Autistic traits in neurotypical adults: correlates of graph theoretical functional network topology and white matter anisotropy patterns

Abstract

Attempts to explicate the neural abnormalities behind autism spectrum disorders frequently revealed impaired brain connectivity, yet our knowledge is limited about the alterations linked with autistic traits in the non-clinical population. In our study, we aimed at exploring the neural correlates of dimensional autistic traits using a dual approach of diffusion tensor imaging (DTI) and graph theoretical analysis of resting state functional MRI data. Subjects were sampled from a public neuroimaging dataset of healthy volunteers. Inclusion criteria were adult age (age: 18-65), availability of DTI and resting state functional acquisitions and psychological evaluation including the Social Responsiveness Scale (SRS) and Autistic Spectrum Screening Questionnaire (ASSQ). The final subject cohort consisted of 127 neurotypicals. Global brain network structure was described by graph theoretical parameters: global and average local efficiency. Regional topology was characterized by degree and efficiency. We provided measurements for diffusion anisotropy. The association between autistic traits and the neuroimaging findings was studied using a general linear model analysis, controlling for the effects of age, gender and IQ profile. Significant negative correlation was found between the degree and efficiency of the right posterior cingulate cortex and autistic traits, measured by the combination of ASSQ and SRS scores. Autistic phenotype was associated with the decrease of whole-brain local efficiency. Reduction of diffusion anisotropy was found bilaterally in the temporal fusiform and parahippocampal gyri. Numerous models describe the autistic brain connectome to be dominated by reduced long-range connections and excessive short-range fibers. Our finding of decreased efficiency supports this hypothesis although the only prominent effect was seen in the posterior limbic lobe, which is known to act as a connector hub. The neural correlates of the autistic trait in neurotypicals showed only limited similarities to the reported findings in clinical populations with low functioning autism.

Figure 1. Demographic data of the study population.

Distribution of subject age, full scale IQ, verbal IQ, ASSQ and SRS scores. Data are depicted as histograms (left panels) and Q-Q (quantile-quantile) probability plots (right panels) in which reference lines of the normal distribution are given (n = 127).

Figure 2. Processing pipeline of resting state functional MRI data.

Panel A, far left: two atlas regions, Inferior Frontal Gyrus, triangular part (red, R23) and Middle Temporal Gyrus temporooccipital part (blue, R58) were overlaid on an atlas-space aligned fMRI image. The corresponding regional BOLD curves and their wavelet coefficients are shown on next images. Accordingly, the TR = 2500 ms repetition time and the four level wavelet decomposition the evaluated wavelet coefficients of four frequency bands (0.1–0.2 Hz, 0.05–0.1 Hz, 0.025–0.05 Hz, 0.0125–0.025 Hz) were generated (middle bottom image). In the 4th column the evaluated wavelet correlation matrix (top) and the distribution of correlation coefficients (bottom) are shown. We used these wavelet correlation values between each pair of brain regions to construct the matrix in which yellow color represents high correlation coefficients while red means low values. Panel B: In the bottom row, three adjacency matrices are shown with wired costs 0.1, 0.4 and 0.9. These matrices were generated from the weighted connectivity matrix (4^th panel, right bottom image) by different weight thresholds. Vertical gray arrows represent the calculation procedure of nodal and global network parameters at different cost levels. The horizontal gray arrows demonstrate the final step of Monte-Carlo based cost-integration procedure in which the summed parameters are divided by the integration steps (MC).

Figure 3. Distribution of global functional network properties.

Global efficiency (E_g) and average local efficiency (E_l). Data are depicted as histograms (left panels) and Q-Q (quantile-quantile) probability plots (right panels) in which reference lines of the normal distribution are given (n = 127).

Figure 4. Small-world properties of functional and synthetic networks.

Global (E_g) and local (E_l) efficiencies are depicted as a function of wired-cost for random (red), a regular (blue) and the investigated human brain networks. In latter case, the averages (black line) and the standard deviation (gray band) of efficiency values are shown. The E_g and E_l of brain networks monotonically increase by the cost with relatively low standard deviation. This means that all brain networks have simultaneously high E_g and E_l values in the [0.34–0.5] cost-range, which verify that these networks have small-world properties.

Figure 5. Regional graph theoretical correlates of self-reported autistic traits.

Left panels: observed vs. predicted values of the dependent variables in the general linear model analysis. Middle panels: predicted values vs. ASSQ score. Right panels: predicted values vs. SRS total scores.

Figure 6. Regional diffusion anisotropy correlates of self-reported autistic traits.

Left panels: observed vs. predicted values of the dependent variables in the general linear model analysis. Middle panels: predicted values vs. ASSQ score. Right panels: predicted values vs. SRS total scores.

Figure 7. Functional network of the right posterior cingulate cortex, calculated for sub-populations with the lowest and highest ASSQ scores.

Graph edges were depicted based on the strongest functional connectivity (threshold criteria for averaged networks: wavelet correlation coefficient >0.634; 95^th percentile strongest connection in high scorers; threshold criterion for the statistical parametric network (SPN) method: p<0.005). The nomenclature for brain region abbreviations is given in Table S1. Raw connectivity data are provided in Table 5. First row: averaged functional network (first degree connections) in subjects with the lowest ASSQ score (n = 52; 5th percentile, cut-off threshold: 2). Second row: lowest ASSQ scorer group, SPN based determination of connectivity strengths. Third row: averaged functional network in subjects with the highest ASSQ score (n = 7; 95th percentile, cut-off threshold: 24). Fourth row: high ASSQ scorer group, connections are visualized using SPN analysis.

Figure 8. Demonstrating the functional connections of the right posterior cingulate cortex with a circular connectivity profile.

Connectivity data were calculated for sub-populations with the lowest and highest ASSQ scores. Key for the abbreviations is given in Table S1.

Figure 9. Circular representation of the functional connections of the right posterior cingulate cortex, SPN method.

Connectivity data were calculated for sub-populations with the lowest and highest ASSQ scores. Key for the abbreviations is given in Table S1.