Aberrant dynamics of cognitive control and motor circuits predict distinct restricted and repetitive behaviors in children with autism

Restricted and repetitive behaviors (RRBs) are a defining clinical feature of autism spectrum disorders (ASD). RRBs are highly heterogeneous with variable expression of circumscribed interests (CI), insistence of sameness (IS) and repetitive motor actions (RM), which are major impediments to effective functioning in individuals with ASD; yet, the neurobiological basis of CI, IS and RM is unknown. Here we evaluate a unified functional brain circuit model of RRBs and test the hypothesis that CI and IS are associated with aberrant cognitive control circuit dynamics, whereas RM is associated with aberrant motor circuit dynamics. Using task-free fMRI data from 96 children, we first demonstrate that time-varying cross-network interactions in cognitive control circuit are significantly reduced and inflexible in children with ASD, and predict CI and IS symptoms, but not RM symptoms. Furthermore, we show that time-varying cross-network interactions in motor circuit are significantly greater in children with ASD, and predict RM symptoms, but not CI or IS symptoms. We confirmed these results using cross-validation analyses. Moreover, we show that brain-clinical symptom relations are not detected with time-averaged functional connectivity analysis. Our findings provide neurobiological support for the validity of RRB subtypes and identify dissociable brain circuit dynamics as a candidate biomarker for a key clinical feature of ASD.


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Study protocol Data collection

Outcomes
The sample size were determined based on samples from previous neuroimaging studies in children with autism and typically-developing children.
Children with autism were excluded if they had any history of known genetic, psychiatric, or neurological disorders (e.g., Fragile X syndrome or Tourette's syndrome), or were currently prescribed anti-psychotic medications. Typically-developing children were screened and excluded if they or a first-degree relative had developmental, language, learning, neurological, psychiatric disorders, or psychiatric medication usage, or if the child met the clinical criteria for a childhood disorder on the Child Symptom Inventory -Fourth Edition or Child and Adolescent Symptom Inventory.
Cross-validation analysis was used to assess replicability of the reported findings.
The experimental groups are children with autism and typically-developing children; therefore, randomization is not applicable.
Blinding is not relevant to the design of the current study.
State number and/or type of variables recorded (e.g. correct button press, response time) and what statistics were used to establish that the subjects were performing the task as expected (e.g. mean, range, and/or standard deviation across subjects).

Functional 3T
A total of 29 axial slices (4.0 mm thickness, 0.5 mm skip) parallel to the AC-PC line and covering the whole brain were imaged with a temporal resolution of 2 s using a T2* weighted gradient echo spiral in-out pulse sequence 7 with the following parameters: TR = 2,000 msec, TE = 30 msec, flip angle = 80 degrees, 1 interleave. The field of view was 20 cm, and the matrix size was 64×64, providing an in-plane spatial resolution of 3.125 mm. To reduce blurring and signal loss arising from field in homogeneities, an automated high-order shimming method based on spiral acquisitions was used before acquiring functional MRI scans. Whole brain scan SPM8 Non-linear normalization was applied.

MNI152 2mm template was used
Head motion and signals from white matter and CSF were regressed out.
Define your software and/or method and criteria for volume censoring, and state the extent of such censoring.
Specify type (mass univariate, multivariate, RSA, predictive, etc.) and describe essential details of the model at the first and second levels (e.g. fixed, random or mixed effects; drift or auto-correlation).
Define precise effect in terms of the task or stimulus conditions instead of psychological concepts and indicate whether ANOVA or factorial designs were used. Specify voxel-wise or cluster-wise and report all relevant parameters for cluster-wise methods.
Describe the type of correction and how it is obtained for multiple comparisons (e.g. FWE, FDR, permutation or Monte Carlo).

Sliding-window Pearson correlations were computed
Mean and variability of CNII/MNII as independent variables and RRB subtype (CI, IS or RM) severity score as dependent variable was used as the input to a linear regression algorithm. Cross-validation analysis was used to assess generalization and reproducibility. Data were divided into five folds. A linear regression model was built/trained using four folds, leaving out one fold. The samples in the leftout fold were then predicted using this trained model, and the predicted values were noted. This procedure was repeated five times, and finally an r(pred, actual) was computed based on the predicted and actual values. r(pred, actual), correlation