Institute of Psychology, Leiden University, Leiden, The Netherlands.
Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
J Alzheimers Dis. 2018;62(4):1827-1839. doi: 10.3233/JAD-170893.
BACKGROUND/OBJECTIVE: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.
Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC).
Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41).
Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.
背景/目的：阿尔茨海默病（AD）和行为变异型额颞叶痴呆（bvFTD）患者的临床表现常常重叠，这使得鉴别诊断变得复杂。磁共振成像（MRI）显示出疾病特异性的结构和功能差异，有助于将 AD 与 bvFTD 患者区分开来。然而，将结构和功能连接测量值相结合，以个体为基础，区分这些痴呆症类型的益处尚不清楚。
收集了 30 名早期 AD 患者、23 名 bvFTD 患者和 35 名对照组患者的解剖学、扩散张量（DTI）和静息状态功能磁共振成像（rs-fMRI）数据，用于计算结构和功能组织状态的测量值。所有测量值均单独或选择性地结合作为弹性网络回归分类器的预测因子。通过计算接收器操作特征曲线下的面积（AUC）来量化每个分类器准确区分痴呆症类型的能力。
当平均扩散系数、rs-fMRI 衍生的独立成分之间的全相关和各向异性分数（FA）结合使用时，AD 和 bvFTD 鉴别分类的 AUC 值最高（0.811）。同样，当结合灰质密度（GMD）、FA 和 rs-fMRI 相关性时，控制和 bvFTD 分类的 AUC 值最高为 0.922。然而，这在控制和 AD 区分中并未观察到。使用 GMD（0.940）和 GMD 和 DTI 组合（0.941）的分类得到了相似的 AUC 值（p=0.41）。
将功能和结构连接测量值相结合可以提高痴呆症类型的区分度，并可能有助于更准确和有根据的 AD 和 bvFTD 患者的鉴别诊断。用于鉴别诊断的成像方案可能受益于还包括 DTI 和 rs-fMRI。
