Evidence map›Paper›PMID 35421567›Full record

ArticleNeuroImage2022

Harmonizing functional connectivity reduces scanner effects in community detection.

Andrew A Chen, Dhivya Srinivasan, Raymond Pomponio, Yong Fan, Ilya M Nasrallah, Susan M Resnick, Lori L Beason-Held, Christos Davatzikos, Theodore D Satterthwaite, Dani S Bassett and 2 more

Abstract read
In one paragraph

Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Article
  2. Article
  3. Two axes of white matter development.Nature communications · 2026
    Article
  4. Article
  5. Article
  6. Calibration of MRI-based reference intervals to new samples.bioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Individual Variability in the Structural Connectivity Architecture of the Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  12. Article
  13. Review
  14. Article
  15. Longitudinal regression of covariance matrix outcomes.Biostatistics (Oxford, England) · 2024
    Article
  16. Neural Systems Underlying the Implementation of Working Memory Removal Operations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  17. Review
  18. Article
  19. Article
  20. 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

12 authors.

Andrew A ChenPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: andrewac@pennmedicine.upenn.edu.
Dhivya SrinivasanCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Raymond PomponioDepartment of Biostatistics, Colorado School of Public Health, Aurora, CO 80045, USA.
Yong FanCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Ilya M NasrallahCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA.
Lori L Beason-HeldLaboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD 21224, USA.
Christos DavatzikosCenter for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Theodore D SatterthwaitePenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
Dani S BassettDepartment of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Nuerology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.
Russell T ShinoharaPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Haochang ShouPenn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Funding

Statistical Methods for Multilevel Multivariate Functional StudiesR01NS060910 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Ciprian M Crainiceanu · 2009 to 2026
$8.3M
Machine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortiumRF1AG054409 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2023
$6.3M
Personalized Functional Network Modeling to Characterize and Predict Psychopathology in YouthR01EB022573 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Yong Fan, Theodore Satterthwaite · 2016 to 2026
$6.0M
Inter-modal Coupling Image AnalyticsR01MH112847 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Theodore Satterthwaite, Russell Takeshi Shinohara · 2017 to 2026
$5.9M
Precision mapping of individualized executive networks in youthR37MH125829 · NIMH · UNIVERSITY OF MINNESOTA · PI Damien A Fair, Theodore Satterthwaite · 2021 to 2026
$4.7M
Harmonization of Multi-Site Neuroimaging Data from Complex Study DesignsR01MH123550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2020 to 2025
$4.0M
Reproducible imaging-based brain growth charts for psychiatryR01MH120482 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI MILHAM, MICHAEL PETER, SATTERTHWAITE, THEODORE · 2019 to 2023
$3.5M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
Statistical methods for large and complex databases of ultra-high-dimensionalR01NS085211 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2013 to 2017
$1.8M
NIA NIH HHS RF1 AG054409NIBIB NIH HHS R01 EB022573NIMH NIH HHS R01 MH112847NIMH NIH HHS R01 MH120482NIMH NIH HHS R01 MH123550NIMH NIH HHS R37 MH125829NINDS NIH HHS R01 NS060910NINDS NIH HHS R01 NS085211
6 · The paper itself

Abstract

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

Indexed as

BrainMagnetic Resonance ImagingBenchmarkingBrain MappingHumansReproducibility of ResultsBrain networksCommunity detectionFunctional connectivityHarmonizationNetwork analysesSite effects

Identifiers

PMID35421567
PMCPMC9202339

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.