted on arXiv 18 Dec 2021.

Builds on

34 of 76 references.

References · 34

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22 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.

5 papers.

  1. 2025SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translationcited by 2
  2. 2023JGAT: a joint spatio-temporal graph attention model for brain decodingcited by 1
  3. 2024Cascaded two-stage feature clustering and selection via separability and consistency in fuzzy decision systemscited by 1
  4. 2025Graph Contrastive Learning for Connectome Classificationcited by 1
  5. 2026Sex-based Network-Specific Differences in Connectomes: A Krakencoder-Based Analysiscited by 0