Faculté de psychologie et des sciences de l'éducation, University of Geneva, Geneva, Switzerland.
D'Or Institute for Research and Education, Rio de Janeiro, Brazil.
Sci Rep. 2020 Feb 14;10(1):2660. doi: 10.1038/s41598-020-59327-2.
Current treatments for Alzheimer's disease are only symptomatic and limited to reduce the progression rate of the mental deterioration. Mild Cognitive Impairment, a transitional stage in which the patient is not cognitively normal but do not meet the criteria for specific dementia, is associated with high risk for development of Alzheimer's disease. Thus, non-invasive techniques to predict the individual's risk to develop Alzheimer's disease can be very helpful, considering the possibility of early treatment. Diffusion Tensor Imaging, as an indicator of cerebral white matter integrity, may detect and track earlier evidence of white matter abnormalities in patients developing Alzheimer's disease. Here we performed a voxel-based analysis of fractional anisotropy in three classes of subjects: Alzheimer's disease patients, Mild Cognitive Impairment patients, and healthy controls. We performed Support Vector Machine classification between the three groups, using Fisher Score feature selection and Leave-one-out cross-validation. Bilateral intersection of hippocampal cingulum and parahippocampal gyrus (referred as parahippocampal cingulum) is the region that best discriminates Alzheimer's disease fractional anisotropy values, resulting in an accuracy of 93% for discriminating between Alzheimer's disease and controls, and 90% between Alzheimer's disease and Mild Cognitive Impairment. These results suggest that pattern classification of Diffusion Tensor Imaging can help diagnosis of Alzheimer's disease, specially when focusing on the parahippocampal cingulum.
目前，阿尔茨海默病的治疗方法仅限于对症治疗，以减缓认知能力下降的速度。轻度认知障碍是一种过渡阶段，患者的认知能力不正常，但不符合特定痴呆症的标准，与发展为阿尔茨海默病的风险高相关。因此，非侵入性技术可以预测个体发展为阿尔茨海默病的风险，考虑到早期治疗的可能性。弥散张量成像作为脑白质完整性的指标，可能会检测到并跟踪正在发展为阿尔茨海默病的患者白质异常的早期证据。在这里，我们对三组受试者进行了基于体素的各向异性分数分析：阿尔茨海默病患者、轻度认知障碍患者和健康对照组。我们使用 Fisher 得分特征选择和留一法交叉验证，在三组之间进行支持向量机分类。双侧海马扣带和海马旁回的交点（称为海马旁扣带）是区分阿尔茨海默病各向异性分数值的最佳区域，区分阿尔茨海默病和对照组的准确率为 93%，区分阿尔茨海默病和轻度认知障碍的准确率为 90%。这些结果表明，弥散张量成像的模式分类有助于阿尔茨海默病的诊断，特别是在关注海马旁扣带时。
