背景:使用[18F]FDG-PET进行脑成像可以支持α-突触核蛋白病患者的诊断工作。经过验证的数据分析方法对于评估神经退行性疾病中特定疾病的脑代谢模式是必要的。这项研究比较了α-突触核蛋白病患者队列中的单变量统计参数映射(SPM)单受试者程序和多变量比例子特征模型/主成分分析(SSM/PCA)。
方法:我们纳入了在α-突触核蛋白病谱内的122名受试者的[18F]FDG-PET扫描:长期随访的帕金森病(PD)正常认知(PD-痴呆风险低(LDR);n=28),在临床随访中发展为痴呆的PD(PD-痴呆(HDR)的高风险;n=16),路易体痴呆(DLB;n=67),和多系统萎缩(MSA;n=11)。我们还纳入了孤立的REM睡眠行为障碍(iRBD;n=51)受试者的[18F]FDG-PET扫描,这些受试者具有明显的α-突触核蛋白病的高风险。使用SPM程序将每个[18F]FDG-PET扫描与112名健康对照进行比较。在SSM/PCA方法中,我们计算了先前确定的PD模式的各个得分,DLB,和MSA:PD相关模式(PDRP),DLBRP,MSARP。我们使用ROC曲线来比较SPMt图(视觉评级)和SSM/PCA个体模式得分在识别整个频谱中的每种临床状况的诊断性能。具体来说,我们使用临床诊断("金标准")作为ROC曲线的参考,以评估两种方法的准确性.运动障碍和痴呆症的专家根据每种疾病的当前临床标准(PD,DLB和MSA)。
结果:SPMt图的视觉评级显示,在将PD-LDR与其他α-突触核蛋白病(PD-HDR,DLB和MSA)。该结果主要是由SPMt图揭示PD-LDR的有限或不存在的大脑低代谢特征的能力驱动的。SPMt-maps视觉评级和SSM/PCAz-评分在识别DLB(DLBRP=AUC:0.909,特异性:0.873,灵敏度0.866;SPMt-maps=AUC:0.892,特异性:0.872,灵敏度0.910)和MSA(MSARP:AUC:0.921,特异性:0.811,灵敏度1.000;SPMt-maps:AUC:1.000,innPD-HDR和DLB在大脑低代谢和高代谢模式方面具有可比性,因此不允许通过SPMt图或SSM/PCA进行区分。值得注意的是,我们发现从iRBD到PD-HDR和DLB的连续体中PDRP和DLBRP表达逐渐增加,其中DLB患者得分最高。SSM/PCA可以区分iRBD和DLB,具体反映了疾病分期和严重程度的差异(AUC:0.938,特异性:0.821,敏感性0.941)。
结论:SPM-单受试者图和SSM/PCA都是支持α-突触核蛋白疾病谱内诊断的有效方法,有不同的优势和陷阱。前者在个体水平上揭示了功能失调的大脑拓扑,对所有特定的亚型模式都具有很高的准确性,尤其是法线贴图;后者提供了可靠的量化,独立于评分者的经验,特别是在跟踪疾病的严重程度和分期。因此,我们的研究结果表明,数据分析方法存在差异,应在临床环境中加以考虑.然而,结合这两种方法可能会提供最佳的诊断性能。
Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies.
We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson\'s Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses (\"gold standard\") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA).
The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941).
SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.