Increased Delta and Theta Power Density in Sickle Cell Disease Individuals with Chronic Pain Secondary to Hip Osteonecrosis: A Resting-State Eeg Study.

PURPOSE: Identify the presence of a dysfunctional electroencephalographic (EEG) pattern in individuals with sickle cell disease (SCD) and hip osteonecrosis, and assess its potential associations with depression, anxiety, pain severity, and serum levels of brain-derived neurotrophic factor (BDNF). METHODS: In this cross-sectional investigation, 24 SCD patients with hip osteonecrosis and chronic pain were matched by age and sex with 19 healthy controls. Resting-state EEG data were recorded using 32 electrodes for both groups. Power spectral density (PSD) and peak alpha frequency (PAF) were computed for each electrode across Delta, Theta, Alpha, and Beta frequency bands. Current Source Density (CSD) measures were performed utilizing the built-in Statistical nonparametric Mapping Method of the LORETA-KEY software. RESULTS: Our findings demonstrated that SCD individuals exhibited higher PSD in delta and theta frequency bands when compared to healthy controls. Moreover, SCD individuals displayed increased CSD in delta and theta frequencies, coupled with decreased CSD in the alpha frequency within brain regions linked to pain processing, motor function, emotion, and attention. In comparison to the control group, depression symptoms, and pain intensity during hip abduction were positively correlated with PSD and CSD in the delta frequency within the parietal region. Depression symptoms also exhibited a positive association with PSD and CSD in the theta frequency within the same region, while serum BDNF levels showed a negative correlation with CSD in the alpha frequency within the left insula. CONCLUSION: This study indicates that individuals with SCD experiencing hip osteonecrosis and chronic pain manifest a dysfunctional EEG pattern characterized by the persistence of low-frequency PSD during a resting state. This dysfunctional EEG pattern may be linked to clinical and biochemical outcomes, including depression symptoms, pain severity during movement, and serum BDNF levels.

Effects of Functional-Task Training on Older Adults With Alzheimer's Disease.

The aim of this study was to verify the effects of functional-task training on cognitive function, activities of daily living (ADL) performance, and functional fitness in community-dwelling older adults with diagnosis of Alzheimer's disease (AD). A total of 57 participants (22 functional-task training group [FTG], 21 social gathering group [SGG], 14 control group [CG]) were recruited. Participants in both intervention groups carried out three 1-hr sessions per week of a functional-task program and social gathering activities for 12 weeks. Significant improvements were observed in executive functions (TMT, t-test, p = .03) in the SGG and in upper limb strength (arm curl, t-test, p = .01) in the FTG. Functional-task training has no significant effect on cognitive function, ADL, and functional fitness among people with AD, although it may contribute to slowing down the process of deterioration this illness causes.

Effects of physical activity on the P300 component in elderly people: a systematic review.

The effects of physical activity on brain function can be assessed through event-related potentials (P300) that reflect cortical activities related to cognitive functions. P300 latency represents the information processing time; longer latencies represent slower processing. P300 amplitude is associated with the attentional system and working memory, with higher amplitudes representing more preserved functions. This systematic review summarizes the literature concerning the effects of physical activity and exercise on P300 in the elderly. Databases, including Web of Science, Scopus, PsycINFO, MEDLINE/PubMed, and Biological Abstracts, were searched for articles up to November 2015. Articles were considered for inclusion if they were studies of the elderly, assessed P300, and evaluated the influence of physical activity on P300 or the effect of physical exercise training on P300. Of the 1227 articles found, 14 investigations matched the inclusion criteria. Nine analyzed the influence of physical activity on P300 in the elderly, and five examined the effects of physical exercise on P300 in the elderly. The obtained results showed that physically active elderly people have shortened P300 latency and higher amplitude. Physical exercise, especially those involving aerobic or resistance training, seems to have marked beneficial effects on P300 in the elderly. Evidence shows that physical activity and physical exercise positively influence cortical activities related to cognitive functions, as indicated by P300, in elderly people.

Comparing methods for determining motor-hand lateralization based on fTCD signals.

