Using Multivariate Machine Learning Methods and Structural MRI to Classify Childhood Onset Schizophrenia and Healthy Controls

Abstract

Introduction: Multivariate machine learning methods can be used to classify groups of schizophrenia patients and controls using structural magnetic resonance imaging (MRI). However, machine learning methods to date have not been extended beyond classification and contemporaneously applied in a meaningful way to clinical measures. We hypothesized that brain measures would classify groups, and that increased likelihood of being classified as a patient using regional brain measures would be positively related to illness severity, developmental delays, and genetic risk. Methods: Using 74 anatomic brain MRI sub regions and Random Forest (RF), a machine learning method, we classified 98 childhood onset schizophrenia (COS) patients and 99 age, sex, and ethnicity-matched healthy controls. We also used RF to estimate the probability of being classified as a schizophrenia patient based on MRI measures. We then explored relationships between brain-based probability of illness and symptoms, premorbid development, and presence of copy number variation (CNV) associated with schizophrenia. Results: Brain regions jointly classified COS and control groups with 73.7% accuracy. Greater brain-based probability of illness was associated with worse functioning (p = 0.0004) and fewer developmental delays (p = 0.02). Presence of CNV was associated with lower probability of being classified as schizophrenia (p = 0.001). The regions that were most important in classifying groups included left temporal lobes, bilateral dorsolateral prefrontal regions, and left medial parietal lobes. Conclusion: Schizophrenia and control groups can be well classified using RF and anatomic brain measures, and brain-based probability of illness has a positive relationship with illness severity and a negative relationship with developmental delays/problems and CNV-based risk.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images

“…In the discriminative analyses of SZ patients, the SVM with RFE classifier used with the significant structural abnormalities identified by the VBM analysis as input features achieved the best performances (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%), which was better than the performances of previous discriminative analyses of SZ patients using structural MRI data. [23,24] …”

“…Recently, machine learning, which is a type of multivariate analysis that can automatically discriminate individuals within a sample group, was used to classify several neuropsychiatric disorders and addictions, such as SZ, [19–24] Alzheimer's disease , [25–28] depression, [29–31] attention-deficit hyperactivity disorder, [32–34] and smoking. [35] …”

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis

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In search of biomarkers for schizophrenia using electroencephalography

“…Greenstein et al applied the Random Forest machine learner on data from 74 anatomic brain MRI subregions obtained from 98 childhood onset schizophrenia patients and 99 age, sex and ethnicity-matched healthy controls [3]. Patients and controls were classified on a combination of brain regions with an accuracy of 73.7%.…”