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

Estradiol regulates brown adipose tissue thermogenesis via hypothalamic AMPK

Copyright © 2014 Elsevier Inc. All rights reserved.Peer reviewedPublisher PD

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

The impact of silent vascular brain burden in cognitive impairment in Parkinson's disease

White matter hyperintensities (WMHs) detected by magnetic resonance imaging (MRI) of the brain are associated with dementia and cognitive impairment in the general population and in Alzheimer's disease. Their effect in cognitive decline and dementia associated with Parkinson's disease (PD) is still unclear. METHODS: We studied the relationship between WMHs and cognitive state in 111 patients with PD classified as cognitively normal (n = 39), with a mild cognitive impairment (MCI) (n = 46) or dementia (n = 26), in a cross-sectional and follow-up study. Cognitive state was evaluated with a comprehensive neuropsychological battery, and WMHs were identified in FLAIR and T2-weighted MRI. The burden of WMHs was rated using the Scheltens scale. RESULTS: No differences in WMHs were found between the three groups in the cross-sectional study. A negative correlation was observed between semantic fluency and the subscore for WMHs in the frontal lobe. Of the 36 non-demented patients re-evaluated after a mean follow-up of 30 months, three patients converted into MCI and 5 into dementia. Progression of periventricular WMHs was associated with an increased conversion to dementia. A marginal association between the increase in total WMHs burden and worsening in the Mini Mental State Examination was encountered. CONCLUSIONS: White matter hyperintensities do not influence the cognitive status of patients with PD. Frontal WMHs have a negative impact on semantic fluency. Brain vascular burden may have an effect on cognitive impairment in patients with PD as WMHs increase overtime might increase the risk of conversion to dementia. This finding needs further confirmation in larger prospective studies

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

In vivo CRISPR/Cas9 targeting of fusion oncogenes for selective elimination of cancer cells

This work was supported by CaixaImpulse (CI18-00017;FuGe) to S.R-P. RT-R. is supported by a postdoctoral fellowship from the Asociación Española Contra el Cáncer (AECC). J.C.S. is supported by the Spanish Cell Therapy cooperative research network (TERCEL)(RD16/0011/0011). P.M. also acknowledges the financial support from the Obra Social La Caixa-Fundaciò Josep Carreras. P.M. is an investigator of the Spanish Cell Therapy cooperative research network (TERCEL). A.M.C. acknowledges funding fromXarxa de Bancs de Tumors de Catalunya (XBTC; sponsored by Pla Director d'Oncologia de Catalunya).Fusion oncogenes (FOs) are common in many cancer types and are powerful drivers of tumor development. Because their expression is exclusive to cancer cells and their elimination induces cell apoptosis in FO-driven cancers, FOs are attractive therapeutic targets. However, specifically targeting the resulting chimeric products is challenging. Based on CRISPR/Cas9 technology, here we devise a simple, efficient and non-patient-specific gene-editing strategy through targeting of two introns of the genes involved in the rearrangement, allowing for robust disruption of the FO specifically in cancer cells. As a proof-of-concept of its potential, we demonstrate the efficacy of intron-based targeting of transcription factors or tyrosine kinase FOs in reducing tumor burden/mortality in in vivo models. The FO targeting approach presented here might open new horizons for the selective elimination of cancer cells

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

Amyloid-β accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer's disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently, only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer's disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer's Dementia cohort. Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex, and education level. Lower static centrality was found in A+ participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A+ participants in the frontal cluster. The default mode network and the dorsal visual networks of A+ participants had lower dynamic eigenvector centrality variability. Centrality variability in the default mode network and dorsal visual networks were associated with cognitive performance in the A- and A+ groups, with lower variability being observed in A+ participants with good cognitive scores. Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer's disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer's disease pathology and cognitive decline

CSF Proteomic Alzheimer's Disease-Predictive Subtypes in Cognitively Intact Amyloid Negative Individuals.