The lateralization index (LI) as determined from functional transcranial Doppler sonography (fTCD) can be used to determine the hemispheric organization of neural activation during a behavioral task. Previous studies have proposed different methods to determine this index, but to our knowledge no studies have compared the performance of these methods. In this study, we compare two established methods with a simpler method proposed here. The aim was to see whether similar results could be achieved with a simpler method and to give an indication of the analysis steps required to determine the LI. A simple unimanual motor task was performed while fTCD was acquired, and the LI determined by each of these methods was compared. In addition, LI determined by each method was related to behavioural output in the form of degree of handedness. The results suggest that although the methods differed in complexity, they yielded similar results when determining the lateralization of motor functions, and its correlation with behavior. Further investigation is needed to expand the conclusions of this preliminary study, however the new method proposed in the paper has great potential as it is much simpler than the more established methods yet yields similar results.

Diagnosis support of major depressive disorder using event-related potentials during affective priming tasks.

Major Depressive Disorder (MDD) is a global problem. Currently, the most common diagnosis is based on criteria susceptible to the subjectivity of the patient and the clinician. A possible solution to this problem is to look for diagnostic biomarkers that can accurately and early detect this mental condition. Some researchers have focused on electroencephalogram (EEG) analysis to identify biomarkers. In this study we used a dataset composed of EEG recordings from 24 subjects with MDD and 29 healthy controls (HC), during the execution of affective priming tasks with three different emotional stimuli (images): fear, sadness, and happiness. We investigated abnormalities in depressed patients using a novel technique, by directly comparing Event-Related Potential (ERP) waveforms to find statistically significant differences between the MMD and HC groups. Compared to the control group (healthy subjects), we found out that for the emotions fear and happiness there is a decrease in cortical activity at temporal regions in MDD patients. Just the opposite, for the emotion sadness, an increase in MDD brain activity occurs in frontal and occipital regions. Our findings suggest that emotions regulate the attentional control of cognitive processing and are promising for clinical application in diagnosing patients with MDD more objectively.

Comparison of subject-independent and subject-specific EEG-based BCI using LDA and SVM classifiers.

Motor imagery brain-computer interface (MI-BCI) is one of the most used paradigms in EEG-based brain-computer interface (BCI). The current state-of-the-art in BCI involves tuning classifiers to subject-specific training data, acquired over several sessions, in order to perform calibration prior to actual use of the so-called subject-specific BCI system (SS-BCI). Herein, the goal is to provide a ready-to-use system requiring minimal effort for setup. Thus, our challenge was to design a subject-independent BCI (SI-BCI) to be used by any new user without the constraint of individual calibration. Outcomes from other studies with the same purpose were used to undertake comparisons and validate our findings. For the EEG signal processing, we used a combination of the delta (0.5-4 Hz), alpha (8-13 Hz), and beta+gamma (13-40 Hz) bands at a stage prior to feature extraction. Next, we extracted features from the 27-channel EEG using common spatial pattern (CSP) and performed binary classification (MI of right- and left-hand) with linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. These analyses were done for both the SS-BCI and SI-BCI models. We employed "leave-one-subject-out" (LOSO) arrangement and 10-fold cross-validation to evaluate our SI-BCI and SS-BCI systems, respectively. Compared with other two studies, our work was the only one that showed higher accuracy for the LDA classifier in SI-BCI as compared to SS-BCI. On the other hand, LDA accuracy was lower than accuracy achieved with SVM in both conditions (SI-BCI and SS-BCI). Our SS-BCI accuracy reached 76.85% using LDA and 94.20% using SVM and for SI-BCI we got 80.30% with LDA and 83.23% with SVM. We conclude that SI-BCI may be a feasible and relevant option, which can be used in scenarios where subjects are not able to submit themselves to long training sessions or to fast evaluation of the so called "BCI illiteracy." Comparatively, our strategy proved to be more efficient, giving us the best result for SI-BCI when faced against the classification performances of other three studies, even considering the caveat that different datasets were used in the comparison of the four studies.

Association between perceived social support and better cognitive performance among caregivers and non-caregivers.

OBJECTIVE: to identify the association between the characteristics of the support network and cognitive performance of older caregivers and compare characteristics between caregivers and non-caregivers. METHODS: we evaluated 85 older caregivers and 84 older non-caregivers registered with primary care units regarding sociodemographic characteristics, cognition, and social support. Multiple linear regression analysis was performed. RESULTS: among non-caregivers, significant associations were found between a better cognitive performance and receiving emotional/affectionate support; each one-point increase in the emotional support score and affectionate support score was related to a 0.43-point and 0.39-point increase in cognitive assessment, respectively. Among older caregivers, each one-point increase in the emotional support score was related to a 0.55-point increase in cognitive assessment. CONCLUSION: strengthening the support networks of older caregivers and encouraging satisfactory exchanges of social support can assist in improving cognitive performance, which can have a positive impact on caregivers' health.

