ArticleNeuroImage2019
Tensor network factorizations: Relationships between brain structural connectomes and traits.
Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 104 citations in OpenAlex.
- An EEG-based digital biomarker for personalizing transcranial magnetic stimulation in major depressive disorder.NPJ digital medicine · 2026Article
- Characterizing Continuous and Discrete Hybrid Latent Spaces for Structural Connectomes.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Latent space-based network analysis for brain-behavior linking in neuroimaging.Nature methods · 2026Article
- White Matter Bundle Reconstruction From Single-Shell Diffusion Magnetic Resonance Imaging: Test-Retest Reliability and Predictive Capability Across Orientation Distribution Function Reconstruction Methods.Human brain mapping · 2025Article
- Cost Efficiency of fMRI Studies Using Resting-State Vs. Task-Based Functional Connectivity.Human brain mapping · 2025Article
- Structural-functional brain network coupling during cognitive demand reveals intelligence-relevant communication strategies.Communications biology · 2025Article
- Bayesian scalar-on-network regression with applications to brain functional connectivity.Biometrics · 2025Article
- Weak and unstable prediction of personality from the structural connectome.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration.Biomedicines · 2024Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Guaranteed Functional Tensor Singular Value Decomposition.Journal of the American Statistical Association · 2024Article
- Inferring neurocognition using artificial intelligence on brain MRIs.Frontiers in neuroimaging · 2024Review
- Motion-invariant variational autoencoding of brain structural connectomes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Identifying vulnerable brain networks associated with Alzheimer's disease risk.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Genetic underpinnings of brain structural connectome for young adults.Journal of the American Statistical Association · 2023Article
- Partially Observed Dynamic Tensor Response Regression.Journal of the American Statistical Association · 2023Article
- 3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2023Article
- Tree representations of brain structural connectivity via persistent homology.Frontiers in neuroscience · 2023Article
- Predicting overall survival in diffuse glioma from the presurgical connectome.Scientific reports · 2022Article
- Outlier detection for multi-network data.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
Advanced brain imaging techniques make it possible to measure individuals' structural connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural connectome extraction pipeline produces a set of connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural connectomes and analysis method can give superior prediction accuracies compared with alternative connectome constructions and other tensor and network regression methods.
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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.