LUIZ ROBERTO KOBUTI FERREIRA
Projetos de Pesquisa
Unidades Organizacionais
LIM/21 - Laboratório de Neuroimagem em Psiquiatria, Hospital das Clínicas, Faculdade de Medicina
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- 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.
- The role of neurocognitive functioning, substance use variables and the DSM-5 severity scale in cocaine relapse: A prospective study(2019) LIM, Danielle Ruiz; GONCALVES, Priscila Dib; OMETTO, Mariella; MALBERGIER, Andre; AMARAL, Ricardo Abrantes; SANTOS, Bernardo dos; CAVALLET, Mikael; CHAIM-AVANCINI, Tiffany; SERPA, Mauricio Henriques; FERREIRA, Luiz Roberto Kobuti; DURAN, Fabio Luis de Souza; ZANETTI, Marcus Vinicius; NICASTR, Sergio; BUSATTO, Geraldo Filho; ANDRAD, Arthur Guerra; CUNH, Paulo JannuzziBackground: The severity of substance use disorder (SUD) is currently defined by the sum of DSM-5 criteria. However, little is known about the validity of this framework or the role of additional severity indicators in relapse prediction. This study aimed to investigate the relationship between DSM-5 criteria, neurocognitive functioning, substance use variables and cocaine relapse among inpatients with cocaine use disorder (CUD). Methods: 128 adults aged between 18 and 45 years were evaluated; 68 (59 males, 9 females) had CUD and 60 (52 males, 8 females) were healthy controls. For the group with CUD, the use of other substances was not an exclusion criterion. Participants were tested using a battery of neurocognitive tests. Cocaine relapse was evaluated 3 months after discharge. Results: Scores for attention span and working memory were worse in patients compared to controls. Earlier onset and duration of cocaine use were related to poorer inhibitory control and global executive functioning, respectively; recent use was related to worse performance in inhibitory control, attention span and working memory. More DSM-5 criteria at baseline were significantly associated with relapse. Conclusions: Recent cocaine use was the most predictive variable for neurocognitive impairments, while DSM-5 criteria predicted cocaine relapse at three months post treatment. The integration of neurocognitive measures, DSM-5 criteria and cocaine use variables in CUD diagnosis could improve severity differentiation. Longitudinal studies using additional biomarkers are needed to disentangle the different roles of severity indicators in relapse prediction and to achieve more individualized and effective treatment strategies for these patients.
- PET and SPECT studies of ageing and cardiovascular risk factors for alzheimer’s disease(2014) BUSATTO, G. F.; TAMASHIRO-DURAN, J. H.; ALVES, T. C. De Toledo Ferraz; FERREIRA, L. K.; DURAN, F. L. De SouzaPositron emission tomography (PET) and single-photon emission computerized tomography (SPECT) have been widely used to document local brain metabolism and regional cerebral blood fl ow reductions associated with ageing-related neurodegenerative disorders such as Alzheimer’s disease (AD), the most common form of dementia. Cardiovascular risk factors (CVRF), such as hypertension, diabetes, dyslipidemia, obesity, and smoking, are highly prevalent in the elderly population and have a signifi cant impact on cognitive performance. These conditions are nowadays recognized as important risk factors for AD. In this chapter, we review PET and SPECT studies which have investigated the impact of CVRF on brain functioning and evaluate how such evidence has helped to provide new insights about the pathophysiology of dementing disorders, particularly AD. We also highlight future directions in this fi eld of research, including longitudinal functional imaging studies to document changes in CVRF-related brain hypoactivity patterns, as well as PET studies assessing possible AD-like brain amyloid deposition abnormalities in proportion to the degree of cardiovascular risk in humans. © Springer-Verlag Berlin Heidelberg 2014.
- 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.
- 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.