LUIZ ROBERTO KOBUTI FERREIRA
Projetos de Pesquisa
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LIM/21 - Laboratório de Neuroimagem em Psiquiatria, Hospital das Clínicas, Faculdade de Medicina
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- White matter abnormalities associated with Alzheimer's disease and mild cognitive impairment: a critical review of MRI studies(2013) RADANOVIC, Marcia; PEREIRA, Fabricio Ramos Silvestre; STELLA, Florindo; APRAHAMIAN, Ivan; FERREIRA, Luiz Kobuti; FORLENZA, Orestes Vicente; BUSATTO, Geraldo F.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. Abnormalities in cortico-cortical and cortico-subcortical WM interconnections are associated with an increased risk of progression from MCI to dementia. It can be concluded that WM abnormalities are detectable in early stages of AD and MCI. Degeneration of WM networks causes disconnection among neural cells and the degree of such changes is related to cognitive decline.
- The link between cardiovascular risk, Alzheimer's disease, and mild cognitive impairment: support from recent functional neuroimaging studies(2014) FERREIRA, Luiz K.; TAMASHIRO-DURAN, Jaqueline H.; SQUARZONI, Paula; DURAN, Fabio L.; ALVES, Tania C.; BUCHPIGUEL, Carlos A.; BUSATTO, Geraldo F.Objective: To review functional neuroimaging studies about the relationship between cardiovascular risk factors (CVRFs), Alzheimer's disease (AD), and mild cognitive impairment (MCI). Methods: We performed a comprehensive literature search to identify articles in the neuroimaging field addressing CVRF in AD and MCI. We included studies that used positron emission tomography (PET), single photon emission computerized tomography (SPECT), or functional magnetic resonance imaging (fMRI). Results: CVRFs have been considered risk factors for cognitive decline, MCI, and AD. Patterns of AD-like changes in brain function have been found in association with several CVRFs (both regarding individual risk factors and also composite CVRF measures). In vivo assessment of AD-related pathology with amyloid imaging techniques provided further evidence linking CVRFs and AD, but there is still limited information resulting from this new technology. Conclusion: There is a large body of evidence from functional neuroimaging studies supporting the hypothesis that CVRFs may play a causal role in the pathophysiology of AD. A major limitation of most studies is their cross-sectional design; future longitudinal studies using multiple imaging modalities are expected to better document changes in CVRF-related brain function patterns and provide a clearer picture of the complex relationship between aging, CVRFs, and AD.
- Neurostructural predictors of Alzheimer's disease: A meta-analysis of VBM studies(2011) FERREIRA, Luiz K.; DINIZ, Breno S.; FORLENZA, Orestes V.; BUSATTO, Geraldo F.; ZANETTI, Marcus V.The identification of biological markers at early stages of Alzheimer's disease (AD) contributes to diagnostic accuracy and adds prognostic value. However, in spite of recent developments, results of neurostructural imaging studies on predicting conversion to AD are not uniform. We conducted a systematic review of voxel-based morphometry (VBM) studies about the neurostructural predictors of conversion to AD. Ten studies met inclusion criteria and nine reported baseline regional gray matter (GM) atrophy in mild cognitive impairment (MCI) or healthy subjects who progressed to AD. Using the method of Activation Likelihood Estimation, we meta-analyzed the coordinates from the six longitudinal VBM studies that enrolled subjects with amnestic MCI (aMCI) at baseline. These comprised a total of 429 aMCI subjects, of which 142 converted to AD. Meta-analysis yielded one significant cluster of GM volumetric reduction in aMCI patients who converted to AD, located in the left hippocampus and parahippocampal gyrus. In conclusion, left medial temporal lobe atrophy is the most consistent neurostructural biomarker to predict conversion from aMCI to AD.
- Resting-state functional connectivity in normal brain aging(2013) FERREIRA, Luiz Kobuti; BUSATTO, Geraldo F.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. Resting-state fMRI (rs-fMRI) has emerged as an important alternative modality of fMRI data acquisition, during which no specific task is required. Due to such simplicity and the reliability of rs-fMRI data, this modality presents increased feasibility and potential for clinical application in the future. With rs-fMRI, fluctuations in regional brain activity can be detected across separate brain regions and the patterns of intercorrelation between the functioning of these regions are measured, affording quantitative indices of resting-state functional connectivity (RSFC). This review article summarizes the results of recent rs-fMRI studies that have documented a variety of aging-related RSFC changes in the human brain, discusses the neurophysiological hypotheses proposed to interpret such findings, and provides an overview of the future, highly promising perspectives in this field.
- Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals(2018) FERREIRA, Luiz K.; RONDINA, Jane M.; KUBO, Rodrigo; ONO, Carla R.; LEITE, Claudia C.; SMID, Jerusa; BOTTINO, Cassio; NITRINI, Ricardo; BUSATTO, Geraldo F.; DURAN, Fabio L.; BUCHPIGUEL, Carlos A.Objective: To conduct the first support vector machine (SVM)-based study comparing the diagnostic accuracy of T1-weighted magnetic resonance imaging (T1-MRI), F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and regional cerebral blood flow single-photon emission computed tomography (rCBF-SPECT) in Alzheimer's disease (AD). Method: Brain T1-MRI, FDG-PET and rCBF-SPECT scans were acquired from a sample of mild AD patients (n=20) and healthy elderly controls (n=18). SVM-based diagnostic accuracy indices were calculated using whole-brain information and leave-one-out cross-validation. Results: The accuracy obtained using PET and SPECT data were similar. PET accuracy was 68 similar to 71% and area under curve (AUC) 0.77 similar to 0.81; SPECT accuracy was 68 similar to 74% and AUC 0.75 similar to 0.79, and both had better performance than analysis with T1-MRI data (accuracy of 58%, AUC 0.67). The addition of PET or SPECT to MRI produced higher accuracy indices (68 similar to 74%; AUC: 0.74 similar to 0.82) than T1-MRI alone, but these were not clearly superior to the isolated neurofunctional modalities. Conclusion: In line with previous evidence, FDG-PET and rCBF-SPECT more accurately identified patients with AD than T1-MRI, and the addition of either PET or SPECT to T1-MRI data yielded increased accuracy. The comparable SPECT and PET performances, directly demonstrated for the first time in the present study, support the view that rCBF-SPECT still has a role to play in AD diagnosis.
article 66 Citação(ões) na Scopus Neuroimaging in Alzheimer's disease: current role in clinical practice and potential future applications(2011) FERREIRA, Luiz Kobuti; BUSATTO, Geraldo F.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. The purpose of this article is to review recent neuroimaging studies of Alzheimer's disease that provide relevant information to clinical practice, including a new modality: in vivo amyloid imaging. Magnetic resonance imaging, single photon emission computed tomography and (18)F-fluorodeoxyglucose-positron emission tomography are currently available for clinical use. Patients with suspected Alzheimer's disease are commonly investigated with magnetic resonance imaging because it provides detailed images of brain structure and allows the identification of supportive features for the diagnosis. Neurofunctional techniques such as single photon emission computed tomography and (18)F-fluorodeoxyglucose-positron emission tomography can also be used to complement the diagnostic investigation in cases of uncertainty. Amyloid imaging is a non-invasive technique that uses positron emission tomography technology to investigate the accumulation of the beta-amyloid peptide in the brain, which is a hallmark of Alzheimer's disease. This is a promising test but currently its use is restricted to very few specialized research centers in the world. Technological innovations will probably increase its availability and reliability, which are the necessary steps to achieve robust clinical applicability. Thus, in the future it is likely that amyloid imaging techniques will be used in the clinical evaluation of patients with Alzheimer's disease.- Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases(2018) RONDINA, Jane Maryam; FERREIRA, Luiz Kobuti; DURAN, Fabio Luis de Souza; KUBO, Rodrigo; ONO, Carla Rachel; LEITE, Claudia Costa; SMID, Jerusa; NITRINI, Ricardo; BUCHPIGUEL, Carlos Alberto; BUSATTO, Geraldo F.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. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). Methods: We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, F-18-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for F-18-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. Results: Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using F-18-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. Conclusion: The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to F-18-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.
