ArticleBrain sciences2020
Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.Scientific reports · 2025Article
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Effect of abdominal adipose tissue glucose uptake on brain aging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Review
- Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.Neuroscience bulletin · 2024Review
- Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.World journal of psychiatry · 2024Article
- Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach.Biomedicines · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach.Frontiers in psychiatry · 2023Article
- A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning.Frontiers in neuroscience · 2022Article
- Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?Frontiers in psychiatry · 2022Article
- Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (
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