Exploring structure-function coupling in alzheimer’s disease: bridging neuroimaging, AI, and policy for future insights

[...]while the study focuses on static SFC metrics, it does not address dynamic network adaptations—such as time-varying functional connectivity or compensatory reorganization—that may influence cognitive resilience. Auto ML technology Just Add Data Bio generated three AD biosignatures using SVM (mi...

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Bibliographic Details
Published in:European journal of nuclear medicine and molecular imaging Vol. 52; no. 13; pp. 5202 - 5203
Main Authors: Wang, Xun, Jiang, Yixin, Dong, Yifan, Zhu, Qian, Zhao, Yan, Zhang, Shuo
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2025
Springer Nature B.V
Subjects:
Alzheimer's disease
Artificial intelligence
Biomarkers
Brain research
Cardiology
Cognitive ability
Disease
Federated learning
Glial fibrillary acidic protein
Imaging
Interdisciplinary aspects
Letter to the Editor
Machine learning
Medical imaging
Medicine
Medicine & Public Health
Meta-analysis
miRNA
mRNA
Neural networks
Neurodegenerative diseases
Neuroimaging
Nuclear Medicine
Oncology
Orthopedics
Pathology
Proteins
Public health
Radiology
Structure-function relationships
ISSN:1619-7070, 1619-7089, 1619-7089
Online Access:Get full text
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Summary:[...]while the study focuses on static SFC metrics, it does not address dynamic network adaptations—such as time-varying functional connectivity or compensatory reorganization—that may influence cognitive resilience. Auto ML technology Just Add Data Bio generated three AD biosignatures using SVM (miRNA, AUC 0.975), Random Forests (mRNA, AUC 0.846), and Ridge Logistic Regression (protein, AUC 0.921) on low-sample blood omics data) [5]. [...]federated learning frameworks could harmonize data from multi-center, addressing current limitations in sample size and diversity [6]. GFAP as a potential biomarker for Alzheimer’s disease: A systematic review and Meta-Analysis.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Correspondence-1
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ISSN:1619-7070
1619-7089
1619-7089
DOI:10.1007/s00259-025-07389-7