Evidence map›Paper›PMID 29322586›Full record

ArticleHuman brain mapping2018

Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity.

Chao Zhang, Chase C Dougherty, Stefi A Baum, Tonya White, Andrew M Michael

Open access · greenAbstract read
In one paragraph

Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed
16.1field-weighted citation impact, top 1% of its field
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

97 citing papers in PubMed, 243 citations in OpenAlex.

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  9. The progression of sex differences in brain networks across the lifespan.bioRxiv : the preprint server for biology · 2026
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  14. Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  18. "Precision Medicine" and the Failed Search for Binary Brain Sex Differences to Address Gender Behavioral Health Disparities.American journal of human biology : the official journal of the Human Biology Council · 2025
    Review
  19. Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.Neural networks : the official journal of the International Neural Network Society · 2025
    Article
  20. Article

37 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

5 authors at 3 institutions in 3 countries.

Chao ZhangAutism and Developmental Medicine Institute, Geisinger, Lewisburg, Pennsylvania.
Chase C DoughertyAutism and Developmental Medicine Institute, Geisinger, Lewisburg, Pennsylvania.
Stefi A BaumChester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, New York.
Tonya WhiteDepartment of Child and Adolescent Psychiatry, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands.ORCID 0000-0003-0271-1896
Andrew M MichaelAutism and Developmental Medicine Institute, Geisinger, Lewisburg, Pennsylvania.
Rochester Institute of Technology · USErasmus MC - Sophia Children’s Hospital · NLPennsylvania State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prevalence of certain forms of psychopathology, such as autism and depression, differs between genders and understanding gender differences of the neurotypical brain may provide insights into risk and protective factors. In recent research, resting state functional magnetic resonance imaging (rfMRI) is widely used to map the inherent functional networks of the brain. Although previous studies have reported gender differences in rfMRI, the robustness of gender differences is not well characterized. In this study, we use a large data set to test whether rfMRI functional connectivity (FC) can be used to predict gender and identify FC features that are most predictive of gender. We utilized rfMRI data from 820 healthy controls from the Human Connectome Project. By applying a predefined functional template and partial least squares regression modeling, we achieved a gender prediction accuracy of 87% when multi-run rfMRI was used. Permutation tests confirmed that gender prediction was reliable ( p<.001). Effects of motion, age, handedness, blood pressure, weight, and brain volume on gender prediction are discussed. Further, we found that FC features within the default mode (DMN), fronto-parietal and sensorimotor networks contributed most to gender prediction. In the DMN, right fusiform gyrus and right ventromedial prefrontal cortex were important contributors. The above regions have been previously implicated in aspects of social functioning and this suggests potential gender differences in social cognition mediated by the DMN. Our findings demonstrate that gender can be reliably predicted using rfMRI data and highlight the importance of controlling for gender in brain imaging studies.

Indexed as

ConnectomeMagnetic Resonance ImagingSex CharacteristicsAdultBrainFemaleHumansMaleNeural PathwaysRestYoung Adult

Identifiers

PMID29322586
PMCPMC6866578
OpenAlexW2784262759

What OpenQuestion holds

Textmetadata
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.