Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

• Published in: Nature communications 2021-04, Vol.12 (1), p.2225-2225, Article 2225
• Main Authors: Park, Bo-yong, Hong, Seok-Jun, Valk, Sofie L., Paquola, Casey, Benkarim, Oualid, Bethlehem, Richard A. I., Di Martino, Adriana, Milham, Michael P., Gozzi, Alessandro, Yeo, B. T. Thomas, Smallwood, Jonathan, Bernhardt, Boris C.
• Format: Article
• Language: English
• Subjects: 59/36; 59/57; 631/378; 631/378/1689/1373; Adolescent; Anomalies; Association analysis; Autism; Autistic Disorder - diagnostic imaging; Autistic Disorder - metabolism; Autistic Disorder - physiopathology; Brain - diagnostic imaging; Brain Mapping; Cerebral Cortex; Child; Children; Circuits; Computational neuroscience; Connectome - methods; Data exchange; Female; Gene Expression Profiling; Group effects; Humanities and Social Sciences; Humans; Learning algorithms; Machine learning; Male; Manifolds (mathematics); Medical imaging; multidisciplinary; Neuroimaging; Perturbation; Schedules; Science; Science (multidisciplinary); Severity of Illness Index; Thalamus; Thalamus - physiopathology; Transcriptome
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-21732-0

The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism. Autism is increasingly recognized to impact multiple scales of neural organization. Here, the authors show alterations in individuals with autism relative to typically developing controls, with findings particularly pointing to atypically organized subcortical-cortical networks.