Evidence map›Paper›PMID 40229250›Full record

ArticleNature communications2025

A genetically informed brain atlas for enhancing brain imaging genomics.

Jingxuan Bao, Junhao Wen, Changgee Chang, Shizhuo Mu, Jiong Chen, Manu Shivakumar, Yuhan Cui, Guray Erus, Zhijian Yang, Shu Yang and 10 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

20 authors.

Jingxuan BaoDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0001-7127-3258
Junhao WenLaboratory of AI and Biomedical Science (LABS), Columbia University, New York, NY, USA.ORCID http://orcid.org/0000-0003-2077-3070
Changgee ChangDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.ORCID http://orcid.org/0000-0003-3426-1295
Shizhuo MuDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Jiong ChenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Manu ShivakumarDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0003-4733-7375
Yuhan CuiCenter for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Guray ErusCenter for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0001-6633-4861
Zhijian YangCenter for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Shu YangDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-8507-7191
Zixuan WenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Alzheimer’s Disease Neuroimaging Initiative
Yize ZhaoDepartment of Biostatistics, Yale University School of Public Health, New Haven, CT, USA.ORCID http://orcid.org/0000-0001-6283-2302
Dokyoon KimDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-4592-9564
Duy Duong-TranDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID http://orcid.org/0009-0009-4496-7575
Andrew J SaykinDepartment of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, USA.ORCID http://orcid.org/0000-0002-1376-8532
Bingxin ZhaoDepartment of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-0979-7891
Christos DavatzikosCenter for AI and Data Science for Integrated Diagnostics (AI2D), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0002-1025-8561
Qi LongDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. qlong@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0003-0660-5230
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. li.shen@pennmedicine.upenn.edu.ORCID http://orcid.org/0000-0002-5443-0503

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Laurel A Beckett · 2016 to 2026
$226.7M
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Artificial Intelligence Strategies for Alzheimer's Disease ResearchU01AG066833 · NIA · CEDARS-SINAI MEDICAL CENTER · PI MOORE, JASON H., RITCHIE, MARYLYN D · 2022 to 2025
$6.7M
Translational big data analytic approaches to advance drug repurposing for Alzheimer's diseaseR01AG071470 · NIA · UNIVERSITY OF PENNSYLVANIA · PI KIM, DOKYOON, NING, XIA · 2021 to 2025
$3.8M
Advancing Analysis of Multi-omics Data in Alzheimer's Disease ResearchRF1AG063481 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LONG, QI · 2019 to 2020
$3.8M
Statistical Modeling of Alzheimer's Disease Progression Integrating Brain Imaging and -Omics DataR01AG071174 · NIA · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI KUNDU, SUPRATEEK, LONG, QI · 2021 to 2025
$3.3M
An integrative Bayesian approach for linking brain to behavioral phenotypeR01EB034720 · NIBIB · YALE UNIVERSITY · PI R Todd Constable, Yize Zhao · 2023 to 2026
$2.4M
Integrative analysis for patient-centered outcomes and time-to-event data in Alzheimer's diseaseRF1AG081413 · NIA · YALE UNIVERSITY · PI SUN, YIFEI, ZHAO, YIZE · 2023 to 2023
$2.3M
Novel integrative imaging genetics analysis for Alzheimer's disease riskand progressionRF1AG068191 · NIA · YALE UNIVERSITY · PI ZHAO, YIZE · 2021 to 2021
$1.9M
Novel integrative imaging genetics analysis for Alzheimer's disease riskand progressionR01AG068191 · NIA · YALE UNIVERSITY · PI ZHAO, YIZE · 2024 to 2025
$1.2M
NIA NIH HHS R01 AG068191NIA NIH HHS R01 AG071174NIA NIH HHS R01 AG071470NIA NIH HHS RF1 AG063481NIA NIH HHS RF1 AG068191NIA NIH HHS RF1 AG081413NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG066833NIA NIH HHS U01 AG068057NIA NIH HHS U19 AG024904NIBIB NIH HHS R01 EB034720
6 · The paper itself

Abstract

Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.

Indexed as

Atlases as TopicBrainImaging GenomicsNeuroimagingBrain MappingFemaleGenomicsHumansMagnetic Resonance ImagingMaleMultifactorial InheritancePolymorphism, Single Nucleotide

Identifiers

PMID40229250
PMCPMC11997130

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.