DeepMultiConnectome: Deep Multi-Task Prediction of Structural Connectomes Directly from Diffusion MRI Tractography
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 62 references that are in the map. First posted on arXiv 27 May 2025.
Builds on
16 of 62 references.
References · 16
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
- 2019FastSurfer -- A fast and accurate deep learning based neuroimaging pipelinecited by 112
- 2018TractSeg - Fast and accurate white matter tract segmentationcited by 52
- 2022Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitionscited by 20
- 2020Tractogram filtering of anatomically non-plausible fibers with geometric deep learningcited by 17
- 2022Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approachcited by 17
- 2022Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellationcited by 14
- 2023TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representationcited by 11
- 2022White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learningcited by 10
- 2023TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractographycited by 10
- 2019DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networkscited by 7
4 more
- 2023Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypescited by 5
- 2024The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning studycited by 4
- 2024Detailed delineation of the fetal brain in diffusion MRI via multi-task learningcited by 3
- 2024TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Cloudscited by 2
Built on by
Not cited yet.