Transdiagnostic interindividual differences in brain coactivation dynamics in autism and ADHD
Poster No:
398
Submission Type:
Abstract Submission
Authors:
Phoebe Thomson1, Patricia Segura1, Shinwon Park1, Michael Milham1, Ting Xu1, Adriana Di Martino1
Institutions:
1Child Mind Institute, New York, NY
First Author:
Co-Author(s):
Introduction:
Collectively, 10% of children worldwide are affected by autism spectrum disorder (ASD) or attention-deficit/hyperactivity disorder (ADHD), and they often co-occur [1–3]. Current evidence points towards altered intrinsic brain functional connectivity with convergence on atypicalities in the default mode network (DMN) in both conditions. However, findings within and across diagnoses have been inconsistent [4–5]. Most prior work has focused on static connectivity and case-control comparisons that may obscure meaningful sources of heterogeneity. As such, dynamic connectivity and dimensional approaches are increasingly used to understand these neurodevelopmental conditions. Prior studies have revealed that greater time spent in a DMN dominant state is associated with fewer ASD symptoms [6] and greater ADHD symptoms [7]. However, these studies have been conducted in ASD and ADHD youth separately, and have not accounted for co-occurring symptoms in a single transdiagnostic sample. This study uses coactivation pattern (CAP) analysis to examine interindividual differences in the associations between dynamic connectivity and symptom severity of ASD and/or ADHD in a transdiagnostic youth sample.
Methods:
Data from 166 children (6–12 years old) with ASD and/or ADHD (75% male) completed T1w structural and 6-minute resting state functional MRI (fMRI) scans on a 3T Prisma Siemens MRI scanner (fMRI: TR=800ms, TE=30ms, voxel size=2.4x2.4x2.4mm). Data with median framewise displacement (FD)<0.2 mm were preprocessed using the Configurable Pipeline for the Analysis of Connectomes (CPAC) version 1.7.2. CAP analysis was then run on fMRI timeseries (using the method from [8]) to derive 8 CAPs across the sample, and dwell times, occurrence rates, and incidence rates for each CAP and person. Pearson partial correlations tested for dimensional associations between CAP properties and ASD/ADHD symptom severity, covarying for age, sex and FD. Symptoms were measured using rigorous clinical measures, Autism Diagnostic Observation Schedule-2 (ADOS-2) total, Social Affect (SA), Restricted and Repetitive Behaviors (RRB) scores for ASD [9], and Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS) total, hyperactivity/impulsivity (HI) and inattention (IN) scores for ADHD [10]. A priori analyses focused on CAPs showing coactivation in the DMN; further analyses explored behavior associations with properties of other identified CAPs.
Results:
Two CAP pairs were identified with strong coactivation in the DMN (states 1/2 and 5/6; see Figure 1). Dwell time of CAP 6 positively correlated with ADOS-2 total scores (r=.18, p=.019; Figure 2A) and negatively correlated with KSADS ADHD totals (r=-.20, p=.011; Figure 2D). When separated by symptom subdomain, these correlations were specific to ADOS SA (r=.16, p=.039; Figure 2B) and KSADS HI (r=-.21, p=.008; Figure 2E) symptoms. There were no other correlations between properties of DMN-related states and ASD/ADHD symptoms. Exploratory analysis of visual and dorsal attention dominant CAP properties revealed that lower incidence rate of the visually dominant CAP (state 3) was associated with higher ADOS-2 total severity (r=-.26, pFDR=.019) and SA (r=-.25, pFDR=.019) scores.
Conclusions:
Our findings show a double dissociation by diagnostic symptom domain involving the brain state characterized by simultaneous deactivation of DMN and frontoparietal network and activation of the somatomotor network. Children with higher ASD symptoms, particularly in the social affective domain, spent more time in this brain state, whereas shorter dwell time occurred in children with higher ADHD symptoms, particularly in the hyperactivity/impulsivity subdomain. Results clarify the role of the DMN in ASD and ADHD symptoms in a transdiagnostic and comorbid sample.
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 2
Keywords:
Attention Deficit Disorder
Autism
FUNCTIONAL MRI
Other - Dynamic fMRI
1|2Indicates the priority used for review
Provide references using author date format
2. Polanczyk, et al. (2014). ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43(2), 434-442.
3. Leitner, Y. (2014). The co-occurrence of autism and attention deficit hyperactivity disorder in children–what do we know?. Frontiers in human neuroscience, 8, 268.
4. Di Martino, et al. (2013). Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder. Biological psychiatry, 74(8), 623-632.
5. Harikumar, et al. (2021). A review of the default mode network in autism spectrum disorders and attention deficit hyperactivity disorder. Brain connectivity, 11(4), 253-263.
6. Kupis, et al. (2020). Evoked and intrinsic brain network dynamics in children with autism spectrum disorder. Neuroimage: Clinical, 28, 102396.
7. Agoalikum, et al. (2021). Differences in disrupted dynamic functional network connectivity among children, adolescents, and adults with attention deficit/hyperactivity disorder: a resting-state fMRI study. Frontiers in Human Neuroscience, 15, 697696.
8. Gutierrez-Barragan, et al. (2023). Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain. bioRxiv, 2023-07.
9. Lord, et al. (2012). Autism diagnostic observation schedule, second edition (ADOS-2). Torrance, CA: Western Psychological Services.
10. Kaufman, et al. (1997). Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): initial reliability and validity data. Journal of the American Academy of Child & Adolescent Psychiatry, 36(7), 980-988.