Poster No:
421
Submission Type:
Abstract Submission
Authors:
Zhixi Chen1, Norihide Maikusa2, Yinghan Zhu2, Atsushi Nishida3, Shuntaro Ando2, Naohiro Okada4, Kiyoto Kasai2, Yuko Nakamura2, Shinsuke Koike2
Institutions:
1Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of, Tokyo, 東京都, 2The University of Tokyo, Tokyo, Tokyo, 3Tokyo Metropolitan Institute of Medical Science, Tokyo, Tokyo, 4University of Tokyo Institutes for Advanced Study, Tokyo, Tokyo
First Author:
Zhixi Chen
Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of
Tokyo, 東京都
Co-Author(s):
Atsushi Nishida
Tokyo Metropolitan Institute of Medical Science
Tokyo, Tokyo
Naohiro Okada
University of Tokyo Institutes for Advanced Study
Tokyo, Tokyo
Introduction:
Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder which is characterized as impairment in reciprocal social interaction and communication, as well as restricted, repetitive, and stereotyped patterns of behavior, interests, and activities (American Psychiatric Association, 2013). In addition, the characteristics in ASD have been extended to a continuum of autistic traits in the general population (Baron-Cohen S, J Autism Dev Disord 2001), since some typically developing individuals would also display autistic traits. MRI studies have revealed morphometric brain differences in patients with ASD. Longitudinal and cross-sectional studies have also demonstrated early brain overgrowth during infancy in ASD, followed by a plateau and an accelerated rate of decline, introduced as "pseudo-normalization", or decrease into adulthood (Brandan et al., Brian 2014). Therefore, adolescence appears to be a key period of brain maturation in ASD, but whether this longitudinal trajectory would extend into the general population is still unclear. Our present study aims to highlight adolescent brain structure trajectories and investigate autistic trait-related morphometric differences and changes in the general population.
Methods:
The study includes 479 participants, who took part in the population neuroscience Tokyo Teen Cohort (pnTTC) study (Okada et al., Psychiatry Clin. Neurosci 2019) every two years. Autistic traits were measured using Autism Spectrum Quotient (AQ) (Baron-Cohen S, J Autism Dev Disord 2001) in two waves (age-11 and age-17) from their main caregivers. Freesurfer image analysis software (Fischl, NeuroImage 2012) was used to parcellate and extract the cortical thickness and cortical surface area in 34 regions of interest per hemisphere and 7 subcortical volumes. Then, a general additive mixed model (GAMM) (Wood, Generalized Additive Models 2017) was used to evaluate adolescent non-linear brain trajectory patterns and explored the relationships between brain features and autistic traits. Multiple testing was corrected using a false discovery rate (FDR) method.
Results:
Autistic traits showed good intraindividual stability and exhibited no significant changes with age. Cross-sectionally, we did not observe strong evidence supporting an association between autistic traits and brain structures in this adolescent sample. However, longitudinal findings revealed that different domains of autistic traits had diverse effects on the trends of brain regions, particularly between males and females. For example, high scores on autistic traits in males were associated with age-related increases in the nucleus accumbens (NAc), whereas males with low autistic trait scores and females exhibited an on-going decrease in NAcc volumes. These results were replicated in the social (SC) domain. Conversely, females with high scores in the Restricted and Repetitive Behaviors (RRB) domain displayed an acceleration of normative thickness decrease in frontotemporal cortical thickness and surface area.
Conclusions:
The results suggest unique non-linear brain structural changes during adolescence, which were partly explained by autistic traits, supporting the neurobiology of autistic traits should be extended into the general population. It also revealed that the SC and RRB domains of autistic traits may be fractionable, underpinned by different brain structures.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Early life, Adolescence, Aging 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Autism
Development
STRUCTURAL MRI
Other - Adolescence
1|2Indicates the priority used for review
Provide references using author date format
American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5). American Psychiatric Journal.
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists, and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5-17.
Zielinski, B. A., Prigge, M. B. D., Nielsen, J. A., et al. (2014). Longitudinal changes in cortical thickness in autism and typical development. Brain, 137(6), 1799-1812.
M, Bundo M, Iwamoto K, Tanaka SC, Kasai K. (2019). Population-neuroscience study of the Tokyo TEEN Cohort (pn-TTC): Cohort longitudinal study to explore the neurobiological substrates of adolescent psychological and behavioral development. Psychiatry Clin Neurosci, 73(5), 231-242. doi: 10.1111/pcn.12814.
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781.
Wood, S. N. (2017). Generalized Additive Models: An Introduction with R, Second Edition (2nd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315370279