ArticlePLoS computational biology2021
Multidimensional analysis and detection of informative features in human brain white matter.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- Neonatal brain-age models in full- and preterm infants.Developmental cognitive neuroscience · 2026Article
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Deviation in development of dorsal association tracts during preadolescence links to concurrent and future cognitive performance and transdiagnostic psychopathology.Nature communications · 2026Article
- Neonatal brain-age models in full- and preterm infants.bioRxiv : the preprint server for biology · 2026Article
- A software ecosystem for brain tractometry processing, analysis, and insight.PLoS computational biology · 2025Article
- Cortical and white matter T1w/T2w development proceed in concert during early infancy.bioRxiv : the preprint server for biology · 2025Article
- Fiber microstructure quantile (FMQ) regression: A novel statistical approach for analyzing white matter bundles from periphery to core.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Tractometry of Human Visual White Matter Pathways in Health and Disease.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- Differences in educational opportunity predict white matter development.Developmental cognitive neuroscience · 2024Article
- Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.Communications medicine · 2024Article
- Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts.Brain communications · 2024Article
- Tractometry of the Human Connectome Project: resources and insights.Frontiers in neuroscience · 2024Article
- TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography.Frontiers in neuroscience · 2024Article
- Human white matter myelinates faster in utero than ex utero.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- An analysis-ready and quality controlled resource for pediatric brain white-matter research.Scientific data · 2022Article
- Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract.Brain communications · 2022Article
- Automated Multiclass Artifact Detection in Diffusion MRI VolumesFrontiers in human neuroscience · 2022Article
- A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Groupyr: Sparse Group Lasso in Python.Journal of open source software · 2021Article
- Evaluating the Reliability of Human Brain White Matter Tractometry.Aperture neuro · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.
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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.