Brain dynamics in toddlers with and without autism spectrum disorder
Poster No:
441
Submission Type:
Abstract Submission
Authors:
Lauren Kupis1, Ashley Kim2, Eric Courchesne3, Jason Nomi4, Lucina Uddin5
Institutions:
1UCLA, LOS ANGELES, CA, 2UCLA, Los Angeles, CA, 3UCSD, San Diego, CA, 4University of California, Los Angeles, Los Angeles, CA, 5Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA
First Author:
Co-Author(s):
Lucina Uddin
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
Los Angeles, CA
Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
Los Angeles, CA
Introduction:
Autism spectrum disorder (ASD) affects one in 36 children.1 Early diagnosis is critical for optimizing outcomes, yet children are not typically diagnosed until 4 years of age.2 In concert with early behavioral signs, early neural markers could identify toddlers at risk of developing ASD to aid earlier diagnosis and targeted interventions. Neuroimaging studies have primarily examined structural brain alterations in toddlers at high risk of developing ASD.3 While innovative dynamic functional magnetic resonance imaging (fMRI) methods reveal candidate brain networks of dysfunction in older children with ASD (7-12 years of age),4–6 little work has been done to examine brain network dynamics in toddlers with ASD. The goal of this project is to identify early functional brain biomarkers of ASD.
Methods:
Participants were enrolled through community referrals and a screening approach in collaboration with pediatricians. All toddlers underwent clinical assessments, including the Autism Diagnostic Observation Schedule (ADOS), and received an official diagnosis at 36 months. The clinical testing took place at the University of California, San Diego Autism Center of Excellence. Clinical scores and fMRI scans were collected from 9 ASD and 9 TD toddlers (age-matched (ASD =27.9 mos; TD = 23.2 mols; p>.05). The small sample size is due to the difficulty of collecting sleep fMRI scans, inclusion criteria (a full rest fMRI scan, a diagnosis of ASD or TD at 36 months of age, a MRI scan prior to their official diagnosis, head motion < .5 mean FD). More participants are planned to be included in the completed study. All toddlers underwent a 10-minute sleep fMRI scan. The fMRI data underwent preprocessing using the multi-echo independent component analysis pipeline 'meica.py' implemented in AFNI and Python. Motion correction parameters were determined based on the first TE images (TE 15 ms) using a rigid-body alignment procedure. Both principal and independent component analyses were used to denoise the data. Data were subsequently smoothed with an 6 mm full-width at half-maximum Gaussian kernel. Head motion was assessed using framewise displacement (FD). There were no significant differences in head motion between the ASD and TD groups (p =.37). Next a group ICA was conducted to identify large-scale brain networks in the sample and the network affiliation of each component was determined based on the brain region. Finally, a dynamic functional connectivity (dFC) analysis was performed using only non-noise ICA components (Figure 1). DFC steps included using a window size of 20 TRs and L1 regularization. Next, an elbow criterion was determined for all participants followed by k means clustering. Lastly, dynamic metrics were computed including transitions, dwell time, and frequency of the states for each participant. This was followed by group comparisons (t-tests) for each dynamic metric and between the groups (ASD, TD).
·Figure 1
·Figure 2
Results:
The elbow criterion indicated 3 states were optimal for the groups included in the study. The networks involved in each state are depicted in Figure 1. There were no significant differences between the groups for dwell time and frequency of occurrence of brain states (p's > .05). However, TD toddlers exhibited more transitions between states compared with ASD toddlers (TD = .6 transitions; ASD = 0 transitions on average; p<.05). Thus, ASD toddlers stayed in one state (either 2 or 3) throughout the scan, whereas TD toddlers transitioned between two of the three states throughout the scan. Across all groups, toddlers spent significantly more time in state 2 compared with states 1 and 3. State 2 also occurred more frequently in all participants (See Figure 2).
Conclusions:
This is one of the first studies to assess brain network dynamics in toddlers later diagnosed with and without ASD and provides a better understanding of early brain network development in this increasingly prevalent neurodevelopmental disorder.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Early life, Adolescence, Aging 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Perception, Attention and Motor Behavior:
Sleep and Wakefulness
Keywords:
Autism
Development
PEDIATRIC
Other - dynamic functional connectivity
1|2Indicates the priority used for review
Provide references using author date format
2. Dawson, G. Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev. Psychopathol. 20, 775–803 (2008).
3. Molnar-Szakacs, I., Kupis, L. & Uddin, L. Q. Neuroimaging Markers of Risk and Pathways to Resilience in Autism Spectrum Disorder. Biol Psychiatry Cogn Neurosci Neuroimaging 6, 200–210 (2021).
4. Kupis, L. et al. Evoked and intrinsic brain network dynamics in children with autism spectrum disorder. Neuroimage Clin 28, 102396 (2020).
5. Watanabe, T. & Rees, G. Brain network dynamics in high-functioning individuals with autism. Nat. Commun. 8, 16048 (2017).
6. Uddin, L. Q. & Karlsgodt, K. H. Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders. J. Clin. Child Adolesc. Psychol. 47, 483–497 (2018).