Beyond attention – new insights into the neural basis of intelligence and cognitive abilities through machine learning-based predictive modeling approaches

• Applicant: Dr. Kirsten Hilger
• Subject Area: Biological Psychology and Cognitive Neuroscience ; Human Cognitive and Systems Neuroscience ; Personality Psychology, Clinical and Medical Psychology, Methodology
• Term: since 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 429016959

Intelligence has predictive relevance for education, occupation, and even for health and longevity. Although the assumption that intelligence has a biological basis within the structure and function of the human brain is relatively established, a comprehensive understanding of this basis is still lacking but constitutes an important aim of ongoing research. The introduction of machine learning-based predictive modeling approaches to neuroscience together with advances in network analyses and the release of large neuroimaging data sets opens new opportunities to address this aim from a new perspective. Building on the published results of the previous project, which revealed brain network reconfiguration as a promising biomarker of intelligence, the here proposed follow-up project will broaden the focus to a) the prediction of individual intelligence scores instead of explaining variance post-hoc (predictive modeling instead of correlation analyses), b) the identification of intelligence-predictive network fingerprints (communication patterns between brain regions), and c) the question of how intelligence-predictive neural characteristics are implemented within the human brain. To achieve these goals, we will first apply the established connectome-based predictive modeling (CPM) approach and the, in our research group developed, covariance maximizing eigenvector-based prediction (CMEP) methodology to functional magnetic resonance imaging (fMRI) data from three large samples (N = 806 from the Human Connectome Project; N = 138 and N = 184 from the Amsterdam Open MRI Collection) to test the following hypotheses: 1. Individual intelligence scores can be significantly predicted from brain network reconfiguration. 2. The contribution of different brain networks to the prediction of intelligence differs significantly. Finally, in the last part of the planned project, Neural Networks will be implemented with the aim of testing the following hypothesis: 3. The prediction of intelligence can be significantly improved by combining different features of neural functioning. Answers to these research questions will enhance our understanding about the neurobiological basis of human intelligence and inform psychological theories about postulated relationships between intelligence and cognitive sub-abilities from a neurobiological perspective - insights which also contribute to our understanding about diseases with cognitive impairments. Finally, the developed methodology will be made available for future neuroscientific research.

• DFG Programme: Research Grants
• International Connection: USA
• Cooperation Partner: Professor Dr. Olaf Sporns