Liu Sidong, Cai Weidong, Wen Lingfeng, Feng David Dagan, Pujol Sonia, Kikinis Ron, Fulham Michael J, Eberl Stefan
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia; Surgical Planning Laboratory (SPL), Brigham and Women's Hospital, Harvard Medical School, United States.
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Australia.
Comput Med Imaging Graph. 2014 Sep;38(6):436-44. doi: 10.1016/j.compmedimag.2014.05.003. Epub 2014 May 14.
Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, as disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could effectively distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to global methods and other pattern analysis methods based on clinical expertise or statistics tests. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.
神经影像学在神经退行性疾病（如阿尔茨海默病和轻度认知障碍）的无创诊断和鉴别中发挥了重要作用。已从神经影像学数据中提取了各种特征来表征这些疾病，这些特征大致可分为全局特征和局部特征。最近的研究表明，在疾病表征中使用局部特征的趋势有所增加，因为它们能够识别与疾病对人脑影响相关的细微疾病特异性模式。然而，如果神经影像学数据库涉及多种疾病或进行性疾病，就会出现问题，因为不同类型或不同进展阶段的疾病可能表现出不同的退化模式。研究人员很难就哪些脑区能够有效区分多种疾病或多个进展阶段达成共识。在本研究中，我们提出了一种多通道模式分析方法，以识别用于神经退行性疾病表征的最具区分性的局部脑代谢特征。我们将我们的方法与基于临床专业知识或统计测试的全局方法和其他模式分析方法进行了比较。初步结果表明，所提出的多通道模式分析方法在阿尔茨海默病表征方面优于其他方法，同时为阿尔茨海默病和轻度认知障碍的潜在病理学提供了重要见解。
