ArticleNeuroImage. Clinical2018
Differences in atypical resting-state effective connectivity distinguish autism from schizophrenia.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 5 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Autism spectrum disorder and schizophrenia: An updated conceptual review.Autism research : official journal of the International Society for Autism Research · 2022Pooled it
- Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review.Frontiers in neuroscience · 2019Pooled it
- Transdiagnostic Profiles of BOLD Signal Variability in Autism and Schizophrenia Spectrum Disorders: Associations With Cognition and Functioning.Human brain mapping · 2026Article
- Differential connectivity between core hubs underlying demanding executive functions in schizophrenia compared to autism and control adults.Frontiers in psychiatry · 2026Article
- Differential brain regional activity and connectivity of anterior cingulate cortex between major depressive disorder with and without suicidal ideation under resting state.Psychological medicine · 2025Article
- Altered temporal lobe connectivity is associated with psychotic symptoms in drug-naïve adolescent patients with first-episode schizophrenia.European child & adolescent psychiatry · 2025Article
- Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.NeuroImage · 2024Article
- Distinct Symptom Network Structure and Shared Central Social Communication Symptomatology in Autism and Schizophrenia: A Bayesian Network Analysis.Journal of autism and developmental disorders · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Editorial: Mapping psychopathology with MRI and connectivity analysis.Frontiers in human neuroscience · 2023Article
- A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder.Human brain mapping · 2022Article
- Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder.Communications biology · 2021Article
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
- Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison.Brain sciences · 2021Article
- Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.Neural computing & applications · 2021Article
- Brain features of nearly drug-naïve female monozygotic twins with first-episode schizophrenia and the classification accuracy of brain feature patterns: A pilot study.Brain and behavior · 2021Article
- Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder.Human brain mapping · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure and common cognitive deficits. A number of studies using resting state fMRI have shown that machine learning algorithms can distinguish between healthy controls and individuals diagnosed with either autism spectrum disorder or schizophrenia. However, it has not yet been determined whether machine learning algorithms can be used to distinguish between the two disorders. Using a linear support vector machine, we identify features that are most diagnostic for each disorder and successfully use them to classify an independent cohort of subjects. We find both common and divergent connectivity differences largely in the default mode network as well as in salience, and motor networks. Using divergent connectivity differences, we are able to distinguish autistic subjects from those with schizophrenia. Understanding the common and divergent connectivity changes associated with these disorders may provide a framework for understanding their shared cognitive deficits.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.