Correlation between volume and morphological changes in the hippocampal formation in Alzheimer’s disease: rounding of the outline of the hippocampal body on coronal MR images

2012 ◽  
Vol 54 (10) ◽  
pp. 1079-1087 ◽  
Author(s):  
Michito Adachi ◽  
Shinobu Kawakatsu ◽  
Takamichi Sato ◽  
Fumi Ohshima
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hippocampal Formation ◽  
Morphological Changes ◽  
Mr Images

scholarly journals The Neuronal Actions of Leptin and the Implications for Treating Alzheimer’s Disease

2021 ◽  
Vol 14 (1) ◽  
pp. 52
Author(s):  
Kirsty Hamilton ◽  
Jenni Harvey
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Energy Homeostasis ◽  
Leptin Receptor ◽  
Hippocampal Formation ◽  
Synaptic Function ◽  
Receptor Expression ◽  
Neuroprotective Actions ◽  
Novel Target ◽  
Leptin System

It is widely accepted that the endocrine hormone leptin controls food intake and energy homeostasis via activation of leptin receptors expressed on hypothalamic arcuate neurons. The hippocampal formation also displays raised levels of leptin receptor expression and accumulating evidence indicates that leptin has a significant impact on hippocampal synaptic function. Thus, cellular and behavioural studies support a cognitive enhancing role for leptin as excitatory synaptic transmission, synaptic plasticity and glutamate receptor trafficking at hippocampal Schaffer collateral (SC)-CA1 synapses are regulated by leptin, and treatment with leptin enhances performance in hippocampus-dependent memory tasks. Recent studies indicate that hippocampal temporoammonic (TA)-CA1 synapses are also a key target for leptin. The ability of leptin to regulate TA-CA1 synapses has important functional consequences as TA-CA1 synapses are implicated in spatial and episodic memory processes. Moreover, degeneration is initiated in the TA pathway at very early stages of Alzheimer’s disease, and recent clinical evidence has revealed links between plasma leptin levels and the incidence of Alzheimer’s disease (AD). Additionally, accumulating evidence indicates that leptin has neuroprotective actions in various AD models, whereas dysfunctions in the leptin system accelerate AD pathogenesis. Here, we review the data implicating the leptin system as a potential novel target for AD, and the evidence that boosting the hippocampal actions of leptin may be beneficial.

Microglia and its genetics in Alzheimer's Disease

2021 ◽  
Vol 18 ◽  
Author(s):  
Xinyan Liang ◽  
Haijian Wu ◽  
Mark Colt ◽  
Xinying Guo ◽  
Brock Pluimer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Immune Cell ◽  
Morphological Changes ◽  
Association Studies ◽  
Unmet Need ◽  
Genome Wide Association Studies ◽  
Protective Effects ◽  
Risk Genes ◽  
Main Component

: Alzheimer’s Disease (AD) is the most prevalent form of dementia across the world. While its discovery and pathological manifestations are centered on protein aggregations of amyloid-beta (Aβ) and hyperphosphorylated tau protein, neuroinflammation has emerged in the last decade as a main component of the disease in both pathogenesis and progression. As the main innate immune cell type in central nervous system (CNS), microglia play a very important role in regulating neuroinflammation, which occurs commonly in neurodegenerative conditions including AD. Under inflammatory response, microglia undergo morphological changes and status transition from homeostatic to activated forms. Different microglia subtypes displaying distinct genetic profiles have been identified in AD, and these signatures often link to AD risk genes identified from the genome-wide association studies (GWAS), such as APOE and TREM2. Furthermore, many of AD risk genes are highly enriched in microglia and specifically influence the functions of microglia in pathogenesis, e.g. releasing inflammatory cytokines and clearing Aβ. Therefore, building up a landscape of these risk genes in microglia, based on current preclinical studies and in the context of their pathogenic or protective effects, would largely help us to understand the complexed etiology of AD and provide new insight for the unmet need of effective treatment.

P4-062: THE RETINAL CHOROID IN SEVERE ALZHEIMER'S DISEASE: A HISTOPATHOLOGICAL STUDY OF MORPHOLOGICAL CHANGES

2006 ◽  
Vol 14 (7S_Part_27) ◽  
pp. P1456-P1456
Author(s):  
Samuel Asanad ◽  
Fred N. Ross-Cisneros ◽  
Ernesto Barron ◽  
Alfredo A. Sadun
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Morphological Changes ◽  
Histopathological Study

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals Visual Features in Alzheimer’s Disease: From Basic Mechanisms to Clinical Overview

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
María Alejandra Cerquera-Jaramillo ◽  
Mauricio O. Nava-Mesa ◽  
Rodrigo E. González-Reyes ◽  
Carlos Tellez-Conti ◽  
Alejandra de-la-Torre
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Morphological Changes ◽  
Indirect Costs ◽  
Therapeutic Interventions ◽  
Disease Stage ◽  
Early Disease ◽  
Early Disease Detection ◽  
Advanced Disease Stage ◽  
Functional Components

