On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

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.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

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 im-provement 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.

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.

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.

Hipertensión arterial y enfermedad de Alzheimer

Hypertension (HT) is the most prevalent cardiovascular risk factor associated with dementia and Alzheimer disease (AD). The evidence about the association within hight blood pressure (BP) level in the middle age and dementia and Alzheimer’s disease incidence in the advanced age has increased. Longitudinal studies show that in the previous years of onset AD, BP is similar or lower in the patients who develop AD with regard to those who not develop. When patients has developed AD the case is the same that the previous one. Most studies show that BP reduction is beneficious to prevent cognitive impairment, dementia and AD. Is spite of some discordant studies, health authorities recommend to treat isolated systolic HT with an ‘A’ evidence degree, level 1, to prevent AD. Animal experimentation prove different ways to act of antihypertensive drugs in the AD prevention and has established the pathophysiological bases in this relation, until now only showed through various clinical studies

Indicadores cognitivos del proceso de envejecimiento en las personas con síndrome de Down

Fundamentos: el aumento de la esperanza de vida en las personas con síndrome de Down plantea nuevos interrogantes sobre el proceso de su envejecimiento. La revisión bibliográfica sobre el tema muestra acuerdo sobre algunos aspectos diferenciales respecto a la población con discapacidad psíquica y la población general. Entre ellos, destacamos dos: a) la precocidad del inicio del proceso y b) el aumento de la probabilidad de desarrollar un envejecimiento patológico a causa de la demencia tipo Alzheimer. El objetivo del presente estudio se centra en la aportación de datos que ayuden a delimitar los posibles indicadores del declive cognitivo de las personas adultas con síndrome de Down relacionados con un posible deterioro propio de la demencia tipo Alzheimer. Método: el estudio se realiza en una muestra de 84 personas adultas con discapacidad psíquica, 42 de las cuales presentan el síndrome de Down. La media de edad se sitúa entorno a los 40 años y su nivel de retraso mental es medio. Se aplica de forma longitudinal en un período de dos años el test d’Aptituds Cognitives per a Deficiència del 65% (Castelló, Carrillo y Barnosell, 1996). Se utiliza un diseño factorial mixto de medidas repetidas controlando las variables etiología, edad cronológica, nivel de retraso mental y paso del tiempo. Resultados: se observa con el paso del tiempo, un declive cognitivo significativo de las personas con síndrome de Down de más de 38 años y nivel de retraso mental ligero respecto al grupo con discapacidad psíquica de referencia. Los indicadores cognitivos se sitúan preferentemente en las áreas de lenguaje y coordinación visomotora. Conclusiones: las personas con síndrome de Down de más de 38 años y nivel de retraso mental ligero manifiestan una probabilidad mayor de desarrollar un declive cognitivo relacionado con un probable deterioro propio de la demencia Alzheimer.

Online drawings for dementia diagnose: in-air and pressure information analysis

In this paper we present experimental results comparing on-line drawings for control population (left and right hand) as well as Alzheimer disease patients. The drawings have been acquired by means of a digitizing tablet, which acquires time information angles and pressures. Experimental measures based on pressure and in-air movements appear to be significantly different for both groups, even when control population performs the tasks with the non-dominant hand.

Gene Set Analysis for improving genetic association studies

Introduction. Genetic epidemiology is focused on the study of the genetic causes that determine health and diseases in populations. To achieve this goal a common strategy is to explore differences in genetic variability between diseased and nondiseased individuals. Usual markers of genetic variability are single nucleotide polymorphisms (SNPs) which are changes in just one base in the genome. The usual statistical approach in genetic epidemiology study is a marginal analysis, where each SNP is analyzed separately for association with the phenotype. Motivation. It has been observed, that for common diseases the single-SNP analysis is not very powerful for detecting genetic causing variants. In this work, we consider Gene Set Analysis (GSA) as an alternative to standard marginal association approaches. GSA aims to assess the overall association of a set of genetic variants with a phenotype and has the potential to detect subtle effects of variants in a gene or a pathway that might be missed when assessed individually. Objective. We present a new optimized implementation of a pair of gene set analysis methodologies for analyze the individual evidence of SNPs in biological pathways. We perform a simulation study for exploring the power of the proposed methodologies in a set of scenarios with different number of causal SNPs under different effect sizes. In addition, we compare the results with the usual single-SNP analysis method. Moreover, we show the advantage of using the proposed gene set approaches in the context of an Alzheimer disease case-control study where we explore the Reelin signal pathway.

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.

