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首页> 外文期刊>Journal of neurology >Spatial metabolic profiles to discriminate dementia with Lewy bodies from Alzheimer disease
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Spatial metabolic profiles to discriminate dementia with Lewy bodies from Alzheimer disease

机译：空间代谢型材以与阿尔茨海默病的用石油尸体鉴别痴呆症

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Background To differentiate dementia with Lewy bodies (DLB) from Alzheimer disease (AD) using a single imaging modality is challenging, because of their common hypometabolic findings. Scaled subprofile modeling/principal component analysis (SSM/PCA), an unsupervised artificial intelligence, has the potential to offer an alternative to image analysis. Objective We aimed to produce spatial metabolic profiles to discriminate DLB from AD and to identify the characteristics of the profiles. Methods Fifty individuals each with DLB, AD, and normal cognition (NL) underwent(18)F-FDG-PET and MRI. The spatial metabolic profile to differentiate DLB from AD (DLB-AD discrimination profile) was determined using SSM/PCA with tenfold cross validation. For comparison, we also produced disease-related profiles that can discriminate AD and DLB from NL (AD- and DLB-related profiles, respectively). Results The DLB-AD discrimination profile significantly differentiated DLB from AD with comparable accuracy to that of discriminating DLB and AD from NL. The AD- and DLB-related profiles comprised metabolic imaging features typical of each pathology. In contrast, the DLB-AD discrimination profile emphasized preservation in the posterior cingulate cortex (cingulate island sign) and medial temporal lobe, and occipital hypometabolism. Common hypometabolic findings between DLB and AD were less noticeable in the profile. The DLB-related profile significantly correlated with cognitive function and three core features of DLB, whereas the DLB-AD discrimination profile did not. Conclusions Spatial metabolic profile that could discriminate DLB from AD emphasized different imaging features and eliminated common findings between DLB and AD. Neither cognitive function nor core features were associated with the profile.
机译：背景，使用单一成像模态的阿尔茨海默病（AD）与石油疾病（DLB）分化的背景是挑战性的，因为它们的常见的抑郁症发现。缩放的子实质建模/主成分分析（SSM / PCA），无监督的人工智能，有可能提供图像分析的替代品。目的我们旨在产生空间代谢型材以区分DLB从广告中并识别谱的特征。方法50个体各自具有DLB，AD和正常认知（NL）（18）F-FDG-PET和MRI。使用具有十倍交叉验证的SSM / PCA测定从AD（DLB-AD鉴别配置文件）中区分DLB的空间代谢谱。为了比较，我们还产生了与NL（分别与DLB相关的轮廓相关的AD和DLB）的疾病相关的曲线。结果DLB-AD鉴别轮廓从广告中显着差异化DLB，可比辨别DLB和来自NL的识别DLB和AD的可比精度。和DLB相关的配置文件包括每个病理学典型的代谢成像特征。相比之下，DLB-AD鉴别轮廓强调在后筒式皮质（CUNULE ISLANE SIGN）和内侧颞叶和枕骨细胞增减中的保存。在概况中，DLB和AD之间的常见低音醇调查结果不太明显。 DLB相关的简档与认知函数和DLB的三个核心特征显着相关，而DLB-AD鉴别配置文件则没有。结论空间代谢型材可以从广告中区分DLB强调不同的成像特征，并在DLB和AD之间消除了常见发现。既不是认知函数也不与核心功能相关联。

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