Evidence map›Paper›PMID 38306043›Full record

ArticleJournal of Alzheimer's disease : JAD2024

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

Yulin Dai, Yu-Chun Hsu, Brisa S Fernandes, Kai Zhang, Xiaoyang Li, Nitesh Enduru, Andi Liu, Astrid M Manuel, Xiaoqian Jiang, Zhongming Zhao and 1 more

Open access · greenAbstract read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Yulin DaiCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Yu-Chun HsuCenter for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Brisa S FernandesCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Kai ZhangCenter for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Xiaoyang LiCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Nitesh EnduruCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Andi LiuCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Astrid M ManuelCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Xiaoqian JiangCenter for Secure Artificial Intelligence for Healthcare, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Zhongming ZhaoCenter for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Alzheimer’s Disease Neuroimaging Initiative
The University of Texas Health Science Center at Houston · USVanderbilt University Medical Center · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learningU01AG079847 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Christopher A. Gaiteri, Xiaoqian Jiang · 2023 to 2026
$5.1M
Finding Combinatorial Drug Repositioning Therapy For Alzheimer'S Disease And Related DementiasR01AG066749 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI JIANG, XIAOQIAN, ZHENG, WENJIN JIM · 2020 to 2024
$4.4M
Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
Training in Precision Environmental Health SciencesT32ES027801 · NIEHS · BAYLOR COLLEGE OF MEDICINE · PI Cheryl L. Walker · 2018 to 2026
$2.8M
Decentralized differentially-private methods for dynamic data release and analysisR01LM013712 · NLM · YALE UNIVERSITY · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2022 to 2025
$2.5M
Robust privacy preserving distributed analysis platform for cancer research: addressing data bias and disparitiesU01CA274576 · NCI · UNIVERSITY OF PENNSYLVANIA · PI JIANG, XIAOQIAN, LONG, QI · 2023 to 2025
$1.2M
Constructing A Transcriptomic Atlas of Retrotransposon in Alzheimer's DiseaseR03AG077191 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ZHAO, ZHONGMING · 2022 to 2022
$312k
NCI NIH HHS U01 CA274576NIA NIH HHS R01 AG066749NIA NIH HHS R03 AG077191NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG079847NIEHS NIH HHS T32 ES027801NLM NIH HHS R01 LM012806NLM NIH HHS R01 LM013712NLM NIH HHS T15 LM007093
6 · The paper itself

Abstract

Background: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results: We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions: Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Indexed as

Alzheimer DiseaseCognitive DysfunctionDeep LearningBrainGenome-Wide Association StudyHumansMagnetic Resonance ImagingNeuroimagingAlzheimer’s diseasecognitive declinedeep learninggenome-wide association studyneuroimaging

Identifiers

PMID38306043
PMCPMC11649026
OpenAlexW4391486149

What Socratic holds

Textmetadata
LicenceTDM
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.