Measuring Neural Correlates of Motor Imitation in Autistic and Non-autistic Adults and Children
Poster No:
2422
Submission Type:
Abstract Submission
Authors:
Tessa George1, Dalin Yang1, Chloe Sobolewski1, Sophia McMorrow2, Sung Min Park3, René Vidal4, Mary Nebel5, Bahar Tunçgenç6, Natasha Marrus7, Stewart Mostofsky5, Adam Eggebrecht8
Institutions:
1Washington University in St. Louis, St. Louis, MO, 2Washington University School of Medicine in St. Louis, Saint Louis, MO, 3Washington University in St. Louis, Saint Louis, MO, 4University of Pennsylvania, Philadelphia, PA, 5Kennedy Krieger Institute, Baltimore, MD, 6University of Nottingham, Nottingham, United Kingdom, 7Washington University School of Medciine, St. Louis, MO, 8Washington University School of Medicine, St. Louis, MO
First Author:
Co-Author(s):
Introduction:
Imitation is crucial to social-communicative development, and imitation deficits are linked to autism spectrum disorder[1,2]. Imitation measures at brain and behavioral levels could thereby provide biomarkers for advancing targeted diagnosis and intervention[1,2].. Development of brain-based biomarkers of autism-associated differences in motor imitation has proved challenging. Gold standard neuroimaging with functional magnetic resonance imaging (fMRI) is limited by sensitivity to motion artifacts (of millimeters in extent), and the constrained, supine position is incompatible with overt naturalistic motion. High-density diffuse optical tomography (HD-DOT) provides fMRI-comparable image quality in an open scanning environment conducive to neuroimaging during naturalistic social behavior, including motor imitation[3]. We herein leverage advances in HD-DOT to investigate brain-behavior associations underlying motor imitation and observation in autistic (ASD) and non-autistic control (NAC) school-age and pre-school-age children, with an initial goal of establishing feasibility in a cohort of ASD and NAC adults and school-age children.
Methods:
We imaged 97 adults (18-74 years) and 53 school-age children (7-16 years) (Table 1) with HD-DOT[3] (Figure 1A) during motor observation and motor imitation (Figure 1B). Participants observed or imitated videos of an actor completing sequences of arm movements while limiting head motion. Participants were concurrently recorded with Kinect 3D cameras for computer-vision-based assessment of motor imitation (CAMI), which has proven highly effective at distinguishing children with ASD from those without ASD2. Adult participants completed auditory, visual, and motor localizers, and passive movie-viewing. Children completed an auditory task and passive movie-viewing. Behavioral metrics included the Kaufmann Brief Intelligence Test (KBIT 2)[6] and the Social Responsiveness Scale (SRS-2)[5].
HD-DOT data were processed using NeuroDOT (https://www.nitrc.org/projects/neurodot) MATLAB pipelines, including head motion detection and censoring with global variance in the temporal derivative (GVTD), an established, data-driven motion censoring technique inspired by DVARS[4]. We used GLM to compute beta values of the stimulus response relative to rest (e.g., imitation>rest), which were then averaged across runs within participants. We then calculated group-level random effects t-tests for motor imitation, observation, and contrast (imitation>observation) for all groups.
HD-DOT data were processed using NeuroDOT (https://www.nitrc.org/projects/neurodot) MATLAB pipelines, including head motion detection and censoring with global variance in the temporal derivative (GVTD), an established, data-driven motion censoring technique inspired by DVARS[4]. We used GLM to compute beta values of the stimulus response relative to rest (e.g., imitation>rest), which were then averaged across runs within participants. We then calculated group-level random effects t-tests for motor imitation, observation, and contrast (imitation>observation) for all groups.
Results:
For both adults and children, the observation, and imitation t-maps each exhibit expected visual and temporal cortex activations, with additional motor cortex activation during imitation (Figure 1D). The ASD adult contrast map indicates stronger imitation response in right pre-frontal and temporal cortex - patterns not observed in NAC adults. Further, during observation, while NAC and ASD children each show parietal activation, ASD children show increased motor and pre-motor activity.
Conclusions:
We herein established the feasibility of utilizing HD-DOT and CAMI to measure brain function underlying motor imitation and observation in ASD and NAC children and adults and highlight differential cortical activity in ASD participants in multiple brain regions such as prefrontal, temporal, and parietal cortex. Our future directions include examining diagnostic and differential transdiagnostic associations of neural signatures and imitation ability with quantitative measures of ASD traits, IQ, and motor ability. Ongoing studies include extending HD-DOT and CAMI to ASD and NAC pre-school-age children (3-6 years; currently n=45).
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Motor Behavior:
Visuo-Motor Functions 2
Novel Imaging Acquisition Methods:
NIRS 1
Keywords:
ADULTS
Autism
Motor
Near Infra-Red Spectroscopy (NIRS)
Optical Imaging Systems (OIS)
PEDIATRIC
1|2Indicates the priority used for review
Provide references using author date format
2. Tunçgenç B. (2021). Computerized assessment of motor imitation (CAMI) as a scalable method for distinguishing autism. Biological Psychiatry: CNNI.6,321-328
3. Eggebrecht AT.(2014). Mapping distributed brain function and networks with diffuse optical tomography. Nature Photonics. 8(6),448-54
4. Sherafati A. (2020) Global motion detection and censoring in high-density diffuse optical tomography. Human Brain Mapping 41(14),4093-4112.
5. Constantino JN. (2012). Social Responsiveness Scale, Second Edition (SRS-2). Torrance, CA: Western Psychological Services.
6. Kaufman, AS. (2004). Kaufman Brief Intelligence Test (2nd ed.). Circle Pines, MN: American Guidance Service.