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Diagnosis of Mild Cognitive Impairment Using Cognitive Tasks: A Functional Near-Infrared Spectroscopy Study
Current Alzheimer Research ( IF 2.1 ) Pub Date : 2020-10-31 , DOI: 10.2174/1567205018666210212154941
So-Hyeon Yoo 1 , Seong-Woo Woo 1 , Myung-Jun Shin 2 , Jin A Yoon 2 , Yong-Il Shin 3 , Keum-Shik Hong 1
Affiliation  

Background: Early diagnosis of Alzheimer’s disease (AD) is essential in preventing its progression to dementia. Mild cognitive impairment (MCI) can be indicative of early-stage AD. In this study, we propose a channel-wise feature extraction method of functional near-infrared spectroscopy (fNIRS) data to diagnose MCI when performing cognitive tasks, including two-back, Stroop, and semantic verbal fluency tasks (SVFT).

Methods: A new channel-wise feature extraction method is proposed as follows: A region-of-interest (ROI) channel is defined as such channel having a statistical difference (p < 0.05) in t-values between two groups. For each ROI channel, features (the mean, slope, skewness, kurtosis, and peak value of oxy- and deoxy-hemoglobin) are extracted. The extracted features for the two classes (MCI, HC) are classified using the linear discriminant analysis (LDA) and support vector machine (SVM). Finally, the classifiers are validated using the area under curve (AUC) of the receiver operating characteristics. Furthermore, the suggested feature extraction method is compared with the conventional approach. Fifteen MCI patients and fifteen healthy controls (HCs) participated in the study.

Results: In the two-back and Stroop tasks, HCs showed activation in the ventrolateral prefrontal cortex (VLPFC). However, in the case of MCI, the VLPFC was not activated. Instead, Ch. 30 was activated. In the SVFT task, the PFC was activated in both groups, but the t-values of HCs were higher than those of MCI. For the SVFT, the classification accuracies using the proposed feature extraction method were 80.77% (LDA) and 83.33% (SVM), showing the highest among the three tasks; for the Stroop task, 79.49% (LDA) and 73.08% (SVM); and for the two-back task, 73.08% (LDA) and 69.23% (SVM).

Conclusion: The cognitive disparities between the MCI and HC groups were detected in the ventrolateral prefrontal cortex using fNIRS. The proposed feature extraction method has shown an improvement in the classification accuracies, see Subsection 3.3. Most of all, the suggested method contains a groupdistinction information per cognitive task. The obtained results successfully discriminated MCI patients from HCs, which reflects that the proposed method is an efficient tool to extract features in fNIRS signals.



中文翻译:

使用认知任务诊断轻度认知障碍:一项功能性近红外光谱研究

背景:阿尔茨海默病 (AD) 的早期诊断对于预防其发展为痴呆症至关重要。轻度认知障碍 (MCI) 可能预示着早期 AD。在这项研究中,我们提出了一种功能性近红外光谱 (fNIRS) 数据的通道特征提取方法,用于在执行认知任务时诊断 MCI,包括两回、Stroop 和语义语言流畅性任务 (SVFT)。

方法:提出了一种新的通道特征提取方法,如下所示:感兴趣区域 (ROI) 通道定义为两组之间 t 值具有统计差异 (p < 0.05) 的通道。对于每个 ROI 通道,提取特征(氧和脱氧血红蛋白的均值、斜率、偏度、峰度和峰值)。使用线性判别分析 (LDA) 和支持向量机 (SVM) 对两个类别 (MCI、HC) 的提取特征进行分类。最后,使用接收器操作特征的曲线下面积 (AUC) 验证分类器。此外,将建议的特征提取方法与传统方法进行比较。15 名 MCI 患者和 15 名健康对照 (HC) 参与了这项研究。

结果:在二背和 Stroop 任务中,HCs 在腹外侧前额叶皮层 (VLPFC) 中显示出激活。然而,在 MCI 的情况下,VLPFC 没有被激活。相反,Ch。30 被激活。在 SVFT 任务中,两组的 PFC 均被激活,但 HCs 的 t 值高于 MCI。对于SVFT,使用提出的特征提取方法的分类准确率分别为80.77%(LDA)和83.33%(SVM),在三个任务中最高;对于 Stroop 任务,79.49% (LDA) 和 73.08% (SVM);对于两回任务,73.08% (LDA) 和 69.23% (SVM)。

结论:使用 fNIRS 在腹外侧前额叶皮层中检测到 MCI 和 HC 组之间的认知差异。所提出的特征提取方法已显示出分类精度的提高,参见第 3.3 小节。最重要的是,建议的方法包含每个认知任务的组区分信息。获得的结果成功地将 MCI 患者与 HC 区分开来,这反映了所提出的方法是提取 fNIRS 信号中特征的有效工具。

更新日期:2020-10-31
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