Evidence map›Paper›PMID 40903913›Full record

ReviewJournal of neuroimaging : official journal of the American Society of Neuroimaging

Reliability and Spatial Consistency of MR Diffusion Tensor Imaging Measures Along the Cerebral Perivascular Space.

Enchao Qiu, Joga Chaganti, Phillip Phan, Mahdi Alizadeh, Devon Middleton, Kiran Talekar, Prabath K Mondel, Scott H Faro, Feroze B Mohamed, Hsiangkuo Yuan

Abstract readReview
In one paragraph

Review in Journal of neuroimaging : official journal of the American Society of Neuroimaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Article
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  6. Reliability and Spatial Consistency of MR Diffusion Tensor Imaging Measures Along the Cerebral Perivascular Space.Journal of neuroimaging : official journal of the American Society of Neuroimaging
    Review
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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

10 authors.

Enchao QiuJefferson Headache Center, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0009-0009-9393-3857
Joga ChagantiJefferson Integrative Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Phillip PhanJefferson Headache Center, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Mahdi AlizadehDepartment of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Devon MiddletonJefferson Integrative Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Kiran TalekarJefferson Integrative Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Prabath K MondelJefferson Integrative Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Scott H FaroJefferson Integrative Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Feroze B MohamedJefferson Integrative Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
Hsiangkuo YuanJefferson Headache Center, Department of Neurology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-4536-9974

Funding

Miles for Migraine, Patient Philanthropy
6 · The paper itself

Abstract

background and purposeDiffusion tensor imaging analysis along the perivascular space (DTI-ALPS) has emerged as a promising noninvasive method for evaluating water motion that may reflect glymphatic system function. However, the reliability of DTI-ALPS measurements across different region-of-interest (ROI) selection methods remains underinvestigated. This study aimed to assess the interrater reliability among three neuroradiologists in native space and compare DTI-ALPS indices derived from ROIs placed in subjects' native space versus standardized Montreal Neurological Institute (MNI) space.

methodsDTI-ALPS indices from 16 healthy subjects were calculated from both left and right hemispheres using two ROI placement approaches: (1) native space ROIs manually placed by three neuroradiologists, and (2) standardized ROIs in MNI space based on the fractional anisotropy template. Interrater reliability was assessed using intraclass correlation coefficients (ICCs). The proportion of ROI overlaps among the three neuroradiologists was also evaluated. Differences between native and MNI space measurements were evaluated using related-samples Friedman's analysis with post hoc pairwise comparisons.

resultsInterrater reliability for native space ROI placement was moderate for left-sided DTI-ALPS indices (ICC = 0.599) and good for right-sided DTI-ALPS indices (ICC = 0.807). Spatial overlap analysis revealed poor Dice similarity coefficients across all ROI types (range: 0.047-0.312), with right association ROIs showing higher spatial consistency. Significant differences were found between native and MNI space measurements for left-sided DTI-ALPS indices (p = 0.002) but not for right-sided DTI-ALPS indices (p = 0.913).

conclusionThese findings highlight the importance of standardized ROI selection approaches for clinical applications of DTI-ALPS.

Indexed as

BrainDiffusion Tensor ImagingGlymphatic SystemAdultFemaleHumansMaleMiddle AgedObserver VariationReproducibility of Resultsdiffusion tensor imagingdiffusion tensor imaging analysis along the perivascular spaceglymphatic systemobserver variationperivascular space

Identifiers

PMID40903913
PMCPMC12409098

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Registered trials

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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.