Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study

Abstract

Background and Hypothesis Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). Study Design Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. Study Results The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). Conclusion We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.

Beijing Language and Culture University, China

“…A lot of current machine learning based approaches distinguish patients with psychiatric disorders from healthy controls utilizing a single modality [5,6]. However, as multi-modality data is able to provide richer information, deep learning methods with multi-modality fusion is a promising direction to improve the model performance in a variety of medical image analysis tasks [7,8,9].…”

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

“…A biomarker of chronic schizophrenia may not predict conversion to psychosis in CHR cases, or response to a given treatment, or which circuits are the most amenable to neuromodulation. But the capacity of machine learning approaches to address these questions is developing, as predicting prognostic trajectories for high-risk or first-episode subjects is an active area of exploration [129][130][131] .…”

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

“…Structural MRI is gaining importance to help differentiate between SCZ and healthy controls, as summarized in de Filippis et al review [91•], as SVM could reach an accuracy of 100% if combined with more recent ML tools [92]. A parameter that has been studied as a potential biomarker of psychosis is the disrupted functional asymmetry: this value in the left thalamus discriminated control vs FEP/UHR individuals with high sensitivity (68.42% and 81.08% respectively) [93]. Antonucci et al found that SVM built with a repeated nested cross-validation framework was able to distinguish schizophrenia patients from HC by computing attentional control task in fMRI, to identify a pattern of connectivity alterations, with an accuracy of 66.9% [94].…”