Evidence map›Paper›PMID 33248255›Full record

ArticleNeuroImage2021

Impact of concatenating fMRI data on reliability for functional connectomics.

Jae Wook Cho, Annachiara Korchmaros, Joshua T Vogelstein, Michael P Milham, Ting Xu

Abstract read
In one paragraph

Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing 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

52 citing papers in PubMed.

  1. Reversible modulation of a deep white matter surgical target for depression with low-intensity focused ultrasound.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Trial
  2. Temporal stability of functional network configurations and its relationship to cognition and psychopathology in early psychoses.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Network homeostasis: functional brain network alterations and relapse in remitted late-life depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Challenges in measuring individual differences of brain function.Imaging neuroscience (Cambridge, Mass.) · 2025
    Article
  18. Jointly estimating individual and group networks from fMRI data.Network neuroscience (Cambridge, Mass.) · 2025
    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

5 authors.

Jae Wook ChoThe Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
Annachiara KorchmarosThe Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
Joshua T VogelsteinDepartment of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, 3400N. Charles St Baltimore, MD 21218, United States.
Michael P MilhamThe Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States.
Ting XuThe Child Mind Institute, 101 East 56th Street, New York, NY 10022, United States. Electronic address: Ting.Xu@childmind.org.

Funding

Project 5: ComputationalP50MH109429 · NIMH · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI Robert Thomas Knight · 2017 to 2026
$25.3M
Defining Neuronal Circuits and Cellular Processes Underlying Resting fMRI SignalsR01MH111439 · NIMH · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI MILHAM, MICHAEL PETER, SCHROEDER, CHARLES E · 2016 to 2020
$5.1M
Reproducible imaging-based brain growth charts for psychiatryR01MH120482 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI MILHAM, MICHAEL PETER, SATTERTHWAITE, THEODORE · 2019 to 2023
$3.5M
Mapping interindividual variation in the aging connectomeR01AG047596 · NIA · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI COLCOMBE, STAN J, MILHAM, MICHAEL PETER · 2015 to 2019
$3.1M
C-PAC: A configurable, compute-optimized, cloud-enabled neuroimaging analysis software for reproducible translational and comparativeR24MH114806 · NIMH · CHILD MIND INSTITUTE, INC. · PI CRADDOCK, RICHARD CAMERON, MILHAM, MICHAEL PETER · 2018 to 2020
$1.6M
NIA NIH HHS R01 AG047596NIMH NIH HHS P50 MH109429NIMH NIH HHS R01 MH111439NIMH NIH HHS R01 MH120482NIMH NIH HHS R24 MH114806
6 · The paper itself

Abstract

Compelling evidence suggests the need for more data per individual to reliably map the functional organization of the human connectome. As the notion that 'more data is better' emerges as a golden rule for functional connectomics, researchers find themselves grappling with the challenges of how to obtain the desired amounts of data per participant in a practical manner, particularly for retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, task, movie). A number of open questions exist regarding the aggregation process and the impact of different decisions on the reliability of resultant aggregate data. We leveraged the availability of highly sampled test-retest datasets to systematically examine the impact of data aggregation strategies on the reliability of cortical functional connectomics. Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal regression, ICA-FIX, and task regression) and estimation procedures. When the total number of time points is equal, and the scan state held constant, concatenating multiple shorter scans had a clear advantage over a single long scan. However, this was not necessarily true when concatenating across different fMRI states (i.e. task conditions), where the reliability from the aggregate data varied across states. Concatenating fewer numbers of states that are more reliable tends to yield higher reliability. Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data.

Indexed as

AdultBrainConnectomeFemaleHumansMagnetic Resonance ImagingMaleNerve NetReproducibility of ResultsRetrospective StudiesYoung Adult

Identifiers

PMID33248255
PMCPMC7983579

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.