Evidence map›Paper›PMID 40818337›Full record

ArticleNeuroImage. Clinical2025

Quantification of brain functional connectivity deviations in individuals: A scoping review of functional MRI studies.

Artur Toloknieiev, Dmytro Voitsekhivskyi, Hlib Kholodkov, Roman Lvovich, Petro Matiushko, Daria Rekretiuk, Andrii Dikhtiar, Antonii Viter, Volodymyr Pokras, Stephan Wunderlich and 1 more

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage. Clinical, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Artur ToloknieievDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany. Electronic address: toloknieiev.artur@campus.lmu.de.
Dmytro VoitsekhivskyiDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany; Munich School of Management, LMU Munich, Munich, Germany.
Hlib KholodkovDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Roman LvovichDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany; Faculty of Electrical Engineering and Information Technology, Technical University of Munich, Munich, Germany.
Petro MatiushkoFaculty of Mathematics, Computer Science and Statistics, LMU Munich, Munich, Germany.
Daria RekretiukSchool of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Andrii DikhtiarSchool of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Antonii ViterSchool of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Volodymyr PokrasSchool of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Stephan WunderlichDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany.
Sophia StoeckleinDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany. Electronic address: sophia.stoecklein@med.uni-muenchen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional connectivity magnetic resonance imaging (fcMRI) is a well-established technique for studying brain networks in both healthy and diseased individuals. However, no fcMRI-based biomarker has yet achieved clinical relevance. To establish better understanding of the state of the art in quantifying abnormal connectivity in comparison to a reference distribution, for potential use in individual patients, we have conducted a scoping review over 5672 entries from the last 10 years. We have located five publications proposing metrics of abnormal connectivity quantification, reported these metrics, formalized their computing methods, assessed their technology readiness and estimated their computational efficiency. Building upon our findings, we have discussed the metrics' lesion data handling, region of interest level of detail and potential clinical use cases. We also proposed methodical and computational strategies for improvement of current and emerging abnormality quantification metrics in fcMRI research.

Indexed as

BrainConnectomeMagnetic Resonance ImagingNerve NetHumansAbnormality detectionBrainConnectivityFunctional connectivityMetric

Identifiers

PMID40818337
PMCPMC12392766

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.