Predicting full-scale and verbal intelligence scores from functional Connectomic data in individuals with autism...

Area of Science:

• Neuroscience
• Cognitive Science
• Psychology

Background:

• Understanding intelligence in the human brain is crucial for neurological disorder research.
• Most studies focus on neurotypical (NT) brains, neglecting how intelligence correlates differ in atypical neurodevelopmental disorders like Autism Spectrum Disorders (ASD).
• A gap exists in characterizing the neural underpinnings of intelligence in ASD populations.

Purpose of the Study:

• To investigate and identify the neural correlates of intelligence scores in both neurotypical (NT) and Autism Spectrum Disorder (ASD) populations.
• To utilize a connectome-based predictive model (CPM) to predict intelligence scores from functional brain connectivity data.
• To explore how functional brain connections associated with intelligence differ between NT and ASD individuals.

Main Methods:

• Employed resting-state functional magnetic resonance imaging (rsfMRI) to acquire functional connectome data.
• Utilized a connectome-based predictive model (CPM) with leave-one-out cross-validation to identify significant functional brain connections predicting intelligence scores (p < 0.01).
• Developed independent positive and negative predictive models to map summary values of brain connections to intelligence scores for both NT and ASD groups.

Main Results:

• The CPM successfully identified distinct functional brain connections associated with intelligence scores in both neurotypical and ASD populations.
• The model identified specific positive and negative connectivity patterns that independently predict intelligence quotients (full-scale and verbal).
• Analysis revealed unique sets of significant brain connections correlating with intelligence in each population, highlighting potential differences in neural organization.

Conclusions:

• Functional brain connectivity patterns significantly predict intelligence scores in both neurotypical and ASD individuals.
• The study provides novel insights into the neural basis of intelligence in ASD, identifying specific connectivity markers.
• This approach offers a framework for understanding intelligence variations and potential neurological underpinnings in diverse neurodevelopmental contexts.