Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies

Abstract

Background: 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.

Methods: 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).

Results: 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).

Conclusions: 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.

Keywords: Imaging biomarkers; SSM/PCA; Univariate analyses; [18F]FDG-PET; α-synucleinopathies.

Fig. 1

Examples of α-synuclein-related patterns of neurodegeneration and atypical α-synuclein patterns. The statistical parametric mapping of [18F]FDG-PET patterns at a single-subject level is depicted for some patients. According to the rating classification, several hypometabolism patterns emerged at the single-subject level in the total cohort of patients (N = 173). Most of the patterns recognised (92%, N = 159) belonged to the α-synucleinopathy spectrum: DLB-like, MSA-like, and typical PD-like patterns. Panel A reports four examples each. Only 8% (14 out of 173) of patients were classified with patterns related to other neurodegenerative conditions ('atypical patterns'): Alzheimer's disease pattern (AD-like), frontotemporal dementia pattern (FTD-like) and corticobasal degeneration pattern (CBD-like). Panel B reports an example each. p < 0.05, FWE corrected at the cluster level.

Fig. 2

The topographies of the PDRP, DLBRP and MSARP and the correlations among them. From top to bottom: the topographies (stable voxels) of the three disease-related metabolic patterns (PDRP, DLBRP, and MSARP), the z-scores expression in each clinical group (violin plots) and Pearson correlations of pattern expression in the combined cohort (scatterplots).

Fig. 3

ROC curves for both methods in iRBD. Diagnostic accuracy of multivariate and univariate metabolism patterns: DLBRP (blue), PDRP (red), MSARP (green), Typical PD-like pattern (violet), DLB-like pattern (brown), MSA-like pattern (orange) and atypical pattern (black). Abbreviations: iRBD: isolated REM sleep behaviour disorder; PD: Parkison'disease, DLB: Dementia with Lewy Bodies, MSA: Multiple System Atrophy, PD-LDR: PD with low risk of dementia, PD-HDR: PD with high risk of dementia, ROC: Received operating curves; PDRP: PD related pattern; DLBRP: DLB related pattern; MSARP: MSA related pattern. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

ROC curves for both methods in overt synucleinopathies. Diagnostic accuracy of multivariate and univariate metabolism patterns: DLBRP (blue), PDRP (red), MSARP (green), Typical PD-like pattern (violet), DLB-like pattern (brown), MSA-like pattern (orange) and atypical pattern (black). Abbreviations: PD: Parkinson'disease, DLB: Dementia with Lewy Bodies, MSA: Multiple System Atrophy, PD-LDR: PD with low risk of dementia, PD-HDR: PD with high risk of dementia, ROC: Received operating curves; PDRP: PD related pattern; DLBRP: DLB related pattern; MSARP: MSA related pattern. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)