ArticleNeuroImage2022
Mahalanobis distance tractometry (MaD-Tract) - a framework for personalized white matter anomaly detection applied to TBI.
Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- The second decade of DTI in TBI Part 2: a systematic review of moderate and severe TBI.Frontiers in neurologyPooled it
- A software ecosystem for brain tractometry processing, analysis, and insight.PLoS computational biology · 2025Article
- Quantification of brain functional connectivity deviations in individuals: A scoping review of functional MRI studies.NeuroImage. Clinical · 2025Article
- MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics.bioRxiv : the preprint server for biology · 2024Article
- Multi-modal MRI of hippocampal morphometry and connectivity after pediatric severe TBI.Brain imaging and behavior · 2024Article
- Sex differences in patterns of white matter neuroplasticity after balance training in young adults.Frontiers in human neuroscience · 2024Article
- A normative modeling approach to quantify white matter changes and predict functional outcomes in stroke patients.Frontiers in neuroscience · 2024Article
- Immersive virtual reality gameplay detects visuospatial atypicality, including unilateral spatial neglect, following brain injury: a pilot study.Journal of neuroengineering and rehabilitation · 2023Article
- MRI-based multivariate gray matter volumetric distance for predicting motor symptom progression in Parkinson's disease.Scientific reports · 2023Article
- Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.Academic radiology · 2023Review
- Quantifying Distances Between Non-Elliptical Clusters to Enhance the Identification of Meaningful Emotional Reactivity Subtypes.Data science in science · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Imaging-based quantitative measures from diffusion-weighted MRI (dMRI) offer the ability to non-invasively extract microscopic information from human brain tissues. Group-level comparisons of such measures represent an important approach to investigate abnormal brain conditions. These types of analyses are especially useful when the regions of abnormality spatially coincide across subjects. When this is not true, approaches for individualized analyses are necessary. Here we present a framework for single-subject multidimensional analysis based on the Mahalanobis distance. This is conducted along specific white matter pathways represented by tractography-derived streamline bundles. A definition for abnormality was constructed from Wilk's criterion, which accounts for normative sample size, number of features used in the Mahalanobis distance, and multiple comparisons. One example of a condition exhibiting high heterogeneity across subjects is traumatic brain injury (TBI). Using the Mahalanobis distance computed from the three eigenvalues of the diffusion tensor along the cingulum, uncinate, and parcellated corpus callosum tractograms, 8 severe TBI patients were individually compared to a normative sample of 49 healthy controls. For all TBI patients, the analyses showed statistically significant deviations from the normative data at one or multiple locations along the analyzed bundles. The detected anomalies were widespread across the analyzed tracts, consistent with the expected heterogeneity that is hallmark of TBI. Each of the controls subjects was also compared to the remaining 48 subjects in the control group in a leave-one-out fashion. Only two segments were identified as abnormal out of the entire analysis in the control group, thus the method also demonstrated good specificity.
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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.