scholarly journals Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Pavol Mikolas ◽  
Jaroslav Hlinka ◽  
Antonin Skoch ◽  
Zbynek Pitra ◽  
Thomas Frodl ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
First Episode ◽  
Schizophrenia Spectrum Disorders ◽  
Machine Learning Classification ◽  
Brain White Matter ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders ◽  
First Episode Schizophrenia

Classification of first-episode schizophrenia spectrum disorders and controls from whole brain white matter fractional anisotropy using machine learning

2017 ◽  
Vol 41 (S1) ◽  
pp. S191-S191 ◽  
Author(s):  
P. Mikolas ◽  
J. Hlinka ◽  
Z. Pitra ◽  
A. Skoch ◽  
T. Frodl ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
Fractional Anisotropy ◽  
Classification Performance ◽  
Classification Model ◽  
First Episode ◽  
Support Vector ◽  
Brain White Matter ◽  
First Episode Schizophrenia

BackgroundSchizophrenia is a chronic disorder with an early onset and high disease burden in terms of life disability. Its early recognition may delay the resulting brain structural/functional alterations and improve treatment outcomes. Unlike conventional group-statistics, machine-learning techniques made it possible to classify patients and controls based on the disease patterns on an individual level. Diagnostic classification in first-episode schizophrenia to date was mostly performed on sMRI or fMRI data. DTI modalities have not gained comparable attention.MethodsWe performed the classification of 77 FES patients and 77 healthy controls matched by age and sex from fractional anisotropy data from using linear support-vector machine (SVM). We further analyzed the effect of medication and symptoms on the classification performance using standard statistical measures (t-test, linear regression) and machine learning (Kernel–Ridge regression).ResultsThe SVM distinguished between patients and controls with significant accuracy of 62.34% (P = 0.005). There was no association between the classification performance and medication nor symptoms. Group level statistical analysis yielded brain-wide significant differences in FA.ConclusionThe SVM in combination with brain white-matter fractional anisotropy might help differentiate FES from HC. The performance of our classification model was not associated with symptoms or medications and therefore reflects trait markers in the early course of the disease.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals S201. HIPPOCAMPAL SUBFIELD VOLUMES AND CHANGE IN BODY MASS OVER 12 MONTHS OF TREATMENT IN FIRST-EPISODE SCHIZOPHRENIA SPECTRUM DISORDERS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S114-S115
Author(s):  
Stéfan Du Plessis ◽  
Hilmar Luckhoff ◽  
Sanja Kilian ◽  
Laila Asmal ◽  
Frederika Scheffler ◽  
...  
Keyword(s):  
Substance Use ◽  
Weight Gain ◽  
Body Mass ◽  
Interactive Effect ◽  
First Episode ◽  
Schizophrenia Spectrum Disorders ◽  
Hippocampal Subfields ◽  
Schizophrenia Spectrum ◽  
Spectrum Disorders ◽  
First Episode Schizophrenia

Abstract Background In this study, we explored the relationship between hippocampal subfield volumes and change in body mass over 12 months of treatment in 90 first-episode schizophrenia spectrum disorder patients (66 males, 24 females; mean age= 24.7±6.8 years). Methods Body mass index was assessed in patients at baseline, and at months 3, 6, 9 and 12. Hippocampal subfields of interest were assessed using a segmentation algorithm included in the FreeSurfer 6.0 software program. Results Linear regression analysis showed a significant interactive effect between sex and anterior hippocampus size as a predictor of change in body mass over 12 months, adjusting for age, substance use, treatment duration, and posterior hippocampal volumes. In an exploratory sub-analysis, partial correlations revealed a significant association between weight gain and smaller CA1, CA3 and subiculum volumes in females, but not males, adjusting for age and substance use, with similar trends evident for the CA4 and presubiculum subfields. Discussion In conclusion, our findings suggest that smaller anterior hippocampal subfields are associated with the development of weight gain over the course of treatment in first-episode schizophrenia spectrum disorders in a sex-specific fashion, and may partly explain the more severe and ongoing increase in body mass evident for female patients.

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