Regional uptakes from early-frame amyloid PET and 18F-FDG PET scans are comparable independent of disease state

Understanding the mechanisms of disease modifying effects of aerobic exercise in people with Alzheimer's disease

Alzheimer's disease (AD) is a very disabling disease. Pathologically, it is characterized by the presence of amyloid plaques and neurofibrillary tangles in the brain that results in neurodegeneration. Its clinical manifestations include progressive memory impairment, language decline and difficulty in carrying out activities of daily living (ADL). The disease is managed using interventions such as pharmacological interventions and aerobic exercise. Use of aerobic exercise has shown some promises in reducing the risk of developing AD, and improving cognitive function and the ability to carry out both basic and instrumental ADL. Although, the mechanisms through which aerobic exercise improves AD are poorly understood, improvement in vascular function, brain glucose metabolism and cardiorespiratory fitness, increase in antioxidant capacity and haemoglobin level, amelioration of immune-related and inflammatory responses, modulation of concentration of circulating Neurotrophins and peptides and decrease in concentration of tau protein and cortisol level among others seem to be the possible mechanisms. Therefore, understanding these mechanisms is important to help characterize the dose and the nature of the aerobic exercise to be given. In addition, they may also help in finding ways to optimize other interventions such as the pharmacological interventions. However, more quality studies are needed to verify the mechanisms.

Mitochondria in Alzheimer's Disease Pathogenesis

Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disorder that primarily affects persons aged 65 years and above. It causes dementia with memory loss and deterioration in thinking and language skills. AD is characterized by specific pathology resulting from the accumulation in the brain of extracellular plaques of amyloid-β and intracellular tangles of phosphorylated tau. The importance of mitochondrial dysfunction in AD pathogenesis, while previously underrecognized, is now more and more appreciated. Mitochondria are an essential organelle involved in cellular bioenergetics and signaling pathways. Mitochondrial processes crucial for synaptic activity such as mitophagy, mitochondrial trafficking, mitochondrial fission, and mitochondrial fusion are dysregulated in the AD brain. Excess fission and fragmentation yield mitochondria with low energy production. Reduced glucose metabolism is also observed in the AD brain with a hypometabolic state, particularly in the temporo-parietal brain regions. This review addresses the multiple ways in which abnormal mitochondrial structure and function contribute to AD. Disruption of the electron transport chain and ATP production are particularly neurotoxic because brain cells have disproportionately high energy demands. In addition, oxidative stress, which is extremely damaging to nerve cells, rises dramatically with mitochondrial dyshomeostasis. Restoring mitochondrial health may be a viable approach to AD treatment.

Static first-minute-frame (FMF) PET imaging after 18F-labeled amyloid tracer injection is correlated to [18F]FDG PET in patients with primary progressive aphasia

OBJECTIVE

To study the correlation between a static PET image of the first-minute-frame (FMF) acquired with ¹⁸F-labeled amyloid-binding radiotracers and brain [¹⁸F]FDG PET in patients with primary progressive aphasia (PPA).

MATERIAL AND METHODS

The study cohort includes 17 patients diagnosed with PPA with the following distribution: 9 nonfluent variant PPA, 4 logopenic variant PPA, 1 semantic variant PPA, 3 unclassifiable PPA. Regional SUVRs are extracted from FMFs and their corresponding [¹⁸F]FDG PET images and Pearson's correlation coefficients are calculated.

RESULTS

SUVRs of both images show similar patterns of regional cerebral alterations. Intrapatient correlation analyses result in a mean coefficient of r=0.94±0.06. Regional interpatient correlation coefficients of the study cohort are greater than 0.81. Radiotracer-specific and variant-specific subcohorts show no difference in the similarity between the images.

CONCLUSIONS

The static FMF could be a valid alternative to dynamic early-phase amyloid PET proposed in the literature, and a neurodegeneration biomarker for the diagnosis and classification of PPA in amyloid PET studies.

