Giulietti Giovanni, Torso Mario, Serra Laura, Spanò Barbara, Marra Camillo, Caltagirone Carlo, Cercignani Mara, Bozzali Marco
Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy.
Institute of Neurology, Catholic University, Rome, Italy.
J Magn Reson Imaging. 2018 Jan 21. doi: 10.1002/jmri.25947.
Amnestic mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer's disease (AD). However, the clinical conversion from MCI to AD is unpredictable. Hence, identification of noninvasive biomarkers able to detect early changes induced by dementia is a pressing need.
To explore the added value of histogram analysis applied to measures derived from diffusion tensor imaging (DTI) for detecting brain tissue differences between AD, MCI, and healthy subjects (HS).
Prospective.
POPULATION/SUBJECTS: A local cohort (57 AD, 28 MCI, 23 HS), and an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (41 AD, 58 MCI, 41 HS).
3T. Dual-echo turbo spin echo (TSE); fluid-attenuated inversion recovery (FLAIR); modified-driven-equilibrium-Fourier-transform (MDEFT); inversion-recovery spoiled gradient recalled (IR-SPGR); diffusion tensor imaging (DTI).
Normal-appearing white matter (NAWM) masks were obtained using the T -weighted volumes for tissue segmentation and T -weighted images for removal of hyperintensities/lesions. From DTI images, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AXD), and radial diffusivity (RD) were obtained. NAWM histograms of FA, MD, AXD, and RD were derived and characterized estimating: peak height, peak location, mean value (MV), and quartiles (C25, C50, C75), which were compared between groups. Receiver operating characteristic (ROC) and area under ROC curves (AUC) were calculated. To confirm our results, the same analysis was repeated on the ADNI dataset.
One-way analysis of variance (ANOVA), post-hoc Student's t-test, multiclass ROC analysis.
For the local cohort, C25 of AXD had the maximum capability of group discrimination with AUC of 0.80 for "HS vs. patients" comparison and 0.74 for "AD vs. others" comparison. For the ADNI cohort, MV of AXD revealed the maximum group discrimination capability with AUC of 0.75 for "HS vs. patients" comparison and 0.75 for "AD vs. others" comparison.
AXD of NAWM might be an early marker of microstructural brain tissue changes occurring during the AD course and might be useful for assessing disease progression.
1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.
遗忘型轻度认知障碍（MCI）是正常衰老与阿尔茨海默病（AD）之间的过渡阶段。然而，MCI向AD的临床转化是不可预测的。因此，识别能够检测痴呆症引起的早期变化的非侵入性生物标志物迫在眉睫。
探讨应用直方图分析从扩散张量成像（DTI）导出的测量值，以检测AD、MCI和健康受试者（HS）之间脑组织差异的附加价值。
前瞻性研究。
一个本地队列（57例AD、28例MCI、23例HS）和一个阿尔茨海默病神经影像倡议（ADNI）队列（41例AD、58例MCI、41例HS）。
3T。双回波快速自旋回波（TSE）；液体衰减反转恢复（FLAIR）；改良驱动平衡傅里叶变换（MDEFT）；反转恢复扰相梯度回波（IR-SPGR）；扩散张量成像（DTI）。
使用T加权体积进行组织分割，T加权图像去除高信号/病变，获得正常外观白质（NAWM）掩膜。从DTI图像中获取分数各向异性（FA）、平均扩散率（MD）、轴向扩散率（AXD）和径向扩散率（RD）。得出FA、MD、AXD和RD的NAWM直方图，并对其进行特征估计：峰值高度、峰值位置、平均值（MV）和四分位数（C25、C50、C75），并在组间进行比较。计算受试者工作特征（ROC）曲线和ROC曲线下面积（AUC）。为了证实我们的结果，在ADNI数据集中重复相同的分析。
单因素方差分析（ANOVA）、事后学生t检验、多类ROC分析。
对于本地队列，AXD的C25在组间区分能力方面最强，“HS与患者”比较的AUC为0.80，“AD与其他组”比较的AUC为0.74。对于ADNI队列，AXD的MV显示出最大的组间区分能力，“HS与患者”比较的AUC为0.75，“AD与其他组”比较的AUC为0.75。
NAWM的AXD可能是AD病程中脑组织微观结构变化的早期标志物，可能有助于评估疾病进展。
1 技术效能：2期 《磁共振成像杂志》2017年
