Evidence map›Paper›PMID 40349743›Full record

ArticleNeuroImage2025

In vivo cortical microstructure mapping using high-gradient diffusion MRI accounting for intercompartmental water exchange effects.

Tanxin Dong, Hong-Hsi Lee, Han Zang, Hansol Lee, Qiyuan Tian, Liang Wan, Qiuyun Fan, SusieY Huang

Abstract read
In one paragraph

Article in NeuroImage, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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

8 authors.

Tanxin DongAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Interaction, Tianjin, China.
Hong-Hsi LeeDepartment of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA.
Han ZangAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Interaction, Tianjin, China.
Hansol LeeDepartment of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA.
Qiyuan TianSchool of Biomedical Engineering, Tsinghua University, Beijing, China.
Liang WanAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Qiuyun FanAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Tianjin Key Laboratory of Brain Science and Neuroengineering, Tianjin, China; Haihe Laboratory of Brain-Computer Interaction and Human-Machine Interaction, Tianjin, China. Electronic address: fanqiuyun@tju.edu.cn.
SusieY HuangDepartment of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.

Funding

CONNECTOME 2.0: DEVELOPING THE NEXT GENERATION HUMAN MRI SCANNER FOR BRIDGING STUDIES OF THE MICRO-, MESO- AND MACRO-CONNECTOMEU01EB026996 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BASSER, PETER J., HUANG, SUSIE YI · 2018 to 2022
$14.2M
Training and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang · 2020 to 2026
$10.9M
Project 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8M
Connectome 2.0: A BRAIN Technology Integration and Dissemination Resource for Ultra-High Gradient Magnetic Resonance Imaging of Human Brain Circuits Across ScalesU24NS137077 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang · 2024 to 2026
$4.0M
Toward a Validated in Vivo Imaging Marker of Axonal Damage Predictive of Progressive Disability in Multiple SclerosisR01NS118187 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI HUANG, SUSIE YI · 2021 to 2025
$2.8M
Revealing tissue microstructure in the brain gray matter in Alzheimer's disease using in vivo high-gradient diffusion MRIDP5OD031854 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI LEE, HONG HSI · 2021 to 2025
$2.1M
Next-generation 3 Tesla Human MRI SystemS10OD032184 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI HUANG, SUSIE YI · 2023 to 2023
$2.0M
Developing a validated biomarker of cortical neurodegeneration in Alzheimer's disease using high-gradient diffusion MRIR21AG085795 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI HUANG, SUSIE YI · 2024 to 2025
$459k
Advancing methods for mapping short-range association fibers in the aging brainK99AG073506 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI TIAN, QIYUAN · 2021 to 2022
$268k
NIA NIH HHS K99 AG073506NIA NIH HHS R21 AG085795NIBIB NIH HHS P41 EB015896NIBIB NIH HHS P41 EB030006NIBIB NIH HHS U01 EB026996NIH HHS DP5 OD031854NIH HHS S10 OD032184NINDS NIH HHS R01 NS118187NINDS NIH HHS U24 NS137077
6 · The paper itself

Abstract

In recent years, mapping tissue microstructure in the cortex using high gradient diffusion MRI has received growing attention. The Soma And Neurite Density Imaging (SANDI) explicitly models the soma compartment in the cortex assuming impermeable membranes. As such, it does not account for diffusion time dependence due to water exchange in the estimated microstructural properties, as neurites in gray matter are much less myelinated than in white matter. In this work, we performed a systematic evaluation of an extended SANDI model for in vivo human cortical microstructural mapping that accounts for water exchange effects between the neurite and extracellular compartments using the anisotropic Kärger model. We refer to this model as in vivo SANDIX, adapting the nomenclature from previous publications. As in the original SANDI model, the soma compartment is modeled as an impermeable sphere due to the much smaller surface-to-volume ratio compared to the neurite compartment. A Monte Carlo simulation study was performed to examine the sensitivity of the in vivo SANDIX model to sphere radii, compartment fractions, and water exchange times. The simulation results indicate that the proposed in vivo SANDIX framework can account for the water exchange effect and provide measures of intra-soma and intra-neurite signal fractions without spurious time-dependence in estimated parameters, whereas the measured water exchange times need to be interpreted with caution. The model was then applied to in vivo diffusion MRI data acquired in 13 healthy adults on the 3-Tesla Connectome MRI scanner equipped with 300 mT/m gradients. The in vivo results exhibited patterns that were consistent with corresponding anatomical characteristics in both cortex and white matter. In particular, the estimated water exchange times in gray and white matter were distinct and differentiated between the two tissue types. Our results show the SANDIX approach applied to high-gradient diffusion MRI data achieves cortical microstructure mapping of the in vivo human brain with the evaluation of water exchange effects. This approach potentially provides a more appropriate description of in vivo cortical microstructure for improving data interpretation in future neurobiological studies.

Indexed as

Brain MappingCerebral CortexDiffusion Magnetic Resonance ImagingAdultFemaleGray MatterHumansMaleModels, NeurologicalMonte Carlo MethodNeuritesWaterWhite MatterWaterConnectomeCortexDiffusion MRIKärger modelMicrostructureWater exchange

Identifiers

PMID40349743
PMCPMC12270005

What OpenQuestion holds

Textmetadata
LicenceTDM
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.