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Identification of variables associated with the graph structure and applications in neuroscience

Grant number: 15/21162-4
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): April 01, 2016
Effective date (End): August 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:André Fujita
Grantee:Suzana de Siqueira Santos
Home Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated scholarship(s):17/12074-0 - Asymptotic behavior of parameter estimators and test statistics for graphs, BE.EP.DR

Abstract

The corpus callosum is the largest white matter structure of the human brain. One of its functions is to facilitate the interaction between the left and right hemispheres. Abnormalities in the corpus callosum morphology have been reported in several neuropsychiatric disorders, such as schizophrenia, autism, attention deficit hyperactivity disorder, alien hand syndrome, personality disorders, and bipolar affective disorder. However the corpus callosum function and its contribution to cognition and behavior is still a topic of debate. Then, finding associations between the corpus callosum morphology and the brain functional connectivity is fundamental for the study of several neuropsychiatric disorders and the corpus callosum function. In this project we propose to attack that problem by measuring the statistical dependence between the structure of the graph representing the brain functional network and corpus callusum morphological features, such as its volume. Then, we will apply the proposed method in data obtained from subjects with autism, which is a complex disorder whose symptoms highly overlap with the agenesis of the corpus callosum (congenital condition resulting in partial or total absence of that structure). One of the main challenges will be to deal with the presence of many sources of variability in the brain functional connectivity. Approaches to attack that problem are essential to integrate graph theory and statistics and can contribute to the study real systems modeled by graphs. (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)
JARDIM, VINICIUS CARVALHO; SANTOS, SUZANA DE SIQUEIRA; FUJITA, ANDRE; BUCKERIDGE, MARCOS SILVEIRA. BioNetStat: A Tool for Biological Networks Differential Analysis. FRONTIERS IN GENETICS, v. 10, JUN 21 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.