Statistical and computational methods for discriminating of anatomic changes, mental states and identification of brain connectivity: an integrative approach based on MRI, fMRI and EEG

• Grant number: 10/01394-4
• Support Opportunities: Regular Research Grants
• Start date: July 01, 2010
• End date: June 30, 2012
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: João Ricardo Sato
• Grantee: João Ricardo Sato
• Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Associated researchers: André Fujita ; Carlos Eduardo Thomaz ; Edson Amaro Junior ; Elisa Harumi Kozasa ; Maria Cecília Oliveira da Fonseca ; Paula Ayako Tiba
• Associated research grant(s): 10/51473-8 - Bridging the gap between statistics, machine learning and clinical practice: an interdisciplinary collaborative study on human brain mapping, AP.R

Abstract

One of the main innovations in human brain mapping is the acquisition of structural and functional magnetic resonance imaging. The extraction and unification of information present in EEG data, structural magnetic resonance imaging (MRI) and functional (fMRI) is of great importance for a deep understanding of cognitive processes, diseases and even clinical applications, such as mapping and characterization of epileptogenic foci. A crucial step for multimodal integration is the development of quantitative methods for the identification of brain connectivity and "decoding" of mental states. To date, the number of scientific studies exploring this issue is negligible, thus, there are no methods available for assessing these aspects especially in multimodal data. The main aim of this project is the development and application of new statistical methodologies and software’s for data analysis and integration of information obtained through MRI, fMRI and electroencephalography (EEG). The approach will be in a non-parametric fashion and exploratory (integrating concepts of support vector machines, neural networks, time series clustering, etc.) in order to avoid the necessity of strong assumptions often assumed by conventional statistical methods, thus obtaining, a method with greater flexibility for use in diseases or complex cognitive processing. Moreover, considering the technological, political and social, this project will promote the integration of Federal University of ABC (UFABC) with other research and technologic centers and in Brazil and also international cooperation, encouraging scientific innovation in the ABC region. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

TAKAHASHI, DANIEL YASUMASA; SATO, JOAO RICARDO; FERREIRA, CARLOS EDUARDO; FUJITA, ANDRE. Discriminating Different Classes of Biological Networks by Analyzing the Graphs Spectra Distribution. PLoS One, v. 7, n. 12, DEC 19 2012. (11/07762-8, 10/01394-4)

GADELHA, ARY; MACHADO, MAURICIO F. M.; YONAMINE, CAMILA M.; SATO, JOAO R.; JULIANO, MARIA A.; OLIVEIRA, VITOR; BRESSAN, RODRIGO A.; HAYASHI, MIRIAN A. F.. Plasma Ndel1 enzyme activity is reduced in patients with schizophrenia - A potential biomarker?. JOURNAL OF PSYCHIATRIC RESEARCH, v. 47, n. 5, p. 657-663, MAY 2013. (09/17726-9, 10/01394-4, 11/50740-5)

SATO, JOAO R.; KOZASA, ELISA H.; RUSSELL, TAMARA A.; RADVANY, JOAO; MELLO, LUIZ E. A. M.; LACERDA, SHIRLEY S.; AMARO, JR., EDSON. Brain Imaging Analysis Can Identify Participants under Regular Mental Training. PLoS One, v. 7, n. 7, JUL 3 2012. (10/01394-4)