• Journal of Multimedia Information System
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• v.9 no.1
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• Korean Journal of Biological Psychiatry
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• v.14 no.1
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• pp.48-54
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• 2007

Objectives : This study aimed to examine the relationship between behavioral and psychological symptoms of dementia(BPSD) and patient and caregiver QOL in Alzheimer's disease(AD). Methods : Fifty-one AD patients and their caregivers participated. Measures about patients were Neuropsychiatric Inventory(NPI), Korean version of QOL-Alzheimer's Disease(KQOL-AD), Activities of Daily Living(ADL), Clinical Dementia Rating(CDR), and Korean version-Mini Mental State Examination(K-MMSE). Caregiver QOL was assessed with KQOL-AD and General Health Questionnaire/Quality of Life-12(GHQ/QOL-12). Results : Patient QOL-AD on patient ratings was negatively correlated with appetite/eating change and NPI scores. Patient QOL-AD on caregiver ratings was negatively correlated with hallucinations, depression/dysphoria, and NPI scores. Caregiver QOL assessed by the GHQ/QOL-12 was negatively correlated with agitation/aggression, depression/dysphoria, and NPI scores and was negatively correlated with distress related to agitation/aggression, depression/dysphoria, and NPI scores. Conclusion : BPSD of AD patients was associated with low QOL of both patients and caregivers. Thus, interventions of BPSD were needed to improve both patient and caregiver QOL.

• Molecules and Cells
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• v.40 no.9
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• pp.613-620
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• 2017

The most common form of senile dementia is Alzheimer's disease (AD), which is characterized by the extracellular deposition of amyloid ${\beta}-peptide$ ($A{\beta}$) plaques and the intracellular formation of neurofibrillary tangles (NFTs) in the cerebral cortex. Tau abnormalities are commonly observed in many neurodegenerative diseases including AD, Parkinson's disease, and Pick's disease. Interestingly, tau-mediated formation of NFTs in AD brains shows better correlation with cognitive impairment than $A{\beta}$ plaque accumulation; pathological tau alone is sufficient to elicit frontotemporal dementia, but it does not cause AD. A growing amount of evidence suggests that soluble $A{\beta}$ oligomers in concert with hyperphosphorylated tau (pTau) serve as the major pathogenic drivers of neurodegeneration in AD. Increased $A{\beta}$ oligomers trigger neuronal dysfunction and network alternations in learning and memory circuitry prior to clinical onset of AD, leading to cognitive decline. Furthermore, accumulated damage to mitochondria in the course of aging, which is the best-known nongenetic risk factor for AD, may collaborate with soluble $A{\beta}$ and pTau to induce synapse loss and cognitive impairment in AD. In this review, I summarize and discuss the current knowledge of the molecular and cellular biology of AD and also the mechanisms that underlie $A{\beta}-mediated$ neurodegeneration.

• BMB Reports
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• v.43 no.10
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• pp.649-655
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• 2010

Alzheimer's disease (AD) is the most common age-dependent neurodegenerative disorder and shows progressive memory loss and cognitive decline. Intraneuronal filaments composed of aggregated hyperphosphorylated tau protein, called neurofibrillary tangles, along with extracellular accumulations of amyloid $\beta$ protein (A$\beta$), called senile plaques, are known to be the neuropathological hallmarks of AD. In light of recent studies, epigenetic modification has emerged as one of the pathogenic mechanisms of AD. Epigenetic changes encompass an array of molecular modifications to both DNA and chromatin, including transcription factors and cofactors. In this review, we summarize how DNA methylation and changes to DNA chromatin packaging by post-translational histone modification are involved in AD. In addition, we describe the role of SIRTs, histone deacetylases, and the effect of SIRT-modulating drugs on AD. Lastly, we discuss how amyloid precursor protein (APP) intracellular domain (AICD) regulates neuronal transcription. Our understanding of the epigenomes and transcriptomes of AD may warrant future identification of novel biological markers and beneficial therapeutic targets for AD.

