ArticleFrontiers in neuroscience2024
TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.
Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography.Human brain mapping · 2025Article
- TractCloud-FOV: Deep Learning-Based Robust Tractography Parcellation in Diffusion MRI With Incomplete Field of View.Human brain mapping · 2025Article
- The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study.Human brain mapping · 2025Review
- Comparative analysis of convolutional neural networks and traditional machine learning models for IVF live birth prediction: a retrospective analysis of 48514 IVF cycles and an evaluation of deployment feasibility in resource-constrained settings.Frontiers in endocrinology · 2025Article
- TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance.Medical image analysis · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.