Classification of Patients with Alzheimer's Disease and Dementia with Lewy Bodies using Resting EEG Selected Features at Sensor and Source Levels: A Proof-of-Concept Study.

BACKGROUND: Early differentiation between Alzheimer's disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. OBJECTIVE: This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. METHODS: Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). RESULTS: The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. CONCLUSION: These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques.

Resting-state posterior alpha rhythms are abnormal in subjective memory complaint seniors with preclinical Alzheimer's neuropathology and high education level: the INSIGHT-preAD study.

Cognitive reserve is present in Alzheimer's disease (AD) seniors with high education attainment making them clinically resilient to extended brain neuropathology and neurodegeneration. Here, we tested whether subjective memory complaint (SMC) seniors with AD neuropathology and high education attainment of the prospective INSIGHT-preAD cohort (Paris) may present abnormal eyes-closed resting state posterior electroencephalographic rhythms around individual alpha frequency peak, typically altered in AD patients. The SMC participants negative to amyloid PET AD markers (SMCneg) with high (over low-moderate) education level showed higher posterior alpha 2 power density (possibly "neuroprotective"). Furthermore, amyloid PET-positive SMC (SMCpos) participants with high (over low-moderate) education level showed higher temporal alpha 3 power density (possibly "neuroprotective") and lower posterior alpha 2 power density (possibly "compensatory"). This effect may reflect cognitive reserve as no differences in brain gray-white matter, and cognitive functions were observed between these SMCpos/SMCneg subgroups. Preclinical Alzheimer's neuropathology may interact with education attainment and neurophysiological mechanisms generating cortical alpha rhythms around individual alpha frequency peak (i.e., alpha 2 and 3) in quiet wakefulness.

Shipbuilding 4.0 Index Approaching Supply Chain.

The shipbuilding industry shows a special interest in adapting to the changes proposed by the industry 4.0. This article bets on the development of an index that indicates the current situation considering that supply chain is a key factor in any type of change, and at the same time it serves as a control tool in the implementation of improvements. The proposed indices provide a first definition of the paradigm or paradigms that best fit the supply chain in order to improve its sustainability and a second definition, regarding the key enabling technologies for Industry 4.0. The values obtained put shipbuilding on the road to industry 4.0 while suggesting categorized planning of technologies.

Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment.

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

Early diagnosis of mild cognitive impairment and Alzheimer's with event-related potentials and event-related desynchronization in N-back working memory tasks.

BACKGROUND AND OBJECTIVE: In this study we investigate whether or not event-related potentials (ERP) and/or event-related (de)synchronization (ERD/ERS) can be used to differentiate between 27 healthy elderly (HE), 21 subjects diagnosed with mild cognitive impairment (MCI) and 15 mild Alzheimer's disease (AD) patients. METHODS: Using 32-channel EEG recordings, we measured ERP responses to a three-level (N-back, N = 0,1,2) visual working memory task. We also performed ERD analysis over the same EEG data, dividing the full-band signal into the well-known delta, theta, alpha, beta and gamma bands. Both ERP and ERD analyses were followed by cluster analysis with correction for multicomparisons whenever significant differences were found between groups. RESULTS: Regarding ERP (full-band analysis), our findings have shown both patient groups (MCI and AD) with reduced P450 amplitude (compared to HE controls) in the execution of the non-match 1-back task at many scalp electrodes, chiefly at parietal and centro-parietal areas. However, no significant differences were found between MCI and AD in ERP analysis whatever was the task. As for sub-band analyses, ERD/ERS measures revealed that HE subjects elicited consistently greater alpha ERD responses than MCI and AD patients during the 1-back task in the match condition, with all differences located at frontal, central and occipital regions. Moreover, in the non-match condition, it was possible to distinguish between MCI and AD patients when they were performing the 0-back task, with MCI presenting more desynchronization than AD on the theta band at temporal and fronto-temporal areas. In summary, ERD analyses have revealed themselves more valuable than ERP, since they showed significant differences in all three group comparisons: HE vs. MCI, HE vs. AD, and MCI vs. AD. CONCLUSIONS: Based on these findings, we conclude that ERD responses to working memory (N-back) tasks could be useful not only for early MCI diagnosis or for improved AD diagnosis, but probably also for assessing the likelihood of MCI progression to AD, after further validated by a longitudinal study.

