Publications by authors named "Luiz Kobuti Ferreira"

Introduction: Functional connectivity (FC)-which reflects relationships between neural activity in different brain regions-has been used to explore the functional architecture of the brain in neurodegenerative disorders. Although an increasing number of studies have explored FC changes in behavioral variant frontotemporal dementia (bvFTD), there is no focused, in-depth review about FC in bvFTD.

Methods: Comprehensive literature search and narrative review to summarize the current field of FC in bvFTD.

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Introduction: Mnemonic strategy training (MST) has been shown to improve cognitive performance and increase brain activation in those with mild cognitive impairment (MCI). However, little is known regarding the effects of MST on functional connectivity (FC) at rest. The aim of the present study was to investigate the MST focused on face-name associations effect on resting-state FC in those with MCI.

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Background: Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification.

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Aging is associated with decreased resting-state functional connectivity (RSFC) within the default mode network (DMN), but most functional imaging studies have restricted the analysis to specific brain regions or networks, a strategy not appropriate to describe system-wide changes. Moreover, few investigations have employed operational psychiatric interviewing procedures to select participants; this is an important limitation since mental disorders are prevalent and underdiagnosed and can be associated with RSFC abnormalities. In this study, resting-state fMRI was acquired from 59 adults free of cognitive and psychiatric disorders according to standardized criteria and based on extensive neuropsychological and clinical assessments.

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To assess the frequency of involuntary psychiatric hospitalizations from 2001 to 2008 and to determine associated clinical and socio-demographic characteristics, a retrospective cohort study was conducted. Adult admission data were collected from a university hospital in Brazil. Hospitalizations were classified as voluntary (VH) or involuntary (IH).

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In this article, the authors aim to present a critical review of recent MRI studies addressing white matter (WM) abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI), by searching PubMed and reviewing MRI studies evaluating subjects with AD or MCI using WM volumetric methods, diffusion tensor imaging and assessment of WM hyperintensities. Studies have found that, compared with healthy controls, AD and MCI samples display WM volumetric reductions and diffusion tensor imaging findings suggestive of reduced WM integrity. These changes affect complex networks relevant to episodic memory and other cognitive processes, including fiber connections that directly link medial temporal structures and the corpus callosum.

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The world is aging and, as the elderly population increases, age-related cognitive decline emerges as a major concern. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), allow the investigation of the neural bases of age-related cognitive changes in vivo. Typically, fMRI studies map brain activity while subjects perform cognitive tasks, but such paradigms are often difficult to implement on a wider basis.

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'Alzheimer's disease is the most common cause of dementia and its prevalence is expected to increase in the coming years. Therefore, accurate diagnosis is crucial for patients, clinicians and researchers. Neuroimaging techniques have provided invaluable information about Alzheimer's disease and, owing to recent advances, these methods will have an increasingly important role in research and clinical practice.

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Vascular risk factors can play an important role in determining the onset of non-genetic Alzheimer's disease (AD). Most cases of AD are sporadic and late-onset, and a complex interaction between genetic predisposition and vascular risk factors has been proposed. Vascular risk factors for AD include stroke, hypertension, diabetes, homocysteine, smoking, hypercholesterolemia, heart failure and atrial fibrillation; it is possible that these can trigger cerebrovascular dysfunction and AD pathology.

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