Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients

Cerebrospinal Fluid 7-Ketocholesterol Level is Associated with Amyloid-β42 and White Matter Microstructure in Cognitively Healthy Adults

Background:Abnormal cholesterol metabolism changes the neuronal membrane and may promote amyloidogenesis. Oxysterols in cerebrospinal fluid (CSF) are related to Alzheimer’s disease (AD) biomarkers in mild cognitive impairment and dementia. Cholesterol turnover is important for axonal and white matter (WM) microstructure maintenance. Objective:We aim to demonstrate that the association of oxysterols, AD biomarkers, and WM microstructure occurs early in asymptomatic individuals. Methods:We studied the association of inter-individual variability of CSF 24-hydroxycholesterol (24-OHC), 27-hydroxycholesterol (27-OHC), 7-ketocholesterol (7-KC), 7β-hydroxycholesterol (7β-OHC), amyloid-β42 (Aβ42), total-tau (t-tau), phosphorylated-tau (p-tau), neurofilament (NfL), and WM microstructure using diffusion tensor imaging, generalized linear models and moderation/mediation analyses in 153 healthy adults. Results:Higher 7-KC levels were related to lower Aβ42, indicative of greater AD pathology (p = 0.041) . Higher 7-KC levels were related to lower fractional anisotropy (FA) and higher mean (MD), axial (AxD), and radial (RD) diffusivity. 7-KC modulated the association between AxD and NfL in the corpus callosum splenium (B = 39.39, p = 0.017), genu (B = 68.64, p = 0.000), and fornix (B = 10.97, p = 0.000). Lower Aβ42 levels were associated to lower FA and higher MD, AxD, and RD in the fornix, corpus callosum, inferior longitudinal fasciculus, and hippocampus. The association between AxD and Aβ42 was moderated by 7K-C (p = 0.048). Conclusion:This study adds clinical evidence to support the role of 7K-C on axonal integrity and the involvement of cholesterol metabolism in the Aβ42 generation process

Functional brain network centrality is related to APOE genotype in cognitively normal elderly

INTRODUCTION: Amyloid plaque deposition in the brain is an early pathological change in Alzheimer's disease (AD), causing disrupted synaptic connections. Brain network disruptions in AD have been demonstrated with eigenvector centrality (EC), a measure that identifies central regions within networks. Carrying an apolipoprotein (APOE)-ε4 allele is a genetic risk for AD, associated with increased amyloid deposition. We studied whether APOE-ε4 carriership is associated with EC disruptions in cognitively normal individuals. METHODS: A total of 261 healthy middle-aged to older adults (mean age 56.6 years) were divided into high-risk (APOE-ε4 carriers) and low-risk (noncarriers) groups. EC was computed from resting-state functional MRI data. Clusters of between-group differences were assessed with a permutation-based method. Correlations between cluster mean EC with brain volume, CSF biomarkers, and psychological test scores were assessed. RESULTS: Decreased EC in the visual cortex was associated with APOE-ε4 carriership, a genetic risk factor for AD. EC differences were correlated with age, CSF amyloid levels, and scores on the trail-making and 15-object recognition tests. CONCLUSION: Our findings suggest that the APOE-ε4 genotype affects brain connectivity in regions previously found to be abnormal in AD as a sign of very early disease-related pathology. These differences were too subtle in healthy elderly to use EC for single-subject prediction of APOE genotype

ZMC 211-3 - KAEDAH MATEMATIK II MAC-APRIL 1989.pdf

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients

On the alzheimer’s disease diagnosis: Automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias and definitive confirmation must be done through a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to the improvement of early diagnosis of AD and its degree of severity from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET) that have the great advantage of being non invasive low cost and without any side effects. The developed system obtains hopeful results for early diagnosis

New Approaches for Alzheimer’s Disease Diagnosis Based on Automatic Spontaneous Speech Analysis and Emotional Temperature

Alzheimer Disease (AD) is one of the most common dementia and their socio-economic relevance is growing. Its diagnosis is sometimes made by excluding other dementias, but definitive confirmation must await the study post-mortem with brain tissue of the patient. According to internationally accepted criteria, we can only speak about probable or possible Alzheimer's disease. The purpose of this paper is to contribute to improve early diagnosis of dementia and severity from automatic analysis performed by non-invasive automated intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET). These methodologies have the great advantage of being non invasive, low cost methodologies and have no side effects

Accelerated long-term forgetting over three months in asymptomatic APOE ɛ4 carriers

Altres ajuts: Fondo Europeo de Desarrollo Regional (FEDER); Agencia Estatal de Investigación (AEI).Accelerated long-term forgetting (ALF) refers to a rapid loss of information over days or weeks despite normal acquisition/encoding. Notwithstanding its potential relevance as a presymptomatic marker of cognitive dysfunction, no study has addressed the relationship between ALF and Alzheimer's disease (AD) biomarkers. We examined ALF in APOE ɛ4 carriers versus noncarriers, and its relationships with AD cerebrospinal fluid (CSF) biomarkers. We found ALF over three months in APOE ɛ4 carriers (F(1,19) = 5.60; P < 0.05; Cohen's d = 1.08), and this performance was associated with abnormal levels of the CSF Aβ/ptau ratio (r = −.614; P < 0.01). Our findings indicate that ALF is detectable in at-risk individuals, and that there is a relationship between ALF and the pathophysiological processes underlying AD