Maikusa Norihide, Shigemoto Yoko, Chiba Emiko, Kimura Yukio, Matsuda Hiroshi, Sato Noriko
Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 113-8654, Japan.
Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan.
J Pers Med. 2022 Sep 21;12(10):1555. doi: 10.3390/jpm12101555.
Alzheimer's disease (AD), the most common type of dementia in elderly individuals, slowly and progressively diminishes the cognitive function. Mild cognitive impairment (MCI) is also a significant risk factor for the onset of AD. Magnetic resonance imaging (MRI) is widely used for the detection and understanding of the natural progression of AD and other neurodegenerative disorders. For proper assessment of these diseases, a reliable database of images from cognitively healthy participants is important. However, differences in magnetic field strength or the sex and age of participants between a normal database and an evaluation data set can affect the accuracy of the detection and evaluation of neurodegenerative disorders. We developed a brain segmentation procedure, based on 30 Japanese brain atlases, and suggest a harmonized Z-score to correct the differences in field strength and sex and age from a large data set (1235 cognitively healthy participants), including 1.5 T and 3 T T1-weighted brain images. We evaluated our harmonized Z-score for AD discriminative power and classification accuracy between stable MCI and progressive MCI. Our procedure can perform brain segmentation in approximately 30 min. The harmonized Z-score of the hippocampus achieved high accuracy (AUC = 0.96) for AD detection and moderate accuracy (AUC = 0.70) to classify stable or progressive MCI. These results show that our method can detect AD with high accuracy and high generalization capability. Moreover, it may discriminate between stable and progressive MCI. Our study has some limitations: the age groups in the 1.5 T data set and 3 T data set are significantly different. In this study, we focused on AD, which is primarily a disease of elderly patients. For other diseases in different age groups, the harmonized Z-score needs to be recalculated using different data sets.
阿尔茨海默病（AD）是老年人中最常见的痴呆类型，它会缓慢且渐进地损害认知功能。轻度认知障碍（MCI）也是AD发病的一个重要风险因素。磁共振成像（MRI）被广泛用于检测和了解AD及其他神经退行性疾病的自然病程。为了对这些疾病进行恰当评估，来自认知健康参与者的可靠图像数据库很重要。然而，正常数据库与评估数据集之间磁场强度、参与者性别和年龄的差异会影响神经退行性疾病检测和评估的准确性。我们基于30个日本脑图谱开发了一种脑分割程序，并提出了一种统一的Z分数，以校正来自一个大数据集（1235名认知健康参与者）的场强、性别和年龄差异，该数据集包括1.5T和3T的T1加权脑图像。我们评估了我们的统一Z分数在AD判别力以及稳定型MCI和进展型MCI分类准确性方面的表现。我们的程序大约能在30分钟内完成脑分割。海马体的统一Z分数在AD检测方面达到了高精度（AUC = 0.96），在区分稳定型或进展型MCI方面达到了中等精度（AUC = 0.70）。这些结果表明，我们的方法能够以高精度和高泛化能力检测AD。此外，它可能区分稳定型和进展型MCI。我们的研究有一些局限性：1.5T数据集和3T数据集的年龄组有显著差异。在本研究中，我们关注的是AD，它主要是一种老年患者的疾病。对于不同年龄组的其他疾病，需要使用不同的数据集重新计算统一的Z分数。
