Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD

Abstract

This study explored various feature extraction methods for use in automated diagnosis of Attention-Deficit Hyperactivity Disorder (ADHD) from functional Magnetic Resonance Image (fMRI) data. Each participant's data consisted of a resting state fMRI scan as well as phenotypic data (age, gender, handedness, IQ, and site of scanning) from the ADHD-200 dataset. We used machine learning techniques to produce support vector machine (SVM) classifiers that attempted to differentiate between (1) all ADHD patients vs. healthy controls and (2) ADHD combined (ADHD-c) type vs. ADHD inattentive (ADHD-i) type vs. controls. In different tests, we used only the phenotypic data, only the imaging data, or else both the phenotypic and imaging data. For feature extraction on fMRI data, we tested the Fast Fourier Transform (FFT), different variants of Principal Component Analysis (PCA), and combinations of FFT and PCA. PCA variants included PCA over time (PCA-t), PCA over space and time (PCA-st), and kernelized PCA (kPCA-st). Baseline chance accuracy was 64.2% produced by guessing healthy control (the majority class) for all participants. Using only phenotypic data produced 72.9% accuracy on two class diagnosis and 66.8% on three class diagnosis. Diagnosis using only imaging data did not perform as well as phenotypic-only approaches. Using both phenotypic and imaging data with combined FFT and kPCA-st feature extraction yielded accuracies of 76.0% on two class diagnosis and 68.6% on three class diagnosis—better than phenotypic-only approaches. Our results demonstrate the potential of using FFT and kPCA-st with resting-state fMRI data as well as phenotypic data for automated diagnosis of ADHD. These results are encouraging given known challenges of learning ADHD diagnostic classifiers using the ADHD-200 dataset (see Brown et al., 2012).

A Review of Feature Reduction Techniques in Neuroimaging

“…This setback has led to the formulation of kernel-pca- which in brief is the application of PCA in a feature space created through a kernel function (Scholkopf and Smola, 2002). Several studies have recently explored the application of kernel-pca to dimensionality reduction problems in neuroimaging (Rasmussen et al 2012; Sidhu et al 2012; Thirion and Faugeras, 2003; Wang et al 2011). …”

A texture-based method for classification of schizophrenia using fMRI data

“…PCA [9] is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. For dimensionality reduction, PCA computes a linear combination of features that have high variance [21] and transforms the original features onto a smaller number of principal components [22]. This is done by finding a linear basis of reduced dimensionality for the data, in which the amount of variance in the data is maximal.…”

Feature selection using genetic algorithm for classification of schizophrenia using fMRI data

“…Principle component analysis (PCA) [9] is a mathematical procedure for solving this problem. PCA transforms the original data onto a smaller number of principal components [18]. It is done by finding a linear basis of reduced dimensionality for the data, which the amount of variance in the data is maximal.…”