From Positron to Pattern: A Conceptual and Practical Overview of 18F-FDG PET Imaging and Spatial Covariance Analysis

Meles, Sanne K.; Kok, J G M; Renken, Remco; Leenders, Klaus L.

doi:10.1007/978-3-030-53168-3_4

Cited by 8 publications

(6 citation statements)

References 61 publications

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“…For ADRP identification SSM/PCA (ScAnVP, Center for Neuroscience, Feinstein Institutes for Medical Research, NY, USA) was applied to 2-[ 18 F]FDG PET scans of 20 AD1 and 20 NC1 subjects as described previously 9 , 13 . The number of 20 diseased and healthy have been shown optimal in previous studies 15 , 35 .…”

Section: Methodsmentioning

confidence: 99%

“…Multivariate analysis approaches of 2-[ 18 F]FDG PET scans, such as scaled subprofile model/principal component analysis (SSM/PCA), have been used in the past to derive specific disease-related patterns to improve diagnostic accuracy and provide insight into pathophysiology 9 – 12 . Multivariate approaches are in general advantageous over univariate models by being able to take into account interactions between voxels/brain regions and have been shown to have better sensitivity, specificity and reproducibility 13 .…”

Section: Introductionmentioning

confidence: 99%

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Identification and validation of Alzheimer’s disease-related metabolic brain pattern in biomarker confirmed Alzheimer’s dementia patients

Perovnik

Tomše

Jamšek

et al. 2022

Sci Rep

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Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer’s disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer’s disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients’ clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76–0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification and validation of Alzheimer’s disease-related metabolic brain pattern in biomarker confirmed Alzheimer’s dementia patients

Perovnik

Tomše

Jamšek

et al. 2022

Sci Rep

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“…For the identification of the DLBRP we applied SSM/PCA (ScAnVP, Center for Neuroscience, Feinstein Institutes for Medical Research, NY, USA) to 2-[ 18 F]FDG PET scans of 20 randomly selected DLB patients and 20 age- and sex-matched NC. The SSM/PCA procedure was described in detail before ( Meles et al, 2021 , Spetsieris and Eidelberg, 2011 ). The output of the procedure are principal components (PCs) and the corresponding subject scores, which underwent further analysis to identify DLBRP.…”

Section: Methodsmentioning

confidence: 99%

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer’s disease topography

Perovnik

Tomše

Jamšek

et al. 2022

NeuroImage: Clinical

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“…The phenoconversion‐related pattern of iRBD (iRBDconvRP) patients was derived using an automated algorithm 27 , 28 developed by the University Medical Center Groningen (UCMG), the Netherlands, based on the SSM‐PCA method of Spetsieris and Eidelberg 29 implemented in MatLab (version 2020a, MathWorks, Natick, MA).…”

Section: Methodsmentioning

confidence: 99%

Derivation and Validation of a Phenoconversion‐Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder

et al. 2022

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Background Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α‐synucleinopathies. Reliable biomarkers are needed to predict phenoconversion. Objective The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM‐PCA]). Methods Seventy‐six consecutive iRBD patients (70 ± 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 ± 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow‐up time: 21 ± 14 months; 46 nonconverters, 69 ± 6 years, follow‐up time: 33 ± 19 months). All patients underwent [18F]FDG‐PET (18F‐fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM‐PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power. Results First, we derived and validated two distinct center‐specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM‐PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6–21.4). Conclusions We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [18F]FDG‐PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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From Positron to Pattern: A Conceptual and Practical Overview of 18F-FDG PET Imaging and Spatial Covariance Analysis

Abstract: Contents 4.1 18 F-FDG PET Imaging 4.1.1 Basic Concepts in PET 4.1.2 18 F-FDG PET Imaging 4.1.3 Studying Brain Function with 18 F-FDG PET 4.2 Analysis of Resting-State 18 F-FDG PET Images 4.

Cited by 8 publications

References 61 publications

Identification and validation of Alzheimer’s disease-related metabolic brain pattern in biomarker confirmed Alzheimer’s dementia patients

Identification and validation of Alzheimer’s disease-related metabolic brain pattern in biomarker confirmed Alzheimer’s dementia patients

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer’s disease topography

Derivation and Validation of a Phenoconversion‐Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder

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