FDG-PET in early AD diagnosis

Multi-stage classification of Alzheimer's disease from 18F-FDG-PET images using deep learning techniques

The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of dementia, including Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), and Alzheimer's disease (AD) from Cognitively Normal (CN), and assess the results. 18F-FDG PET imaging modality for brain were procured from Alzheimer's disease neuroimaging initiative's (ADNI) repository. The ResNet50V2 model layers were utilised for feature extraction, with the final convolutional layers fine-tuned for this dataset's multi-classification objectives. Multiple metrics and feature maps were utilized to scrutinize and evaluate the model's statistical and qualitative inference. The multi-classification model achieved an overarching accuracy of 98.44% and Area under the receiver operating characteristic curve of 95% on the testing set. Feature maps aided in deducing finer aspects of the model's overall operation. This framework helped classifying from the 18F-FDG PET brain images, the subtypes of Mild Cognitive Impairment (MCI) which include EMCI, LMCI, from AD, CN groups and achieved an all-inclusive sensitivity of 94% and specificity of 95% respectively.

Optimizing Use of Neuroimaging Tools in Evaluation of Prodromal Alzheimer's Disease and Related Disorders

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by preclinical, pre-dementia, and dementia phases. Progression of the disease leads to cognitive decline and is associated with loss of functional independence, personality changes, and behavioral disturbances. Current guidelines for AD diagnosis include the use of neuroimaging tools as biomarkers for identifying and monitoring pathological changes. Various imaging modalities, namely magnetic resonance imaging (MRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and PET with amyloid-beta tracers are available to facilitate early accurate diagnoses. Enhancing diagnosis in the early stages of the disease can allow for timely interventions that can delay progression of the disease. This paper will discuss the characteristic findings associated with each of the imaging tools for patients with AD, with a focus on FDG-PET due to its established accuracy in assisting with the differential diagnosis of dementia and discussion of other methods including MRI. Diagnostically-relevant features to aid clinicians in making a differential diagnosis will also be pointed out and multimodal imaging will be reviewed. We also discuss the role of quantification software in interpretation of brain imaging. Lastly, to guide evaluation of patients presenting with cognitive deficits, an algorithm for optimal integration of these imaging tools will be shared. Molecular imaging modalities used in dementia evaluations hold promise toward identifying AD-related pathology before symptoms are fully in evidence. The work describes state of the art functional and molecular imaging methods for AD. It will also overview a clinically applicable quantitative method for reproducible assessments of such scans in the early identification of AD.

Metabolic Activity Value in the Posterior Cingulate Cortex Using F-18 Fluorodeoxyglucose Positron Emission Tomography Brain to Predict the Severity of Alzheimer's

Alzheimer's disease (AD) is a type of dementia which is known as one of a major problem in elderly. Clinicians commonly use mini-mental state examination (MMSE) score to determine the severity of cognitive decline, but MMSE has some limitations such as more subjective, influenced by age, educational degree, and local culture. F-18 fluorodeoxyglucose positron emission tomography (F-18 FDG PET) can be used to assess the process of glucose metabolism in posterior cingulate cortex (PCC) area which endures a central role in supporting cognitive function directly. The purpose of this study is to observe a correlation between metabolic activity value of PCC and MMSE score in predicting the severity of AD. A cross-sectional study was done to 30 subjects suspect AD disease with aged 60 years and older. Characteristic data including gender, age, and education, MMSE scoring by psychiatrist, and imaging of F-18 FDG PET were established. The results of correlation test between the value of FDG metabolic activity and MMSE score shows that the value of metabolic activity in the PCC area tends to increase along with the increase of MMSE score (rs = 0.411, P = 0.024). While from the results of multiple regression test with predictor variable consisting of F-18 FDG metabolic activity in the PCC, gender, age, education level, and the interaction between the metabolic activity of F-18 FDG at PCC and gender, a regression model was obtained. There is a significant correlation observed between the captured of F-18 FDG radioactivity with MMSE score in PCC area which can be used as a tool to predict the severity of AD.

Multimodality Imaging Approaches in Alzheimer's disease. Part II: 1H MR spectroscopy, FDG PET and Amyloid PET

In this Part II review, as a complement to the Part I published in this supplement, the authors cover the imaging techniques that evaluates the Alzheimer's disease according to the different metabolic and molecular profiles. In this section MR spectroscopy, FDG-PET and amyloid PET are deeply discussed.

Early detection of cerebral glucose uptake changes in the 5XFAD mouse

Brain glucose hypometabolism has been observed in Alzheimer’s disease (AD) patients, and is detected with ¹⁸F radiolabelled glucose, using positron emission tomography. A pathological hallmark of AD is deposition of brain β-amyloid plaques that may influence cerebral glucose metabolism. The five times familial AD (5XFAD) mouse is a model of brain amyloidosis exhibiting AD-like phenotypes. This study examines brain β-amyloid plaque deposition and ¹⁸FDG uptake, to search for an early biomarker distinguishing 5XFAD from wild-type mice. Thus, brain ¹⁸FDG uptake and plaque deposition was studied in these mice at age 2, 5 and 13 months. The 5XFAD mice demonstrated significantly reduced brain ¹⁸FDG uptake at 13 months relative to wild-type controls but not in younger mice, despite substantial β-amyloid plaque deposition. However, by comparing the ratio of uptake values for glucose in different regions in the same brain, 5XFAD mice could be distinguished from controls at age 2 months. This method of measuring altered glucose metabolism may represent an early biomarker for the progression of amyloid deposition in the brain. We conclude that brain ¹⁸FDG uptake can be a sensitive biomarker for early detection of abnormal metabolism in the 5XFAD mouse when alternative relative uptake values are utilized.