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

Yang H, Liu J, Sui J, Pearlson G, Calhoun VD - Front Hum Neurosci (2010)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Environment Engineering, Northwestern Polytechnical University Xi'an, China.

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.

No MeSH data available.


Related in: MedlinePlus

The location of selected voxels.
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Figure 3: The location of selected voxels.

Mentions: At the second stage, the FSA selected a certain number of voxels that containing the most discriminating information from 261 large voxels and trained a SVM at each iteration. The number of voxels to be selected at each iteration was estimated by the LOO algorithm with weighted training dataset used in that iteration. 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 FSA. Figure 3 shows the location of selected voxels in the brain and their importance. The volume of each region represents the importance of voxels. Yellow indicates the highly important region, followed by orange and red. Table 3 lists the anatomical brain regions of selected voxels.


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

Yang H, Liu J, Sui J, Pearlson G, Calhoun VD - Front Hum Neurosci (2010)

The location of selected voxels.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2990459&req=5

Figure 3: The location of selected voxels.
Mentions: At the second stage, the FSA selected a certain number of voxels that containing the most discriminating information from 261 large voxels and trained a SVM at each iteration. The number of voxels to be selected at each iteration was estimated by the LOO algorithm with weighted training dataset used in that iteration. 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 FSA. Figure 3 shows the location of selected voxels in the brain and their importance. The volume of each region represents the importance of voxels. Yellow indicates the highly important region, followed by orange and red. Table 3 lists the anatomical brain regions of selected voxels.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Environment Engineering, Northwestern Polytechnical University Xi'an, China.

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.

No MeSH data available.


Related in: MedlinePlus