Altered Periodic Dynamics in the Default Mode Network in Autism and Attention-Deficit/Hyperactivity Disorder

Abstract

Background: Altered resting-state functional connectivity in the default mode network (DMN) is characteristic of both autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Standard analytical pipelines for resting-state functional connectivity focus on linear correlations in activation time courses between neural networks or regions of interest. These features may be insensitive to temporally lagged or nonlinear relationships.

Methods: In a twin cohort study comprising 292 children, including 52 with a diagnosis of ASD and 70 with a diagnosis of ADHD, we applied nonlinear analytical methods to characterize periodic dynamics in the DMN. Using recurrence quantification analysis and related methods, we measured the prevalence, duration, and complexity of periodic processes within and between DMN regions of interest. We constructed generalized estimating equations to compare these features between neurotypical children and children with ASD and/or ADHD while controlling for familial relationships, and we leveraged machine learning algorithms to construct models predictive of ASD or ADHD diagnosis.

Results: In within-pair analyses of twins with discordant ASD diagnoses, we found that DMN signal dynamics were significantly different in dizygotic twins but not in monozygotic twins. Considering our full sample, we found that these patterns allowed a robust predictive classification of both ASD (81.0% accuracy; area under the curve = 0.85) and ADHD (82% accuracy; area under the curve = 0.87) cases.

Conclusions: These findings indicate that synchronized periodicity among regions comprising the DMN relates both to neurotypical function and to ASD and/or ADHD, and they suggest generally that a dynamical analysis of network interconnectivity may be a useful methodology for future neuroimaging studies.

Keywords: Attention-deficit/hyperactivity disorder; Autism; Default mode network; Dynamical systems; Recurrence quantification analysis; Resting-state connectivity.

Figure 1.. Anatomical localization of the Default Mode Network (DMN).

(A) From top-to-bottom, the DMN in the brain from left, right, left-medial, and right-medial sections. (B) seed-to-voxel (positive) correlations in ROIs of the DMN, with panel slices of the DMN from the central reference slice z = 18 (coordinates = 0 0 18 mm/75 109 91 voxels).

Figure 2.. Construction of recurrence and cross-recurrence plots.

(A) Example BOLD signal from posterior cingulate cortex (PCC). (B) Phase portrait derived from Taken’s delay embedding of BOLD signal shown in (A). (C) Recurrence plot derived from (B). Examples of the features we quantified from recurrence plots are provided in the magnified box. Recurrence plots represent periodic processes with diagonal lines (orange dotted line) and non-periodic processes with vertical/horizontal (laminar) lines (purple/blue dotted lines). (D) Example BOLD signal from posterior cingulate cortex (PCC) (red) and medial prefrontal cortex (MPFC) (blue). (E) Phase portrait derived from Taken’s delay embedding of BOLD signals shown in (D). (F) Cross-recurrence plot derived from (E). We quantified the same features from cross-recurrence plots as recurrence plots, e.g., diagonal lines or cyclical processes.

Figure 3.. ASD-Related Dysregulation of Determinism in DMN BOLD Signals.

P-values reflect Tukey-adjusted post-hoc comparisons between discordant twins with ASD diagnosis (10 MZ pairs; 13 DZ pairs; 1 DZ triplet).

Figure 4.. Predictive efficacy of periodic features derived from RQA/CRQA of the default mode network.

(A) Receiver operating characteristic (ROC) curve illustrating performance of a model trained to classify ASD on a validation dataset following 5-fold cross-validation in training set. (B) ROC curve illustrating performance of a model trained to classify ADHD on a validation dataset. (C) Feature importance (Gain) associated with each feature in predicting ASD. (D) Feature importance (Gain) associated with each feature in predicting ADHD.