Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

Abstract

Importance: Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks.

Objectives: To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories.

Design, setting, and participants: We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012.

Main outcomes and measures: We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale.

Results: Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r ≥ 0.21, P < .0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r ≤ -0.09, P < .012 corrected), although this association was significant only in typically developing participants (all r ≤ -0.13, P < .009 corrected).

Conclusions and relevance: Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior.

Figure 1. Components selected for FNC analysis

The metaICA on 359 participants (166 ASD + 193 TD) extracted 52 independent components (IC), 19 of which were selected for FNC analyses using a semi-supervised procedure detailed in the eMaterials. For each IC, we indicate the component order in the results of the metaICA, reflecting the amount of variance explained by that IC (in decreasing order), along with an anatomical labeling. Abbreviations are listed in Table 2. Discarded ICs are shown in eFigure 3. The similarity of these RSNs with those previously found in Biswal et al. (2010) and in Smith et al. (2009) was quantified by means of spatial correlation, and is reported in eFigure 7.

Figure 2. Group differences in between-network connectivity

Group differences in FNC strength are shown as lines (red indicates increased FNC in ASD with respect to TD participants, blue the reverse situation) together with boxplots of the Pearson correlation values per each group. Boxplots report the mean (red line), standard deviation (blue bars) and standard error of the mean (black rectangle around the mean) of group-level FNC values. Stars above the boxplots indicate those cases in which the mean FNC was found significantly different from zero (at q(FDR)=.05 - see also eTable 5). Results were obtained by comparing the between-network functional connectivity of 166 ASD and 193 TD participants, using nonparametric permutation testing (20,000 permutations) and correcting the final results with q(FDR)=.05, leading to a final threshold of p<0.001. Converting the correlation scores to Z values using Fisher r to Z transformation yielded almost identical results (see eFigure 5).

Figure 3. Correlation between somatosensory-subcortical FNC and group-wise demeaned SRS scores

These scatterplots illustrate the association between FNC and SRS (after group-wise demeaning) in ASD (red) and TD (blue) participants for the interaction between the subcortical RSN and the two RSNs centered around the ventral (IC29) and dorsal (IC5) primary somatosensory and motor cortex. The association between SRS scores and FNC was found significant only for the ASD group after correction with q(FDR)=.053. These results were confirmed by repeating the analysis using robust regression (p<.024, q(FDR)=.05). The lines in the scatterplot represent the linear fit witihin each group: red for ASD, blue for TD. Detailed statistics for these within-group correlations, as well as for the correlation analysis in the entire sample, are reported in the Table 3. Scatterplots and statistics for the correlation of SRS with other FNC scores are reported in eFigure 8 and eTable 2.