Evidence map›Paper›PMID 35840117›Full record

ArticleNeuroImage2022

Mahalanobis distance tractometry (MaD-Tract) - a framework for personalized white matter anomaly detection applied to TBI.

Jose M Guerrero-Gonzalez, Benjamin Yeske, Gregory R Kirk, Michael J Bell, Peter A Ferrazzano, Andrew L Alexander

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Jose M Guerrero-GonzalezDepartment of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA. Electronic address: jguerrerogon@wisc.edu.
Benjamin YeskeWaisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA.
Gregory R KirkWaisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA.
Michael J BellCritical Care Medicine, Children's National Medical Center, Washington, DC, USA.
Peter A FerrazzanoDepartment of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA.
Andrew L AlexanderDepartment of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA.
University of Wisconsin–Madison · USChildren's National · US

Funding

U.W. RADIOLOGICAL SCIENCES TRAINING PROGRAMT32CA009206 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI Bryan Patrick Bednarz · 1985 to 2026
$12.1M
Waisman Center Intellectual and Developmental Disabilities Research CenterP50HD105353 · NICHD · UNIVERSITY OF WISCONSIN-MADISON · PI Brittany Gail Travers · 2021 to 2026
$8.5M
MRI markers of functional outcome after severe pediatric TBIR01NS092870 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI ALEXANDER, ANDREW L, FERRAZZANO, PETER A · 2016 to 2019
$2.2M
NCI NIH HHS T32 CA009206NICHD NIH HHS P50 HD105353NINDS NIH HHS R01 NS092870
6 · The paper itself

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.

Indexed as

Brain Injuries, TraumaticWhite MatterDiffusion Magnetic Resonance ImagingDiffusion Tensor ImagingHumansDTIMultivariatePrecision-medicineTBITractometry

Identifiers

PMID35840117
PMCPMC9531540
OpenAlexW4285405136

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.