Evidence map›Paper›PMID 41382197›Full record

ArticleJournal of translational medicine2025

Multimodal MR imaging for quantification of brain lipid in mice at 9.4T.

Sunil Kumar Khokhar, Anshuman Swain, Narayan Datt Soni, Halvor Juul, Abeer Mathur, Dipak Roy, Blake Benyard, Dushyant Kumar, Ravi Prakash Reddy Nanga, Mohammad Haris and 1 more

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Sunil Kumar Khokhar *Center for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Anshuman Swain *Center for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Narayan Datt SoniCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Halvor JuulCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Abeer MathurCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Dipak RoyCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Blake BenyardCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Dushyant KumarCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Ravi Prakash Reddy NangaCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Mohammad HarisCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Ravinder ReddyCenter for Advanced Metabolic Imaging in Precision Medicine, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. krr@pennmedicine.upenn.edu.ORCID 0000-0003-4580-2392

Funding

High Spatial and Temporal Resolution MRI Mapping of Oxygen Consumption in HumansP41EB029460 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Ravinder Reddy · 2021 to 2026
$7.6M
Imaging of glutamatergic system changes in Alzheimer's disease in vivoR01AG063869 · NIA · UNIVERSITY OF PENNSYLVANIA · PI REDDY, RAVINDER · 2020 to 2024
$3.0M
Novel Imaging Biomarkers of Lipid and Ketone Metabolism in Alzheimer's DiseaseRF1AG087306 · NIA · UNIVERSITY OF PENNSYLVANIA · PI REDDY, RAVINDER · 2024 to 2024
$2.1M
Novel imaging biomarkers of neuroinflammation in Alzheimer's diseaseR01AG091760 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Mohammad Haris · 2025 to 2026
$1.6M
NIA NIH HHS R01 AG063869NIA NIH HHS R01AG063869NIA NIH HHS R01 AG091760NIA NIH HHS R01AG091760NIA NIH HHS RF1 AG087306NIA NIH HHS RF1AG087306NIBIB NIH HHS P41 EB029460NIBIB NIH HHS P41EB029460
6 · The paper itself

Abstract

backgroundAdvanced MR imaging techniques like steady state Nuclear Overhauser enhancement (ssNOE), transient NOE (tNOE), and myelin water fraction (MWF) provide a non-invasive way to assess the biochemical and structural integrity of brain tissue. Their sensitivity to endogenous lipids and macromolecules allows for the early detection of neuropathological changes, making them valuable tools in studying brain health and disease progression. In this study, we systematically evaluate the repeatability and sensitivity of NOEMTR, tNOE, and MWF for quantifying lipid and myelin content in the brains of wild-type (WT) mice, correlating the results with immunohistochemistry (IHC).

methodsFive 6-month-old C57BL6/J mice were imaged using 3D-NOE, and four mice underwent imaging with 2D tNOE and MWF across four repeated sessions using a 9.4T Scanner. For ssNOE imaging, CEST-weighted images at 56 frequency offsets were acquired using B1rms of 1.0 µT and 3s saturation duration. For tNOE, 52 offsets were acquired with a hyperbolic secant inversion pulse (bandwidth = 400 Hz, duration = 44ms) and a mixing time of 200ms. For MWF, a multi-echo spin-echo (MESE) sequence was acquired with 40 evenly spaced echoes from 5.5ms to 200ms. For both ssNOE and tNOE, B0 correction was performed using WASSR. Repeatability was quantified using intra- and inter-subject coefficients of variation (COV%). Pearson correlation was performed to see the association between imaging matrices and IHC measures, Luxol fast blue (LFB) stained sections, and myelin basic protein (MBP).

resultsAll techniques demonstrated high repeatability across the whole brain (WB) and selected regions of interest (ROIs). Whole-brain intra-subject COV% for NOEMTR ranged from 1.92% to 3.40%, with corresponding inter-subject COVs of 1.50%. tNOE exhibited improved intra-subject repeatability with COVs ranging from 0.75% to 5.57%, but a reduced inter-subject COV of 2.97%. MWF imaging showed the highest stability overall, with an intra-subject COV ranging from 0.47% to 2.03% and an inter-subject COV of 0.75%. Visually, tNOE offers superior contrast in myelin-rich areas compared to NOEMTR and MWF imaging, showing greater sensitivity to myelinated regions. tNOE strongly correlates with histological markers: r = 0.83 with MBP staining and r = 0.72 with LFB staining (both p < 0.001). MWF and NOEMTR showed correlations with MBP (r = 0.63 and r = 0.57, respectively).

conclusionNOEMTR, tNOE, and MWF imaging are reliable and repeatable methods for quantifying macromolecules in the brain. Among these, tNOE emerges as the most sensitive for detecting myelin lipids, as confirmed by histological validation. These findings highlight the translational potential of tNOE for studying demyelinating disorders and neurodegenerative diseases. CLINICAL TRIAL NUMBER: not applicable.

Indexed as

BrainLipidsMagnetic Resonance ImagingMultimodal ImagingAnimalsMiceMice, Inbred C57BLMyelin SheathReproducibility of ResultsLipidsBrainLipid imagingMWFMyelinationNOEMTRRepeatabilitytNOE

Identifiers

PMID41382197
PMCPMC12817864

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.