Stable Biomarker Identification For Predicting Schizophrenia in the Human Connectome

Abstract

Schizophrenia, as a mental disorder, has been well documented with both structural and functional magnetic resonance imaging. The developing field of connectomics has attracted much attention as it allows researchers to take advantage of powerful tools of network analysis in order to study structural and functional connectivity abnormalities in schizophrenia. Many methods have been proposed to identify biomarkers in schizophrenia, focusing mainly on improving the classification performance or performing statistical comparisons between groups. However, the stability of biomarkers selection has been for long overlooked in the connectomics field. In this study, we follow a machine learning approach where the identification of biomarkers is addressed as a feature selection problem for a classification task. We perform a recursive feature elimination and support vector machines (RFE-SVM) approach to identify the most meaningful biomarkers from the structural, functional, and multi-modal connectomes of healthy controls and patients. Furthermore, the stability of the retrieved biomarkers is assessed across different subsamplings of the dataset, allowing us to identify the affected core of the pathology. Further analysis of the connectivity strength of the affected core quantified with the generalized fractional anisotropy (gFA) and apparent diffusion coefficient (ADC) reveal a significant alteration in patients. Considering our technique altogether demonstrates a principled way to achieve both accurate and stable biomarkers while highlighting the importance of multi-modal approaches to brain pathology as they tend to reveal complementary information. schizophrenia diagnosis | stable biomarkers | RFE-SVM | human connectome

Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants

“…Our model had a mean accuracy of 85%, which is slightly better than those reported in recent machine-learning studies based on brain-wide FC: 82.4% [15], 81.74% [16], and 82.61% [41]. Notably, the performance of these SVMs was highly consistent—between 80 and 85%—suggesting that brain-wide FC is a reliable feature for automatic classification of patients with schizophrenic disorder.…”

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

“…ML was also employed to discriminate subjects with schizophrenia from healthy control with an accuracy of 0.72% [96], or predict the response to treatment in first-episode drug naïve subjects, with an accuracy of 82.5% [97]. Also, deep neural network models were applied to identify brain abnormalities with an accuracy of 81.5% [98] supervised SVM-RFE combining functional and structural MRI was able to distinguish schizophrenia patients from HC with an accuracy of up to 80% [99], and combining polygenic risk score and structural imaging methods with 71.6% accuracy by exploring data of more than 1000 subjects from eight independent sites across China [100].…”

FAM171B as a Novel Biomarker Mediates Tissue Immune Microenvironment in Pulmonary Arterial Hypertension

“…As a regression analysis technique, LASSO analysis sets the coefficients of less significant variables to zero by applying an L1-penalty (lambda) to screen for significant variables and construct the best classification model [11]. The SVM-RFE analysis is a supervised machine learning technique for classifying data points by maximizing the margin between distinct classes in a high-dimensional space [12]. The RF analysis is a nonparametric approach for carrying out classification under supervision [13].…”