Effects of Physical Exercise on the P300 of Elderly With Alzheimer's Disease.

BACKGROUND: Evidence regarding the benefits of physical activity on the mental processing information of patients with Alzheimer's disease assessed objectively is scarce and can be observed through event-related potentials, such as the P300. The aim of the study was to identify the effects of physical exercises on mental processing information in the elderly with Alzheimer's disease through neurophysiological measures (P300 amplitude and latency) and reaction time. METHODS: A total of 31 patients with Alzheimer's disease participated in this study: 14 in functional exercise (FE) group and 17 in social gathering (SG) group who carried out three 1-hour sessions per week of FE and SG activities, respectively, for a 12-week period. All groups performed an auditory oddball task. A healthy elderly control group also participated. RESULTS: Significant (P < .05) improvements were observed as a reduction of reaction time after intervention in the FE group (pre = 421.5 ms and post = 360.9 ms). Also, an increase of P300 amplitude at central midline (pre = 5.9 µV and post = 6.9 µV) and parietal midline (pre = 4.7 µV and post = 5.7 µV) was observed in the FE. Finally, a decrease in the P300 latency at frontal midline (pre = 377 ms and post = 367 ms) was observed in the SG after the intervention. CONCLUSION: Physical exercise decreases reaction time and suggests a recovery in cortical activity, whereas SG activities could probably facilitate information processing.

EEG epochs with less alpha rhythm improve discrimination of mild Alzheimer's.

BACKGROUND AND OBJECTIVE: Eyes-closed-awake electroencephalogram (EEG) is a useful tool in the diagnosis of Alzheimer's. However, there is eyes-closed-awake EEG with dominant or rare alpha rhythm. In this paper, we show that random selection of EEG epochs disregarding the alpha rhythm will lead to bias concerning EEG-based Alzheimer's Disease diagnosis. METHODS: We compared EEG epochs with more than 30% and with less than 30% alpha rhythm of mild Alzheimer's Disease patients and healthy elderly. We classified epochs as dominant alpha scenario and rare alpha scenario according to alpha rhythm (8-13 Hz) percentage in O1, O2 and Oz channels. Accordingly, we divided the probands into four groups: 17 dominant alpha scenario controls, 15 mild Alzheimer's patients with dominant alpha scenario epochs, 12 rare alpha scenario healthy elderly and 15 mild Alzheimer's Disease patients with rare alpha scenario epochs. We looked for group differences using one-way ANOVA tests followed by post-hoc multiple comparisons (p < 0.05) over normalized energy values (%) on the other four well-known frequency bands (delta, theta, beta and gamma) using two different electrode configurations (parieto-occipital and central). RESULTS: After carrying out post-hoc multiple comparisons, for both electrode configurations we found significant differences between mild Alzheimer's patients and healthy elderly on beta- and theta-energy (%) only for the rare alpha scenario. No differences were found for the dominant alpha scenario in any of the five frequency bands. CONCLUSIONS: This is the first study of Alzheimer's awake-EEG reporting the influence of alpha rhythm on epoch selection, where our results revealed that, contrarily to what was most likely expected, less synchronized EEG epochs (rare alpha scenario) better discriminated mild Alzheimer's than those presenting abundant alpha (dominant alpha scenario). In addition, we find out that epoch selection is a very sensitive issue in qEEG research. Consequently, for Alzheimer's studies dealing with resting state EEG, we propose that epoch selection strategies should always be cautiously designed and thoroughly explained.

Association between psychosocial and functional factors and language and verbal fluency performance in older adults / Associação entre fatores psicossociais e funcionais e desempenho de idosos em linguagem e fluência verbal

ABSTRACT Purpose: to identify psychosocial and functional factors associated with language and verbal fluency performance in older adults. Methods: a study conducted with 149 older adults registered in a primary health care service in a city in inland São Paulo. Instruments such as a sociodemographic questionnaire, the Addenbrooke Cognitive Examination-Revised (verbal fluency and language domains), Geriatric Depression Scale, Perceived Stress Scale, Medical Outcomes Study, WHOQOL-OLD, and Instrumental Activities of Daily Living Scale, were used. Participants were divided into tertiles, according to their language and verbal fluency performance: T1 = best performance, T2 = median performance, and T3 = worst performance. Groups T1 and T3 were compared, and a binary regression was conducted to analyze the factors associated with the best language and verbal fluency performance. Results: higher educational attainment, higher income, and better functioning were the factors associated with the best language performance comparing T1 and T3, while higher educational attainment and better functioning were associated with the best verbal fluency. Conclusion: educational attainment and functioning proved to be important factors associated with good language and verbal fluency performance, while income was associated only with good language performance.

