Mitigating the dilemma in dementia: A case series of the first amyloid brain PET scans in the Philippines

@#Diagnosis of Alzheimer dementia is done clinically using criteria set by different neurological associations. Inevitably, clinicians encounter cases that do not fulfill the set definitions and have to resort to supporting data to form a clinical judgment. Part of the ancillary work-up for dementia is the brain amyloid PET scan that has recently been available in the Philippines. It involves a radiopharmaceutical with high-affinity binding to amyloid plaques which for a time were thought to be central pathological finding for Alzheimer dementia. This study describes the first four amyloid PET scans in the Philippines and detail the protocol as well as interpretation of such studies. The procedure is not as simple and reproducible as one might think hence following the recommended protocol and interpretation guidelines are of utmost importance. We recommend standardization of the reporting of results for all centers that will cater to patients being worked up for dementia, which include reporting SUVRs for both whole cerebellum and cerebellar cortex. More studies are recommended to generate a local Florbetaben SUVR cutoff.

Topography of 11C-Pittsburgh compound B uptake in Alzheimer's disease: a voxel-based investigation of cortical and white matter regions

Objective: To compare results of positron emission tomography (PET) with carbon-11-labeled Pittsburgh compound B (11C-PIB) obtained with cerebellar or global brain uptake for voxel intensity normalization, describe the cortical sites with highest tracer uptake in subjects with mild Alzheimer's disease (AD), and explore possible group differences in 11C-PIB binding to white matter. Methods: 11C-PIB PET scans were acquired from subjects with AD (n=17) and healthy elderly controls (n=19). Voxel-based analysis was performed with statistical parametric mapping (SPM). Results: Cerebellar normalization showed higher 11C-PIB uptake in the AD group relative to controls throughout the cerebral cortex, involving the lateral temporal, orbitofrontal, and superior parietal cortices. With global uptake normalization, greatest cortical binding was detected in the orbitofrontal cortex; decreased 11C-PIB uptake in white matter was found in the posterior hippocampal region, corpus callosum, pons, and internal capsule. Conclusion: The present case-control voxelwise 11C-PIB PET comparison highlighted the regional distribution of amyloid deposition in the cerebral cortex of mildly demented AD patients. Tracer uptake was highest in the orbitofrontal cortex. Decreased 11C-PIB uptake in white-matter regions in this patient population may be a marker of white-matter damage in AD.

Prediction of Alzheimer's Pathological Changes in Subjective Cognitive Decline Using the Self-report Questionnaire and Neuroimaging Biomarkers

BACKGROUND AND PURPOSE: Subjective cognitive decline (SCD) may be the first symptomatic stage of Alzheimer's disease (AD). Hence, a screening tool to characterize the patients' complaints and assess the risk of AD is required. We investigated the SCD neuroimaging biomarker distributions and the relevance between the self-report questionnaire and Alzheimer's pathologic changes. METHODS: Individuals aged 50 and above with consistent cognitive complaints without any objective cognitive impairments were eligible for the study. The newly developed questionnaire consisted of 2 parts; 10 questions translated from the ‘SCD-plus criteria’ and a Korean version of the cognitive failure questionnaire by Broadbent. All the subjects underwent physical examinations such as blood work, detailed neuropsychological tests, the self-report questionnaire, brain magnetic resonance imagings, and florbetaben positron emission tomography (PET) scans. Amyloid PET findings were interpreted using both visual rating and quantitative analysis. Group comparisons and association analysis were performed using SPSS (version 18.0). RESULTS: A total of 31 participants with SCD completed the study and 25.8% showed positive amyloid depositions. The degree of periventricular white matter hyperintensities (WMH) and hippocampal atrophy were more severe in amyloid-positive SCDs compared to the amyloid-negative group. In the self-reported questionnaire, the ‘informant's report a decline’ and ‘symptom's onset after 65 years of age’ were associated with more Alzheimer's pathologic changes. CONCLUSIONS: Amyloid-positive SCDs differed from amyloid-negative SCDs on WMH, hippocampal atrophy, and a few self-reported clinical features, which gave clues on the prediction of AD pathology.

Amyloid PET Quantification Via End-to-End Training of a Deep Learning / 대한핵의학회잡지

PURPOSE: Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers.METHODS: Amyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method.RESULTS: The mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PETand a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively.CONCLUSION: We suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers.