A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals

Yuxiang Wei, Anees Abrol, James Lah, Deqiang Qiu, Vince D. Calhoun
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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.
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动态功能网络连接的深度时空注意力模型显示无症状个体对阿尔茨海默氏症的敏感性
功能磁共振成像(fMRI),尤其是动态功能网络连接(dFNC),已成为阿尔茨海默病(AD)的重要生物标志物。然而,利用dFNC探测症状前阶段高危人群的研究非常有限。为了识别高危人群并了解不同阶段dFNC的变化,我们利用深度学习的进步,引入了一个基于dFNC的变压器-卷积框架来预测高危人群,并结合空间-时间自我关注来捕捉脑网络的依赖性和时间动态。我们的模型明显优于其他流行的机器学习方法。通过分析已确诊的AD和轻度认知障碍(MCI)患者,我们研究了AD的发展过程,发现MCI和无症状AD之间有更高的相似性。可解释的分析凸显了认知控制网络在诊断中的重要性,该模型在预测无症状AD受试者时侧重于视觉域内的dFNC。
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