Enhanced brain signal variability in children with autism spectrum disorder during early childhood

Abstract

Extensive evidence shows that a core neurobiological mechanism of autism spectrum disorder (ASD) involves aberrant neural connectivity. Recent advances in the investigation of brain signal variability have yielded important information about neural network mechanisms. That information has been applied fruitfully to the assessment of aging and mental disorders. Multiscale entropy (MSE) analysis can characterize the complexity inherent in brain signal dynamics over multiple temporal scales in the dynamics of neural networks. For this investigation, we sought to characterize the magnetoencephalography (MEG) signal variability during free watching of videos without sound using MSE in 43 children with ASD and 72 typically developing controls (TD), emphasizing early childhood to older childhood: a critical period of neural network maturation. Results revealed an age-related increase of brain signal variability in a specific timescale in TD children, whereas atypical age-related alteration was observed in the ASD group. Additionally, enhanced brain signal variability was observed in children with ASD, and was confirmed particularly for younger children. In the ASD group, symptom severity was associated region-specifically and timescale-specifically with reduced brain signal variability. These results agree well with a recently reported theory of increased brain signal variability during development and aberrant neural connectivity in ASD, especially during early childhood. Results of this study suggest that MSE analytic method might serve as a useful approach for characterizing neurophysiological mechanisms of typical-developing and its alterations in ASD through the detection of MEG signal variability at multiple timescales.

Keywords: autism spectrum disorder; early childhood; magnetoencephalography; multiscale entropy; signal variability; typical-development.

Figure 1

Stereographic projection of MEG sensors onto a color‐coded planar image showing dots corresponding to different brain regions.

Figure 2

Group differences of the MSE value. (A) Each panel represents average MSE curves for TD children (blue line) and children with ASD (red line) for each subregion corresponding to Fig. 1. Post‐hoc comparisons between groups were highlighted with p < 0.005 (light‐green shaded areas), p < 0.001 (green shaded areas), and p < 0.0005 (dark‐green shaded areas). FDR (controlling for 20 SFs × five subregions) q values of 0.01 and 0.005, respectively, correspond to p values of 0.0040 and 0.0019. (B) Array plot showing sensor‐wise group comparisons of MSE values (X‐axis, SFs; Y‐axis, sensors sorted by subregions according to Fig. 1). Values represent t values for each comparison (151 sensors × 20 SFs). Each panel shows all children, younger children, and older children. T values for FDR (controlling for 20 SFs × 151 sensors) adjusted q = 0.01 correspond to 3.06 (p = 0.0031) for all children and 2.97 (p = 0.0044) for younger children. T values of 4.0 correspond respectively to p = 0.00011 for all children and p = 0.00018 for young children. No significant difference was found in older children. (C) Topography of t values of group difference in all children (left). Brain regions that showed lower than FDR adjusted q = 0.01 are depicted (right).

Figure 3

Associations between age and MSE values. (A) Topography of correlation coefficient values between age and MSE values for TD children (left) and children with ASD (right). Values represent correlation coefficients. Positive values indicate age‐related MSE increase (and vice versa). (B) Array plot representing sensor‐wise correlations of age vs MSE values (X‐axis, SFs; Y‐axis, sensors sorted by subregions according to Fig. 1) for TD children (left) and children with ASD (right). Rho value for FDR (controlling for 20 SFs × 151 sensors) adjusted q = 0.01 corresponds to 0.34 (p = 0.0030) for TD children. (C) Scatter plots showing age vs average MSE values in TD children and children with ASD: all sensors at SF 10 (top left panel), frontal (top right panel) and central (middle left panel) and occipital (middle right panel) sensors at SF 1–5, and frontal (bottom left panel) and occipital sensors at SF 11–15.

Figure 4

Association between disease severity and MSE values. (A) Array plot of the sensor‐wise correlation coefficient between symptom severity (communication score) and MSE values (X‐axis, SFs; Y‐axis, sensors sorted by subregions according to Fig. 1) in children with ASD (left). Topography of correlation coefficient values between symptom severity (communication score). (B) Array plot of the sensor‐wise correlation coefficient between symptom severity (social interaction score) and MSE values (X‐axis, SFs; Y‐axis, sensors sorted by subregions according to Fig. 1) in children with ASD (left). Topography of correlation coefficient values between symptom severity (social interaction score) and MSE values. Positive values denote severity‐related MSE increase (and vice versa). (C) Overlapped topographies of correlation with symptom severity (Fig. 4A, right (depicted by thresholding lower than FDR adjusted q = 0.05 controlling for 151 sensors)) and t values of group difference in all children (Fig. 2C, right). Superscription of topography of correlation with symptom severity on topography of t values of group difference in all children (left), and vice versa (right).