Evidence map›Paper›PMID 41424643›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2026

A Unified Continuous Staging Framework for Alzheimer's Disease and Lewy Body Dementia via Hierarchical Anatomical Features.

Tong Chen, Minheng Chen, Jing Zhang, Yan Zhuang, Chao Cao, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu and 1 more

Abstract read
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2026. 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
–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

2 citing papers in PubMed.

  1. DEEP-GYRALNET: ENABLING MACHINE LEARNING IN GYRAL FOLDING PATTERN EXTRACTION ON CORTICAL SURFACE.Proceedings. IEEE International Symposium on Biomedical Imaging · 2026
    Article
  2. 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

11 authors.

Tong ChenDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.
Minheng ChenDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.
Jing ZhangDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.
Yan ZhuangDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.
Chao CaoDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.
Xiaowei YuDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.
Yanjun LyuDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.
Lu ZhangDepartment of Computer Science, Indiana University Indianapolis, USA.
Li SuSheffield Institute for Translational Neuroscience, University of Sheffield, UK.
Tianming LiuSchool of Computing, University of Georgia, USA.
Dajiang ZhuDepartment of Computer Science and Engineering, University of Texas at Arlington, USA.

Funding

Mapping Trajectories of Alzheimer's Progression via Personalized Brain Anchor-nodesR01AG075582 · NIA · UNIVERSITY OF TEXAS ARLINGTON · PI Gang Li, Dajiang Zhu · 2022 to 2026
$2.7M
Developing an Individualized Deep Connectome Framework for ADRD AnalysisRF1NS128534 · NINDS · UNIVERSITY OF TEXAS ARLINGTON · PI LI, GANG, LIU, TIANMING · 2022 to 2022
$1.7M
Developing an Individualized Deep Connectome Framework for ADRD AnalysisR01NS128534 · NINDS · UNIVERSITY OF TEXAS ARLINGTON · PI Gang Li, Tianming Liu · 2025 to 2026
$878k
NIA NIH HHS R01 AG075582NINDS NIH HHS R01 NS128534NINDS NIH HHS RF1 NS128534Wellcome Trust
6 · The paper itself

Abstract

Alzheimer's Disease (AD) and Lewy Body Dementia (LBD) often exhibit overlapping pathologies, leading to common symptoms that make diagnosis challenging and protracted in clinical settings. While many studies achieve promising accuracy in identifying AD and LBD at earlier stages, they often focus on discrete classification rather than capturing the gradual nature of disease progression. Since dementia develops progressively, understanding the continuous trajectory of dementia is crucial, as it allows us to uncover hidden patterns in cognitive decline and provides critical insights into the underlying mechanisms of disease progression. To address this gap, we propose a novel multi-scale learning framework that leverages hierarchical anatomical features to model the continuous relationships across various neurodegenerative conditions, including Mild Cognitive Impairment, AD, and LBD. Our approach employs the proposed hierarchical graph embedding fusion technique, integrating anatomical features, cortical folding patterns, and structural connectivity at multiple scales. This integration captures both fine-grained and coarse anatomical details, enabling the identification of subtle patterns that enhance differentiation between dementia types. Additionally, our framework projects each subject onto continuous tree structures, providing intuitive visualizations of disease trajectories and offering a more interpretable way to track cognitive decline. To validate our approach, we conduct extensive experiments on our in-house dataset of 308 subjects spanning multiple groups. Our results demonstrate that the proposed tree-based model effectively represents dementia progression, achieves promising performance in intricate classification task of AD and LBD, and highlights discriminative brain regions that contribute to the differentiation between dementia types. Our code is available at https://github.com/tongchen2010/haff.

Indexed as

3-Hinge GyrusAlzheimer’s DiseaseCortical folding patternDementia ProgressionLewy Body DementiaMulti-scale fusion

Identifiers

PMID41424643
PMCPMC12714422

What OpenQuestion holds

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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.