We recently discovered three distinct pathophysiological subtypes in Alzheimer's disease (AD) using cerebrospinal fluid (CSF) proteomics: one with neuronal hyperplasticity, a second with innate immune system activation, and a third subtype with blood-brain barrier dysfunction. It remains unclear whether AD proteomic subtype profiles are a consequence of amyloid aggregation, or might exist upstream from aggregated amyloid. We studied this question in 127 older individuals with intact cognition and normal AD biomarkers in two independent cohorts (EMIF-AD MBD and ADNI). We clustered 705 proteins measured in CSF that were previously related to AD. We identified in these cognitively intact individuals without AD pathology three subtypes: two subtypes were seen in both cohorts (n = 49 with neuronal hyperplasticity and n = 44 with blood-brain barrier dysfunction), and one only in ADNI (n = 12 with innate immune activation). The proteins specific for these subtypes strongly overlapped with AD subtype protein profiles (overlap coefficients 92%-71%). Longitudinal p <sub>181</sub> -tau and amyloid β 1-42 (Aβ42) CSF analysis showed that in the hyperplasticity subtype p <sub>181</sub> -tau increased (β = 2.6 pg/mL per year, p = 0.01) and Aβ42 decreased over time (β = -4.4 pg/mL per year, p = 0.03), in the innate immune activation subtype p <sub>181</sub> -tau increased (β = 3.1 pg/mL per year, p = 0.01) while in the blood-brain barrier dysfunction subtype Aβ42 decreased (β = -3.7 pg/mL per year, p = 0.009). These findings suggest that AD proteomic subtypes might already manifest in cognitively normal individuals and may predispose for AD before amyloid has reached abnormal levels

Effects of age, amyloid, sex, and APOE ε4 on the CSF proteome in normal cognition

Introduction: It is important to understand which biological processes change with aging, and how such changes are associated with increased Alzheimer's disease (AD) risk. We studied how cerebrospinal fluid (CSF) proteomics changed with age and tested if associations depended on amyloid status, sex, and apolipoprotein E Ɛ4 genotype. Methods: We included 277 cognitively intact individuals aged 46 to 89 years from Alzheimer's Disease Neuroimaging Initiative, European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery, and Metabolic Syndrome in Men. In total, 1149 proteins were measured with liquid chromatography mass spectrometry with multiple reaction monitoring/Rules-Based Medicine, tandem mass tag mass spectrometry, and SOMAscan. We tested associations between age and protein levels in linear models and tested enrichment for Reactome pathways. Results: Levels of 252 proteins increased with age independently of amyloid status. These proteins were associated with immune and signaling processes. Levels of 21 proteins decreased with older age exclusively in amyloid abnormal participants and these were enriched for extracellular matrix organization. Discussion: We found amyloid-independent and -dependent CSF proteome changes with older age, perhaps representing physiological aging and early AD pathology

Feature selection for spontaneous speech analysis to aid in Alzheimer’s disease diagnosis: A fractal dimension approach

Alzheimer’s disease (AD) is the most prevalent form of degenerative dementia; it has a high socio-economic impact in Westerncountries. The purpose of our project is to contribute to earlier diagnosis of AD and allow better estimates of its severity by usingautomatic analysis performed through new biomarkers extracted through non-invasive intelligent methods. The method selectedis based on speech biomarkers derived from the analysis of spontaneous speech (SS). Thus the main goal of the present work isfeature search in SS, aiming at pre-clinical evaluation whose results can be used to select appropriate tests for AD diagnosis. Thefeature set employed in our earlier work offered some hopeful conclusions but failed to capture the nonlinear dynamics of speechthat are present in the speech waveforms. The extra information provided by the nonlinear features could be especially useful whentraining data is limited. In this work, the fractal dimension (FD) of the observed time series is combined with linear parameters inthe feature vector in order to enhance the performance of the original system while controlling the computational cost.© 2014 Elsevier Ltd. All rights reserved