How covariate control can bias our insights into brain architecture and pathology.

Overview

Authors: Christoph Sperber¹, Laura Gallucci¹, Marcel Arnold¹, Roza M Umarova¹

ORCID iDs: Roza M Umarova

Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Freiburgstr. 16, 3010 Bern, Switzerland

Institutions: University of Bern (Switzerland); University Hospital of Bern (Switzerland)

Journal: Scientific reports, volume 16, issue 1, article 17804

Dates: received 14 September 2025; accepted 30 March 2026; published online 17 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41598-026-47122-4 · PMID 41998106 · PMCID PMC13247238 · OpenAlex W7154714366

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: structural MRI / diffusion (modality), human (organism), stroke (population)

Methods: Statistics, Connectivity, fMRI & imaging

Keywords: Sex, Age, Omics, Big data, Inference, Brain mapping, Diseases, Medical research, Neurology, Neuroscience

MeSH: Brain*, Brain Mapping*, Stroke*, Aged, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Swiss National Science Foundation (10003589); Heidi Seiler-Stiftung; Stiftung Synapsis - Alzheimer Forschung Schweiz AFS (2019-PI05)

Citations: not cited yet (Europe PMC); 38 references in the paper

Abstract

Inferential analysis of normal or pathological brain imaging data—as in brain mapping or the identification of neurological imaging markers—is often controlled for secondary variables. However, a rationale for covariate control is rarely given and formal criteria to identify appropriate covariates in such complex data are lacking. We investigated the impact and adequacy of covariate control in large-scale imaging data using the example of stroke lesion-deficit mapping. In 183 stroke patients, we evaluated control for age, sex, hypertension, or lesion volume when mapping real or simulated deficits. We examined (i) the impact of covariate control when mapping cognitive poststroke deficits, (ii) the association of covariates and brain pathology (iii) the impact of covariate control when mapping simulated deficits, and (iv) the impact of covariate control under a verified null hypothesis. We found that the impact of covariate control varies across covariates and deficits in both real and simulated data. However, simulations showed that covariate control does not necessarily improve the precision of lesion–deficit inference. Instead, it systematically reshapes statistical maps according to the associations between covariates and lesion anatomy. As a result, covariate control can bias statistical inference and, under a verified null hypothesis, may even generate spurious associations. These findings suggest that the widespread use of covariate control in clinical brain imaging—and likely other biological high-dimensional data—should be reconsidered, as it may introduce substantial analytical flexibility without necessarily improving inference.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-47122-4.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

data.mendeley.com/datasets/gvn3k8cj9r, at Mendeley Data; found in “Data availability”

Data availability

Statistical maps and scripts are available at https://data.mendeley.com/datasets/gvn3k8cj9r/1. Original clinical data are not publicly available, but qualified researchers may request access to anonymised data. Proposals need to be approved by the local ethics commission.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 10 keywords, 8 MeSH terms, 3 funders, 32 references.

Cite

This paper

Sperber, C., Gallucci, L., Arnold, M., & Umarova, R. M. (2026). How covariate control can bias our insights into brain architecture and pathology. Scientific reports, 16(1), 17804. https://doi.org/10.1038/s41598-026-47122-4

BibTeX

@article{sperber2026how, author = {Sperber, Christoph and Gallucci, Laura and Arnold, Marcel and Umarova, Roza M}, title = {{How covariate control can bias our insights into brain architecture and pathology}}, journal = {Scientific reports}, year = {2026}, month = apr, volume = {16}, number = {1}, pages = {17804}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-47122-4}, url = {https://doi.org/10.1038/s41598-026-47122-4}, pmid = {41998106}, pmcid = {PMC13247238} }

RIS

TY - JOUR AU - Sperber, Christoph AU - Gallucci, Laura AU - Arnold, Marcel AU - Umarova, Roza M TI - How covariate control can bias our insights into brain architecture and pathology T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/04/17 VL - 16 IS - 1 SP - 17804 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-47122-4 UR - https://doi.org/10.1038/s41598-026-47122-4 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41598-026-47122-4", "type": "article-journal", "title": "How covariate control can bias our insights into brain architecture and pathology", "container-title": "Scientific reports", "author": [ { "family": "Sperber", "given": "Christoph" }, { "family": "Gallucci", "given": "Laura" }, { "family": "Arnold", "given": "Marcel" }, { "family": "Umarova", "given": "Roza M" } ], "container-title-short": "Sci Rep", "volume": "16", "issue": "1", "page": "17804", "DOI": "10.1038/s41598-026-47122-4", "PMID": "41998106", "PMCID": "PMC13247238", "ISSN": "2045-2322", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41598-026-47122-4", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 17 ] ] } }

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.