ArticleNeuroImage2018
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 33 citations in OpenAlex.
- Article
- Predictive modeling of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients.medRxiv : the preprint server for health sciences · 2025Article
- Discovering the gene-brain-behavior link in autism via generative machine learning.Science advances · 2024Article
- Invariance encoding in sliced-Wasserstein space for image classification with limited training data.Pattern recognition · 2023Article
- Optimal transport features for morphometric population analysis.Medical image analysis · 2023Article
- Effects of Mild Traumatic Brain Injury on Resting State Brain Network Connectivity in Older Adults.Brain imaging and behavior · 2022Article
- Radon Cumulative Distribution Transform Subspace Modeling for Image Classification.Journal of mathematical imaging and vision · 2021Article
- Examining the effect of explanation on satisfaction and trust in AI diagnostic systems.BMC medical informatics and decision making · 2021Article
- PARTITIONING SIGNAL CLASSES USING TRANSPORT TRANSFORMS FOR DATA ANALYSIS AND MACHINE LEARNING.Sampling theory, signal processing, and data analysis · 2021Article
- A Heteromodal Word-Meaning Binding Site in the Visual Word Form Area under Top-Down Frontoparietal Control.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Investigating impact of cardiorespiratory fitness in reducing brain tissue loss caused by ageing.Brain communications · 2021Article
- Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Trajectories from Distribution-valued Functional Curves: A Unified Wasserstein FrameworkMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency.NPJ digital medicine · 2020Article
- Assessing Postconcussive Reaction Time Using Transport-Based Morphometry of Diffusion Tensor Images.AJNR. American journal of neuroradiology · 2019Article
- Exploratory Population Analysis with Unbalanced Optimal Transport.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e. deformation-based methods), are prone to missing a vast array of physical changes that are not well-encapsulated by these metrics. In this paper, we have developed a technique for automated pattern analysis that can fully determine the relationship between brain structure and observable phenotype without requiring any a priori features. Our technique, called transport-based morphometry (TBM), is an image transformation that maps brain images losslessly to a domain where they become much more separable. The new approach is validated on structural brain images of healthy older adult subjects where even linear models for discrimination, regression, and blind source separation enable TBM to independently discover the characteristic changes of aging and highlight potential mechanisms by which aerobic fitness may mediate brain health later in life. TBM is a generative approach that can provide visualization of physically meaningful shifts in tissue distribution through inverse transformation. The proposed framework is a powerful technique that can potentially elucidate genotype-structural-behavioral associations in myriad diseases.
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What OpenQuestion holds
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.