- Neuroanatomical Classification in a Population-Based Sample of Psychotic Major Depression and Bipolar I Disorder with 1 Year of Diagnostic Stability(2014) SERPA, Mauricio H.; OU, Yangming; SCHAUFELBERGER, Maristela S.; DOSHI, Jimit; FERREIRA, Luiz K.; MACHADO-VIEIRA, Rodrigo; MENEZES, Paulo R.; SCAZUFCA, Marcia; DAVATZIKOS, Christos; BUSATTO, Geraldo F.; ZANETTI, Marcus V.The presence of psychotic features in the course of a depressive disorder is known to increase the risk for bipolarity, but the early identification of such cases remains challenging in clinical practice. In the present study, we evaluated the diagnostic performance of a neuroanatomical pattern classification method in the discrimination between psychotic major depressive disorder(MDD), bipolar I disorder (BD-I), and healthy controls (HC) using a homogenous sample of patients at an early course of their illness. Twenty-three cases of first-episode psychotic mania (BD-I) and 19 individuals with a first episode of psychotic MDD whose diagnosis remained stable during 1 year of followup underwent 1.5 T MRI at baseline. A previously validated multivariate classifier based on support vector machine (SVM) was employed and measures of diagnostic performance were obtained for the discrimination between each diagnostic group and subsamples of age-and gender-matched controls recruited in the same neighborhood of the patients. Based on T1-weighted images only, the SVM-classifier afforded poor discrimination in all 3 pairwise comparisons: BD-I versus HC; MDD versus HC; and BD-I versus MDD. Thus, at the population level and using structural MRI only, we failed to achieve good discrimination between BD-I, psychotic MDD, and HC in this proof of concept study.
- Aging Effects on Whole-Brain Functional Connectivity in Adults Free of Cognitive and Psychiatric Disorders(2016) FERREIRA, Luiz Kobuti; REGINA, Ana Carolina Brocanello; KOVACEVIC, Natasa; MARTIN, Maria da Graca Morais; SANTOS, Pedro Paim; CARNEIRO, Camila de Godoi; KERR, Daniel Shikanai; AMARO JR., Edson; MCINTOSH, Anthony Randal; BUSATTO, Geraldo F.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. We tested for associations between age and whole-brain RSFC using Partial Least Squares, a multivariate technique. We found that normal aging is not only characterized by decreased RSFC within the DMN but also by ubiquitous increases in internetwork positive correlations and focal internetwork losses of anticorrelations (involving mainly connections between the DMN and the attentional networks). Our results reinforce the notion that the aging brain undergoes a dedifferentiation processes with loss of functional diversity. These findings advance the characterization of healthy aging effects on RSFC and highlight the importance of adopting a broad, system-wide perspective to analyze brain connectivity.
- Neuroanatomical pattern classification in a population-based sample of first-episode schizophrenia(2013) ZANETTI, Marcus V.; SCHAUFELBERGER, Maristela S.; DOSHI, Jimit; OU, Yangming; FERREIRA, Luiz K.; MENEZES, Paulo R.; SCAZUFCA, Marcia; DAVATZIKOS, Christos; BUSATTO, Geraldo F.Recent neuroanatomical pattern classification studies have attempted to individually classify cases with psychotic disorders using morphometric MRI data in an automated fashion. However, this approach has not been tested in population-based samples, in which variable patterns of comorbidity and disease course are typically found. We aimed to evaluate the diagnostic accuracy (DA) of the above technique to discriminate between incident cases of first-episode schizophrenia identified in a circumscribed geographical region over a limited period of time, in comparison with next-door healthy controls. Sixty-two cases of first-episode schizophrenia or schizophreniform disorder and 62 age, gender and educationally-matched controls underwent 1.5 T MRI scanning at baseline, and were naturalistically followed-up over 1 year. T1-weighted images were used to train a high-dimensional multivariate classifier, and to generate both spatial maps of the discriminative morphological patterns between groups and ROC curves. The spatial map discriminating first-episode schizophrenia patients from healthy controls revealed a complex pattern of regional volumetric abnormalities in the former group, affecting fronto-temporal-occipital gray and white matter regions bilaterally, including the inferior fronto-occipital fasciculus, as well as the third and lateral ventricles. However, an overall modest DA (73.4%) was observed for the individual discrimination between first-episode schizophrenia patients and controls, and the classifier failed to predict 1-year prognosis (remitting versus non-remitting course) of first-episode schizophrenia (DA = 58.3%). In conclusion, using a ""real world"" sample recruited with epidemiological methods, the application of a neuroanatomical pattern classifier afforded only modest DA to classify first-episode schizophrenia subjects and next-door healthy controls, and poor discriminative power to predict the 1-year prognosis of first-episode schizophrenia.