Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features.
To date, there are no reliable markers for predicting onset of schizophrenia in individuals at high risk (HR). Substantial promise is, however, shown by a variety of pattern classification approaches to neuroimaging data. Here, we examined the predictive accuracy of support vector machine (SVM) in later diagnosing schizophrenia, at a single-subject level, using a cohort of HR individuals drawn from multiply affected families and a combination of neuroanatomical, schizotypal and neurocognitive variables. Baseline structural magnetic resonance imaging (MRI), schizotypal and neurocognitive data from 17 HR subjects, who subsequently developed schizophrenia and a matched group of 17 HR subjects who did not make the transition, yet had psychotic symptoms, were included in the analysis. We employed recursive feature elimination (RFE), in a nested cross-validation scheme to identify the most significant predictors of disease transition and enhance diagnostic performance. Classification accuracy was 94% when a self-completed measure of schizotypy, a declarative memory test and structural MRI data were combined into a single learning algorithm; higher than when either quantitative measure was used alone. The discriminative neuroanatomical pattern involved gray matter volume differences in frontal, orbito-frontal and occipital lobe regions bilaterally as well as parts of the superior, medial temporal lobe and cerebellar regions. Our findings suggest that an early SVM-based prediction of schizophrenia is possible and can be improved by combining schizotypal and neurocognitive features with neuroanatomical variables. However, our predictive model needs to be tested by classifying a new, independent HR cohort in order to estimate its validity.
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK. Electronic address: E.Zarogianni@sms.ed.ac.uk.
Eleni
E
Zarogianni
Institute for Adaptive and Neural Computation, University of Edinburgh, UK.
Amos J
AJ
Storkey
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK.
Eve C
EC
Johnstone
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK.
David G C
DG
Owens
Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK.
Stephen M
SM
Lawrie
D000293:Adolescent; D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging*; D003071:Cognition; D003936:Diagnosis, Computer-Assisted; D005190:Family; D005240:Feasibility Studies; D005260:Female; D005500:Follow-Up Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D008568:Memory; D015999:Multivariate Analysis; D009483:Neuropsychological Tests; D012559:Schizophrenia / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D012565:Schizophrenic Psychology; D012569:Schizotypal Personality Disorder / Q000523:psychology*; D060388:Support Vector Machine; D055815:Young Adult
Familial HR; MRI; Machine learning; Prediction; Recursive feature elimination; Schizophrenia; Support vector machine