Mapping Brain-Behavior Correlations in Autism Using Heat Kernel Smoothing

TL;DR

This study introduces a heat kernel smoothing-based framework for mapping brain-behavior correlations in autism, effectively removing confounding variables and visualizing localized cortical regions associated with behavioral measures.

Contribution

It presents a novel partial correlation mapping method that accounts for age and cortical size effects, improving the accuracy of brain-behavior correlation analysis in autism.

Findings

Successfully correlated facial emotion discrimination scores with cortical thickness.

Demonstrated removal of age and cortical size effects in correlation maps.

Localized brain regions associated with behavioral performance in autism.

Abstract

This paper presents a streamlined image analysis framework for correlating behavioral measures to anatomical measures on the cortex and detecting the regions of abnormal brain-behavior correlates. We correlated a facial emotion discrimination task score and its response time to cortical thickness measurements in a group of high functioning autistic subjects. Many previous correlation studies in brain imaging neglect to account for unwanted age effect and other variables and the subsequent statistical parametric maps may report spurious results. We demonstrate that the partial correlation mapping strategy proposed here can remove the effect of age and global cortical area difference effectively while localizing the regions of high correlation difference. The advantage of the proposed correlation mapping strategy over the general linear model framework is that we can directly visualize more intuitive correlation measures across the cortex in each group.