Prediction of individual performance and verbal intelligence scores from resting‐state fMRI in children and adolescents

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome-based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting-state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting-state features from whole-brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10-fold cross-validation framework, our findings revealed that prediction models based on whole-brain functional connectivity effectively predicted performance IQ scores( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.35</mml:mn> <mml:mo>,</mml:mo> <mml:mi>P</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2.2</mml:mn> <mml:mo>×</mml:mo> <mml:msup><mml:mn>10</mml:mn> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>4</mml:mn></mml:mrow> </mml:msup> </mml:math> ) but not verbal IQ scores( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.12</mml:mn> <mml:mo>,</mml:mo> <mml:mi>P</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.20</mml:mn></mml:math> ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto-parietal task control network (FTPN) for performance IQ scores ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>R</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0.71</mml:mn> <mml:mo>,</mml:mo> <mml:mi>P</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2.2</mml:mn> <mml:mo>×</mml:mo> <mml:msup><mml:mn>10</mml:mn> <mml:mrow><mml:mo>-</mml:mo> <mml:mn>18</mml:mn></mml:mrow> </mml:msup> </mml:math> ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.