Evidence map›Paper›PMID 31927129›Full record

ArticleNeuroImage2020

The effect of network thresholding and weighting on structural brain networks in the UK Biobank.

Colin R Buchanan, Mark E Bastin, Stuart J Ritchie, David C Liewald, James W Madole, Elliot M Tucker-Drob, Ian J Deary, Simon R Cox

Abstract read
In one paragraph

Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.

0numbers the graph read from it
0cells of the map it votes in
72citing 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

72 citing papers in PubMed.

  1. Article
  2. A solution to generalized learning from small training sets found in infants' repeated visual experiences of individual objects.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  5. General cognitive function and the brain's structural connectome.bioRxiv : the preprint server for biology · 2026
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12 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

8 authors.

Colin R BuchananLothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK. Electronic address: colin.buchanan@ed.ac.uk.
Mark E BastinLothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK.
Stuart J RitchieSocial, Genetic and Developmental Psychiatry Centre, King's College London, London, UK.
David C LiewaldLothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK.
James W MadoleDepartment of Psychology, University of Texas at Austin, Austin, TX, USA.
Elliot M Tucker-DrobDepartment of Psychology, University of Texas at Austin, Austin, TX, USA.
Ian J DearyLothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK.
Simon R CoxLothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.

Funding

Scientific & Technical CoreP2CHD042849 · NICHD · UNIVERSITY OF TEXAS AT AUSTIN · PI MARK D. HAYWARD · 2017 to 2026
$5.2M
Evaluating Longitudinal Changes in the Human Structural Connectome in Relation to Cognitive AgingR01AG054628 · NIA · UNIVERSITY OF TEXAS AT AUSTIN · PI TUCKER-DROB, ELLIOT MAX · 2017 to 2021
$2.7M
Biotechnology and Biological Sciences Research CouncilMedical Research Council MC_PC_12028Medical Research Council MC_PC_17228Medical Research Council MC_QA137853Medical Research Council MR/K026992/1Medical Research Council MR/L015382/1Medical Research Council MR/M013111/1Medical Research Council MR/R024065/1NIA NIH HHS R01 AG054628NICHD NIH HHS P2C HD042849
6 · The paper itself

Abstract

Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44-77 years), we constructed whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 ​≤ ​|β| ​≤ ​0.409); and 2) the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ​≤ ​|β| ​≤ ​0.406) than the consistency-based approach which retained only 30% of connections (0.140 ​≤ ​|β| ​≤ ​0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC ​= ​0.84), when matched at 70% network sparsity. Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ​≤ ​|0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ​≤ ​|0.219|, p ​< ​0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.

Indexed as

AdultAgedAge FactorsBiological Specimen BanksDiffusion Magnetic Resonance ImagingFemaleHumansMaleMiddle AgedNerve NetNeuroimagingUnited KingdomWhite MatterAgingBrain networkConnectomeDiffusion MRIThresholding

Identifiers

PMID31927129
PMCPMC7085460

What OpenQuestion holds

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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.