Evidence map›Paper›PMID 32428883›Full record

ArticleJournal of neural engineering2020

Connectomic consistency: a systematic stability analysis of structural and functional connectivity.

Yusuf Osmanlıoğlu, Jacob A Alappatt, Drew Parker, Ragini Verma

Abstract read
In one paragraph

Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Altered network efficiency in isolated REM sleep behavior disorder: A multicentric study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Article
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  8. Article
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  10. Article
  11. Review
  12. Article
  13. Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders.Uncertainty for safe utilization of machine learning in medical imaging, and graphs in biomedical image analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI... · 2020
    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

4 authors.

Yusuf OsmanlıoğluDiffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America.ORCID 0000-0002-9997-9479
Jacob A AlappattDiffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, United States of America.ORCID 0000-0003-0909-2506
Drew ParkerAuthor to whom any correspondence should be addressed.ORCID 0000-0001-8017-0525
Ragini VermaAuthor to whom any correspondence should be addressed.ORCID 0000-0002-7479-1007

Funding

Harmonization for multisite Connectomics: parsing heterogeneity and creating markers in ASDR01MH117807 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI VERMA, RAGINI · 2019 to 2023
$3.6M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
NIH HHS S10 OD023495NIMH NIH HHS R01 MH117807
6 · The paper itself

Abstract

objectiveConnectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans. APPROACH: In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes. MAIN

resultsOur results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated. SIGNIFICANCE: Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.

Indexed as

ConnectomeAlgorithmsBrainHumansMagnetic Resonance ImagingNeurobiology

Identifiers

PMID32428883
PMCPMC7584380

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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.