基于图像和网络的脑数据分析技术用于阿尔茨海默病诊断的综述揭示了发展预测方法的差距
A Review on Image- and Network-based Brain Data Analysis Techniques for Alzheimer's Disease Diagnosis Reveals a Gap in Developing Predictive Methods for Prognosis
摘要：
揭示与阿尔茨海默病（AD）相关的病理性大脑变化是一项具有挑战性的任务，特别是人们直到晚期才表现出痴呆症状。在过去的几年里，神经影像学技术为基于计算机的诊断和预后铺平了道路，以促进医疗决策支持的自动化，并帮助临床医生识别认知完整的受试者，这些受试者具有发展AD的高风险。作为一种进行性神经退行性疾病，研究人员使用不同的方法研究了AD如何影响大脑：1）基于图像的方法，其中主要使用神经成像方式来提供早期AD生物标志物，以及2）基于网络的方法，专注于功能和结构大脑连接，以深入了解AD如何改变大脑布线。在这项研究中，我们回顾了为AD和轻度认知障碍（MCI）分类和预测任务开发的基于神经成像的技术方法，这些方法是通过筛选2010年至2016年期间发表的所有MICCAI论文集而选择的。我们纳入了适合基于图像或基于网络类别的论文。大多数论文集中在对MCI与AD大脑状态进行分类，这使得能够发现有区别或改变的大脑区域和连接。然而，很少有研究旨在根据早期基于神经成像的观察来预测MCI进展。尽管可靠地识别哪些早期MCI患者将转化为AD，在数月/数年内保持稳定或逆转至正常非常重要，但预测模型仍然落后。
英文摘要：
Unveiling pathological brain changes associated with Alzheimer's disease (AD) is a challenging task especially that people do not show symptoms of dementia until it is late. Over the past years, neuroimaging techniques paved the way for computer-based diagnosis and prognosis to facilitate the automation of medical decision support and help clinicians identify cognitively intact subjects that are at high-risk of developing AD. As a progressive neurodegenerative disorder, researchers investigated how AD affects the brain using different approaches: 1) image-based methods where mainly neuroimaging modalities are used to provide early AD biomarkers, and 2) network-based methods which focus on functional and structural brain connectivities to give insights into how AD alters brain wiring. In this study, we reviewed neuroimaging-based technical methods developed for AD and mild-cognitive impairment (MCI) classification and prediction tasks, selected by screening all MICCAI proceedings published between 2010 and 2016. We included papers that fit into image-based or network-based categories. The majority of papers focused on classifying MCI vs. AD brain states, which has enabled the discovery of discriminative or altered brain regions and connections. However, very few works aimed to predict MCI progression based on early neuroimaging-based observations. Despite the high importance of reliably identifying which early MCI patient will convert to AD, remain stable or reverse to normal over months/years, predictive models are still lagging behind.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
