• Smart Media Journal
• /
• v.12 no.7
• /
• pp.52-58
• /
• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• Dementia and Neurocognitive Disorders
• /
• v.23 no.1
• /
• pp.1-10
• /
• 2024

Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer's disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer's disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions: Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

• Journal of Preventive Medicine and Public Health
• /
• v.49 no.2
• /
• pp.134-138
• /
• 2016

Objectives: Season of birth, an exogenous indicator of early life environment, has been related to higher risk of adverse psychiatric outcomes but the findings for Alzheimer's disease (AD) have been inconsistent. We investigated whether the month or season of birth are associated with AD. Methods: A nationwide nested case-control study including all community-dwellers with clinically verified AD diagnosed in 2005 to 2012 (n=70 719) and up to four age- sex- and region of residence-matched controls (n=282 862) residing in Finland. Associations between month and season of birth and AD were studied with conditional logistic regression. Results: Month of birth was not associated with AD (p=0.09). No strong associations were observed with season (p=0.13), although in comparison to winter births (December-February) summer births (June-August) were associated with higher odds of AD (odds ratio, 1.03; 95% confidence interval, 1.00 to 1.05). However, the absolute difference in prevalence in winter births was only 0.5% (prevalence of those born in winter were 31.7% and 32.2% for cases and controls, respectively). Conclusions: Although our findings do not support the hypothesis that season of birth is related to AD/dementia risk, they do not invalidate the developmental origins of health and disease hypothesis in late-life cognition. It is possible that season does not adequately capture the early life circumstances, or that other (postnatal) risk factors such as lifestyle or socioeconomic factors overrule the impact of prenatal and perinatal factors.

• Journal of Digital Convergence
• /
• v.19 no.12
• /
• pp.353-358
• /
• 2021

Parkinson's disease is the second most common degenerative brain disease after Alzheimer's in old age. Symptoms of Parkinson's disease are factors that reduce the quality of life in daily life, such as shaking hands, slowing behavior and cognitive function. Parkinson's disease that can slow the progression of the disease through early diagnosis. To diagnoze Parkinson's disease early, an algorithm was implemented to extract features using wav2vec and to diagnose the presence or absence of Parkinson's disease with deep learning(ANN). As a results of the experiment, the accuracy was 97.47%. It was better than the results of diagnosing Parkinson's disease using the existing neural network. The audio voice file could simply reduce the experiment process and obtain improved results.

• Journal of Integrative Natural Science
• /
• v.6 no.3
• /
• pp.143-150
• /
• 2013

Prevailing role of intracellular amyloid ${\beta}$ ($iA{\beta}$) in Alzheimer's disease (AD) initiation and progression attracts more and more attention in recent years. To address whether $iA{\beta}$ induces early alterations of electrophysiological properties in cultured human primary neurons, we delivered $iA{\beta}$ with adenovirus and measured the electrophysiological properties of infected neurons with whole-cell recordings. Our results show that $iA{\beta}$ induces an increase in neuronal resting membrane potentials, a decrease in $K^+$ currents and a hyperpolarizing shift in voltage-dependent activation of $K^+$ currents. These results suggest the electrophysiological impairments induced by $iA{\beta}$ may be responsible for its neuronal toxicity.

• Proceedings of the Korean Society of Toxicology Conference
• /
• 2002.05a
• /
• pp.109-109
• /
• 2002

Alzheimer's disease (AD) is characterized by $\beta$-amyloid deposition and associated with loss of neuron cells in brain regions involved in learning and memory process. Many cases of early onset autosomal dominant inherited forms of AD are caused by mutation in the genes encoding presenilin-2 (PS-2).(omitted)

• Proceedings of the Korean Society of Toxicology Conference
• /
• 2002.05a
• /
• pp.111-111
• /
• 2002

Many cases of early onset autosomal dominant inherited forms of Alzheimer's disease (AD) are caused by mutation in the genes encoding presenilin-2 (PS-2) on chromosome 1. It is characterized by amyloid deposition and associated with loss of neuron. However, molecular mechanisms underlying the role of PS-2 mutation in the pathogenic AD are not known.(omitted)

• Dementia and Neurocognitive Disorders
• /
• v.17 no.4
• /
• pp.174-175
• /
• 2018

• Journal of Korean geriatric psychiatry
• /
• v.16 no.1
• /
• pp.17-23
• /
• 2012

The diagnosis of Alzheimer's disease (AD) is still obscure even to specialists. To improve the diagnostic accuracy, to find at-risk people as early as possible, to predict the efficacy or adverse reactions of pharmacotherapy on an individual basis, to attain more reliable results of clinical trials by recruiting better defined participants, to prove the disease-modifying ability of new candidate drugs, to establish prognosis-based therapeutic plans, and to do more, is now increasing the need for biomarkers for AD. Among AD-related biochemical markers, cerebrospinal beta-amyloid and tau have been paid the most attention since they are materials directly interfacing the brain interstitium and can be obtained through the lumbar puncture. Level of beta-amyloid is reduced whereas tau is increased in cerebrospinal fluid of AD patients relative to cognitively normal elderly people. Remarkably, such information has been found to help predict AD conversion of mild cognitive impairment. Despite inconsistent findings from previous studies, plasma beta-amyloid is thought to be increased before the disease onset, but show decreasing change as the disease progress. Regarding other peripheral biochemical markers, omics tools are being widely used not only to find useful biomarkers but also to generate novel hypotheses for AD pathogenesis and to lead new personalized future medicine.

• Journal of Sensor Science and Technology
• /
• v.21 no.5
• /
• pp.339-344
• /
• 2012

This article describes a novel method for the detection of amyloid-${\beta}$($A{\beta}$) peptide that utilizes a photo-sensitive field-effect transistor (p-FET). According to a recent study, $A{\beta}$ protein has been known to play a central role in the pathogenesis of Alzheimer's disease (AD). Accordingly, we investigated the variation of photo current generated from p-FET with and without intracellular magnetic beads conjugated with $A{\beta}$ peptides, which are placed on the p-FET sensing areas. The decrease of photo current was observed due to the presence of the magnetic beads on the channel region. Moreover, a similar characteristic was shown when the Raw 264 cells take in magnetic beads treated with $A{\beta}$ peptide. This means that it is possible to simply detect a certain protein using magnetic beads and a p-FET device. Therefore, in this paper, we suggest that our method could detect tiny amounts of $A{\beta}$ for early diagnosis of AD using the p-FET devices.