Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer’s disease

IF 4.5 2区医学 Q1 NEUROIMAGING

NeuroImage Pub Date : 2025-09-22 DOI:10.1016/j.neuroimage.2025.121448

Jacopo Tessadori , Ilaria Boscolo Galazzo , Silvia F. Storti , Lorenzo Pini , Lorenza Brusini , Federica Cruciani , Diego Sona , Gloria Menegaz , Vittorio Murino

{"title":"Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer’s disease","authors":"Jacopo Tessadori , Ilaria Boscolo Galazzo , Silvia F. Storti , Lorenzo Pini , Lorenza Brusini , Federica Cruciani , Diego Sona , Gloria Menegaz , Vittorio Murino","doi":"10.1016/j.neuroimage.2025.121448","DOIUrl":null,"url":null,"abstract":"<div><div>Alterations in brain connectivity provide early indications of neurodegenerative diseases like Alzheimer’s disease (AD). Here, we present a novel framework that integrates a Hidden Markov Model (HMM) within the architecture of a convolutional neural network (CNN) to analyze dynamic functional connectivity (dFC) in resting-state functional magnetic resonance imaging (rs-fMRI). Our unsupervised approach captures recurring connectivity states in a large cohort of subjects spanning the Alzheimer’s disease continuum, including healthy controls, individuals with mild cognitive impairment (MCI), and patients with clinically diagnosed AD.</div><div>We propose a deep neural model with embedded HMM dynamics to identify stable recurring brain states from resting-state fMRI. These states exhibit distinct connectivity patterns and are differentially expressed across the Alzheimer’s disease continuum. Our analysis shows that the fraction of time each state is active varies systematically with disease severity, highlighting dynamic network alterations that track neurodegeneration.</div><div>Our findings suggest that the disruption of dynamic connectivity patterns in AD may follow a two-stage trajectory, where early shifts toward integrative network states give way to reduced connectivity organization as the disease progresses. This framework offers a promising tool for early diagnosis and monitoring of AD, and may have broader applications in the study of other neurodegenerative conditions.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"320 ","pages":"Article 121448"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NeuroImage","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1053811925004513","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROIMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Alterations in brain connectivity provide early indications of neurodegenerative diseases like Alzheimer’s disease (AD). Here, we present a novel framework that integrates a Hidden Markov Model (HMM) within the architecture of a convolutional neural network (CNN) to analyze dynamic functional connectivity (dFC) in resting-state functional magnetic resonance imaging (rs-fMRI). Our unsupervised approach captures recurring connectivity states in a large cohort of subjects spanning the Alzheimer’s disease continuum, including healthy controls, individuals with mild cognitive impairment (MCI), and patients with clinically diagnosed AD.

We propose a deep neural model with embedded HMM dynamics to identify stable recurring brain states from resting-state fMRI. These states exhibit distinct connectivity patterns and are differentially expressed across the Alzheimer’s disease continuum. Our analysis shows that the fraction of time each state is active varies systematically with disease severity, highlighting dynamic network alterations that track neurodegeneration.

Our findings suggest that the disruption of dynamic connectivity patterns in AD may follow a two-stage trajectory, where early shifts toward integrative network states give way to reduced connectivity organization as the disease progresses. This framework offers a promising tool for early diagnosis and monitoring of AD, and may have broader applications in the study of other neurodegenerative conditions.

查看原文本刊更多论文

动态连接状态与阿尔茨海默病认知能力下降和解剖学变化的联系。

大脑连通性的改变提供了阿尔茨海默病（AD）等神经退行性疾病的早期迹象。在这里，我们提出了一个新的框架，该框架将隐马尔可夫模型（HMM）集成到卷积神经网络（CNN）的架构中，以分析静息状态功能磁共振成像（rs-fMRI）中的动态功能连接（dFC）。我们的无监督方法捕获了跨越阿尔茨海默病连续体的大量受试者的反复连接状态，包括健康对照，轻度认知障碍（MCI）个体和临床诊断为AD的患者。我们提出了一个嵌入HMM动力学的深度神经模型，用于从静息状态fMRI识别稳定的反复出现的大脑状态。这些状态表现出不同的连接模式，并在阿尔茨海默病连续体中表现出差异。我们的分析表明，每种状态的活跃时间随疾病严重程度而系统变化，突出了跟踪神经退行性变的动态网络变化。我们的研究结果表明，AD动态连接模式的破坏可能遵循两个阶段的轨迹，随着疾病的发展，早期向整合网络状态的转变让位于连接组织的减少。该框架为阿尔茨海默病的早期诊断和监测提供了一个有前途的工具，并可能在其他神经退行性疾病的研究中有更广泛的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

NeuroImage 医学-核医学

CiteScore

11.30

自引率

10.50%

发文量

809

审稿时长

63 days

期刊介绍： NeuroImage, a Journal of Brain Function provides a vehicle for communicating important advances in acquiring, analyzing, and modelling neuroimaging data and in applying these techniques to the study of structure-function and brain-behavior relationships. Though the emphasis is on the macroscopic level of human brain organization, meso-and microscopic neuroimaging across all species will be considered if informative for understanding the aforementioned relationships.