Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence

2011 ◽  
Vol 197 (1) ◽  
pp. 165-170 ◽  
Author(s):  
Ziad Sankari ◽  
Hojjat Adeli
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Wavelet Coherence ◽  
Probabilistic Neural Networks

scholarly journals Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06226
Author(s):  
Diedre Carmo ◽  
Bruna Silva ◽  
Clarissa Yasuda ◽  
Letícia Rittner ◽  
Roberto Lotufo
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Convolutional Neural Networks ◽  
Hippocampus Segmentation

scholarly journals Alzheimer disease neuropathology:understanding autonomic dysfunction

2008 ◽  
Vol 2 (3) ◽  
pp. 183-191 ◽  
Author(s):  
Eliasz Engelhardt ◽  
Jerson Laks
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Autonomic Dysfunction ◽  
Disease Process ◽  
Functional Impairments ◽  
Cortical Areas ◽  
Manual Search ◽  
Neurodegenerative Process ◽  
Vital Functions

Abstract Alzheimer's disease is a widely studied disorder with research focusing on cognitive and functional impairments, behavioral and psychological symptoms, and on abnormal motor manifestations. Despite the importance of autonomic dysfunctions they have received less attention in systematic studies. The underlying neurodegenerative process of AD, mainly affecting cortical areas, has been studied for more than one century. However, autonomic-related structures have not been studied neuropathologically with the same intensity. The autonomic nervous system governs normal visceral functions, and its activity is expressed in relation to homeostatic needs of the organism's current physical and mental activities. The disease process leads to autonomic dysfunction or dysautonomy possibly linked to increased rates of morbidity and mortality. Objective: The aim of this review was to analyze the cortical, subcortical, and more caudal autonomic-related regions, and the specific neurodegenerative process in Alzheimer's disease that affects these structures. Methods: A search for papers addressing autonomic related-structures affected by Alzheimer's degeneration, and under normal condition was performed through MedLine, PsycInfo and Lilacs, on the bibliographical references of papers of interest, together with a manual search for classic studies in older journals and books, spanning over a century of publications. Results: The main central autonomic-related structures are described, including cortical areas, subcortical structures (amygdala, thalamus, hypothalamus, brainstem, cerebellum) and spinal cord. They constitute autonomic neural networks that underpin vital functions. These same structures, affected by specific Alzheimer's disease neurodegeneration, were also described in detail. The autonomic-related structures present variable neurodegenerative changes that develop progressively according to the degenerative stages described by Braak and Braak. Conclusion: The neural networks constituted by the central autonomic-related structures, when damaged by progressive neurodegeneration, represent the neuropathological substrate of autonomic dysfunction. The presence of this dysfunction and its possible relationship with higher rates of morbidity, and perhaps of mortality, in affected subjects must be kept in mind when managing Alzheimer's patients.

scholarly journals Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer’s disease neuropathologies

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicasia Beebe-Wang ◽  
Safiye Celik ◽  
Ethan Weinberger ◽  
Pascal Sturmfels ◽  
Philip L. De Jager ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Immune Response ◽  
Deep Neural Networks ◽  
Linear Models ◽  
Joint Analysis ◽  
Analysis Framework ◽  
Complex Relationships

AbstractDeep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer’s Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require “harmonized” phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer’s Disease.

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