ASD and Inattention: Multimodal Imaging Implicates the Salience Network

Background: Attention impairments are common in youth with autism spectrum disorder (ASD), and these impairments are associated with a number of negative outcomes. While stimulants are effective in reducing attention impairments, fewer children with ASD responded positively to stimulants (~50%) compared to children with attention deficit/hyperactivity disorder (70-90%). This suggests that in a sizable portion of children with ASD inattention may have a different underlying neural basis. There is limited data on the neural basis of inattention in the context of ASD across multiple imaging modalities, such as functional MRI (fMRI), resting state MRI (rsMRI), and diffusion tensor imaging (DTI).

Objectives:  To characterize the neural basis of inattention symptoms in children with ASD using a modified flanker fMRI task, rsMRI, and DTI.

Methods:  A total of 146 youth with ASD and 113 typically developing controls were enrolled across all three modalities.  The flanker fMRI task (ASD n=36; TDC n=24) had three conditions: Neutral, Incongruent, and Congruent. Each trial started with a middle arrow surrounded by flanking stimuli. Participants responded to the direction of a middle arrow and ignored flanking diamonds (Neutral), flanking arrows pointing in the opposite (Incongruent), or the same direction (Congruent) as the target. We utilized a novel multivariate pattern analysis with the Interference Suppression contrast (Incongruent vs. Neutral) to identify unique brain signatures in the ASD group relative to the TDC group. For resting state data (ASD n=83; TDC n=83) and DTI data (ASD n=140; TDC n=109) we examined within and cross-network connectivity for regions distinguishing ASD subgroups in the fMRI analysis.

Results: The multivariate analysis found two distinct clusters within the ASD group. Cluster 1 was characterized by a prominent difference in the dorsal anterior cingulate that falls within the salience network, as well as middle cingulate, primary motor, left dorso- and ventrolateral prefrontal cortex, left orbitofrontal cortex, bilateral thalamus, bilateral hippocampus, and bilateral cerebellum. Cluster 2 was characterized by differences in the left inferior frontal and inferior parietal cortices associated with language, and the precuneus. Cluster 1 showed medium-to-large effects for more severe attention impairments and a larger interference suppression effect (RT) on the Flanker task. Resting state analyses demonstrated that the ASD group had lower within-network functional connectivity in the salience network, and this correlated with Inattention symptoms (r=-.29, p<.05). This correlation increased to r=-.50, p<.05 when probing the subset in Cluster 1. Finally, the DTI data revealed significantly reduced tracks between the salience and dorsal attention networks in youth with ASD and 6+ Inattention symptoms compared to the TDC group and youth with ASD and <6 Inattention symptoms.

Conclusions:  Deviations in the salience network’s function, and functional and structural connectivity associates with greater inattention symptoms in youth with ASD. Interestingly the salience network is not a primary target of stimulant medication. Thus this initial work provides insight into the neural basis of inattention for children with ASD. Moreover, we hypothesize the salience network may play a greater role for those who do not respond to stimulants.