Profiling brain morphology for autism spectrum disorder with two cross-culture large-scale consortia
Poster No:
314
Submission Type:
Abstract Submission
Authors:
Xueru Fan1, Xujun Duan2, Xi-Nian Zuo1
Institutions:
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 2University of Electronic Science and Technology of China, Chengdu, China
First Author:
Xue-Ru Fan
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Co-Author(s):
Xi-Nian Zuo
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Introduction:
Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition characterized by impairments in social communication and the presence of repetitive, unusual sensory–motor behaviors. Over time, the understanding of ASD has developed from a categorical diagnosis to a dimensional, neurodiversity perspective. This change redefined ASD as a spectrum including a wide range of characteristics within a unified framework. The current level of heterogeneity observed in ASD suggests that the umbrella term "autism spectrum" encompasses multiple subgroups with distinct etiological contributions to phenotype, leading to challenges of reproducible findings across studies (Lombardo et al., 2019). The present study applies normative models to investigate neurodevelopmental heterogeneity in ASD using cross-culture cohorts.
Methods:
We employed the largest normative brain charts (Bethlehem et al., 2022), focusing on childhood developmental stages to explore the heterogeneity of ASD populations before adolescence. Using Out-of-Sample (OoS) centile scoring, we quantified deviations from normative brain growth and applied Spectral Clustering approach to group individuals with ASD from Autism Brain Imaging Data Exchange (ABIDE) (Di Martino et al., 2014; Di Martino et al., 2017) based on their brain morphological profiles. We used mechine learning method further to identify key brain regions driving the subgroup differentiation, complemented by statistical tests to characterize subgroup-specific features. We then applied the ABIDE-based classifier on an independent dataset China Autism Brain Imaging Consortium (CABIC) (https://php.bdnilab.com) to get the similar subgroups. For each dataset, we separately did brain-behavior correlation analysis to characterize subgroup-specific features that would be reproduced across the two cross-culture large-scale consortia, to identify stable neurobiological imaging markers of ASD diversity.
Results:
We identified two distinct grouping clusters of ASD participants within the ABIDE cohort. One subgroup "L" exhibited overall smaller OoS scores compared to demographically matched typically developing controls, while another one "H" subgroup displayed higher OoS scores. Subgroup L has the highest prevalence of morphological abnormalities in regions such as the middle temporal gyrus, inferior temporal gyrus, pericalcarine cortex, and medial orbital frontal gyrus (Fig.1b, bottom). In contrast, subgroup H has in general less severe abnormalities, with a concentration in the insula, transverse temporal gyrus, and caudal anterior cingulate (Fig.1b, top). Among all the cortical regions, the isthmus cingulate, entorhinal cortex, precuneus, and middle temporal gyrus emerged as the most predictive features to divide ASD into these two subgroups. Group comparisons have shown that participants in subgroup L were significantly younger than those in subgroup H. All the significant brain-behavior correlations which were reproduced across ABIDE and CABIC were detected within subroup H (Fig.1a). The transverse temporal gyrus showed positive correlations with ADOS Total and ADOS Social Affect scores. The inferior temporal gyrus was positively correlated with ADOS RRB. The isthmus cingulate is linked to SRS Autistic Mannerisms.
Conclusions:
We employed normative modeling to disentangle the heterogeneity of brain morphology in ASD. By breaking down the ASD population into smaller yet more homogeneous subgroups, we identified two subtypes of morphology abnormality. From the abnormal prevalence of regional volumes, we propose two distinct structural impairment pathways with potential disruptions in sensory-to-higher cognitive functions (Fig.1c) from the perspective of the latest 15 large-scale brain functional networks (Du et al., 2024). These findings emphasize the importance of tangling mixed biological mechanisms and provide insights to enhance the development of effective subgroup-driven individualized interventions.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Autism
MRI
Other - Brain morphology;Brain charts;Heterogeneity;Neurosubtyping;Normative modelling;Spectral clustering
1|2Indicates the priority used for review
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Di Martino, A., O'Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L., Beggiato, A., Bernaerts, S., Blanken, L. M., Bookheimer, S. Y., Braden, B. B., Byrge, L., Castellanos, F. X., Dapretto, M., Delorme, R., Fair, D. A., Fishman, I., Fitzgerald, J., … Milham, M. P. (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific data, 4, 170010.
Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., Deen, B., Delmonte, S., Dinstein, I., Ertl-Wagner, B., Fair, D. A., Gallagher, L., Kennedy, D. P., Keown, C. L., Keysers, C., Lainhart, J. E., … Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659–667.
Du, J., DiNicola, L. M., Angeli, P. A., Saadon-Grosman, N., Sun, W., Kaiser, S., Ladopoulou, J., Xue, A., Yeo, B. T. T., Eldaief, M. C., & Buckner, R. L. (2024). Organization of the human cerebral cortex estimated within individuals: networks, global topography, and function. Journal of neurophysiology, 131(6), 1014–1082.
Lombardo, M. V., Lai, M. C., & Baron-Cohen, S. (2019). Big data approaches to decomposing heterogeneity across the autism spectrum. Molecular psychiatry, 24(10), 1435–1450.