Dysfunctional Glucose Metabolism in Alzheimer’s Disease Onset and Potential Pharmacological Interventions

Alzheimer’s disease (AD) is the most common age-related dementia. The alteration in metabolic characteristics determines the prognosis. Patients at risk show reduced glucose uptake in the brain. Additionally, type 2 diabetes mellitus increases the risk of AD with increasing age. Therefore, changes in glucose uptake in the cerebral cortex may predict the histopathological diagnosis of AD. The shifts in glucose uptake and metabolism, insulin resistance, oxidative stress, and abnormal autophagy advance the pathogenesis of AD syndrome. Here, we summarize the role of altered glucose metabolism in type 2 diabetes for AD prognosis. Additionally, we discuss diagnosis and potential pharmacological interventions for glucose metabolism defects in AD to encourage the development of novel therapeutic methods.

Classification of Alzheimer’s Disease Using Dual-Phase 18F-Florbetaben Image with Rank-Based Feature Selection and Machine Learning

18F-florbetaben (FBB) positron emission tomography is a representative imaging test that observes amyloid deposition in the brain. Compared to delay-phase FBB (dFBB), early-phase FBB shows patterns related to glucose metabolism in 18F-fluorodeoxyglucose perfusion images. The purpose of this study is to prove that classification accuracy is higher when using dual-phase FBB (dual FBB) versus dFBB quantitative analysis by using machine learning and to find an optimal machine learning model suitable for dual FBB quantitative analysis data. The key features of our method are (1) a feature ranking method for each phase of FBB with a cross-validated F1 score and (2) a quantitative diagnostic model based on machine learning methods. We compared four classification models: support vector machine, naïve Bayes, logistic regression, and random forest (RF). In composite standardized uptake value ratio, RF achieved the best performance (F1: 78.06%) with dual FBB, which was 4.83% higher than the result with dFBB. In conclusion, regardless of the two quantitative analysis methods, using the dual FBB has a higher classification accuracy than using the dFBB. The RF model is the machine learning model that best classifies a dual FBB. The regions that have the greatest influence on the classification of dual FBB are the frontal and temporal lobes.

Measurement of neurodegeneration using a multivariate early frame amyloid PET classifier

Introduction

Amyloid measurement provides important confirmation of pathology for Alzheimer's disease (AD) clinical trials. However, many amyloid positive (Am+) early-stage subjects do not worsen clinically during a clinical trial, and a neurodegenerative measure predictive of decline could provide critical information. Studies have shown correspondence between perfusion measured by early amyloid frames post-tracer injection and fluorodeoxyglucose (FDG) positron emission tomography (PET), but with limitations in sensitivity. Multivariate machine learning approaches may offer a more sensitive means for detection of disease related changes as we have demonstrated with FDG.

Methods

Using summed dynamic florbetapir image frames acquired during the first 6 minutes post-injection for 107 Alzheimer's Disease Neuroimaging Initiative subjects, we applied optimized machine learning to develop and test image classifiers aimed at measuring AD progression. Early frame amyloid (EFA) classification was compared to that of an independently developed FDG PET AD progression classifier by scoring the FDG scans of the same subjects at the same time point. Score distributions and correlation with clinical endpoints were compared to those obtained from FDG. Region of interest measures were compared between EFA and FDG to further understand discrimination performance.

Results

The EFA classifier produced a primary pattern similar to that of the FDG classifier whose expression correlated highly with the FDG pattern (R-squared 0.71), discriminated cognitively normal (NL) amyloid negative (Am-) subjects from all Am+ groups, and that correlated in Am+ subjects with Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale (R = 0.59, 0.63, 0.73) and with subsequent 24-month changes in these measures (R = 0.67, 0.73, 0.50).

Discussion

Our results support the ability to use EFA with a multivariate machine learning-derived classifier to obtain a sensitive measure of AD-related loss in neuronal function that correlates with FDG PET in preclinical and early prodromal stages as well as in late mild cognitive impairment and dementia.

Highlights

The summed initial post-injection minutes of florbetapir positron emission tomography  correlate with fluorodeoxyglucose.A machine learning classifier enabled sensitive detection of early prodromal Alzheimer's disease.Early frame amyloid (EFA) classifier scores correlate with subsequent change in Mini-Mental State Examination, Clinical Dementia Rating Sum of Boxes, and Alzheimer's Disease Assessment Scale-13-item Cognitive subscale.EFA classifier effect sizes and clinical prediction outperformed region of interest standardized uptake value ratio.EFA classification may aid in stratifying patients to assess treatment effect.