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Early onset schizophrenia (onset before adulthood) is a rare, severe, and chronic form of schizophrenia. The clinical presentation of schizophrenia at this unusually early age of onset has been associated with premorbid developmental abnormalities, poor response to neuroleptic treatment, greater admission rates, and poor prognosis. This is a brief, condensed review of current treatment strategies for the early onset population highlighting the need for novel treatment strategies for these generally treatment-refractory cases. Based on the current literature, second-generation antipsychotics remain the mainstay of treatment, although current medications provide suboptimal response at best. Based on the adult literature, combining antipsychotic treatment with psychotherapeutic intervention may be a more comprehensive treatment strategy. Indeed, early detection, identification of relevant biomarkers, coupled with advancing knowledge of the neurochemical and neuroanatomic pathways may help design informed and novel treatment strategies.

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.