Data-driven spatiotemporal modeling reveals personalized trajectories of cortical atrophy in Alzheimer's disease.

Chunyan Li, Yutong Mao, Xiao Liu, Wenrui Hao

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In one paragraph

Article in ArXiv, 2025. 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

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chunyan LiDepartment of Mathematics, The Pennsylvania State University, University Park, PA, 16802, USA.

Yutong MaoDepartment of Biomedical Engineering, The Pennsylvania State University, PA, 16802, USA.

Xiao LiuDepartment of Biomedical Engineering, The Pennsylvania State University, PA, 16802, USA, Institute for Computational and Data Sciences, The Pennsylvania State University, PA, 16802, USA.

Wenrui HaoDepartment of Mathematics, The Pennsylvania State University, University Park, PA, 16802, USA.

Funding

A pathophysiology driven spatial dynamic modeling framework for personalized prediction and precision medicineR35GM146894 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI WENRUI HAO · 2022 to 2026

$2.0M

NIGMS NIH HHS R35 GM146894

6 · The paper itself

Abstract

Alzheimer's disease (AD) is characterized by the progressive spread of pathology across brain networks, yet forecasting this cascade at the individual level remains challenging. We present a personalized graph-based dynamical model that captures the spatiotemporal evolution of cortical atrophy from longitudinal MRI and PET data. The approach constructs individualized brain graphs and learns the dynamics driving regional neurodegeneration. Applied to 1,891 participants from the Alzheimer's Disease Neuroimaging Initiative, the model accurately predicts key AD biomarkers-including amyloid-

Indexed as

Alzheimer’s diseaseAlzheimer’s Disease Neuroimaging Initiative (ADNI)amyloid-βneurofunctional losssensitivity analysisspatiotemporal mathematical modeltau

Identifiers

PMID41293533

PMCPMC12642761

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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.