Atypical developmental trajectory of local spontaneous brain activity in autism spectrum disorder

Autism spectrum disorder (ASD) is marked by atypical trajectory of brain maturation, yet the developmental abnormalities in brain function remain unclear. The current study examined the effect of age on amplitude of low-frequency fluctuations (ALFF) in ASD and typical controls (TC) using a cross-sectional design. We classified all the participants into three age cohorts: child (<11 years, 18ASD/20TC), adolescent (11–18 years, 28ASD/26TC) and adult (≥18 years, 18ASD/18TC). Two-way analysis of variance (ANOVA) was performed to ascertain main effects and interaction effects on whole brain ALFF maps. Results exhibited significant main effect of diagnosis in ASD with decreased ALFF in the right precuneus and left middle occipital gyrus during all developmental stages. Significant diagnosis-by-age interaction was observed in the medial prefrontal cortex (mPFC) with ALFF lowered in autistic children but highered in autistic adolescents and adults. Specifically, remarkable quadratic change of ALFF with increasing age in mPFC presented in TC group was absent in ASD. Additionally, abnormal ALFF values in diagnosis-related brain regions predicted the social deficits in ASD. Our findings indicated aberrant developmental patterns of spontaneous brain activity associated with social deficits in ASD and highlight the crucial role of the default mode network in the development of disease.


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Methods and results

Main effect of age
A distributed set of brain regions showed significant main effect of age, such as the frontal gyrus, cerebellum, thalamus, temporal gyrus, parietal gyrus, angular gyrus and striatum (Gaussian random field (GRF) correction, Z > 2.3 combined with cluster size > 88 voxels) ( Figure S1 and Table S1).

Replication analysis using male subjects 4
Considering recent reports on differential effects of sex on neurobiology and neurocognitive profiles of ASD 1-3 , we conducted additional reproducible analysis excluding female subjects to explore whether the reported neurodevelopmental effects hold for the male subsample. All the selected participants (n = 108) were classified into child (17ASD/19TC), adolescent (23ASD/21TC) and adult (14ASD/14TC) groups. No significant differences of the age, FIQ and mean frame-wise displacement (FD) were found between diagnostic groups within each age group. Two-way analysis of variance (ANOVA) was performed to ascertain main effects and interaction effects on whole brain ALFF maps.

Robustness of findings to head motion
To demonstrate the robustness of our findings against head motion, we verified the results using more stringent criterion. Subjects with excessive motion (i.e., translational or rotational motion greater than 2 mm or 2°) were excluded from the subsequent analysis. All the selected participants (n = 124) were classified into child (17ASD/20TC), adolescent (28ASD/25TC) and adult (17ASD/17TC) groups. No significant differences of the age, gender, FIQ and mean FD were found between diagnostic groups within each age group. Two-way ANOVA was performed to ascertain main effects and interaction effects on whole brain ALFF maps. Gender, FIQ and mean

Additional analyses of FIQ and mean FD
Though the ANOVA analysis in primary analysis was adequately corrected for nuisance covariates, we conducted additional analyses to exclude the effects of FIQ and mean FD on diagnosis-by-age interaction effect. Two-way ANOVA analyses with diagnosis (two levels: ASD and TC) and age (three levels: child, adolescent and adult) as between-subject factors were performed using FIQ and mean FD, respectively. No significant interaction between diagnosis and age was observed in both analyses. In addition, Pearson correlation analyses were performed between ALFF values in the identified interaction cluster and FIQ, as well as mean FD. No significant correlation was found in the correlation analyses. These results suggest that the identified diagnosis-by-age interaction effect wasn't contributed by FIQ or mean FD.

Replication analysis using other datasets
To demonstrate the robustness of our results, primary findings were verified using other datasets. We examined the other ABIDE datasets and chose the dataset which contains most subjects at childhood, adolescence and adulthood, respectively. Stanford

Replication analysis of symptom severity prediction analysis
Since we didn't include nuisance regressors (i.e., gender, FIQ and mean FD) in the regression models in original analyses, we conducted additional multivariate regression analyses including gender, FIQ and mean FD in the regression models. Given that ALFF interacts with age during the development of ASD, age was also included as a factor in the regression model. For standardization, age, gender, FIQ and mean FD were

Regression analyses with age as a continuous regressor
We also conducted whole-brain analyses exploring linear as well as quadratic relationships with age as a continuous regressor to see whether it similarly identifies the diagnosis-by-age interaction in the mPFC. To do so, we performed voxel-based multiple regressions with voxel-wise ALFF values as dependent variable and age as a continuous regressor of interest for ASD and TC groups, respectively. Gender, FIQ and 11 mean FD were taken as nuisance regressors in the regression model. Gaussian random field theory was employed for multiple comparisons correction (voxel-level p < 0.05, cluster-level p < 0.05). Quadratic results showed a significant positive quadratic relationship between age and ALFF values in the mPFC in TC group (U-shaped), while we didn't identified any mPFC cluster in quadratic effects of ASD group. Additionally, we performed two-way ANOVA analysis with diagnosis (two levels: ASD and TC) and age as between-subject factors on ALFF values in the mPFC cluster found in TC group.
Significant interaction effect was observed for ALFF in the mPFC cluster (p < 0.05). In linear regression analysis, both ASD and TC group showed increased ALFF with increasing age in several mPFC clusters. However, we also found a cluster in the mPFC where TC group showed a negative linear relationship between age and ALFF values, while ASD group showed no negative relationship in the mPFC. Additional ANOVA analysis observed significant diagnosis-by-age interaction effect in the mPFC cluster identified negative relationship in TC group (p < 0.01). No significant interaction effect was found in mPFC clusters which exhibited positive relationship in ASD or TC group.
These findings demonstrated that both linear and quadratic analyses similarly identified significant diagnosis-by-age interaction effects in the mPFC as our primary analysis ( Figure S5).
12 Figure S5 Regression analyses with age as a continuous regressor. Quadratic relationships between ALFF and age in ASD (A) and TC groups (B). Linear relationships between ALFF and age in ASD (C) and TC groups (D).