Evidence map›Paper›PMID 37792280›Full record

ArticleHuman brain mapping2023

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods.

Jianzhong He, Fan Zhang, Yiang Pan, Yuanjing Feng, Jarrett Rushmore, Erickson Torio, Yogesh Rathi, Nikos Makris, Ron Kikinis, Alexandra J Golby and 1 more

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.1field-weighted citation impact, top 5% 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, 18 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Multimodal Neuroimaging Computing: Basics and Applications in Neurosurgery.Advances in experimental medicine and biology · 2024
    Review
  10. Review
  11. 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

11 authors at 3 institutions in 2 countries.

Jianzhong HeInstitution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China.ORCID 0000-0002-1797-8334
Fan ZhangDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-5032-6039
Yiang PanInstitution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China.
Yuanjing FengInstitution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China.
Jarrett RushmoreDepartments of Psychiatry, Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Erickson TorioDepartment of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Yogesh RathiDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-9946-2314
Nikos MakrisDepartments of Psychiatry, Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Ron KikinisDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Alexandra J GolbyDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Lauren J O'DonnellDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0003-0197-7801
Brigham and Women's Hospital · USZhejiang University of Science and Technology · CNBoston University · US

Funding

Training and DisseminationP41EB015902 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI KIKINIS, RON, WESTIN, CARL-FREDRIK · 2012 to 2022
$20.9M
TRD 3 - Enabling Technologies for Intraprocedural GuidanceP41EB028741 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI Nobuhiko Hata, CLARE M TEMPANY · 2021 to 2026
$10.7M
Neural substrates of diffusion imaging in cognitively aging rhesus monkeysR01AG042512 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI KUBICKI, MAREK, MAKRIS, NIKOLAOS · 2013 to 2023
$6.5M
Lymph Node Quantification System for Multisite Clinical TrialsR01CA235589 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI GORDON J HARRIS, Ron Kikinis · 2019 to 2026
$4.5M
Mapping the superficial white matter connectome of the human brain using ultra high resolution multi-contrast diffusion MRIR01MH125860 · NIMH · BRIGHAM AND WOMEN'S HOSPITAL · PI MAKRIS, NIKOLAOS, O'DONNELL, LAUREN JEAN · 2021 to 2025
$4.1M
High Resolution, Comprehensive Atlases of the Human Brain MorphologyR01MH112748 · NIMH · BRIGHAM AND WOMEN'S HOSPITAL · PI BOUIX, SYLVAIN, KUBICKI, MAREK · 2018 to 2022
$4.1M
Harmonizing multi-site diffusion MRI acquisitions for neuroscientific analysis across ages and brain disordersR01MH119222 · NIMH · BRIGHAM AND WOMEN'S HOSPITAL · PI O'DONNELL, LAUREN JEAN, RATHI, YOGESH · 2019 to 2023
$4.0M
Quantitative Glioblastoma Margin and Infiltration Mapping with Advanced Diffusion-Relaxation MRIR01NS125781 · NINDS · BRIGHAM AND WOMEN'S HOSPITAL · PI ALEXANDRA J GOLBY, Carl-Fredrik Westin · 2022 to 2026
$3.6M
Unraveling the Superficial White Matter of the Primate Brain: Tracer-Based Histology and dMRI Tractography ValidationR01NS125307 · NINDS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI NIKOLAOS MAKRIS, RICHARD Jarrett RUSHMORE · 2022 to 2026
$3.4M
Patient-specific, Effective, and Rational Functional Connectivity Targeting for DBS in OCDR01MH111917 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI DOUGHERTY, DARIN D, MAKRIS, NIKOLAOS · 2017 to 2019
$2.1M
Mentoring and neuroimaging research on new targets for DBS in OCDK24MH116366 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI MAKRIS, NIKOLAOS · 2018 to 2022
$817k
Modulating Inhibitory Control Networks In Gambling Disorder With Theta Burst StimulationR21DA042271 · NIDA · MASSACHUSETTS GENERAL HOSPITAL · PI CAMPRODON, JOAN A, MAKRIS, NIKOLAOS · 2017 to 2018
$439k
NCI NIH HHS HHSN261201500003CNCI NIH HHS HHSN261201500003INCI NIH HHS R01 CA235589NIA NIH HHS R01 AG042512NIBIB NIH HHS P41 EB015902NIBIB NIH HHS P41 EB028741NIDA NIH HHS R21 DA042271NIH HHS K24MH116366NIH HHS P41EB015902NIH HHS P41EB028741NIH HHS R01AG042512NIH HHS R01CA235589NIH HHS R01MH111917NIH HHS R01MH112748NIH HHS R01MH119222NIH HHS R01MH125860NIH HHS R01NS125307NIH HHS R01NS125781NIH HHS R21DA042271NIMH NIH HHS K24 MH116366NIMH NIH HHS R01 MH111917NIMH NIH HHS R01 MH112748NIMH NIH HHS R01 MH119222NIMH NIH HHS R01 MH125860NINDS NIH HHS R01 NS125307NINDS NIH HHS R01 NS125781
6 · The paper itself

Abstract

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

Indexed as

Brain NeoplasmsDiffusion Tensor ImagingBrainDiffusion Magnetic Resonance ImagingHumansPyramidal Tractscorticospinal tractdiffusion magnetic resonance imagingmotor cortexsomatotopic organizationtractography

Identifiers

PMID37792280
PMCPMC10619402
OpenAlexW4387325984

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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.