Distinct Developmental Trajectory of Brain Structural Features in Autism Across Intelligence Quotien
Poster No:
330
Submission Type:
Abstract Submission
Authors:
I-Jou Chi1, Albert Chih-Chieh Yang2
Institutions:
1Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taipei, 2School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taipei
First Author:
I-Jou Chi
Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University
Taipei, Taipei
Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University
Taipei, Taipei
Co-Author:
Albert Chih-Chieh Yang
School of Medicine, College of Medicine, National Yang Ming Chiao Tung University
Taipei, Taipei
School of Medicine, College of Medicine, National Yang Ming Chiao Tung University
Taipei, Taipei
Introduction:
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition with significant heterogeneity in cognitive abilities and brain development. Understanding how brain structural features develop in ASD individuals with varying Intelligence Quotient (IQ) levels can provide critical insights into neurodevelopmental differences and potential clinical implications. This study aimed to investigate and compare the developmental trajectories of brain structural features, such as gray matter volume and surface area, among autism subgroups classified by IQ and healthy controls.
Methods:
Structural MRI data from the Autism Brain Imaging Data Exchange (ABIDE I) dataset were analyzed, including participants with autism who were stratified into three IQ-based subgroups: low IQ (below 90), average IQ (90–109), and high IQ (above 110), alongside a healthy control group. Gray matter volume, surface area, and cortical thickness were extracted from specific brain regions. To explore the developmental trajectories, participant age, and brain structural data were processed using a framework that incorporated the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Linear interpolation was applied to resample brain structural data into evenly spaced time points at intervals of 0.25 years. The EEMD algorithm decomposed the resampled data into intrinsic mode functions (IMFs), from which long-term trends were extracted to represent developmental patterns. Differences in the intercepts and slopes of these trends were analyzed using linear regression models to compare trajectories across the autism subgroups and controls.
Results:
Demographic data as follows: Low IQ (N = 57, mean age = 15.44 ± 6.85 years), Average IQ (N = 152, mean age = 16.48 ± 7.59 years), High IQ (N = 135, mean age = 17.74 ± 8.21 years), Healthy Controls (N = 412, mean age = 17.35 ± 8.06 years). The findings revealed distinct developmental trajectories of brain structural features among autism subgroups with different IQ levels. In the opercular part of the inferior frontal gyrus, gray matter volume declined at varying rates across groups. Specifically, autistic individuals with low IQ exhibited a steeper decline with an intercept of 5.607 and a slope of -0.071. The average IQ group (intercept = 5.265, slope = -0.022) and the high IQ group (intercept = 5.309, slope = -0.018) had relatively gradual patterns. In contrast, the healthy control group showed a similar trend to the high IQ subgroup with an intercept of 5.245 and a slope of -0.018. A similar pattern of divergence was observed in the surface area of the pars orbitalis within the inferior frontal gyrus. Autistic individuals with low IQ showed a pronounced decline, with an intercept of 869.7 and a slope of -4.69, whereas the average IQ group demonstrated a positive trajectory with an intercept of 721.2 and a slope of 3.23. In comparison, the high IQ group displayed a slower increase (intercept = 770.0, slope = 1.27), while the healthy control group exhibited a relatively stable trajectory with an intercept of 782.7 and a slope of 0.27.
·Inferior frontal gyrus, opercular part gray matter volume developmental trajectories across IQ subgroups and healthy control
·Inferior frontal gyrus, pars orbitalis surface area developmental trajectories across IQ subgroups and healthy control
Conclusions:
The results demonstrate that brain structural development in autism varies across IQ subgroups, with individuals in the low IQ group exhibiting atypically accelerated declines in gray matter volume and surface area. These differences were particularly prominent in regions such as the opercular part and pars orbitalis of the inferior frontal gyrus. In contrast, the brain development trajectories of individuals in the average and high IQ groups were more closely aligned with those of healthy controls, although subtle deviations were still present. These findings emphasize the importance of stratifying autism populations by IQ to capture the heterogeneity in neurodevelopmental trajectories. By identifying distinct developmental patterns, this study contributes to a better understanding of brain maturation in autism and improves precision prognostics and intervention tailored to specific subgroups.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Lifespan Development Other 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Autism
Computational Neuroscience
Development
MRI
1|2Indicates the priority used for review
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Wang, T., Zhang, M., Yu, Q., & Zhang, H. (2012). Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. Journal of applied Geophysics, 83, 29-34.