An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography.

Early detection is crucial to control the progression of Alzheimer's disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.

A self-driven approach for multi-class discrimination in Alzheimer's disease based on wearable EEG.

Early detection is critical to control Alzheimer's disease (AD) progression and postpone cognitive decline. Traditional medical procedures such as magnetic resonance imaging are costly, involve long waiting lists, and require complex analysis. Alternatively, for the past years, researchers have successfully evaluated AD detection approaches based on machine learning and electroencephalography (EEG). Nonetheless, these approaches frequently rely upon manual processing or involve non-portable EEG hardware. These aspects are suboptimal regarding automated diagnosis, since they require additional personnel and hinder portability. In this work, we report the preliminary evaluation of a self-driven AD multi-class discrimination approach based on a commercial EEG acquisition system using sixteen channels. For this purpose, we recorded the EEG of three groups of participants: mild AD, mild cognitive impairment (MCI) non-AD, and controls, and we implemented a self-driven analysis pipeline to discriminate the three groups. First, we applied automated artifact rejection algorithms to the EEG recordings. Then, we extracted power, entropy, and complexity features from the preprocessed epochs. Finally, we evaluated a multi-class classification problem using a multi-layer perceptron through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best in literature (0.88 F1-score), what suggests that AD can potentially be detected through a self-driven approach based on commercial EEG and machine learning. We believe this work and further research could contribute to opening the door for the detection of AD in a single consultation session, therefore reducing the costs associated to AD screening and potentially advancing medical treatment.

The cognitive and psychiatric subacute impairment in severe Covid-19.

Neurologic impairment persisting months after acute severe SARS-CoV-2 infection has been described because of several pathogenic mechanisms, including persistent systemic inflammation. The objective of this study is to analyze the selective involvement of the different cognitive domains and the existence of related biomarkers. Cross-sectional multicentric study of patients who survived severe infection with SARS-CoV-2 consecutively recruited between 90 and 120 days after hospital discharge. All patients underwent an exhaustive study of cognitive functions as well as plasma determination of pro-inflammatory, neurotrophic factors and light-chain neurofilaments. A principal component analysis extracted the main independent characteristics of the syndrome. 152 patients were recruited. The results of our study preferential involvement of episodic and working memory, executive functions, and attention and relatively less affectation of other cortical functions. In addition, anxiety and depression pictures are constant in our cohort. Several plasma chemokines concentrations were elevated compared with both, a non-SARS-Cov2 infected cohort of neurological outpatients or a control healthy general population. Severe Covid-19 patients can develop an amnesic and dysexecutive syndrome with neuropsychiatric manifestations. We do not know if the deficits detected can persist in the long term and if this can trigger or accelerate the onset of neurodegenerative diseases.

PET-Amyloid After Inconclusive Cerebrospinal Fluid Biomarkers in Clinical Practice. Is it Necessary to Duplicate Procedures?

INTRODUCTION: In the absence of a gold standard for in vivo Alzheimer disease (AD) diagnosis, AD biomarkers such as cerebrospinal fluid biomarkers (CSF-B) and PET-Amyloid are considered diagnostically useful in clinical practice guidelines and have consensual appropriate use criteria (AUC). However, little evidence has been published on their utilization in the clinical setting or on approaches to mismatched results. The objective of this work was to evaluate the use of AD biomarkers in clinical practice, focusing on the implementation of PET-Amyloid in cases of inconclusive CSF-B. METHODS: This naturalistic, ambispective case series included patients fulfilling AUC for CSF-B and PET-Amyloid whose CSF-B results were non-diagnostic (target population), analyzing the diagnostic certainty, the treatment approach, and the relationship between CSF-B and PET-Amyloid results. RESULTS: Out of 2373 eligible patients, AD biomarkers were studied in 417 (17.6%), most frequently due to cognitive impairment in under 65-year-olds, using CSF-B in 311 patients and PET-Amyloid in 150. CSF-B results were non-diagnostic for 44 patients (52.3% male; aged 60.9±6.6 years), who then underwent PET-Amyloid study, which was positive in 31. A 'k' coefficient of 0.108 was obtained between CSF-B and PET-amyloid (54.5% concordance). In multivariate regression analysis, Aß42 was the only significant predictor (p= 0.018) of a positive PET-Amyloid result. In the target population, PETAmyloid increased diagnostic confidence by 53.7% (p <0.001) and modified the therapeutic approach in 36.4% of cases. CONCLUSION: These findings support the duplication of AD biomarkers and demonstrate that the implementation of PET-Amyloid provides an early and certain diagnosis to guide appropriate treatment.