Evidence map›Paper›PMID 29986834›Full record

ArticleNeuroImage2018

Tracking and validation techniques for topographically organized tractography.

Dogu Baran Aydogan, Yonggang Shi

Abstract read
In one paragraph

Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Flow-based Geometric Interpolation of Fiber Orientation Distribution Functions.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023
    Article
  9. Article
  10. Review
  11. Article
  12. Parallel Transport Tractography.IEEE transactions on medical imaging · 2021
    Article
  13. Article
  14. Topographic Filtering of Tractograms as Vector Field Flows.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019
    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

2 authors.

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. Electronic address: yshi@loni.usc.edu.

Funding

VASCULAR RISK AND COGNITIVE STATUS IN A LATINO POPULATIONP50AG005142 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ARANDA, MARIA P. · 1985 to 2019
$41.0M
TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
The Impact of Emotional Regulation on Cognition in AgingR01AG025340 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MATHER, MARA · 2004 to 2021
$7.9M
Human Connectomes for Low Vision, Blindness, and Sight RestorationU01EY025864 · NEI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AGUIRRE, GEOFFREY KARL, PATEL, VIVEK R. · 2015 to 2018
$4.0M
Surface-Based Fiber Tracking and Modeling Techniques for Mapping the Superficial White Matter Connectome with Diffusion MRIR01EB022744 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Yonggang Shi · 2016 to 2026
$3.7M
The structural and functional connectome across Alzheimer's disease subtypesU01AG051218 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI RINGMAN, JOHN M · 2015 to 2018
$3.6M
NEI NIH HHS U01 EY025864NIA NIH HHS P50 AG005142NIA NIH HHS R01 AG025340NIA NIH HHS U01 AG051218NIBIB NIH HHS P41 EB015922NIBIB NIH HHS R01 EB022744
6 · The paper itself

Abstract

Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

Indexed as

AlgorithmsAdultCerebral CortexConnectomeDiffusion Tensor ImagingHumansImage Processing, Computer-AssistedInternal CapsulePyramidal TractsReproducibility of ResultsVisual PathwaysConnectomeCorticospinal tractdMRIRetinotopy

Identifiers

PMID29986834
PMCPMC6139055

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.