背景:海马和杏仁核亚区对神经退行性疾病的认知障碍有不同的影响。使用现代神经成像工具可以很好地分割这些区域的子场,但它们在神经退行性疾病中的作用正在积极研究中。在这项研究中,我们确定了通过MoCA(蒙特利尔认知评估)和MDS-UPDRSIII测量的预测认知表现和运动症状严重程度的海马和杏仁核亚区,分别,路易体痴呆(DLB)患者。
方法:我们选择了所有可能患有DLB的参与者(N=48,平均年龄=71±7岁,截至2022年7月,15%的女性)参加了路易体痴呆联盟(DLBC),同时进行了3DT1MRI序列的测量,MoCA,和MDS-UPDRSIII评分。我们使用FreeSurfer(v7.2)对海马子场和杏仁核核进行了皮质重建和体积分割。我们使用战斗协调来解决场地和扫描仪的差异。我们训练并应用了自举,29个预测变量的双向逐步回归模型,包括子场和平均皮质厚度对MoCA和MDS-UPDRSIII,分别,列车测试分流比为80-20,和5000次重复,纠正年龄和性别。
结果:子场分段如图1A所示。MoCA的最佳拟合模型包括平均皮质厚度,伞下,海马和杏仁核过渡区,皮质杏仁状过渡区,和CA3主体(图1B,调整后的R2=0.51)。MDS-UPDRSIII的最佳拟合模型包括杏仁核和CA1体的皮质核(图1C,调整后的R2=0.22)。该模型被认为是不合适的。我们考虑了MoCA进行进一步分析,并密切预测了我们20%分区测试样本中的分数(图2A,R2=0.38)。
结论:我们报告了基于模型的海马和杏仁核子场选择来预测DLB中的MoCA评分。这些地区的萎缩与轻度认知障碍和阿尔茨海默病队列中的整体认知缺陷有关。MDS-UPDRSIII分数的模型拟合度较差,提供这些大脑区域在运动控制中不起作用的证据。
BACKGROUND: Hippocampal and
amygdala subfields variably affect cognitive impairment in neurodegenerative diseases. Subfields of these regions can be well segmented using modern neuroimaging tools but their role in neurodegenerative disease is under active investigation. In this study, we identified hippocampal and
amygdala subregions predictive of cognitive performance and motor symptoms severity measured by MoCA (Montreal Cognitive Assessment) and MDS-UPDRS III, respectively, in patients with dementia with Lewy Bodies (DLB).
METHODS: We selected all participants with probable DLB (N = 48, mean age = 71±7 years, 15% female) enrolled in the Dementia with Lewy Bodies Consortium (DLBC) as of July 2022, with concurrent measures of 3D T1 MRI sequence, MoCA, and MDS-UPDRS III scores. We performed cortical reconstruction and volumetric segmentation of hippocampal subfields and nuclei of the amygdala using FreeSurfer (v 7.2). We used combat harmonization to account for site and scanner differences. We trained and applied a bootstrapped, bidirectional stepwise regression model of 29 predictor variables comprised of sub-fields and mean cortical thickness against MoCA and MDS-UPDRS III, respectively, with an 80-20 train-test split ratio, and 5000 repetitions, corrected for age and sex.
RESULTS: Subfield segmentation is shown in Figure 1A. The best fitting model for MoCA included mean cortical thickness, parasubiculum, hippocampal and
amygdala transition area, corticoamygdaloid transition area, and CA3 body (Figure 1B, adjusted R2 = 0.51). The best fitting model for MDS-UPDRS III included the cortical nucleus of the
amygdala and CA1 body (Figure 1C, adjusted R2 = 0.22). This model was considered a poor fit. We considered MoCA for further analysis and closely predicted scores in our 20% partitioned test sample (Figure 2A, R2 = 0.38).
CONCLUSIONS: We report model-based selection of hippocampal and
amygdala subfields to predict MoCA scores in DLB. Atrophy in these regions has been associated with global cognitive deficit in mild cognitive impairment and Alzheimer disease cohorts. The model fit for MDS-UPDRS III scores was poor, providing evidence that these brain regions do not serve a role in motor control.