DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography

2025-05-27

AI Summary

Conventional structural connectome construction relies on time-consuming gray-matter parcellation, limiting scalability. Method: We propose the first end-to-end deep learning framework that directly predicts multi-atlas (84- and 164-region) structural connectomes from whole-brain dMRI tractography—bypassing parcellation entirely. Our approach innovatively integrates point cloud neural networks with multi-task learning to enable cross-atlas shared representation learning and simultaneous connectome prediction. Contribution/Results: Trained and validated on the HCP-YA dataset (n=1000), our model generates multi-scale connectivity matrices for ~1M streamlines in ~40 seconds per inference. It achieves near-perfect correlation with ground-truth connectomes (r = 0.992 and 0.986), matches test–retest reliability of conventional methods, and maintains consistent performance in age and cognitive-behavioral prediction tasks—effectively overcoming the parcellation bottleneck.

Technology Category

• Machine Learning: Structured Learning
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Diffusion MRI (dMRI) tractography enables in vivo mapping of brain structural connections, but traditional connectome generation is time-consuming and requires gray matter parcellation, posing challenges for large-scale studies. We introduce DeepMultiConnectome, a deep-learning model that predicts structural connectomes directly from tractography, bypassing the need for gray matter parcellation while supporting multiple parcellation schemes. Using a point-cloud-based neural network with multi-task learning, the model classifies streamlines according to their connected regions across two parcellation schemes, sharing a learned representation. We train and validate DeepMultiConnectome on tractography from the Human Connectome Project Young Adult dataset ($n = 1000$), labeled with an 84 and 164 region gray matter parcellation scheme. DeepMultiConnectome predicts multiple structural connectomes from a whole-brain tractogram containing 3 million streamlines in approximately 40 seconds. DeepMultiConnectome is evaluated by comparing predicted connectomes with traditional connectomes generated using the conventional method of labeling streamlines using a gray matter parcellation. The predicted connectomes are highly correlated with traditionally generated connectomes ($r = 0.992$ for an 84-region scheme; $r = 0.986$ for a 164-region scheme) and largely preserve network properties. A test-retest analysis of DeepMultiConnectome demonstrates reproducibility comparable to traditionally generated connectomes. The predicted connectomes perform similarly to traditionally generated connectomes in predicting age and cognitive function. Overall, DeepMultiConnectome provides a scalable, fast model for generating subject-specific connectomes across multiple parcellation schemes.

Problem

• Predicts structural connectomes directly from tractography
• Eliminates need for time-consuming gray matter parcellation
• Supports multiple parcellation schemes simultaneously

Innovation

• Deep learning predicts connectomes from tractography
• Multi-task learning supports multiple parcellation schemes
• Point-cloud neural network classifies streamline connections