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The roles of choline and of choline-containing phospholipids (CCPLs) on the maintenance and progress of neurovascular unit (NVU) integrity are analyzed. NVU is composed of neurons, glial and vascular cells ensuring the correct homeostasis of the blood-brain barrier (BBB) and indirectly the function of the central nervous system. The CCPLs phosphatidylcholine (lecithin), cytidine 50-diphosphocholine (CDP-choline), choline alphoscerate or α-glyceryl-phosphorylcholine (α-GPC) contribute to the modulation of the physiology of the NVU cells. A loss of CCPLs contributes to the development of neurodegenerative diseases such as Alzheimer’s disease, multiple sclerosis, Parkinson’s disease. Our study has characterized the cellular components of the NVU and has reviewed the effect of lecithin, of CDP-choline and αGPC documented in preclinical studies and in limited clinical trials on these compounds. The interesting results obtained with some CCPLs, in particular with α-GPC, probably would justify reconsideration of the most promising molecules in larger attentively controlled studies. This can also contribute to better define the role of the NVU in the pathophysiology of brain disorders characterized by vascular impairment. [ABSTRACT FROM AUTHOR] more...

Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score ≥ 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer's disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age. [ABSTRACT FROM AUTHOR] more...

Background: It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia.Objective: This study introduces a new CADS for deep learning of magnetic resonance image (MRI) data to identify changes in the brain during Alzheimer's disease (AD) dementia.Methods: The proposed algorithm employed a decision tree with genetic algorithm rule-based optimization to classify input data which were extracted from MRI. This pipeline is applied to the healthy and AD subjects of the Open Access Series of Imaging Studies (OASIS).Results: Final evaluation of the CADS and its comparison with other systems supported the potential of the proposed model as a novel tool for investigating the progression of AD and its great ability as an innovative computerized help to facilitate the decision-making procedure for the diagnosis of AD.Conclusion: The one-second time response, together with the identified high accurate performance, suggests that this system could be useful in future cognitive and computational neuroscience studies. [ABSTRACT FROM AUTHOR] more...

This paper has reviewed the state‐of‐the‐art approaches for Computer Aided Diagnosis Systems (CADS) for Alzheimer's Disease (AD) using neuroimaging. Identification of the current approaches leads to improving the efficiency of these techniques. The analysis covered 110 articles published between 2009 and January 2018. Papers were chosen according to the Newcastle‐Ottawa criteria. MeSH terms were "computer aided diagnosis systems for Alzheimer's disease" and "computer aided diagnosis systems methods for diagnosis of Alzheimer's disease". CADS algorithms have been presented with specific methods. There is no standardized approach to determine the best one. This study has tables that aimed to conclude all methods in a precise way. Among them, Statistical Parametric Mapping (SPM), Principal Component Analysis (PCA), and Support Vector Machine (SVM) were the most common, respectively. CADS for AD could become important in clinical practice in the near future. The evaluation criteria approved their efficiency as a second opinion besides the neurologist. [ABSTRACT FROM AUTHOR] more...

The article offers information on multimodal machine learning algorithms associated with neuroimaging for early detection of alzheimer's disease (AD). Topics include information on how adult-onset dementias are a main health issue ; memory problems often referred to the age-related physiological reduction of cognitive capabilities; and strategies for early detection of AD. more...