Evidence map›Paper›PMID 28054634›Full record

ArticleScientific reports2017

Within brain area tractography suggests local modularity using high resolution connectomics.

Peter N Taylor, Yujiang Wang, Marcus Kaiser

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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  20. Statistical Challenges of Big Brain Network Data.Statistics & probability letters · 2018
    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

3 authors.

Peter N TaylorInstitute of Neuroscience, Newcastle University, United Kingdom.
Yujiang WangInterdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing Science, Newcastle University, United Kingdom.
Marcus KaiserInstitute of Neuroscience, Newcastle University, United Kingdom.

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
NIMH NIH HHS U54 MH091657Wellcome TrustWellcome Trust 105617/Z/14/Z
6 · The paper itself

Abstract

Previous structural brain connectivity studies have mainly focussed on the macroscopic scale of around 1,000 or fewer brain areas (network nodes). However, it has recently been demonstrated that high resolution structural connectomes of around 50,000 nodes can be generated reproducibly. In this study, we infer high resolution brain connectivity matrices using diffusion imaging data from the Human Connectome Project. With such high resolution we are able to analyse networks within brain areas in a single subject. We show that the global network has a scale invariant topological organisation, which means there is a hierarchical organisation of the modular architecture. Specifically, modules within brain areas are spatially localised. We find that long range connections terminate between specific modules, whilst short range connections via highly curved association fibers terminate within modules. We suggest that spatial locations of white matter modules overlap with cytoarchitecturally distinct grey matter areas and may serve as the structural basis for function specialisation within brain areas. Future studies might elucidate how brain diseases change this modular architecture within brain areas.

Indexed as

ConnectomeDiffusion Tensor ImagingAdultBrainFemaleGray MatterHumansMaleWhite Matter

Identifiers

PMID28054634
PMCPMC5213837

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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.