Shared atypical default mode and salience network functional connectivity between autism and schizophrenia.
Autism and schizophrenia share the same at-rest brain signature, so screen for both and borrow strategies across labels.
01Research in Context
What this study did
Chen et al. (2017) scanned teens and young adults while they rested.
Some had autism, some had schizophrenia, and some had neither.
The team used fMRI to map two brain networks: the default mode and the salience networks.
A computer learned the patterns and tried to tell who had which diagnosis.
What they found
Both autism and schizophrenia groups showed the same odd wiring.
The shared odd pattern let the computer pick out either diagnosis about four times in five.
This means the two labels may share one brain signature.
How this fits with other research
Fine et al. (2005) already showed that kids with 22q11.2 deletion often meet autism cut-offs.
Heng’s finding extends that work: the shared biology may sit in these two networks.
Sirao et al. (2026) also used imaging plus machine learning on autistic kids.
They looked at live two-brain data, not resting wiring, yet both papers land near 80 % accuracy—suggesting the method, not the moment, spots autism-linked quirks.
Why it matters
If one brain pattern sits under two labels, your autism tools may help clients with schizophrenia traits, and vice versa.
When you see rigid thinking or social slip-ups, ask about family history of psychosis—then teach self-monitoring or coping plans that work for both groups.
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02At a glance
03Original abstract
UNLABELLED: Schizophrenia and autism spectrum disorder (ASD) are two prevalent neurodevelopmental disorders sharing some similar genetic basis and clinical features. The extent to which they share common neural substrates remains unclear. Resting-state fMRI data were collected from 35 drug-naïve adolescent participants with first-episode schizophrenia (15.6 ± 1.8 years old) and 31 healthy controls (15.4 ± 1.6 years old). Data from 22 participants with ASD (13.1 ± 3.1 years old) and 21 healthy controls (12.9 ± 2.9 years old) were downloaded from the Autism Brain Imaging Data Exchange. Resting-state functional networks were constructed using predefined regions of interest. Multivariate pattern analysis combined with multi-task regression feature selection methods were conducted in two datasets separately. Classification between individuals with disorders and controls was achieved with high accuracy (schizophrenia dataset: accuracy = 83%; ASD dataset: accuracy = 80%). Shared atypical brain connections contributing to classification were mostly present in the default mode network (DMN) and salience network (SN). These functional connections were further related to severity of social deficits in ASD (p = 0.002). Distinct atypical connections were also more related to the DMN and SN, but showed different atypical connectivity patterns between the two disorders. These results suggest some common neural mechanisms contributing to schizophrenia and ASD, and may aid in understanding the pathology of these two neurodevelopmental disorders. Autism Res 2017, 10: 1776-1786. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism spectrum disorder (ASD) and schizophrenia are two common neurodevelopmental disorders which share several genetic and behavioral features. The present study identified common neural mechanisms contributing to ASD and schizophrenia using resting-state functional MRI data. The results may help to understand the pathology of these two neurodevelopmental disorders.
Autism research : official journal of the International Society for Autism Research, 2017 · doi:10.1002/aur.1834