Oligomers of the amyloid β-protein (Aβ) have been implicated in the pathogenesis of Alzheimer's disease (AD) through their toxicity towards neurons. Understanding the process of oligomerization may contribute to the development of therapeutic agents, but this has been difficult due to the complexity of oligomerization and the metastability of the oligomers thus formed. To understand the kinetics of oligomer formation, and how that relates to the progression of AD, we developed models of the oligomerization process. Here, we use experimental data from cell viability assays and proxies for rate constants involved in monomer-dimer-trimer kinetics to develop a simple mathematical model linking Aβ assembly to oligomer-induced neuronal degeneration. This model recapitulates the rapid growth of disease incidence with age. It does so through incorporation of age-dependent changes in rates of Aβ monomer production and elimination. The model also describes clinical progression in genetic forms of AD (e.g., Down's syndrome), changes in hippocampal volume, AD risk after traumatic brain injury, and spatial spreading of the disease due to foci in which Aβ production is elevated. Continued incorporation of clinical and basic science data into the current model will make it an increasingly relevant model system for doing theoretical calculations that are not feasible in biological systems. In addition, terms in the model that have particularly large effects are likely to be especially useful therapeutic targets.
Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder-based imputation of missing key features of heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history.
Brain waste clearance from the interstitial fluid environment is challenging to measure, which has contributed to controversy regarding the significance of glymphatic transport impairment for neurodegenerative processes. Dynamic contrast enhanced MRI (DCE-MRI) with cerebrospinal fluid administration of Gd-tagged tracers is often used to assess glymphatic system function. We previously quantified glymphatic transport from DCE-MRI data utilizing regularized optimal mass transport (rOMT) analysis, however, information specific to glymphatic clearance was not directly derived.
Dementia with Lewy bodies (DLB) and its prodromal presentation with mild cognitive impairment is characterized by prominent deficits in attention/executive domains and in visual processing abilities with relative sparing of memory. Neuropsychological research is continuously refining the tools to define more in detail the patterns of relatively preserved and impaired cognitive abilities that help differential diagnosis between DLB and Alzheimer disease (AD). This review summarizes the main studies exploring specific cognitive tasks investigating different visual processing abilities and verbal memory that better differentiate DLB from AD.
Department of Neurology, Erenkoy Mental Health and Neurological Diseases Training and Research Hospital, University of Health Sciences, Istanbul, Turkey.
Background And Purpose: Alzheimer's disease (AD) and epileptic seizure are among the most common health problems in the elderly population. This study aimed to estimate the prevalence rate and predictors of seizures in sporadic AD patients.
Methods: The study was conducted by retrospectively for a period of 10 years examining the file records.
