LUIZ ROBERTO KOBUTI FERREIRA
Projetos de Pesquisa
Unidades Organizacionais
LIM/21 - Laboratório de Neuroimagem em Psiquiatria, Hospital das Clínicas, Faculdade de Medicina
6 resultados
Resultados de Busca
Agora exibindo 1 - 6 de 6
- 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.
- Clinical and demographic differences between voluntary and involuntary psychiatric admissions in a university hospital in Brazil(2013) CHANG, Tais Michele Minatogawa; FERREIRA, Luiz Kobuti; FERREIRA, Montezuma Pimenta; HIRATA, Edson ShiguemiTo assess the frequency of involuntary psychiatric hospitalizations from 2001 to 2008 and to determine associated clinical and sociodemographic 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). Groups were compared using chi-square test for categorical variables and Mann-Whitney test for continuous non-parametric variables. The relative risk of certain events was estimated by the odds ratio statistic. Of 2,289 admissions, 13.3% were IH. The proportion of IH increased from 2.5% to 21.2% during the eight year period. IH were more frequently associated with female gender, unmarried status, unemployment, and more than 9 years of schooling. Psychotic symptoms were more common among IH. There were no differences in age, duration of hospitalization, or rate of attendance at first appointment after hospital discharge. Understanding of the characteristics associated with IH is necessary to improve the treatment of psychiatric disorders.
- 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.