Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI

Xiao, Yuan; Yan, Zhihan; Zhao, Youjin; Tao, Bo; Sun, Huaiqiang; Li, Fēi; Yao, Li; Zhang, Wenjing; Shah, Chandan; Liu, Jieke; Gong, Qiyong; Sweeney, John A.; Lui, Su

doi:10.1016/j.schres.2017.11.037

Cited by 64 publications

(54 citation statements)

References 45 publications

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“…e proposed method in the current study revealed sensitive and accurate information about anatomically abnormal patterns in the frontal lobe, postcentral gyrus, corpus callosum, and cuneus, especially in the thalamus, fusiform gyrus, temporal lobe, and cerebellum. ese identified abnormal biomarkers were consistent with those found in the literature [8,[76][77][78][79]. Some brain regions were found in both GM and WM, including the cerebellum, fusiform gyrus, temporal lobe, occipital lobe, and frontal lobe, which showed that SZ could indeed cause structural changes in these brain regions.…”

Section: Discussionsupporting

confidence: 89%

“…However, the specific mechanisms involved in producing these structural deficits remain incompletely understood. In recent years, it has been consistently reported that SZ patients have structural abnormalities in the brain, including the middle temporal gyrus, middle frontal gyrus, thalamus, and corpus callosum (CcSum) [6][7][8]. e brain structure location and neurobiological processes underlying these structural abnormalities are central to the pathophysiology of schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

“…A recent study [33] utilized a support vector machine (SVM) to learn the decision model to classify Alzheimer's disease patients and normal controls, which achieved an area under the curve (AUC) of over 88.82%. Another study [8] used cortical thickness in conjunction with surface area in schizophrenia patients to perform discriminative analysis and obtained an accuracy of 85.0%. ough the good performance with machine learning produced an excessive number of features (voxels), there is a risk of overfitting [34,35].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

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Utilizing neuroimaging and machine learning (ML) to differentiate schizophrenia (SZ) patients from normal controls (NCs) and for detecting abnormal brain regions in schizophrenia has several benefits and can provide a reference for the clinical diagnosis of schizophrenia. In this study, structural magnetic resonance images (sMRIs) from SZ patients and NCs were used for discriminative analysis. is study proposed an ML framework based on coarse-to-fine feature selection. e proposed framework used two-sample t-tests to extract the differences between groups first, then further eliminated the nonrelevant and redundant features with recursive feature elimination (RFE), and finally utilized the support vector machine (SVM) to learn the decision models with selected gray matter (GM) and white matter (WM) features. Previous studies have tended to report differences at the group level instead of at the individual level and cannot be widely applied. e method proposed in this study extends the diagnosis to the individual level and has a higher recognition rate than previous methods. e experimental results of this study demonstrate that the proposed framework distinguishes SZ patients from NCs, with the highest classification accuracy reaching over 85%. e identified biomarkers are also consistent with previous literature findings. As a universal method, the proposed framework can be extended to diagnose other diseases.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Chen

Yan

Wang

et al. 2020

Computational Intelligence and Neuroscience

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“…The predictive signature is also consistent with discriminative regions identified in previous machine learning studies. For example, in a recent study with a large population of 326 participants of drug‐naïve first‐episode psychosis (FEP) patients and demographically matched healthy controls, the regions contributing to the classification mainly included the left inferior parietal, left rostral anterior cingulate, left rostral middle frontal, right caudal middle frontal, right inferior parietal, right lingual and right temporal pole cortex. Findings in this sample of never‐treated FES patients are related to the regions identified in this current study.…”

Section: Discussionmentioning

confidence: 99%

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Pierrefeu

Löfstedt

Laidi

et al. 2018

Acta Psychiatr Scand

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Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity Objective: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings' reproducibility. Method: We propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross-site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first-episode patients. Results: Machine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first-episode psychosis patients (73% accuracy). Conclusion: These results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder. Significant outcomes• Significant intersite prediction accuracy of clinical diagnosis based on sMRI.• Identification of a robust and interpretable structural brain signature of schizophrenia.• The predictive signature generalizes to the detection of patients at the early stage of the disorder. Limitations• At this stage, this does not imply that such predictive models are able to distinguish patients with various psychiatric conditions. • A minority of patients do not present such brain abnormalities, which directly questions the need for a disorder stratification.

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“…As deep learning algorithms have achieved superior performance in visual image recognition (11), their clinical significance has increased in certain diagnostic tasks, such as detecting pulmonary nodules on chest CT scans (12) and diagnosing diabetic retinopathy from retinal fundus photographs (13). Similar studies have been conducted in schizophrenia patients using structural MRI data, and acceptable accuracy rates have been achieved (68.1% to 85.0%) (14)(15)(16)(17). A deep belief network achieved a higher accuracy rate than a classical machine learning algorithm in discriminating schizophrenia patients from healthy controls (15).…”

Section: Introductionmentioning

confidence: 99%

Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm

Lee

et al. 2020

Front. Psychiatry

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Objective: Although distinctive structural abnormalities occur in patients with schizophrenia, detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This study aimed to detect schizophrenia in structural MRI data sets using a trained deep learning algorithm. Method: Five public MRI data sets (BrainGluSchi, COBRE, MCICShare, NMorphCH, and NUSDAST) from schizophrenia patients and normal subjects, for a total of 873 structural MRI data sets, were used to train a deep convolutional neural network. Results: The deep learning algorithm trained with structural MR images detected schizophrenia in randomly selected images with reliable performance (area under the receiver operating characteristic curve [AUC] of 0.96). The algorithm could also identify MR images from schizophrenia patients in a previously unencountered data set with an AUC of 0.71 to 0.90. The deep learning algorithm's classification performance degraded to an AUC of 0.71 when a new data set with younger patients and a shorter duration of illness than the training data sets was presented. The brain region contributing the most to the performance of the algorithm was the right temporal area, followed by the right parietal area. Semitrained clinical specialists hardly discriminated schizophrenia patients from healthy controls (AUC: 0.61) in the set of 100 randomly selected brain images. Conclusions: The deep learning algorithm showed good performance in detecting schizophrenia and identified relevant structural features from structural brain MRI data; it had an acceptable classification performance in a separate group of patients at an earlier stage of the disease. Deep learning can be used to delineate the structural characteristics of schizophrenia and to provide supplementary diagnostic information in clinical settings.

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Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI

Cited by 64 publications

References 45 publications

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning

Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity

Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm

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