ArticleInternational journal of methods in psychiatric research2020
Diagnosing schizophrenia with network analysis and a machine learning method.
Article in International journal of methods in psychiatric research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.Journal of medical Internet research · 2022Pooled it
- SNP-based prediction of schizophrenia using machine learning.Bioinformatics advances · 2026Article
- Multimodal prediction of psychotic-like experiences using elastic net modeling: external validation in a clinical sample.Psychological medicine · 2025Article
- Individualized functional brain mapping machine learning prediction of symptom-change resulting from selective kappa-opioid antagonism in an anhedonic sample from a Fast-Fail trial.Journal of mood and anxiety disorders · 2025Article
- miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.International journal of molecular sciences · 2025Article
- Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.Scientific reports · 2025Article
- Psychological disorder detection: A multimodal approach using a transformer-based hybrid model.MethodsX · 2024Article
- Network integration and segregation changes in schizophrenia: impact of electroconvulsive therapy.BMC psychiatry · 2024Article
- Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis.Scientific reports · 2024Article
- Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.The clinical respiratory journal · 2024Article
- Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia.BMC neuroscience · 2024Article
- Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis.Healthcare (Basel, Switzerland) · 2023Review
- Single classifier vs. ensemble machine learning approaches for mental health prediction.Brain informatics · 2023Article
- Unraveling schizophrenia replicable functional connectivity disruption patterns across sites.Human brain mapping · 2023Article
- Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis.Frontiers in neuroscience · 2023Article
- Violent behavior and the network properties of psychopathological symptoms and real-life functioning in patients with schizophrenia.Frontiers in psychiatry · 2023Article
- Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSchizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research.
methodsWe investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls.
resultsIn classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns.
conclusionIn conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.
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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 Socratic graph.