Evidence map›Paper›PMID 39371469›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2024

MAPPING ALZHEIMER'S DISEASE PSEUDO-PROGRESSION WITH MULTIMODAL BIOMARKER TRAJECTORY EMBEDDINGS.

Lina Takemaru, Shu Yang, Ruiming Wu, Bing He, Christos Davtzikos, Jingwen Yan, Li Shen

Abstract read
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Staging and Pseudotime Inference of Alzheimer's Disease Progression using Multimodal Imaging Data.Proceedings. IEEE International Conference on Healthcare Informatics · 2026
    Article
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

7 authors.

Lina TakemaruPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Shu YangPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ruiming WuPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Bing HeSchool of Informatics and Computing, Indiana University, Indianapolis, IN, USA.
Christos DavtzikosPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Jingwen YanSchool of Informatics and Computing, Indiana University, Indianapolis, IN, USA.
Li ShenPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Funding

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
ENIGMA World Aging CenterR01AG058854 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2021 to 2025
$3.3M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
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 AG058854NIA NIH HHS R01 AG068191NIA NIH HHS R01 AG071470NIA NIH HHS RF1 AG068191NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG066833NIA NIH HHS U01 AG068057NIH HHS S10 OD023495
6 · The paper itself

Abstract

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by progressive cognitive degeneration and motor impairment, affecting millions worldwide. Mapping the progression of AD is crucial for early detection of loss of brain function, timely intervention, and development of effective treatments. However, accurate measurements of disease progression are still challenging at present. This study presents a novel approach to understanding the heterogeneous pathways of AD through longitudinal biomarker data from medical imaging and other modalities. We propose an analytical pipeline adopting two popular machine learning methods from the single-cell transcriptomics domain, PHATE and Slingshot, to project multimodal biomarker trajectories to a low-dimensional space. These embeddings serve as our pseudotime estimates. We applied this pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to align longitudinal data across individuals at various disease stages. Our approach mirrors the technique used to cluster single-cell data into cell types based on developmental timelines. Our pseudotime estimates revealed distinct patterns of disease evolution and biomarker changes over time, providing a deeper understanding of the temporal dynamics of AD. The results show the potential of the approach in the clinical domain of neurodegenerative diseases, enabling more precise disease modeling and early diagnosis.

Indexed as

Alzheimer’s Diseasedisease progressionimagingpseudotime

Identifiers

PMID39371469
PMCPMC11452153

What OpenQuestion 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 OpenQuestion graph.