摘要
背景:最近,人们在评估阿尔茨海默型痴呆方面,已经做出了相辅相成的努力。特别能引起人们兴趣的是被称为轻度认知障碍或前驱阿尔茨海默氏病的疾病早期指纹的定义。已经证明机器学习方法非常适合于实现这样的组合。 方法:在目前的试点研究中,我们将机器学习方法与结构磁共振成像和认知测试评估相结合,对11名健康参与者和11名经历轻度认知障碍的患者进行分组。认知评估包括一系列标准化测试和一系列实验性视觉空间记忆测试。在100%的参与者中实现了正确的分类,表明神经成像与更复杂的认知测试的组合适合早期检测阿尔茨海默病。 结果:结果特别强调了实验性视觉空间记忆测试电池在分类效率方面的重要性,表明在这些生态测试中支持参与者表现的高级脑计算框架可能代表了一种“自然过滤器”信息的认知模式能够识别疾病的早期征兆。
关键词: 视空间记忆,空间能力,支持向量机,磁共振成像,轻度认知障碍,分类。
Current Alzheimer Research
Title:Combining Structural Magnetic Resonance Imaging and Visuospatial Tests to Classify Mild Cognitive Impairment
Volume: 15 Issue: 3
关键词: 视空间记忆,空间能力,支持向量机,磁共振成像,轻度认知障碍,分类。
摘要: Background: Recently, efforts have been made to combine complementary perspectives in the assessment of Alzheimer type dementia. Of particular interest is the definition of the fingerprints of an early stage of the disease known as Mild Cognitive Impairment or prodromal Alzheimer's Disease. Machine learning approaches have been shown to be extremely suitable for the implementation of such a combination.
Methods: In the present pilot study we combined the machine learning approach with structural magnetic resonance imaging and cognitive test assessments to classify a small cohort of 11 healthy participants and 11 patients experiencing Mild Cognitive Impairment. Cognitive assessment included a battery of standardised tests and a battery of experimental visuospatial memory tests. Correct classification was achieved in 100% of the participants, suggesting that the combination of neuroimaging with more complex cognitive tests is suitable for early detection of Alzheimer Disease.
Results: In particular, the results highlighted the importance of the experimental visuospatial memory test battery in the efficiency of classification, suggesting that the high-level brain computational framework underpinning the participant's performance in these ecological tests may represent a “natural filter” in the exploration of cognitive patterns of information able to identify early signs of the disease.
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Cite this article as:
Combining Structural Magnetic Resonance Imaging and Visuospatial Tests to Classify Mild Cognitive Impairment, Current Alzheimer Research 2018; 15 (3) . https://dx.doi.org/10.2174/1567205014666171030112339
DOI https://dx.doi.org/10.2174/1567205014666171030112339 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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