Reorganization of functionally connected brain subnetworks in high-functioning autism

Abstract

Previous functional connectivity studies have found both hypo- and hyper-connectivity in brains of individuals having autism spectrum disorder (ASD). Here we studied abnormalities in functional brain subnetworks in high-functioning individuals with ASD during free viewing of a movie containing social cues and interactions. Twenty-six subjects (13 with ASD) watched a 68-min movie during functional magnetic resonance imaging. For each subject, we computed Pearson's correlation between haemodynamic time-courses of each pair of 6-mm isotropic voxels. From the whole-brain functional networks, we derived individual and group-level subnetworks using graph theory. Scaled inclusivity was then calculated between all subject pairs to estimate intersubject similarity of connectivity structure of each subnetwork. Additional 54 individuals (27 with ASD) from the ABIDE resting-state database were included to test the reproducibility of the results. Between-group differences were observed in the composition of default-mode and ventro-temporal-limbic (VTL) subnetworks. The VTL subnetwork included amygdala, striatum, thalamus, parahippocampal, fusiform, and inferior temporal gyri. Further, VTL subnetwork similarity between subject pairs correlated significantly with similarity of symptom gravity measured with autism quotient. This correlation was observed also within the controls, and in the reproducibility dataset with ADI-R and ADOS scores. Our results highlight how the reorganization of functional subnetworks in individuals with ASD clarifies the mixture of hypo- and hyper-connectivity findings. Importantly, only the functional organization of the VTL subnetwork emerges as a marker of inter-individual similarities that co-vary with behavioral measures across all participants. These findings suggest a pivotal role of ventro-temporal and limbic systems in autism.

Keywords: autism spectrum disorder; fMRI; functional connectivity; graph theory; intersubject similarity; network modularity.

Figure 1

A schematic representation of the intersubject analysis framework. For two groups of subjects (bottom layer), we can compute the similarity between each subject pair by using functional brain data at the level of subnetworks (middle layer) or behavioral scores (top layer). These layers are described as networks using adjacency matrices also known in this case as intersubject similarity matrices. Two types of statistical tests can then be run: a group difference within a layer, in which the within groups values of the adjacency matrix are compared (bottom adjacency matrix, where the group comparison tests whether the within NT group similarity is higher than the within ASD group similarity). The second test is the so‐called Mantel test, in which the two adjacency matrices are compared with each other by correlating the corresponding values of the top off‐diagonal triangle. In the latter case, also the between group similarity values are used making the Mantel approach more strict. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 2

Functional subnetworks and their reorganization between NT and ASD. Functional subnetwork similarities and differences between NT (left) and ASD (right) subjects. The subnetworks are color‐coded and projected on lateral and medial surfaces of both hemispheres. The alluvial diagram in the middle uses the same color‐coding. The height of each ribbon representing a subnetwork corresponds to the number of nodes that belong to the given subnetwork. Stars indicate statistically significant group difference: *significant at P < 0.05, see also Table I; plus signs indicate median consistency of all nodes within a subnetwork: +median subnetwork consistency > 0.5, ++median subnetwork consistency > 0.75. Group consensus modules and consistency values for each node are available at http://neurovault.org/collections/437/. Ribbons with same color show related areas partitioned into similar subnetworks for both the groups. ACG: Anterior cingulum; AMYG: Amygdala; ANG: Angular gyrus; BST: Brainstem; CAL: Calcarine gyrus; CAU: Caudate; CUN: Cuneus; DCG: Middle cingulum; FFG: Fusiform gyrus; HES: Heschl gyrus; HIP: Hippocampus; IFGoperc: Opercular inferior frontal gyrus; IFGtriang: Triangular inferior frontal gyrus; INS: Insula; IOG: Inferior occipital gyrus; IPL: Inferior parietal lobule; ITG: Inferior temporal gyrus; LING: Lingual gyrus; MFG: Middle frontal gyrus; MOG: Middle occipital gyrus; MTG: Middle temporal gyrus; NAcc: Nucleus accumbens; OLF: Olfactory cortex; ORBinf: Orbital inferior frontal gyrus; ORBmid: Orbital middle frontal gyrus; ORBsupmed: Orbital medial frontal gyrus; ORBsup: Orbital superior frontal gyrus; PAL: Pallidum; PCG: Posterior cingulum; PCL: Paracentra lobule; PCL: Paracentral lobule; PCUN: Precuneus; PHG: Parahippocampal gyrus; PUT: Putamen; PoCG: Postcentral gyrus; PreCG: Precentral gyrus; REC: Gyrus rectus; ROL: Rolandic operculum; SFGdor: Superior frontal gyrus; SFGmed: Medial superior frontal gyrus; SMA: Supplementary motor area; SMG: Supramarginal gyrus; SOG: Superior occipital gyrus; SPG: Superior parietal lobule; STG: Superior temporal gyrus; THA: Thalamus; TPOmid: Temporal pole (middle); TPOsup: Temporal pole (superior).

Figure 3

Intersubject analysis between subnetwork similarity and autistic symptoms. Mantel test showing association between VTL subnetwork structure and autistic symptoms. Each dot is a pair of subjects showing their subnetwork similarity with median scaled inclusivity and behavioral similarity with AQ score vectors. Pairs are coded based on within groups (blue NT, red AS) and across groups (green). Mantel test results in the black interpolation line that was performed using all data points. Mantel test results in blue (NT) and red (ASD) interpolation lines are only for within group values. Effect sizes are reported as correlation values and P values were computed with permutations. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 4

Node‐level and link‐level regression with autism quotient scores. (A) Map of nodes whose strength values correlate with individual AQ scores. (B) Summary plot of link weight correlations with individual AQ scores. Only the strongest positively and negatively correlated links are reported (links in the 1st percentile). For a full summary connectivity matrix, see Supporting Information Figure S3. Each element of the pairwise connectivity matrix indicates the average of the correlations between AQ and link weights for all the links between a pair of anatomical regions. The main diagonal shows the average correlation for links within the respective region. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Figure 5

Results from the ABIDE dataset. Mantel test showing association between VTL subnetwork structure and autistic symptoms in the ABIDE replication dataset. Each dot is a pair of ASD subjects (351 pairs for 27 subjects) showing their VTL subnetwork similarity with median scaled inclusivity and behavioral similarity with ADI‐R and ADOS score vectors. Effect size is reported as correlation value and P value was computed with permutations. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]