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

Autor:	Carli G; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands., Meles SK; Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands., Reesink FE; Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands., de Jong BM; Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands., Pilotto A; Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy., Padovani A; Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy., Galbiati A; School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy., Ferini-Strambi L; School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Department of Clinical Neuroscience, Sleep Disorders Center, San Raffaele Hospital, Milan, Italy., Leenders KL; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. Electronic address: k.l.leenders@umcg.nl., Perani D; School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; In Vivo Human Molecular and Structural Neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan; Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy. Electronic address: perani.daniela@hsr.it.
Jazyk:	angličtina
Zdroj:	NeuroImage. Clinical [Neuroimage Clin] 2023; Vol. 39, pp. 103475. Date of Electronic Publication: 2023 Jul 13.
DOI:	10.1016/j.nicl.2023.103475
Abstrakt:	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. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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