• Korean Journal of Biological Psychiatry
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• v.17 no.4
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• pp.194-202
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• 2010

Alzheimer's disease(AD) is the most common neurodegenerative disorder and constitutes about two thirds of dementia. Despite a lot of effort to find drugs for AD worldwide, an efficient medicine that can cure AD has not come yet, which is due to the complicated pathogenic pathways and progressively degenerative properties of AD. In its early clinical phase, it is important to find the subtle alterations in synapses responsible for memory because symptoms of AD patients characteristically start with pure impairment of memory. Attempts to find the target synaptic proteins and their pathogenic pathways will be the most powerful alternative strategy for developing AD medicine. Here we review recent progress in deciphering the role of target synaptic proteins related to AD in hippocampal glutamatergic synapses.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.486-507
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• 2022

Cerebral small vessel disease (CSVD) includes vascular lesions detected on brain MRI, such as white matter hyperintensities, lacunar infarctions, microbleeds, or enlarged perivascular spaces. There is accumulating evidence that vascular changes may play an important role in development of Alzheimer's disease (AD), and CSVD lesions detected on brain MRI were reported to be associated with β-amyloid and tau proteins accumulation. As the vascular contribution has therapeutic potential, it is important to understand the association of CSVD with AD and AD biomarkers. This review begins with a brief introduction of AD and AD biomarkers, explains the association between AD and vascular changes, and then details the pathogenesis and MR imaging findings of CSVD. Afterwards, we discuss the association of CSVD with AD and AD biomarkers.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.178-190
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• 2021

Alzheimer's disease (AD) is an insidious and degenerative neurological disease. It is a new topic for AD patients to use magnetic resonance imaging (MRI) and computer technology and is gradually explored at present. Preprocessing and correlation analysis on MRI data are firstly made in this paper. Then kernel principal component analysis (KPCA) is used to extract features of brain gray matter images. Finally supervised classification schemes such as AdaBoost algorithm and support vector machine algorithm are used to classify the above features. Experimental results by means of AD program Alzheimer's Disease Neuroimaging Initiative (ADNI) database which contains brain structural MRI (sMRI) of 116 AD patients, 116 patients with mild cognitive impairment, and 117 normal controls show that the proposed method can effectively assist the diagnosis and analysis of AD. Compared with principal component analysis (PCA) method, all classification results on KPCA are improved by 2%-6% among which the best result can reach 84%. It indicates that KPCA algorithm for feature extraction is more abundant and complete than PCA.

• Journal of Biomedical Engineering Research
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• v.31 no.1
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• pp.27-32
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• 2010

Noninvasive detection of patients with probable Alzheimer's disease (AD) is of great importance for assisting a medical doctor's decision for early treatment of AD patients. In the present study, we have extracted quantitative electroencephalogram (qEEG) variables, which can be potentially used to diagnose AD, from resting eyes-closed continuous EEGs of 22 AD patients and 27 age-matched normal control (NC) subjects. We have extracted qEEG variables from mean phase coherence (MPC) and EEG coherence, evaluated for all possible combinations of electrode pairs. Preliminary trials to discriminate the two groups with the extracted qEEG variables demonstrated that the use of MPC as a supplementary or alternative measure for the EEG coherence may enhance the accuracy of noninvasive diagnosis of AD.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• BMB Reports
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• v.53 no.1
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• pp.35-46
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• 2020

Despite enduring diverse insults, mitochondria maintain normal functions through mitochondrial quality control. However, the failure of mitochondrial quality control resulting from excess damage and mechanical defects causes mitochondrial dysfunction, leading to various human diseases. Recent studies have reported that mitochondrial defects are found in Alzheimer's disease (AD) and worsen AD symptoms. In AD pathogenesis, mitochondrial dysfunction-driven generation of reactive oxygen species (ROS) and their contribution to neuronal damage has been widely studied. In contrast, studies on mitochondrial dysfunction-associated inflammatory responses have been relatively scarce. Moreover, ROS produced upon failure of mitochondrial quality control may be linked to the inflammatory response and influence the progression of AD. Thus, this review will focus on inflammatory pathways that are associated with and initiated through defective mitochondria and will summarize recent progress on the role of mitochondria-mediated inflammation in AD. We will also discuss how reducing mitochondrial dysfunction-mediated inflammation could affect AD.