Mapping Neural Correlates of Motor Observation and Imitation in Autistic and Non-autistic Individual
Poster No:
1694
Submission Type:
Abstract Submission
Authors:
Dalin Yang1, Tessa George2, Chloe Sobolewski2, Sophia McMorrow2, SungMin Park2, Mary Nebel3, Bahar Tuncgenc4, Rene Vidal5, Natasha Marrus2, Stewart Mostofsky6, Adam Eggebrecht7
Institutions:
1Washington University in St. Louis, St. Louis, MO, 2Washington University School of Medicine in St. Louis, St. Louis, MO, 3Johns Hopkins University, Baltimore, MD, 4Nottingham Trent University, Nottingham , Nottingham , 5University of Pennsylvania, Philadelphia, PA, 6Johns Hopkins University School of Medicine, Baltimore, MD, 7Washington University School of Medicine, Eureka, MO
First Author:
Co-Author(s):
Introduction:
Autism spectrum disorder (ASD) is characterized by social-communication deficits, alongside restricted interests and repetitive behaviors. However, autistic individuals frequently exhibit early motor impairments that persist throughout childhood and into adulthood [1]. A notable motor feature associated with ASD is difficulty in imitating the actions of others. Investigating the brain mechanisms underlying autism-related differences in motor imitation has been challenging, largely due to logistical limitations of functional MRI (fMRI) that limit natural movements. High-density diffuse optical tomography (HD-DOT) overcomes these challenges with an open scanning environment suitable for studying gross motor imitation [2]. Additionally, the mirror neuron system (MNS) is crucial for understanding and imitating actions, and its dysfunction is hypothesized to underlie key ASD features [3]. We hypothesize that HD-DOT will reveal greater MNS activity during motor imitation tasks and a negative correlation between the autistic traits and MNS brain activation.
Methods:
The study included 184 participants, comprising 81 children (10.94 ± 2.59 yrs; 37 with ASD) and 103 adults (29.7 ± 13.2 yrs, 19 with ASD), who completed OBS and IM tasks while undergoing HD-DOT imaging (Table 1, Fig. 1A-B). During the tasks, participants either observed or imitated videos of an actor performing sequences of meaningless arm movements (Fig. 1C). Their performances were recorded using Kinect 3D cameras to enable computer-vision-based motor imitation assessment (CAMI) [4]. We assessed data quality using the pulse signal-to-noise ratio, good measurements percentage, and motion levels with the global variance in the temporal derivative [5]. Beta values reflecting the stimulus response relative to rest (e.g., task > rest) were calculated using general linear modeling [6], and group-level t-tests were performed for motor OBS, IM, and their contrast (IM > OBS). Multiple comparisons were controlled using a false discovery rate correction. Additionally, multiple regression analyses were conducted to examine brain-behavioral associations, focusing on autistic traits assessed via the Social Responsiveness Scale-Version 2 and CAMI (Fig. 1D). Furthermore, we defined 58 parcels representing the MNS within the HD-DOT field of view to run the hypothesis-driven analysis [7].
·Figure 1 | Establishing feasibility for HD-DOT to measure brain function in autistic and non-autistic control (NAC) adults and school-age children. A HD-DOT array on different age group participants.
Results:
The adults' statistical maps exhibit expected activations in visual and temporal cortex on IM and OBS tasks (Fig. 1E, F). School-age children displayed a consistent pattern of OBS/IM brain activation as the adult participants (Fig. 1F). Across all participants, the dorsal attention network and the somatosensory hand function network exhibited larger involvement during the IM task as compared to the OBS task (Fig. 1G). More specifically, while both OBS and IM tasks revealed significant activation in MNS regions, IM activation in MNS areas was more extensive (Fig. I, J). Additionally, we observed a negative correlation between higher (more impaired) SRS-2 t-scores and greater brain activation in the temporoparietal junction and superior temporal gyrus in the right hemisphere during the OBS task. A similar bilateral negative correlation was observed during the IM task, although these correlations lacked statistical significance (Fig. 1K). Further, during the IM task, motor cortex activation showed a positive correlation with higher (better) CAMI scores (Fig. 1L).
·Figure 1 | Establishing feasibility for HD-DOT to measure brain function in autistic and non-autistic control (NAC) adults and school-age children. A HD-DOT array on different age group participants.
Conclusions:
In this study, we demonstrated the feasibility of combining HD-DOT and CAMI to assess brain function in adult and school-age individuals during naturalistic motor imitation tasks, surpassing the limitations of fMRI. Significant task differences were identified, along with neural correlates linking autism traits to brain activity during motor observation and imitation. Future research will focus on extending the application of HD-DOT and CAMI to younger populations and to develop brain-based biomarkers for early-stage ASD diagnosis and intervention.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Motor Behavior:
Mirror System
Motor Behavior Other 1
Novel Imaging Acquisition Methods:
NIRS
Keywords:
Autism
Computational Neuroscience
Immitation
Motor
OPTICAL
1|2Indicates the priority used for review
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[1] Mostofsky S. H. et al., (2006). “Developmental dyspraxia is not limited to imitation in children with autism spectrum disorders.” Journal of the International Neuropsychological Society, vol. 12, pp. 314-326.
[2] Eggebrecht, A. T., et al. (2014). “Mapping distributed brain function and networks with diffuse optical tomography.” Nature photonics, vol. 8, pp. 448-454.
[3] Chan, M. M. Y. & Han, Y. M. Y. (2020). “Differential mirror neuron system (MNS) activation during action observation with and without social-emotional components in autism: a meta-analysis of neuroimaging studies.” Molecular Autism, vol. 11, article no. 72.
[4] Tuncgenc, B. et al. (2021). “Computerized assessment of motor imitation as a scalable method for distinguishing children with Autism”. Biol Psychiatry Cogn Neurosci Neuroimaging vol. 6, pp. 321-328.
[5] Sherafati, A. et al. (2020). “Global motion detection and censoring in high-density diffuse optical tomography.” Human Brain Mapping vol 41, pp. 4093-4112.
[6] Hassanpour, M. S., et al. (2014). “Statistical analysis of high density diffuses optical tomography”, NeuroImage, vol. 85, pp. 104-116.
[7] Iacoboni, M. & Dapretto, M. (2006). “The mirror neuron system and the consequences of its dysfunction. Nature Reviews Neuroscience vol 7, pp. 942-95.