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Prediction of human intelligence through neuroimaging features

Abstract

Human intelligence is commonly understood as the capability to acquire novel knowledge or abilities, which may be useful in actively solving cognitive problems and in adapting to new situations. This project will develop and apply novel information-theory-based measures of the brain connectivity and state-of-the-art machine learning methods to predict human intelligence from the brain connectivity results. It will address existing challenges regarding the estimation of the functional connectivity of the brain from the resting state fMRI data and the structural connectivity of the brain from the diffusion tensor MRI (DT-MRI) data, besides of network parameters and morphometric results. It will use well-established tests of the human intelligence for the prediction study. Both Brazilian and Iranian teams will use international and local previously acquired data, as well as collecting additional data. The expert investigators from Iran and Brazil will collaborate in this project to complement each other and boost their performance. The results of the study are also expected to be beneficial for all projects that deal with the human intelligence in health and disease as well as growth and aging. The project is also expected to produce data that will subsequently be made available and to generate software modules with the new technical developments. (AU)

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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)
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, . (18/11881-1, 17/02752-0)
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, . (18/11881-1, 17/02752-0)

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