In <sup>18</sup>F-FDG PET images, the changes of voxels' intensities reflect the differences of glucose rates, therefore voxel intensity is usually used as a feature to distinguish AD from Normal Control (NC), or at earlier stage to distinguish between progressive and stable Mild Cognitive Impairment (pMCI and sMCI).
The value of <sup>18</sup>F-FDG PET for predicting conversion from mild cognitive impairment (MCI) to Alzheimer dementia (AD) is currently under debate.
Globally normalized 18F-FDG-PET values and levels of NFL and tau were obtained from 149 patients with mild cognitive impairment (MCI) from the baseline cohort of the Alzheimer's Disease Neuroimaging Initiative database.
The research in this paper proved that the novel approach based on high-order radiomic features extracted from <sup>18</sup>F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.
The model was applied to FDG PET data of Alzheimer's disease (AD) and mild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality and seizures.
To evaluate the incremental diagnostic value of FDG-PET over CSF biomarkers, and vice versa, in patients with mild cognitive impairment (MCI) and suspected AD, in which the first biomarker resulted inconclusive.
Thirty-nine patients with MCI who had undergone [<sup>18</sup>F]FDG as well as [<sup>11</sup>C]PIB PET were identified from a single-centre clinical registry.
To update the evidence and reassess the accuracy of 18F-FDG-PET for detecting people with MCI at baseline who would clinically convert to Alzheimer's disease (AD) dementia at follow-up.
This study attests the complementary value of amyloid and FDG PET in MCI assessment and the efficiency of combined cognitive, amyloid, and metabolic scores to predict AD conversion.
Thus, the goal of this study was to identify the combination of cerebrospinal fluid (CSF) biomarkers, MRI morphometry, FDG PET metabolism and neuropsychological test scores to that best differentiate between a sample of normal aging subjects and those with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative.
Using florbetapir PET and cerebrospinal fluid (CSF) measures to define amyloid-β (Aβ) positivity, 40 Aβ negative (Aβ-) cognitively unimpaired controls (CU; 76 ± 5y), 76 Aβ positive (Aβ+) persons with MCI (76 ± 7y) and 51 Aβ + persons with probable AD dementia (75 ± 7y) from the AD Neuroimaging Initiative (ADNI) were included in this study with baseline and 2-year follow-up FDG PET scans.
Materials and Methods Maps of cerebral blood flow (CBF; pulsed arterial spin-labeling [ASL] MRI), glucose metabolism (fluorine 18 [<sup>18</sup>F] fluorodeoxyglucose [FDG] PET), and gray matter (GM) volume (structural T1-weighted MRI) were calculated from integrated PET/MR data in 45 patients with AD (mean age, 69 years ± 9 [standard deviation]; age range, 51-89 years), 20 patients with MCI (mean age, 64 years ± 10; age range, 45-82 years), and 11 HC participants (mean age, 65 years ± 8; age range, 54-80 years) between 2011 and 2014.
We evaluated the utility of 18F-FDG-PET to differentiate flortaucipir tau PET negative from flortaucipir positive amnestic mild cognitive impairment and dementia and used an autopsy confirmed cohort to test the hypothesis that hippocampal sclerosis might account for the observed pattern.
A significant correlation was found between baseline biomarkers, specifically CSF Aβ and FDG PET, and IADL change over a 3-year period in individuals with MCI.
To investigate the correspondence between neuropsychological single measures and variation in fludeoxyglucose positron emission tomography (FDG PET) glucose metabolism and magnetic resonance imaging (MRI) cortical thickness in mild cognitive impairment (MCI) patients.
<b>Methods:</b> We implemented independent-component analysis of <sup>18</sup>F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups.
Respectively, we investigate the accuracy of an optimized SPM analysis for 18F-FDG-PET and of standardized uptake value ratio semiquantification for 11C-PiB-PET in predicting ADD conversion in 30 MCI subjects (age 63.57±7.78 years).