Joint Attention and Brain Functional Connectivity in Infants and Toddlers

Background:  Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder defined by impaired social interactions, repetitive behaviors, and altered communication. The Communication and Symbolic Behavior Scale (CSBS) is a semi-structured assessment of social communication, a domain profoundly affected in individuals with ASD.

Objectives:  The purpose of this work is to investigate whether particular patterns of correlations between brain regions, as measured with resting state functional connectivity MRI (fcMRI), relate to initiation of joint attention (IJA) as measured with the CSBS.

Methods:  

This work focuses on fcMRI and CSBS data collected from N=107 12- and N=94 24- month old (m.o.) children across four sites within IBIS, an NIH ACE Network study.
Cohorts: Children were classified as high risk (HR) if they had a sibling with ASD, or low risk (LR) if they had only siblings without ASD. Groups were assigned by research clinical best estimate using the DSM-IV-TR checklist at 24-m.o. (HR+/HR-/LR-: 12-m.o. n=17/62/28; 24-m.o.: n=22/49/23).
fcMRI data: For each child, fcMRI data were acquired in up to three resting state BOLD runs (130 frames, each TR=2.5 seconds), and processed using standard protocols including motion scrubbing (FD level 0.2 mm). Analyses utilized 150 frames of clean fcMRI data from each subject. Time traces from 230 functionally-defined regions of interest (ROI) throughout the cerebrum and cerebellum (Figure 1a) were correlated to yield fc values (Figure 1b), and sorted into one of 15 putative functional networks (Figure 1a) based on Infomap clustering of fcMRI data from the 24-m.o. children.
CSBS data: Question 7 (Q7) of the CSBS provided a dimensional metric of IJA: acts used to direct another’s attention to an object, event, or topic (Figure 1c).
Brain-behavior analysis: For each age group, fc values were correlated against CSBS Q7 score (Figure 1d,e). A 2x1 χ2 test was calculated to determine if the number of strong correlations (uncorrected p< 0.05, Figure 1e) within each network-pair of the matrix was significantly greater than the number expected by chance (Figure 1f); only a few blocks survived Bonferroni correction (Figure 1g). For each block, a 2-sided t-test was calculated across age groups.

Results:  Analyses revealed specific groupings of significant network-pair correlations of fc with IJA (Figure 1d-g) that differ between age groups (Figure 1h), e.g. the Dorsal Attention Network (DAN) and the Supplementary Visual (SV) exhibit strong negative correlations with behavior in 12-m.o. (blue shading in DAN-SV block in Figure 1d, top) and modest correlation magnitudes in 24-m.o. (green shading, Figure 1d, bottom). Age-dependent differences are also apparent between other key network pairs (Figure 1h). Further analyses on the risk and diagnostic groups are forthcoming as we acquire more subjects. 

Conclusions: Herein we have shown that IJA correlates with fcMRI, with different specific network-pair interactions in 12-m.o. and 24-m.o. children. Inclusion of the children at high risk for ASD and those with a positive diagnosis provided a unique opportunity to explore a wide range of IJA behavior. Thus we have shown evolving brain-behavior relationships for one affected domain in ASD over the second year of life, when ASD symptoms emerge.