Evidence map›Paper›PMID 41292986›Full record

ArticlebioRxiv : the preprint server for biology2025

Comparative Evaluation of Assumption Lean Community Detection Methods for Human Connectome Networks.

Ayoushman Bhattacharya, Nilanjan Chakraborty, Xintian Wang, Jiaxin Cindy Tu, Donna Dierker, Andy Eck, Soumen Lahiri, Adam Eggebrecht, Muriah D Wheelock

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

9 authors.

Ayoushman BhattacharyaDepartment of Statistics and Data Science, Washington University in St. Louis.ORCID 0009-0007-1750-4175
Nilanjan ChakrabortyDepartment of Statistics and Data Science, Washington University in St. Louis.
Xintian WangMallinckrodt Institute of Radiology, Washington University in St. Louis.
Jiaxin Cindy TuMallinckrodt Institute of Radiology, Washington University in St. Louis.
Donna DierkerMallinckrodt Institute of Radiology, Washington University in St. Louis.
Andy EckMallinckrodt Institute of Radiology, Washington University in St. Louis.
Soumen LahiriDepartment of Statistics and Data Science, Washington University in St. Louis.
Adam EggebrechtMallinckrodt Institute of Radiology, Washington University in St. Louis.
Muriah D WheelockMallinckrodt Institute of Radiology, Washington University in St. Louis.

Funding

Leveraging pediatric state-specific functional brain network dynamics to predict developmental outcomesR01HD115540 · NICHD · WASHINGTON UNIVERSITY · PI Muriah D Wheelock · 2024 to 2026
$1.9M
Innovative biostatistical approaches to network level analyses of connectome-behavior relationshipsR00EB029343 · NIBIB · WASHINGTON UNIVERSITY · PI WHEELOCK, MURIAH D · 2022 to 2024
$955k
NIBIB NIH HHS R00 EB029343NICHD NIH HHS R01 HD115540
6 · The paper itself

Abstract

Community detection provides a principled lens on mesoscale organization in functional brain networks, yet many widely used methods presume assortative structure and depend on arbitrary thresholding, which complicates the selection of the community count

Indexed as

Brain NetworksCommunity detectionFunctional ConnectivityInfantsVariational BayesWeighted Stochastic Block Model

Identifiers

PMID41292986
PMCPMC12642645

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.