Evidence map›Paper›PMID 30317017›Full record

ArticleNeuroImage2019

Limits to anatomical accuracy of diffusion tractography using modern approaches.

Kurt G Schilling, Vishwesh Nath, Colin Hansen, Prasanna Parvathaneni, Justin Blaber, Yurui Gao, Peter Neher, Dogu Baran Aydogan, Yonggang Shi, Mario Ocampo-Pineda and 32 more

Abstract read
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
155citing papers in PubMed, 3 pooled it
–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

155 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  20. Frontiers in neuroscience · 2026
    Article

95 more citing papers are in PubMed but not listed here.

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

42 authors.

Kurt G SchillingVanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA. Electronic address: kurt.g.schilling.1@vumc.org.
Vishwesh NathElectrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, USA.
Colin HansenDepartment of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
Prasanna ParvathaneniDepartment of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
Justin BlaberDepartment of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.
Yurui GaoVanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
Peter NeherDivision of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
Dogu Baran AydoganLaboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Yonggang ShiLaboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Mario Ocampo-PinedaComputer Science Department, University of Verona, Verona, Italy.
Simona SchiaviComputer Science Department, University of Verona, Verona, Italy.
Alessandro DaducciComputer Science Department, University of Verona, Verona, Italy.
Gabriel GirardSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Muhamed BarakovicSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Jonathan Rafael-PatinoSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
David RomascanoSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Gaëtan RensonnetSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Marco PizzolatoSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Alice BatesSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Elda FischiSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Jean-Philippe ThiranSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.
Erick J Canales-RodríguezSignal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland.
Chao HuangDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Hongtu ZhuDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Liming ZhongGuangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Ryan CabeenLaboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Arthur W TogaLaboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Francois RheaultSherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada.
Guillaume TheaudSherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada.
Jean-Christophe HoudeSherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada.
Jasmeen SidhuSherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada.
Maxime ChamberlandCardiff University, Brain Research Imaging Centre, School of Psychology, Cardiff, UK.
Carl-Fredrik WestinDepartment of Radiology, Harvard Medical School, Boston, MA, USA.
Tim B DyrbyDanish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Ragini VermaCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Yogesh RathiBrigham and Women's Hospital, Harvard Medical School, USA.
M Okan IrfanogluNational Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA.
Cibu ThomasSection on Learning and Plasticity, Laboratory of Brain and Cognition, NIMH, Bethesda, MD, USA.
Carlo PierpaoliNational Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA.
Maxime DescoteauxSherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada.
Adam W AndersonVanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
Bennett A LandmanVanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA.

Funding

VANDERBILT UNIVERSITY CTSA FOR PEDIATRIC RESEARCHUL1RR024975 · NCRR · VANDERBILT UNIVERSITY · PI BERNARD, GORDON RAPHAEL · 2007 to 2011
$45.7M
Vascular Contributions to Dementia and Genetic Risk Factors for Alzheimer's DiseaseP01AG052350 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Daniel A Nation, ARTHUR W TOGA · 2016 to 2026
$28.4M
Postdoctoral Training in Biomedical MRI and MRST32EB001628 · NIBIB · VANDERBILT UNIVERSITY MEDICAL CENTER · PI John C Gore · 2003 to 2026
$7.0M
Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRIR01EB017230 · NIBIB · VANDERBILT UNIVERSITY · PI LANDMAN, BENNETT A. · 2015 to 2024
$5.1M
Processing and Analysis of Quantitative Diffusion MRI DataZIHEB000088 · NIBIB · NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING · PI PIERPAOLI, CARLO · 2017 to 2022
$4.5M
The Biological Basis of Diffusion MRI of the BrainR01NS058639 · NINDS · VANDERBILT UNIVERSITY · PI ANDERSON, ADAM W · 2008 to 2016
$3.7M
Connectomics meets neuro-oncology: mapping the brain for treatment planningR01NS096606 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BREM, STEVEN, VERMA, RAGINI · 2016 to 2020
$2.7M
NCRR NIH HHS UL1 RR024975NIA NIH HHS P01 AG052350NIBIB NIH HHS R01 EB017230NIBIB NIH HHS T32 EB001628NIH HHS R01EB017230NIH HHS T32EB001628NIH HHS UL1 RR024975-01NINDS NIH HHS R01 NS058639NINDS NIH HHS R01 NS096606
6 · The paper itself

Abstract

Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

Indexed as

BrainBrain MappingDiffusion Tensor ImagingHumansImage Processing, Computer-AssistedNeural PathwaysConnectivityDiffusion MRIPhantomTracerTractographyValidationWhite matter

Identifiers

PMID30317017
PMCPMC6551229

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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.