A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia

Godfrey Pearlson,Honghui Yang,Jing Sui,Jingyu Liu,Vince D Calhoun

doi:10.3389/fnhum.2010.00192

Abstract

We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP) data. The method consists of four stages: (1) SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME). (2) Voxels in the fMRI map contributing to classification are selected to build another SVME (Voxel-SVME). (3) Components of fMRI activation obtained with independent component analysis (ICA) are used to construct a single SVM classifier (ICA-SVMC). (4) The above three models are combined into a single module using a majority voting approach to make a final decision (Combined SNP-fMRI). The method was evaluated by a fully validated leave-one-out method using 40 subjects (20 patients and 20 controls). The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects, of schizophrenia etiopathology. This study suggests an effective way to reassess biological classification of individuals with schizophrenia, which is also potentially useful for identifying diagnostically important markers for the disorder.

Highlights

Schizophrenia is a severe, chronic, brain disease that disrupts normal thinking, speech, and behavior
While most such studies focus on identifying associations between genetics and brain function in schizophrenia, we look at this problem from a different perspective, using biological and genetic information to help classify the disorder
The importance of each voxel to the classification task can be denoted by the ratio of the number of times each voxel selected over the number of iterations of feature selective AdaBoost method (FSA)

Summary

Introduction

Schizophrenia is a severe, chronic, brain disease that disrupts normal thinking, speech, and behavior. Schizophrenia diagnosis currently relies on clinical examination and the illness course, with many subcategories reflecting different aspects of this complex and likely biologically heterogeneous mental disease. There have been increasing efforts to utilize brain functional magnetic resonance imaging (fMRI) and examine genetic variation to study potential schizophrenia biomarkers, in order to better understand the pathology of schizophrenia. While most such studies focus on identifying associations between genetics and brain function in schizophrenia, we look at this problem from a different perspective, using biological and genetic information to help classify the disorder. We predict that by achieving better classification, intrinsic connections between genetic variation and biological function can be identified

Methods

Results

Discussion

Conclusion