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首页> 外文会议>IEEE International Symposium on Biomedical Imaging >Deep Learning Identifies Neuroimaging Signatures Of Alzheimer’s Disease Using Structural And Synthesized Functional Mri Data
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Deep Learning Identifies Neuroimaging Signatures Of Alzheimer’s Disease Using Structural And Synthesized Functional Mri Data

机译：深入学习用结构和合成功能MRI数据识别阿尔茨海默病的神经影像症状

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Current neuroimaging techniques provide paths to investigate the structure and function of the brain in vivo and have made great advances in understanding Alzheimer's disease (AD). However, the group-level analyses prevalently used for investigation and understanding of the disease are not applicable for diagnosis of individuals. More recently, deep learning, which can efficiently analyze large-scale complex patterns in 3D brain images, has helped pave the way for computer-aided individual diagnosis by providing accurate and automated disease classification. Great progress has been made for classifying AD with deep learning models developed upon increasingly available structural MRI data. The lack of scale-matched functional neuroimaging data prevents such models from being further improved by observing functional changes in pathophysiology. Here we propose a potential solution by first learning a structural-to-functional transformation in brain MRI, and further synthesizing spatially matched functional images from large-scale structural scans. We evaluated our approach by building computational models to discriminate patients with AD from healthy normal subjects and demonstrated a performance boost after combining the structural and synthesized functional brain images into the same model. Furthermore, our regional analyses identified the temporal lobe to be the most predictive structural region and the parieto-occipital lobe to be the most predictive functional-region of our model, which are both in concordance with previous group-level neuroimaging findings. Together, we demonstrate the potential of deep learning with large-scale structural and synthesized functional MRI to impact AD classification and to identify AD's neuroimaging signatures.
机译：目前的神经影像技术提供了研究体内大脑的结构和功能的路径，并且在了解阿尔茨海默病（广告）方面取得了很大进展。然而，普遍用于调查和理解该疾病的群体水平分析不适用于诊断个人。最近，深入学习，可以通过提供准确和自动疾病分类，有效地分析3D脑图像中的大规模复杂模式，帮助为计算机辅助的个人诊断铺平了铺平道路。在越来越多的结构MRI数据上开发的深度学习模型，对分类广告进行了巨大进展。通过观察病理生理学的功能变化，缺乏比例匹配的功能性神经影像数据可防止这些模型进一步改善。在这里，我们通过首先学习脑MRI中的结构对功能转换，并进一步合成来自大规模结构扫描的空间匹配的功能图像的潜在解决方案。我们通过建立计算模型来评估我们的方法，以区分来自健康正常对象的患者，并在将结构和合成的功能性脑图像结合到同一模型中后表现出性能提升。此外，我们的区域分析确定了颞叶是最预测的结构区域和促耳枕的是我们模型最预测的功能区，这与先前的群体级神经影像调查结果相一致。我们一起展示了大规模结构和合成功能MRI的深度学习的潜力，以影响广告分类并识别广告的神经影像签名。

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