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Advanced analysis of MR imaging in epilepsy to predict surgery outcome: a machine learning approach

Grant number: 20/00019-7
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): July 01, 2020
Effective date (End): June 30, 2023
Field of knowledge:Health Sciences - Medicine - Medical Clinics
Principal researcher:Fernando Cendes
Grantee:Raphael Fernandes Casseb
Home Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology, AP.CEPID

Abstract

Surgery outcome prediction based on neuroimaging has great potential and importance in the clinical practice. In this project we propose to use machine learning approaches to 1) perform a variety of functional and structural analysis of EEG-fMRI and other functional and structural MRI datasets, including modern techniques (eg: segmentation of brain structures using multi-atlas approaches, dynamic functional connectivity), to complement the neurologic profile of epilepsy patients; and 2) explore different machine learning approaches to predict surgical outcome (favourable vs. unfavourable) based on the extracted features. We believe that the results may help identify with more confidence patients who can benefit from surgical resection and revisit carefully the cases in which the algorithm advises against it. Moreover, we can potentially isolate characteristics or brain patterns to aid in the presurgical decision, since this is part of machine learning capabilities. (AU)

News published in Agência FAPESP Newsletter about the scholarship:
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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)
CASSEB, RAPHAEL F.; DE CAMPOS, BRUNNO M.; MORITA-SHERMAN, MARCIA; MORSI, AMR; KONDYLIS, EFSTATHIOS; BINGAMAN, WILLIAM E.; JONES, STEPHEN E.; JEHI, LARA; CENDES, FERNANDO. ResectVol: A tool to automatically segment and characterize lacunas in brain images. EPILEPSIA OPEN, v. 6, n. 4, . (20/00019-7)
FEITOSA, JAMILLE A.; FERNANDES, CORINA A.; CASSEB, RAPHAEL F.; CASTELLANO, GABRIELA. ffects of virtual reality-based motor rehabilitation: a systematic review of fMRI studie. JOURNAL OF NEURAL ENGINEERING, v. 19, n. 1, . (20/00019-7, 20/10644-6)

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