RESUMO Objetivo: identificar fatores psicossociais e funcionais associados ao desempenho de idosos em linguagem e fluência verbal. Métodos: estudo realizado com 149 idosos cadastrados no serviço de atenção primária à saúde de uma cidade do interior de São Paulo. Foram utilizados um questionário de caracterização sociodemográfica, Exame Cognitivo de Addenbrooke - Revisado (domínios de fluência verbal e de linguagem), Escala de Depressão Geriátrica, Escala de Estresse Percebido, Medical Outcomes Study, WHOQOL-OLD e Escala de Atividades Instrumentais da Vida Diária. Os participantes foram divididos em tercis de acordo com o desempenho em linguagem e fluência verbal: T1 = melhor desempenho, T2 = desempenho mediano e T3 = pior desempenho. Foram comparados os grupos T1 e T3 e realizada uma regressão binária para analisar os fatores associados ao melhor desempenho em linguagem e fluência verbal. Resultados: as variáveis associadas ao melhor desempenho de linguagem comparando T1 e T3 foram: maior escolaridade, maior renda e melhor funcionalidade. Para a melhor fluência verbal, os fatores associados foram: maior escolaridade e melhor funcionalidade. Conclusão: a escolaridade e a funcionalidade mostraram-se importantes fatores associados ao bom desempenho da linguagem e da fluência verbal, enquanto a renda mostrou-se associada apenas ao bom desempenho de linguagem.

EEG epoch selection: lack of alpha rhythm improves discrimination of Alzheimer's disease.

In this work we propose a detailed EEG epoch selection method and compare epochs with rare and abundant alpha rhythm (AR) of patients with Alzheimer's disease (AD) and normal controls. Epochs were classified as Dominant Alpha Scenario (DAS) and Rare Alpha Scenario (RAS) according to the AR percentage (energy within the 8-13 Hz bandwidth) in O1, O2 and Oz electrodes. Participants were divided into four groups: 17 DAS controls (N1), 15 DAS mild-AD patients (AD1), 12 RAS controls (N2) and 15 RAS mild-AD patients (AD2). We found out that scenario factor (DAS vs. RAS, two-way ANOVA) is significant over a great amount of electrode-bandwidth situations. Furthermore, one-way ANOVA showed significant differences between RAS AD and RAS controls in much more situations as compared to DAS. This is the first study using AD awake EEG reporting the decisive influence of alpha rhythm on epoch selection, where our results revealed that, contrary to what was initially expected, EEG epochs with poor alpha (RAS) discriminate mild AD much better than those presenting richer alpha content (DAS).

Simultaneous evaluation of prepulse inhibition with EMG and EEG using advanced artifact removal techniques.

Prepulse inhibition (PPI) consists of a reduction of the acoustic startle reflex (SR) magnitude (measured with EMG) when a startling stimulus is preceded by a non-startling one. This behavior has been extensively investigated in studies related to schizophrenia, since sensory-motor deficit plays a central role in its pathophysiology. However, the same auditory stimuli that trigger the SR also provoke intense auditory evoked responses (AEP), which can be measured with EEG. Comparing these two types of responses, acquired simultaneously, is a great opportunity to investigate the dependence and interdependence of their neural pathways. Nonetheless, so far very few studies have dared to perform such simultaneous recordings, because SR produces strong eye blinks and muscle contraction artifacts that contaminate EEG electrodes placed on the scalp. In this study we investigated the possibility of simultaneously obtaining both the acoustic SR (using EMG) and the AEP (using EEG) measures, through the use of advanced artifact removal techniques, to better characterize PPI in healthy humans.

