Evidence map›Paper›PMID 39953164›Full record

ReviewNature protocols2025

Structural connectome construction using constrained spherical deconvolution in multi-shell diffusion-weighted magnetic resonance imaging.

Marlene Tahedl, J-Donald Tournier, Robert E Smith

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

3 authors.

Marlene TahedlDepartment of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany. marlene.tahedl@tum.de.ORCID 0000-0003-1762-4824
J-Donald TournierCentre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Robert E SmithFlorey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.ORCID 0000-0003-3636-4642

Funding

ERC
6 · The paper itself

Abstract

Connectional neuroanatomical maps can be generated in vivo by using diffusion-weighted magnetic resonance imaging (dMRI) data, and their representation as structural connectome (SC) atlases adopts network-based brain analysis methods. We explain the generation of high-quality SCs of brain connectivity by using recent advances for reconstructing long-range white matter connections such as local fiber orientation estimation on multi-shell dMRI data with constrained spherical deconvolution, which yields both increased sensitivity to detecting crossing fibers compared with competing methods and the ability to separate signal contributions from different macroscopic tissues, and improvements to streamline tractography such as anatomically constrained tractography and spherical-deconvolution informed filtering of tractograms, which have increased the biological accuracy of SC creation. Here, we provide step-by-step instructions to creating SCs by using these methods. In addition, intermediate steps of our procedure can be adapted for related analyses, including region of interest-based tractography and quantification of local white matter properties. The associated software MRtrix3 implements the relevant tools for easy application of the protocol, with specific processing tasks deferred to components of the FSL software. The protocol is suitable for users with expertise in dMRI and neuroscience and requires between 2 h and 13 h to complete, depending on the available computational system.

Indexed as

BrainConnectomeDiffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedDiffusion Tensor ImagingHumansSoftwareWhite Matter

Identifiers

What OpenQuestion holds

Textmetadata
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.