Analysis of functional magnetic resonance imaging data of patients diagnosed with Schizophrenia

• Grant number: 19/12098-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2019
• End date: June 30, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Principal Investigator: André Fujita
• Grantee: Ravi do Valle Luz
• Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 18/21934-5 - Network statistics: theory, methods, and applications, AP.TEM

Abstract

Schizophrenia is a psychiatric disorder with a generally poor prognosis, leading patients to a broad deterioration of their social and cognitive capacities. Carriers often need constant familiar support and multiple hospitalizations in mental hospitals. Although this disorder has a low prevalence in the general population, around 0.4%, the economic burden is high, reaching almost three percent of total public health expenditures in developed countries. Since this disease presents high complexity structural and functional cerebral disturbances, a few decades of scientific research in schizophrenia has not been enough to unveil a solid background in its causes. Nevertheless, magnetic resonance imaging has been a great tool to provide a detailed overview of cerebral connectivity, bringing up a higher-level comprehension of the schizophrenia. This project proposes the implementation of mathematical models with stochastic processes inference on functional magnetic resonance imaging (fMRI) data of DSM-IV strict schizophrenia diagnosed patients. Trials of Granger causality tests will be carried out to identify functional brain networks in resting state fMRI protocol. From the inferred functional networks, statistic significant differences will be looked up between experimental and control (no diagnosed psychiatric disorder) groups. From the contrast between groups functional networks, we will make dismemberments of it, aiming at the identification of structural aspects of the disorder. Also, we will make efforts to associate these aspects to the clinical variability of the disease and, then, promote advancements in schizophrenia comprehension.