Association between perceived social support and better cognitive performance among caregivers and non-caregivers / Associação entre suporte social percebido e melhor desempenho cognitivo entre cuidadores e não cuidadores / Asociación entre apoyo social percibido y mejor desempeño cognitivo entre cuidadores y no cuidadores

ABSTRACT Objective: to identify the association between the characteristics of the support network and cognitive performance of older caregivers and compare characteristics between caregivers and non-caregivers. Methods: we evaluated 85 older caregivers and 84 older non-caregivers registered with primary care units regarding sociodemographic characteristics, cognition, and social support. Multiple linear regression analysis was performed. Results: among non-caregivers, significant associations were found between a better cognitive performance and receiving emotional/affectionate support; each one-point increase in the emotional support score and affectionate support score was related to a 0.43-point and 0.39-point increase in cognitive assessment, respectively. Among older caregivers, each one-point increase in the emotional support score was related to a 0.55-point increase in cognitive assessment. Conclusion: strengthening the support networks of older caregivers and encouraging satisfactory exchanges of social support can assist in improving cognitive performance, which can have a positive impact on caregivers' health.

RESUMO Objetivo: identificar a associação entre as características da rede de apoio e o desempenho cognitivo de cuidadores idosos e comparar características entre cuidadores e não cuidadores. Método: foram avaliados 85 idosos cuidadores e 84 idosos não cuidadores cadastrados em unidades da atenção primária de saúde quanto às características sociodemográficas, cognitivas e suporte social. Foi realizada análise de regressão linear múltipla. Resultados: entre os não cuidadores, foram encontradas associações significativas entre melhor desempenho cognitivo e receber apoio emocional/afetivo; cada aumento de um ponto no escore de apoio emocional e escore de suporte afetivo foi relacionado a um aumento de 0,43 pontos e 0,39 pontos na avaliação cognitiva, respectivamente. Entre os cuidadores mais velhos, cada aumento de um ponto na pontuação do apoio emocional teve relação com um aumento de 0,55 ponto na avaliação cognitiva. Conclusão: o fortalecimento das redes de apoio de cuidadores mais velhos e o incentivo a trocas satisfatórias de apoio social podem auxiliar na melhoria do desempenho cognitivo, o que pode impactar positivamente na saúde dos cuidadores.

RESUMEN Objetivo: identificar la asociación entre las características de la red de apoyo y el desempeño cognitivo de los cuidadores mayores y comparar características entre cuidadores y no cuidadores. Métodos: se evaluaron 85 cuidadores mayores y 84 no cuidadores mayores registrados en unidades de atención primaria en cuanto a características sociodemográficas, cognitivas y de apoyo social. Se realizó un análisis de regresión lineal múltiple. Resultados: entre los no cuidadores, se encontraron asociaciones significativas entre un mejor desempeño cognitivo y recibir apoyo emocional/afectivo; cada aumento de un punto en la puntuación de apoyo emocional y la puntuación de apoyo afectivo se relacionó con un aumento de 0,43 puntos y 0,39 puntos en la evaluación cognitiva, respectivamente. Entre los cuidadores mayores, cada aumento de un punto en la puntuación de apoyo emocional se relacionó con un aumento de 0,55 puntos en la evaluación cognitiva. Conclusión: el fortalecimiento de las redes de apoyo para los cuidadores mayores y el fomento de intercambios satisfactorios de apoyo social puede ayudar a mejorar el rendimiento cognitivo, lo que puede tener un impacto positivo en la salud de los cuidadores.

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis.

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system "semi-automated." Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.

Clinician's road map to wavelet EEG as an Alzheimer's disease biomarker.

Alzheimer's disease (AD) is considered the main cause of dementia in Western countries. Consequently, there is a need for an accurate, universal, specific and cost-effective biomarker for early AD diagnosis, to follow disease progression and therapy response. This article describes a new diagnostic approach to quantitative electroencephalogram (QEEG) diagnosis of mild and moderate AD. The data set used in this study was composed of EEG signals recorded from 2 groups: (S1) 74 normal subjects, 33 females and 41 males (mean age 67 years, standard deviation = 8) and (S2) 88 probable AD patients (NINCDS-ADRDA criteria), 55 females and 33 males (mean age 74.7 years, standard deviation = 7.8) with mild to moderate symptoms (DSM-IV-TR). Attention is given to sample size and the use of state of the art open source tools (LetsWave and WEKA) to process the EEG data. This innovative technique consists in associating Morlet wavelet filter with a support vector machine technique. A total of 111 EEG features (attributes) were obtained for 162 probands. The results were accuracy of 92.72% and area under the curve of 0.92 (percentage split test). Most important, comparing a single patient versus the total data set resulted in accuracy of 84.56% (leave-one-patient-out test). Particular emphasis was on clinical diagnosis and feasibility of implementation of this low-cost procedure, because programming knowledge is not required. Consequently, this new method can be useful to support AD diagnosis in resource-limited settings.