Predictors of schizophrenia spectrum disorders in early-onset first episodes of psychosis: a support vector machine model

Abstract

Identifying early-onset schizophrenia spectrum disorders (SSD) at a very early stage remains challenging. To assess the diagnostic predictive value of multiple types of data at the emergence of early-onset first-episode psychosis (FEP), various support vector machine (SVM) classifiers were developed. The data were from a 2-year, prospective, longitudinal study of 81 patients (age 9-17 years) with early-onset FEP and a stable diagnosis during follow-up and 42 age- and sex-matched healthy controls (HC). The input was different combinations of baseline clinical, neuropsychological, magnetic resonance imaging brain volumetric and biochemical data, and the output was the diagnosis at follow-up (SSD vs. non-SSD, SSD vs. HC, and non-SSD vs. HC). Enhanced recursive feature elimination was performed for the SSD vs. non-SSD classifier to select and rank the input variables with the highest predictive value for a diagnostic outcome of SSD. After validation with a test set and considering all baseline variables together, the SSD vs. non-SSD, SSD vs. HC and non-SSD vs. HC classifiers achieved an accuracy of 0.81, 0.99 and 0.99, respectively. Regarding the SSD vs. non-SSD classifier, a combination of baseline clinical variables (severity of negative, disorganized symptoms and hallucinations or poor insight) and neuropsychological variables (impaired attention, motor coordination, and global cognition) showed the highest predictive value for a diagnostic outcome of SSD. Neuroimaging and biochemical variables at baseline did not add to the predictive value. Thus, comprehensive clinical/cognitive assessment remains the most reliable approach for differential diagnosis during early-onset FEP. SVMs may constitute promising multivariate tools in the search for predictors of diagnostic outcome in FEP.

Deficits in Neurite Density Underlie White Matter Structure Abnormalities in First-Episode Psychosis

“…We extracted the mean FA, MD, ODI, and ND for control subjects and patients with FEP in the corpus callosum body (Johns Hopkins University ICBM-DTI-81 atlas), masking the region of interest by the mean_FA_skeleton_mask to include only skeleton voxels (Supplemental Figure S2). Years of Education 13.37 (11)(12)(13)(14)(15)(16)(17) 13.84 (11)(12)(13)(14)(15)(16)(17) .…”

“…These structural changes are associated with dysfunctional interactions between brain regions (12) and predict symptom severity in FEP (13). Moreover, neuroimaging indices of white matter integrity predict longer term outcomes, including response to treatment (2,14). White matter structural abnormalities may thus underpin early psychosis.…”

“…Approaches such as NODDI benefit from long MRI acquisition times; however, newer clinically feasible protocols have been developed (21). These permit detailed characterization of white matter integrity that can shed light on the etiology of brain disorders and provide indicators for diagnosis, treatment response, and prognosis (14,22,23).…”

Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM

“…The experimental results demonstrated that there was an accuracy rate of 88.3% for classification in the first group, and the discrimination function derived from the first group correctly differentiated 75% of the subjects in the second group. To integrate spatial and temporal information in multichannel fNIRS, [8] employed a novel probabilistic pattern recognition method called Gaussian process classifier for the diagnostic classification of schizophrenia. Using the temporal patterns of fNIRS data measured during a working memory task, an overall accuracy of 76% was achieved in a group of 80 samples.…”

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross-sectional study

“…One study used a K-nearest neighbors (KNN) algorithm, random forest model, and a support vector machine (SVM) with a linear kernel function and radial basis function (RBF) to classify the different levels of airway obstruction in patients with chronic pulmonary diseases in a binary fashion [ 8 ]. A binary SVM classifier was used for the identification of schizophrenia spectrum disorders (SSD) in the early stages and the assessment of the predictive value of early diagnosis of different types of data in the emergence of first episode psychosis (FEP) [ 9 ]. Another study has been conducted on the methods of diagnosis of lymphglands based on SVM with different kernel functions such as linear, quadratic, and Gaussian functions [ 10 ].…”