Another general objective of feature selection is to build interpretable models which are able to support or reject hypothesis with domain knowledge.
Hence, the regularized logistic regression (LR) such as least absolute shrinkage and selection operator (LASSO) and elastic net (or ENet for short) are found to be particularly useful in addressing sparsity. 5.Ī major challenge in MVPA, as stated previously, comes from the high dimensionality and sparsity in fMRI data. 4, followed by the conclusion and possible directions for future work in Sect. Results are reported and discussed in Sect. Section 3 illustrates the methodology, including experimental settings, data description, feature extraction and selection methods, classification algorithms, and methodological framework. The remainder of this paper is organized as follows: Section 2 provides a brief review of existing studies, including stability selection algorithms and their applications to neuroimaging data. Therefore, the objective of our study is to explore for an integrated stability-based feature selection approach. Selecting stable features across various datasets, on the other hand, has not been completely investigated. This evaluation criterion may suffer from the variance in training data with a limited sample size and result in unstable generalization error when the trained model is applied to an unknown dataset. In current studies, a common criterion of evaluating the subset selection is classification accuracy. Hence, selecting the active voxels associated with particular stimuli or states is an important and challenging task before training classifiers in MVPA, which is called feature selection or feature reduction. Moreover, only a portion of the brain will be activated with respect to specific stimulus or mental states. However, fMRI-based data analysis using machine learning approaches has a challenging small- n large- p problem, i.e., there are many thousands of voxels in the brain, but the sample size is relatively small because of the expensive cost of fMRI data collection. Unlike conventional univariate approaches, MVPA constructs a pattern classification problem to decode neural information processing by characterizing multivariate brain activity patterns. The precise spatial localization of brain activation, therefore, is an essential advantage of fMRI compared to other non-invasive neuroimaging techniques.
#THEBRAIN 9 STABILITY SERIES#
fMRI is a popular, non-invasive neuroimaging technique to measure brain activity via blood-oxygen-level dependent (BOLD) signals, recorded as time series in a three-dimensional (3D) brain space. In this study, we would like to focus on multi-voxel pattern analysis (MVPA), which is a commonly used methodological framework for analyzing functional magnetic resonance imaging (fMRI) data with machine learning algorithms (see Fig. A comprehensive review of previous studies has been provided in. With the aid of modern neuroimaging techniques, the capability of machine learning algorithms to identify distributed patterns of voxels in response to stimuli allows for decoding brain activities using data-driven models. In recent years, with the advent of machine learning techniques, the interdisciplinary field of machine learning and neuroscience has drawn growing attention to both communities. The results indicate that regularization-based methods are generally more stable in StarPlus dataset, but in Haxby dataset they failed to perform as well as others.Įxploring the mysteries of brain function is one of the most challenging and fascinating tasks in the domain of science.
Top-k feature selection with a ranking score of mutual information and correlation, recursive feature elimination integrated with support vector machine, and L1 and L2-norm regularizations were adapted to a bootstrapped stability selection framework, and the selected algorithms were compared based on both accuracy and stability scores. In this study, we aim to investigate the stability of feature selection methods and test the stability-based feature selection scheme on two benchmark datasets. Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights. Feature selection plays a key role in multi-voxel pattern analysis because functional magnetic resonance imaging data are typically noisy, sparse, and high-dimensional.
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