A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia

Abstract

Schizophrenia is diagnosed based largely upon behavioral symptoms. Currently no quantitative, biologically based diagnostic technique has yet been developed to identify patients with schizophrenia. Classification of individuals into patient with schizophrenia and healthy control groups based on quantitative biologically-based data is of great interest to support and refine psychiatric diagnoses. We applied a novel projection pursuit technique on various components obtained with independent component analysis (ICA) of 70 subjects' fMRI activation maps obtained during an auditory oddball task. The validity of the technique was tested with a leave-one-out method and the detection performance varied between 80% and 90%. The findings suggest that the proposed data reduction algorithm is effective in classifying individuals into schizophrenia and healthy control groups and may eventually prove useful as a diagnostic tool.

Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements

“…These include Mahalanobis distance (McLachlan, 2004), which is also part of this study, the Bhattacharyya distance (Jimenez and Landgrebe, 1999), and the Kullback-Leibler divergence (Hastie et al, 2003). In addition a projection pursuit algorithm closely coupled to classification error minimization based on an appropriate optimization algorithm can be devised (Demirci et al, 2008). As mentioned before here we focus on Chang's method.…”

Brain Image Classification Based on Automated Morphometry and Penalised Linear Discriminant Analysis with Resampling

“…The efficiency of the algorithm is comparable or superior to the other state-of-the-art studies dealing with the classification of schizophrenia patients [5]- [18]. However, the classification performance is smaller than in [19], in which the COMPARE algorithm enabled classification of schizophrenia and healthy females with accuracy equal to 91.8% and the classification of diseased and healthy males with accuracy of 90.8%.…”

“…In [19] the COMPARE algorithm, classification of schizophrenia patients with very high classification accuracy (91.8% for female subjects and 90.8% for male subjects) was applied. Thus, the complex pipeline seems to enable classification with a higher efficiency than other commonly used methods that have reported classification accuracy between 70% and 90% [5]- [18].…”

“…The most commonly used classification methods have been the linear discriminant analysis (LDA) [6]- [10], support vector machines (SVM) [11]- [13] or the k-nearest neighbour algorithm [14]- [16]. Due to the large amount of features obtained from 3-D medical images, the classification is often preceded by data reduction performed by principal component analysis [17], independent component analysis [18], selection of regions of interests (ROI) [6]- [8], and other data reduction methods. So far, only few studies have presented complex pipelines for data reduction and classification, such as the COMPARE method [19], which combines deformation-based morphometry with machine learning methods (watershed segmentation algorithm and support vector machine-recursive feature elimination technique).…”

A Projection Pursuit framework for supervised dimension reduction of high dimensional small sample datasets

“…A key advantage of PP is its flexibility to fit different pattern recognition tasks, depending on the PP index used. For example, PP can be used to perform clustering analysis [19,20], classification [21][22][23][24], regression analysis [25] and density estimation [26] (some reviews of PP indexes can be found in [21,27,28]). Another advantage of PP is its out-of-sample mapping capability, that is, the possibility to map new examples in the projection space after the construction of it.…”