Evidence map›Paper›PMID 42239205›Full record

ArticlebioRxiv : the preprint server for biology2026

Robust lesion network mapping reveals genuine symptom-specific networks.

Weigang Cui, Jiayi Zhu, Yang Long, Wei Zhang, Jianting Huang, Feng Wang, Evan M Gordon, Nico U F Dosenbach, Danhong Wang, Jianxun Ren and 1 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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.

Weigang CuiChangping Laboratory, Beijing, China.
Jiayi ZhuChangping Laboratory, Beijing, China.
Yang LongChangping Laboratory, Beijing, China.
Wei ZhangChangping Laboratory, Beijing, China.
Jianting HuangChangping Laboratory, Beijing, China.
Feng WangDepartment of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Evan M GordonMallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA.
Nico U F DosenbachMallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA.
Danhong WangChangping Laboratory, Beijing, China.
Jianxun RenChangping Laboratory, Beijing, China.
Hesheng LiuChangping Laboratory, Beijing, China.ORCID 0000-0002-7233-1509

Funding

Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASDR01MH096773 · NIMH · UNIVERSITY OF MINNESOTA · PI DOSENBACH, NICO, FAIR, DAMIEN A · 2012 to 2024
$9.4M
Motion-robust brain MRI for infantsR44MH124567 · NIMH · TURING MEDICAL TECHNOLOGIES INC · PI BERTOLERO, MAXWELL, FAIR, DAMIEN A · 2020 to 2023
$7.3M
Visual biofeedback to reduce head motion during MRI scansR44MH122066 · NIMH · TURING MEDICAL TECHNOLOGIES INC · PI BRUENER, KEN, FAIR, DAMIEN A · 2019 to 2022
$6.2M
Solving the MRI motion problem with Framewise Integrated Real-Time MRI Monitoring (FIRMM) softwareR44MH121276 · NIMH · TURING MEDICAL TECHNOLOGIES INC · PI FAIR, DAMIEN A, ROTHNEY, MEGAN PEARL · 2020 to 2023
$5.4M
Behavioral feedback and rewards for improving functional brain mapping in presurgical pediatric patientsR44NS129521 · NINDS · TURING MEDICAL TECHNOLOGIES INC · PI FAIR, DAMIEN A, KING, KEVIN · 2022 to 2025
$3.7M
PEDIATRIC BRAIN INJURY RECOVERY VIA USE-DRIVEN FUNCTIONAL NETWORK REORGANIZATIONK23NS088590 · NINDS · WASHINGTON UNIVERSITY · PI DOSENBACH, NICO · 2015 to 2019
$886k
NIMH NIH HHS R01 MH096773NIMH NIH HHS R44 MH121276NIMH NIH HHS R44 MH122066NIMH NIH HHS R44 MH124567NINDS NIH HHS K23 NS088590NINDS NIH HHS R44 NS129521
6 · The paper itself

Abstract

Lesion network mapping (LNM) and its derivatives successfully integrate anatomically distributed brain loci into common symptom-associated functional networks, however, their statistical validity and specificity have recently become topics of debate. Here, we introduce a null model-based robust LNM (rLNM) framework to perform sensitivity and symptom-specificity testing. Across multiple lesion-based (10 conditions, 333 lesions) and task-based (4 conditions, 706 experiments) datasets, rLNM reveals biologically meaningful and symptom-specific networks while effectively controlling for false positives.

Identifiers

PMID42239205
PMCPMC13228464

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Registered trials

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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.