The neurogenetics of functional connectivity changes in Autism: Insights from subtyping 657 patients
Poster No:
399
Submission Type:
Abstract Submission
Authors:
Javier Rasero1, Antonio Jimenez-Marin2, Ibai Diez3, Roberto Toro4, Mazahir Hasan5, Jesus Cortes2
Institutions:
1University of Virginia, Charlottesville, VA, United States of America, 2Biobizkaia Health Research Institute, Barakaldo, Bizkaia, Spain, 3Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America, 4Institut Pasteur, Paris, France, 5Achucarro Basque Center for Neuroscience, Leioa, Bizkaia, Spain
First Author:
Co-Author(s):
Ibai Diez
Massachusetts General Hospital and Harvard Medical School
Boston, MA, United States of America
Massachusetts General Hospital and Harvard Medical School
Boston, MA, United States of America
Introduction:
Autism Spectrum Disorder (ASD) is a diverse condition with social and behavioral variations, and whose origin involves complex interactions of genetic, cellular, and environmental factors, potentially linked to developmental excitation/inhibition imbalances. Neurobiologically, ASD exhibits a great deal of heterogeneity in brain morphology and network patterns. Subtyping efforts may offer a solution to overcome such multiscale heterogeneity, which is the most significant challenge in the development of effective therapies. This study, combining functional connectivity profiles, consensus clustering and transcriptomics, aims to further understand ASD heterogeneity.
Methods:
A subtyping approach based on consensus clustering of multi-study harmonized functional brain connectivity patterns was applied to a population of 657 ASD individuals with quality-assured neuroimaging data from the ABIDE consortium. Subsequently, by means of a Multivariate Distance Matrix Regression analysis, functional architecture alterations of each subtype relative to the typically developing control (TDC) group (884 subjects) were estimated. The resulting brain maps were associated with high-resolution gene transcriptomics while controlling for spatial autocorrelations. The subset of genes showing a significant spatial similarity in these associations were then submitted to an enrichment analysis and a protein interaction analysis to characterize the molecular mechanism behind each subtype.
·A) ABIDE data preparation. B) Preprocessing. C) Harmonization. D) Transcriptomic data preparation. E) Subtyping. F) Physical gene interaction network analysis. G) Gene Set Enrichment Analysis.
Results:
Two major stable subtypes were found (panel A): subtype 1, comprising about 53% of ASD subjects and exhibiting hypoconnectivity (less average connectivity than TDC participants); and subtype 2, involving about 43% of ASD subjects and showing hyperconnectivity. Both subtypes did not differ statistically in structural imaging metrics in any of the regions (68 cortical and 14 subcortical) or in any of the behavioral scores (IQ, Autism Diagnostic Interview, and Autism Diagnostic Observation Schedule) analyzed.
Functional network alterations of subtype 1 relative to TDC mainly involved the superior temporal gyrus, posterior cingulate cortex, and the insula. For subtype 2, higher differences were found in the thalamus, putamen, and precentral gyrus. Thus, alterations affecting the default mode network were common to both subtypes, but one (subtype 1) also showed specific disruptions involving the salience network and the other (subtype 2) in the somatomotor network (panel B).
Subsequently, an association analysis of such alterations with transcriptomic data found 195 significant negative-associated (NEG) genes and 364 positive-associated (POS) genes for subtype 1. Significant NEG genes, also present in the SFARI gene human database with a relevance score of 1, were GFAP, CHD7, SKI, SHANK3, ANK3, and CACNA1E, while POS genes were ASXL3, MAP1A, STXBP1, DPYSL2, KNCB1, SCN8A, RIMS1, and CDKL5. Similarly, for subtype 2, we found 142 NEG genes, of which GRIA2, RFX3, SHANK2, GRIN2B, DLG4, LRRC4C, ARX, and GABRB3 were also present in the SFARI list, and 180 POS genes, including MAGEL2 and IQSEC2.
Finally, a gene enrichment analysis showed (panel C) significant enrichments after multiple testing corrections only for subtype 2 (not even using instead the whole ASD group), and included GO biological processes and Reactome pathways related to glutamate signaling (affecting both AMPA and NMDA receptors) and synapse organization in relation to the E/I imbalance occurring during the development of brain circuits. Likewise, NEG genes participated in each biological process and pathway, mostly prominent by genes DLG4, GRIN2B, GRIA2, and SHANK2.
Functional network alterations of subtype 1 relative to TDC mainly involved the superior temporal gyrus, posterior cingulate cortex, and the insula. For subtype 2, higher differences were found in the thalamus, putamen, and precentral gyrus. Thus, alterations affecting the default mode network were common to both subtypes, but one (subtype 1) also showed specific disruptions involving the salience network and the other (subtype 2) in the somatomotor network (panel B).
Subsequently, an association analysis of such alterations with transcriptomic data found 195 significant negative-associated (NEG) genes and 364 positive-associated (POS) genes for subtype 1. Significant NEG genes, also present in the SFARI gene human database with a relevance score of 1, were GFAP, CHD7, SKI, SHANK3, ANK3, and CACNA1E, while POS genes were ASXL3, MAP1A, STXBP1, DPYSL2, KNCB1, SCN8A, RIMS1, and CDKL5. Similarly, for subtype 2, we found 142 NEG genes, of which GRIA2, RFX3, SHANK2, GRIN2B, DLG4, LRRC4C, ARX, and GABRB3 were also present in the SFARI list, and 180 POS genes, including MAGEL2 and IQSEC2.
Finally, a gene enrichment analysis showed (panel C) significant enrichments after multiple testing corrections only for subtype 2 (not even using instead the whole ASD group), and included GO biological processes and Reactome pathways related to glutamate signaling (affecting both AMPA and NMDA receptors) and synapse organization in relation to the E/I imbalance occurring during the development of brain circuits. Likewise, NEG genes participated in each biological process and pathway, mostly prominent by genes DLG4, GRIN2B, GRIA2, and SHANK2.
·A) Two major stable ASD subtypes (pink and orange). B) Association between transcriptomics and connectivity patterns for each ASD subtype. C) Excitation/inhibition imbalance enrichment for subtype 2.
Conclusions:
Our results support a link between excitation/inhibition imbalance, a leading well-known primary mechanism in the pathophysiology of ASD, and functional connectivity alterations. This, however, affects only one subpart of ASD, overall characterized by brain hyperconnectivity and major alterations in somatomotor and default mode networks.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Genetics:
Transcriptomics 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Other Methods
Keywords:
Autism
FUNCTIONAL MRI
Machine Learning
Other - Allen Human Brain Atlas
1|2Indicates the priority used for review
Provide references using author date format
- Hawrylycz, M. (2023), 'Linking Neurogenetics and Functional Connectivity in Autism', Biological Psychiatry, vol. 94, no 10, pp 765-766.