Machine learning prediction of intellectual abilities from magnetic resonance neuroimaging

• Grant number: 18/11881-1
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: April 01, 2019
• End date: November 30, 2021
• Field of knowledge: Biological Sciences - Biophysics - Biophysics of Processes and Systems
• Principal Investigator: Carlos Ernesto Garrido Salmon
• Grantee: Bruno Hebling Vieira
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

Intelligence encompasses several cognitive capabilities that allow the individual to gather information from and act on its environment. It is known that these cognitive processes are centered in the brain and, comparing the human brain to the brain of other animals less proficient at diverse cognitive processes usual to humans, it is possible to infer that neuroanatomical and neurophysiological divergences are, somehow, associated to differences in cognitive abilities. In another scale, these divergences are also noticed between human individuals, where there exists a correlation between intracranial volume and intelligence quotients, for example. Knowing that, it is possible to employ neuroimaging, more specifically anatomical and functional magnetic resonance imaging, to uncover correlates of intelligence in humans. Alternatively, it is also possible to derive models to predict, with machine learning, intelligence quotients in subjects from their neuroimaging data. In this project, we propose the use of both methodologies to uncover relationships between intelligence and brain morphometric and connectivity features in a large sample of healthy subjects. (AU)

Scientific publications

(6)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

VIEIRA, BRUNO HEBLING; HIAR, NATHALIA HANNA; CARDOSO, GEORGE C.. Uncertainty Reduction in Logistic Growth Regression Using Surrogate Systems Carrying Capacities: a COVID-19 Case Study. Brazilian Journal of Physics, v. 52, n. 1, FEB 2022. (18/11881-1)

DE SOUZA, ERICK ALMEIDA; VIEIRA, BRUNO HEBLING; SALMON, CARLOS ERNESTO GARRIDO. Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep convolutional-recurrent model. CEREBRAL CORTEX, v. 34, n. 10, p. 11-pg., 2024-10-23. (18/11881-1, 22/16182-0)

VIEIRA, BRUNO HEBLING; DUBOIS, JULIEN; CALHOUN, VINCE D.; SALMON, CARLOS ERNESTO GARRIDO. A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI. Human Brain Mapping, v. 42, n. 18, SEP 2021. (18/11881-1, 17/02752-0)

VIEIRA, BRUNO HEBLING; PAMPLONA, GUSTAVO SANTO PEDRO; FACHINELLO, KARIM; SILVA, ALICE KAMENSEK; FOSS, MARIA PAULA; SALMON, CARLOS ERNESTO GARRIDO. On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. INTELLIGENCE, v. 93, p. 28-pg., 2022-07-01. (18/11881-1)

DIZAJI, ASLAN; VIEIRA, BRUNO HEBLING; KHODAEI, MOHMMAD REZA; ASHRAFI, MAHNAZ; PARHAM, ELAHE; HOSSEIN-ZADEH, GHOLAM ALI; GARRIDO SALMON, CARLOS ERNESTO; ZADEH, HAMID SOLTANIAN. Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data. BASIC AND CLINICAL NEUROSCIENCE, v. 12, n. 1, p. 1-27, JAN-FEB 2021. (18/11881-1, 17/02752-0)

DE SOUZA, ERICK ALMEIDA; SILVA, STEPHANIE ANDRADE; VIEIRA, BRUNO HEBLING; SALMON, CARLOS ERNESTO GARRIDO. fMRI functional connectivity is a better predictor of general intelligence than cortical morphometric features and ICA parcellation order affects predictive performance. INTELLIGENCE, v. 97, p. 9-pg., 2023-01-20. (18/11881-1)