Multimodal imaging in mild cognitive impairment: Metabolism, morphometry and diffusion of the temporal-parietal memory network
Multimodal imaging in mild cognitive impairment: Metabolism, morphometry and diffusion of the temporal-parietal memory network
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DOI：
10.1016/j.neuroimage.2008.10.053
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发表时间：
2009-03-01
期刊：
影响因子：
5.7
通讯作者：
Fladby, T.
中科院分区：
文献类型：
--
作者：
Walhovd, K. B.;Fjell, A. M.;Fladby, T.
本研究比较了 FDG-PET、MR 形态测量和扩散张量成像 (DTI) 衍生的分数各向异性 (FA) 测量对轻度认知障碍 (MCI) 诊断和记忆功能的敏感性。患者 (n = 44) 和正常对照 (NC，n = 22) 接受 FDG-PET 和 MRI 扫描，测量受阿尔茨海默氏病影响并参与情景记忆网络的 9 个颞叶和顶叶区域的代谢、形态测量和 FA。患者还接受了记忆测试（RAVLT）。当所有方法和 ROI 结合时，逻辑回归分析产生 100% 的诊断准确性，但没有任何变量可以作为唯一的预测因子。在单独的 ROI 内，组合方法的诊断准确度范围为 65.6%（海马旁回）到 73.4%（顶下皮质）。形态测量预测大多数 ROI 的诊断组。 PET 和 FA 不能唯一预测群体，但可以看到楔前叶代谢的趋势。对于 MCI 组，使用相同的方法和 ROI 进行预测记忆评分的逐步回归分析。海马体积和压后 WM 的 FA 预测学习，海马代谢和海马旁皮质厚度预测 5 分钟回忆。没有任何变量能够预测独立于学习的 30 分钟回忆。总之，结合多种方法和 ROI 可以实现更高的诊断准确性，但形态测量显示出更高的诊断灵敏度。新陈代谢、形态测定和 FA 都以独特的方式解释了记忆性能，使得多模式方法更加优越。 MCI 的记忆变化可能与转换风险有关，结果表明使用多模态成像可以提高预测能力。 (C) 2008 Elsevier Inc. 保留所有权利。
This study compared sensitivity of FDG-PET, MR morphometry, and diffusion tensor imaging (DTI) derived fractional anisotropy (FA) measures to diagnosis and memory function in mild cognitive impairment (MCI). Patients (n=44) and normal controls (NC, n=22) underwent FDG-PET and MRI scanning yielding measures of metabolism, morphometry and FA in nine temporal and parietal areas affected by Alzheimer's disease and involved in the episodic memory network. Patients also underwent memory testing (RAVLT). Logistic regression analysis yielded 100% diagnostic accuracy when all methods and ROIs were combined, but none of the variables then served as unique predictors. Within separate ROIs, diagnostic accuracy for the methods combined ranged from 65.6% (parahippocampal gyrus) to 73.4 (inferior parietal cortex). Morphometry predicted diagnostic group for most ROIs. PET and FA did not uniquely predict group, but a trend was seen for the precuneus metabolism. For the MCI group, stepwise regression analyses predicting memory scores were performed with the same methods and ROIs. Hippocampal volume and FA of the retrosplenial WM predicted learning, and hippocampal metabolism and parahippocampal cortical thickness predicted 5 minute recall. No variable predicted 30 minute recall independently of learning. In conclusion, higher diagnostic accuracy was achieved when multiple methods and ROIs were combined, but morphometry showed superior diagnostic sensitivity. Metabolism, morphometry and FA all uniquely explained memory performance, making a multimodal approach superior. Memory variation in MCI is likely related to conversion risk, and the results indicate potential for improved predictive power by the use of multimodal imaging. (C) 2008 Elsevier Inc. All rights reserved.