To this end, we developed a deep learning-based cognitive signature of FDG brain PET adaptable for Parkinson's disease (PD) as well as Alzheimer's disease (AD).
In Parkinson's disease (PD), spatial covariance analysis of <sup>18</sup>F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP).
Voxel-based analysis of relative [<sup>18</sup>F]FDG uptake showed a dynamic pattern of PD-related metabolic changes throughout the disease progression (weeks 2-9).
Moreover, iRBD patients showed several differences in [18F]FDG uptake than PD, DLB, or AD groups, with the main differences documented in the comparison with AD patients.
In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.
18F-FDG-PET scans were used to independently discriminate subjects belonging to four categories: controls (RBD no, PD no), iRBD (RBD yes, PD no), PD (RBD no, PD yes) and PDRBD (RBD yes, PD yes).
<sup>11</sup>C-CFT and <sup>18</sup>F-FDG PET/CT can be analyzed quantitatively with NeuroQ software, which provides an accurate method for the diagnosis and severity evaluation of PD.
The overall diagnostic accuracy of 18F-FDG in differentiating PD from APDs and HCs was quite high, with a pooled sensitivity of 0.88 [95% confidence interval (95% CI), 0.85-0.91] and a pooled specificity of 0.92 (95% CI, 0.89-0.94), with sensitivity analyses indicating statistically consistent results.
We retrospectively compared <sup>18</sup>F-FDG PET-CT imaging patterns from seven iNPH patients (mean age 74 ± 6 years) to age and sex matched controls, as well as patients diagnosed with clinical Alzheimer's disease dementia (AD), Dementia with Lewy Bodies (DLB) and Parkinson's Disease Dementia (PDD), and behavioral variant frontotemporal dementia (bvFTD).
However, in the opinion of the majority of the panellists, FDG PET is a clinically useful imaging biomarker for idiopathic PD and atypical parkinsonism associated with dementia.
We investigated the ability of pCIT to differentiate atypical Parkinson disorder from Parkinson disease (PD) compared to FDG and the image quality for optimizing the acquisition time.
Next, we discuss how <sup>18</sup>F-FDG PET studies have advanced understanding of the relation between distinct brain regions and associated symptoms in Parkinson disease, including cognitive decline.
Taken together, the current literature underscores the utility of <sup>18</sup>F-FDG PET for diagnostic evaluation of parkinsonism and the promising role of <sup>18</sup>F-FDG PET for assessment and risk stratification of cognitive impairment in PD.