Alzheimer’s disease (AD) is the leading cause of dementia worldwide. It compromises patients’ daily activities owing to progressive cognitive deterioration, which has elevated direct and indirect costs. Although AD has several risk factors, aging is considered the most important. Unfortunately, clinical diagnosis is usually performed at an advanced disease stage when dementia is established, making implementation of successful therapeutic interventions difficult. Current biomarkers tend to be expensive, insufficient, or invasive, raising the need for novel, improved tools aimed at early disease detection. AD is characterized by brain atrophy due to neuronal and synaptic loss, extracellular amyloid plaques composed of amyloid-beta peptide (Aβ), and neurofibrillary tangles of hyperphosphorylated tau protein. The visual system and central nervous system share many functional components. Thus, it is plausible that damage induced by Aβ, tau, and neuroinflammation may be observed in visual components such as the retina, even at an early disease stage. This underscores the importance of implementing ophthalmological examinations, less invasive and expensive than other biomarkers, as useful measures to assess disease progression and severity in individuals with or at risk of AD. Here, we review functional and morphological changes of the retina and visual pathway in AD from pathophysiological and clinical perspectives.

Fasu-Net: Fast Alzheimer’s Disease Screening with Undersampled MRI Using Convolutional Neural Networks

2021 ◽  
Vol 11 (8) ◽  
pp. 2211-2221
Author(s):  
Yuanbo Xie ◽  
Haitao Jiang ◽  
Hongwei Du ◽  
Jinzhang Xu ◽  
Bensheng Qiu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Mri ◽  
Potential Method ◽  
Disease Screening ◽  
Acquisition Time ◽  
Mr Images ◽  
Brain Mr Images ◽  
Magnetic Resonance Imaging Mri ◽  
Questionnaire Method

Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative condition, which results in dementia. Mild Cognitive Impairment (MCI) is an intermediate state between normal aging and AD. Instead of traditional questionnaire method, magnetic resonance imaging (MRI) can be used by radiologists to diagnose and screening AD recently, but long acquisition time is not conducive to screening AD and MCI. To solve this problem, we develop a Fasu-Net (Fast Alzheimer’s disease Screening neural network with Undersampled MRI) for AD and MCI clinical classification. The network uses undersampled structural MRI with a shorter acquisition time to improve the screening and diagnosis efficiency of AD. For achieving the best classification result, three axial planes of brain MR images were feed into the Fasu-Net with transfer learning method. The experiment results on undersampled 3D T1-weighted images database (ADNI) show that in the AD versus MCI versus HC (Healthy Controls) classification, the Fasu-Net achieved the accuracy of 91.41%, thus can be a potential method for fast clinical screening of AD.

Neuropathology

2012 ◽  
pp. 177-185
Author(s):  
Peter Falkai ◽  
Bernhard Bogerts
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Affective Disorders ◽  
Morphological Changes ◽  
Brain Diseases ◽  
Nigrostriatal System ◽  
Brain Pathology ◽  
Huntington's Chorea

The traditional domains of neuropathology are well-defined organic brain diseases with an obvious pathology, such as tumours, infections, vascular diseases, trauma, or toxic and hypoxemic changes, as well as degenerative brain diseases (e.g. Alzheimer's disease, Parkinson's disease, and Huntington's chorea). Neuropathological investigations of these brain disorders have been rewarding, because patients with any of these conditions can be expected to have gross morphological or more or less specific neurohistological anomalies related to the clinical symptoms of the disorders. Moreover, the type of brain pathology of these well-defined disease entities is quite homogenous. For example, it is highly unlikely that a patient with Parkinson's disease would not exhibit morphological changes and Lewy bodies in the nigrostriatal system, just as much a person with Huntington's chorea would have a normal striatum, or a patient with Pick'sor Alzheimer's disease would have no changes in the cerebralcortex. In contrast, the history of the neuropathology of psychiatric disorders outside primary degenerative diseases is much more controversial, because no such obvious and homogenous types of brain pathology (as seen in neurological disorders) have yet been detected for the major psychiatric illnesses such as schizophrenia, affective disorders, substance-related disorders, or personality disorders. The scope of this chapter is to summarize the neuropathological findings in schizophrenia, affective disorders, and alcoholism. Tables 2.3.5.1, 2.3.5.2, 2.3.5.3, and 2.3.5.4 highlight the significant findings. It goes beyond the scope of this chapter to review thelarge body of literature on the dementias, including specifically Alzheimer's disease. Concerning this matter, the reader is referred to several comprehensive reviews (e.g. Jellinger and Bancher 1998).

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