FDG PET and the genetics of dementia

Heterogeneous multimodal biomarkers analysis for Alzheimer's disease via Bayesian network

By 2050, it is estimated that the number of worldwide Alzheimer's disease (AD) patients will quadruple from the current number of 36 million, while no proven disease-modifying treatments are available. At present, the underlying disease mechanisms remain under investigation, and recent studies suggest that the disease involves multiple etiological pathways. To better understand the disease and develop treatment strategies, a number of ongoing studies including the Alzheimer's Disease Neuroimaging Initiative (ADNI) enroll many study participants and acquire a large number of biomarkers from various modalities including demographic, genotyping, fluid biomarkers, neuroimaging, neuropsychometric test, and clinical assessments. However, a systematic approach that can integrate all the collected data is lacking. The overarching goal of our study is to use machine learning techniques to understand the relationships among different biomarkers and to establish a system-level model that can better describe the interactions among biomarkers and provide superior diagnostic and prognostic information. In this pilot study, we use Bayesian network (BN) to analyze multimodal data from ADNI, including demographics, volumetric MRI, PET, genotypes, and neuropsychometric measurements and demonstrate our approach to have superior prediction accuracy.

Rethinking on the concept of biomarkers in preclinical Alzheimer's disease

The neuropathological processes eventually leading to Alzheimer's disease (AD) are thought to start decades before the appearance of clinical symptoms and the clinical diagnosis of AD dementia. The term "preclinical AD" has been recently introduced to identify this "silent stage" of AD, when the disease is already present, but symptoms are not yet clinically evident. Advances in AD biomarkers have dramatically improved the ability to detect AD pathological processes in vivo in cognitively intact subjects, thus demonstrating the presence of AD pathology in the preclinical phase. This review focuses on the recent advances in the field of neuroimaging and CSF AD biomarkers specifically in the preclinical phase of AD, and aims to discuss the significance that such biomarkers could have in cognitively intact subjects. Even though the use of such biomarkers in AD preclinical phase has contributed to improve our understanding of AD early pathological processes, it raised also a number of new challenges that still remain to be overcome, such as a better definition of the clinical and individual significance of currently known biomarkers in preclinical stages and the development of novel biomarkers of different early AD-related events.