Yuxiang Wei, Anees Abrol, James Lah, Deqiang Qiu, Vince D. Calhoun
{"title":"A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals","authors":"Yuxiang Wei, Anees Abrol, James Lah, Deqiang Qiu, Vince D. Calhoun","doi":"arxiv-2408.00378","DOIUrl":null,"url":null,"abstract":"Alzheimer's disease (AD) progresses from asymptomatic changes to clinical\nsymptoms, emphasizing the importance of early detection for proper treatment.\nFunctional magnetic resonance imaging (fMRI), particularly dynamic functional\nnetwork connectivity (dFNC), has emerged as an important biomarker for AD.\nNevertheless, studies probing at-risk subjects in the pre-symptomatic stage\nusing dFNC are limited. To identify at-risk subjects and understand alterations\nof dFNC in different stages, we leverage deep learning advancements and\nintroduce a transformer-convolution framework for predicting at-risk subjects\nbased on dFNC, incorporating spatial-temporal self-attention to capture brain\nnetwork dependencies and temporal dynamics. Our model significantly outperforms\nother popular machine learning methods. By analyzing individuals with diagnosed\nAD and mild cognitive impairment (MCI), we studied the AD progression and\nobserved a higher similarity between MCI and asymptomatic AD. The interpretable\nanalysis highlights the cognitive-control network's diagnostic importance, with\nthe model focusing on intra-visual domain dFNC when predicting asymptomatic AD\nsubjects.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computational Engineering, Finance, and Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.00378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Alzheimer's disease (AD) progresses from asymptomatic changes to clinical
symptoms, emphasizing the importance of early detection for proper treatment.
Functional magnetic resonance imaging (fMRI), particularly dynamic functional
network connectivity (dFNC), has emerged as an important biomarker for AD.
Nevertheless, studies probing at-risk subjects in the pre-symptomatic stage
using dFNC are limited. To identify at-risk subjects and understand alterations
of dFNC in different stages, we leverage deep learning advancements and
introduce a transformer-convolution framework for predicting at-risk subjects
based on dFNC, incorporating spatial-temporal self-attention to capture brain
network dependencies and temporal dynamics. Our model significantly outperforms
other popular machine learning methods. By analyzing individuals with diagnosed
AD and mild cognitive impairment (MCI), we studied the AD progression and
observed a higher similarity between MCI and asymptomatic AD. The interpretable
analysis highlights the cognitive-control network's diagnostic importance, with
the model focusing on intra-visual domain dFNC when predicting asymptomatic AD
subjects.