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通过独立成分分析评估早期阿尔茨海默病中的突触密度模式。

Synaptic density patterns in early Alzheimer's disease assessed by independent component analysis.

作者信息

Fang Xiaotian T, Raval Nakul R, O'Dell Ryan S, Naganawa Mika, Mecca Adam P, Chen Ming-Kai, van Dyck Christopher H, Carson Richard E

机构信息

Yale PET Center, Yale University School of Medicine, New Haven, CT 06520, USA.

Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT 06520, USA.

出版信息

Brain Commun. 2024 Mar 26;6(2):fcae107. doi: 10.1093/braincomms/fcae107. eCollection 2024.

Abstract

Synaptic loss is a primary pathology in Alzheimer's disease and correlates best with cognitive impairment as found in studies. Previously, we observed reductions of synaptic density with [C]UCB-J PET (radiotracer for synaptic vesicle protein 2A) throughout the neocortex and medial temporal brain regions in early Alzheimer's disease. In this study, we applied independent component analysis to synaptic vesicle protein 2A-PET data to identify brain networks associated with cognitive deficits in Alzheimer's disease in a blinded data-driven manner. [C]UCB-J binding to synaptic vesicle protein 2A was measured in 38 Alzheimer's disease (24 mild Alzheimer's disease dementia and 14 mild cognitive impairment) and 19 cognitively normal participants. [C]UCB-J distribution volume ratio values were calculated with a whole cerebellum reference region. Principal components analysis was first used to extract 18 independent components to which independent component analysis was then applied. Subject loading weights per pattern were compared between groups using Kruskal-Wallis tests. Spearman's rank correlations were used to assess relationships between loading weights and measures of cognitive and functional performance: Logical Memory II, Rey Auditory Verbal Learning Test-long delay, Clinical Dementia Rating sum of boxes and Mini-Mental State Examination. We observed significant differences in loading weights among cognitively normal, mild cognitive impairment and mild Alzheimer's disease dementia groups in 5 of the 18 independent components, as determined by Kruskal-Wallis tests. Only Patterns 1 and 2 demonstrated significant differences in group loading weights after correction for multiple comparisons. Excluding the cognitively normal group, we observed significant correlations between the loading weights for Pattern 1 (left temporal cortex and the cingulate gyrus) and Clinical Dementia Rating sum of boxes ( = -0.54, = 0.0019), Mini-Mental State Examination ( = 0.48, = 0.0055) and Logical Memory II score ( = 0.44, = 0.013). For Pattern 2 (temporal cortices), significant associations were demonstrated between its loading weights and Logical Memory II score ( = 0.34, = 0.0384). Following false discovery rate correction, only the relationship between the Pattern 1 loading weights with Clinical Dementia Rating sum of boxes ( = -0.54, = 0.0019) and Mini-Mental State Examination ( = 0.48, = 0.0055) remained statistically significant. We demonstrated that independent component analysis could define coherent spatial patterns of synaptic density. Furthermore, commonly used measures of cognitive performance correlated significantly with loading weights for two patterns within only the mild cognitive impairment/mild Alzheimer's disease dementia group. This study leverages data-centric approaches to augment the conventional region-of-interest-based methods, revealing distinct patterns that differentiate between mild cognitive impairment and mild Alzheimer's disease dementia, marking a significant advancement in the field.

摘要

突触丧失是阿尔茨海默病的主要病理特征,并且在研究中发现其与认知障碍的关联最为密切。此前,我们通过[C]UCB-J PET(用于突触囊泡蛋白2A的放射性示踪剂)观察到,在早期阿尔茨海默病患者的整个新皮层和内侧颞叶脑区,突触密度有所降低。在本研究中,我们对突触囊泡蛋白2A-PET数据应用独立成分分析,以一种盲法数据驱动的方式识别与阿尔茨海默病认知缺陷相关的脑网络。对38名阿尔茨海默病患者(24名轻度阿尔茨海默病痴呆患者和14名轻度认知障碍患者)以及19名认知正常的参与者进行了[C]UCB-J与突触囊泡蛋白2A结合情况的测量。使用全小脑参考区域计算[C]UCB-J分布容积比值。首先使用主成分分析提取18个独立成分,然后对其应用独立成分分析。使用Kruskal-Wallis检验比较各组之间每个模式的受试者负荷权重。采用Spearman等级相关性来评估负荷权重与认知和功能表现指标之间的关系:逻辑记忆II、雷伊听觉词语学习测验-长时延迟、临床痴呆评定量表总分和简易精神状态检查表。通过Kruskal-Wallis检验确定,在18个独立成分中的5个成分上,认知正常、轻度认知障碍和轻度阿尔茨海默病痴呆组之间的负荷权重存在显著差异。经过多重比较校正后,只有模式1和模式2在组间负荷权重上显示出显著差异。排除认知正常组后,我们观察到模式1(左侧颞叶皮质和扣带回)的负荷权重与临床痴呆评定量表总分(r = -0.54,p = 0.0019)、简易精神状态检查表(r = 0.48,p = 0.0055)以及逻辑记忆II得分(r = 0.44,p = 0.013)之间存在显著相关性。对于模式2(颞叶皮质),其负荷权重与逻辑记忆II得分之间存在显著关联(r = 0.34,p = 0.0384)。经过错误发现率校正后,只有模式1的负荷权重与临床痴呆评定量表总分(r = -0.54,p = 0.0019)和简易精神状态检查表(r = 0.48,p = 0.0055)之间的关系仍具有统计学意义。我们证明了独立成分分析可以定义突触密度的连贯空间模式。此外,仅在轻度认知障碍/轻度阿尔茨海默病痴呆组中,常用的认知表现测量指标与两种模式的负荷权重显著相关。本研究利用以数据为中心的方法来补充传统的基于感兴趣区域的方法,揭示了区分轻度认知障碍和轻度阿尔茨海默病痴呆的不同模式,标志着该领域的重大进展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91e6/11004947/0aea6745376f/fcae107_ga.jpg

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