Brain state dynamics and behavioral associations in a transdiagnostic sample of autism and ADHD
Poster No:
346
Submission Type:
Abstract Submission
Authors:
Phoebe Thomson1, Patricia Segura1, Rowen Gesue1, Page Freeman1, Shinwon Park2, Stewart Mostofsky3, Michael Milham1, Ting Xu1, Adriana Di Martino1
Institutions:
1Child Mind Institute, New York, NY, 2Autism Center, Child Mind Institute, New York, NY, 3Kennedy Krieger Institute, Baltimore, MD
First Author:
Co-Author(s):
Introduction:
Independent studies of autism or attention-deficit/hyperactivity disorder (ADHD) have reported atypical connectivity patterns across multiple large-scale networks, with default mode network (DMN) findings being a common observation [1–4]. However, there is an incomplete picture of the shared and distinct connectivity patterns among conditions. In response, we address several gaps in prior work. First, most work in autism or ADHD has focused on time-averaged measures that only capture a static representation of connectivity, ignoring dynamic shifts in network interactions. Second, studies have focused on 'case-control' group mean comparisons. Third, limited studies have assessed inter-individual variability across continuous ranges of clinical measures. Accordingly, we conducted dynamic functional connectivity analyses in a rigorously-characterized sample of children with autism and/or ADHD. Using co-activation pattern (CAP) analysis we aimed to identify prominent brain states and transdiagnostically examine associations between their dynamic properties and clinical symptoms. We focused a priori on CAPs involving the DMN [5–6]. As a secondary aim, we begin to explore the replicability of results in a convenience autism sample.
Methods:
Our discovery sample comprised high quality resting-state functional MRI data (median framewise displacement [FD]<0.2mm) from n=166 children with rigorously confirmed autism and/or ADHD (75% male, 8.9±1.7 years old). Using the method from [7], CAP analysis was run on preprocessed functional MRI timeseries to derive 8 CAPs across the sample. Properties of these CAPs were computed for each person including: dwell time -mean continuous time spent in a given brain state; occurrence rate -proportion of total time spent per state; incidence rate -number of times entering each state. Spearman partial correlations tested dimensional associations between these CAP properties, and either autism or ADHD symptoms (covariates: age, sex, in-scanner motion, and symptoms of non-interest) across all children. False-discovery rate (FDR) corrections (q<0.05) were applied as appropriate. A linear regression with interaction term tested if identified associations were driven by diagnostic status. An initial replication analysis was conducted in a convenience autism sample of n=92 from ABIDE [1,8] with the same age range (91% male, 8.8±1.7 years old).
Results:
In the discovery transdiagnostic sample, analyses revealed eight brain states organized into pairs of states with opposing network activation (Fig.1A). Two CAP pairs were identified with strong coactivation in the DMN. Dwell time of one of them (CAP 6) was positively correlated with autism symptoms (r=.17, p=.027, q=.220; Fig.2A), and negatively correlated with ADHD symptoms (r=-.19, p=.016, q=.220; Fig.2B). Follow up analyses revealed that the association between autism symptoms and CAP 6 dwell time was driven by the autism subgroup (Fig.2A). Analysis in the replication autism convenience sample revealed CAPs with spatial distributions similar to those observed in the discovery sample (Fig.1B). Similarly, the positive correlation between autism symptoms and CAP 6 dwell time was replicated (r=.31, p=.003, q=.017; Fig.2C).
Conclusions:
Preliminary findings revealed a double dissociation by symptom domain and, possibly, diagnostic status. On the one hand, the positive association between dwell time in a DMN-related state and autism symptoms appeared to be specific to autism diagnosis. Importantly, examining a convenience autism sample revealed that the CAP 6 association with autism symptoms was replicable. On the other hand, the negative association with ADHD symptoms appears to be transdiagnostic. Results underscore the importance of assessing clinical symptoms within and across diagnoses to clarify the role of atypical DMN connectivity in autism and ADHD. Future planned analyses will assess replicability of findings in transdiagnostic samples and consider the effect of comorbid diagnoses.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Attention Deficit Disorder
Autism
FUNCTIONAL MRI
Other - Dynamic functional methods
1|2Indicates the priority used for review
Abstract Information
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