Synthesis and multi-target biological profiling of a novel family of rhein derivatives as disease-modifying anti-Alzheimer agents

We have synthesized a family of rheinhuprine hybrids to hit several key targets for Alzheimer"s disease. Biological screening performed in vitro and in Escherichia coli cells has shown that these hybrids exhibit potent inhibitory activities against human acetylcholinesterase butyrylcholinesterase, and BACE-1, dual Aβ42 and tau anti-aggregating activity, and brain permeability. Ex vivo studies with the leads (+)- and ()-7e in brain slices of C57bl6 mice have revealed that they efficiently protect against the Aβ-induced synaptic dysfunction , preventing the loss of synaptic proteins and/or have a positive effect on the induction of long term potentiation. In vivo studies in APP-PS1 transgenic mice treated i.p. for 4 weeks with (+)- and ()-7e have shown a central soluble Aβ lowering effect, accompanied by an increase in the levels of mature amyloid precursor protein (APP). Thus, (+)- and ()-7e emerge as very promising disease-modifying anti-Alzheimer drug candidates.

Synthesis and multi-target biological profiling of a novel family of rhein derivatives as disease-modifying anti-Alzheimer agents

We have synthesized a family of rheinhuprine hybrids to hit several key targets for Alzheimer"s disease. Biological screening performed in vitro and in Escherichia coli cells has shown that these hybrids exhibit potent inhibitory activities against human acetylcholinesterase butyrylcholinesterase, and BACE-1, dual Aβ42 and tau anti-aggregating activity, and brain permeability. Ex vivo studies with the leads (+)- and ()-7e in brain slices of C57bl6 mice have revealed that they efficiently protect against the Aβ-induced synaptic dysfunction , preventing the loss of synaptic proteins and/or have a positive effect on the induction of long term potentiation. In vivo studies in APP-PS1 transgenic mice treated i.p. for 4 weeks with (+)- and ()-7e have shown a central soluble Aβ lowering effect, accompanied by an increase in the levels of mature amyloid precursor protein (APP). Thus, (+)- and ()-7e emerge as very promising disease-modifying anti-Alzheimer drug candidates.

Rapid improvement of canine cognitive dysfunction with immunotherapy designed for Alzheimer's disease

Immunotherapy against amyloid-β(Aβ) may improve rodent cognitive function by reducing amyloid neuropathology and is being validated in clinical trials with positive preliminary results. However, for a complete understanding of the direct and long-term immunization responses in the aged patient, and also to avoid significant side effects, several key aspects remain to be clarified. Thus, to investigate brain Aβ clearance and Th2 responses in the elderly, and the reverse inflammatory events not found in the immunized rodent, better Alzheimer"s disease (AD) models are required. In the aged familiar canine with a Cognitive Dysfunction Syndrome (CDS) we describe the rapid effectiveness and the full safety profile of a new active vaccine candidate for human AD prevention and treatment. In these aged animals, besidesa weak immune system, the antibody response activated a coordinated central and peripheral Aβ clearance, that rapidly improved their cognitive function in absence of any side effects. Our results also confirm the interest to use familiar dogs to develop innovative and reliable therapies for AD.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

Dendritic Spine Abnormalities in Hippocampal CA1 Pyramidal Neurons Underlying Memory Deficits in the SAMP8 Mouse Model of Alzheimer's Disease

SAMP8 is a strain of mice with accelerated senescence. These mice have recently been the focus of attention as they show several alterations that have also been described in Alzheimer"s disease (AD) patients. The number of dendritic spines, spine plasticity, and morphology are basic to memory formation. In AD, the density of dendritic spines is severely decreased. We studied memory alterations using the object recognition test. We measured levels of synaptophysin as a marker of neurotransmission and used Golgi staining to quantify and characterize the number and morphology of dendritic spines in SAMP8 mice and in SAMR1 as control animals. While there were no memory differences at 3 months of age, the memory of both 6- and 9-month-old SAMP8 mice was impaired in comparison with age-matched SAMR1 mice or young SAMP8 mice. In addition, synaptophysin levels were not altered in young SAMP8 animals, but SAMP8 aged 6 and 9 months had less synaptophysin than SAMR1 controls and also less than 3-month-old SAMP8 mice. Moreover, while spine density remained stable with age in SAMR1 mice, the number of spines started to decrease in SAMP8 animals at 6 months, only to get worse at 9 months. Our results show that from 6 months onwards SAMP8 mice show impaired memory. This age coincides with that at which the levels of synaptophysin and spine density decrease. Thus, we conclude that together with other studies that describe several alterations at similar ages, SAMP8 mice are a very suitable model for studying AD.