Voxel Selection Framework with Feature Extraction for Classification of Brain Activity in fMRI

Vol. 28, No. 7, (2019), pp. 33-37

Voxel Selection Framework with Feature Extraction for Classification of Brain Activity in fMRI

1 D.M.Yadav

1 Universal College of Engineering and research Pune,Maharashtra,India

[email protected]

2S.V.Raut

2Rajarshi Shahu College of Engineering, Pune,Maharashtra,India

[email protected]

3S.S.Kanse

3Rajarshi Shahu College of Engineering, Pune,Maharashtra,India

[email protected]

Abstract—This paper presents fMRI (functional Magnetic Resonance Imaging) signal analysis methodology using Principal Component Analysis (PCA) and Mutual Information (MI) based voxel selection framework. Previously, the fMRI signal analysis has been carried out either using Principal Component Analysis (PCA) model or voxel selection on raw fMRI signal. The first methodology does feature extraction that makes voxel selection process easy while the latter methodology does selection of relevant voxels (or features)

.

Keywords— fMRI, voxel, decomposition, PCA, MI.

I. INTRODUCTION

Functional magnetic resonance imaging (fMRI) is one of the most popularly used techniques for the study of human brain function. It is possible to safely, non invasively observe correlations of neural activity across the entire human with good spatial resolution [3]. The brain functions are spatially divided into different brain regions. In functional brain studies, it is shown by Ogawa et. that by measuring the neural activity using fMRI, one can localize the brain functions. This measurement of neural activity is based on the Blood Oxygenation Level Dependent (BOLD) contrast. In the BOLD effect, a neural activity in the brain caused by some sensory or motor tasks produces localized changes in the blood flow and

activity of brain [3]. fMRI is popular technique in the field of neuroscientific as well as cognitive studies [5] from last two decades. The objective of fMRI signal analysis is to find the activation areas in the brain, which corresponds to stimulus in time axis. The fMRI signal analysis achieved either Signal detection or Characterization of Haemodynamic response function. In signal detection, the area of brain that gets activated after giving a stimulus is identified [6]. On the other hand, the Haemodynamic response gives us changes in the blood flow that occur after the stimulus is given [7]. There are many factors that affect the analysis of the fMRI signal like signal acquisition [8], pre-processing [9] as well as de-noising algorithms [12] [13], enhancement procedures [10] [11], evaluation of Haemodynamics [14] [15] and post-processing schemes [16] [17]. All the above methodologies, as a whole, can be grouped into two main methods (1) The hypothesis- driven methods and (2) The data-driven methods [3].

Traditional hypothesis driven methods include Markov random field (MRF) models [18], [19] and hidden Markov models (HMM) [20].These models require more information about the stimulus and are complex [3]. General linear model (GLM) is used in these models [21] and the analysis is highly univariate [22]. These models consider both the hemodynamic response function (HRF) and the stimulus timing function while generating reference functions. In contrast, for analysis of fMRI signals the data-driven methods handle multivariate approach. They contain both the supervised [23] [24] and the unsupervised methods [25] [26] [27] [28]. In opposite to the hypothesis-driven approaches, data-driven approaches do not require prior knowledge about the stimulus timings also it make very less or no assumptions about the HRF shape [3].

The supervised methods have many advantages than

Vol. 28, No. 7, (2019), pp. 33-37 II. PROPOSED METHOD

In investigating fMRI data to classify brain activity, feature selection (voxel selection) and feature based classification are two cardinal and essential methodologies. The selection of applicable voxels is difficult task in fMRI signal analysis.

Selection of informative voxels for efficient classifications is called as Feature selection. This step is more important because it directly affects the fMRI prediction. Each fMRI time series dataset represents thousands of voxels. Not all of them contain specific information related to stimulus. There could be noise and immaterial, information. If we consider all these voxels for training our classification algorithm there may be overfitting problem. Thus it is important to select only those voxels that would contain information about stimulus.

For voxel selection the analysis of variance (ANOVA) method is largely used. The voxels selected should have maximum relevance with the fMRI signals. In multivariate classification methods multiple voxel correlations and the obtained structural patterns are integrated.

For fMRI multivariate pattern analysis, Gaussian Naïve Bayes (GNB), Neural Networks (NNs) and Support Vector Machine (SVM) are few eminent classification algorithms are nominated. As per literature, these algorithms are used together with the informative voxel selection framework [35].

More than ten millions voxels in the raw fMRI data make the fMRI-related pattern recognition difficult to classify brain activity. In the multivariate signal analysis there is need to add feature extraction methodology [32][33]. By introducing feature extraction using PCA methodology before voxel selection framework, multivariate fMRI pattern analysis can be performed in a more precise way than the existing methodologies.

The algorithm of our methodology contain following three steps:

1. PCA allows us to compute a linear transformation which portrays data from a high dimensional space to a low dimensional space. Brain activity classification using fMRI is performed with PCA based feature extraction..

2. Informative voxels are selected after feature extraction of the fMRI signal, depending on its mutual information. Informative voxels plays important role for achieving accurate prediction of neural activity.

3. This paper focuses the challenges in fMRI analysis and by integrating two contributions achieved improved accuracy. Principal Component Analysis (PCA) [36]

and MI – based voxel selection framework [35] are our two contributions. The above methodologies are well known for its accurate solutions, still there is necessity to improve its performance. By incorporating these two methods individual performance can be improve.

This paper is presented as follows. In section III, we illustrate the concepts and algorithm of PCA used for feature extraction. In section IV we explain the concepts of Mutual Information applied for feature selection. In section V, Experimental results are shown on a standard dataset and

compare with existing algorithms in the literature followed by discussion and conclusion.

III Feature Extraction using PCA

PCA is transformed based feature extraction method which transforms the data from high dimensional coordinate system to a new low dimensional coordinate system.

Let Z be the d*n matrix of measured fMRI data, containing n samples of a d-dimensional vector.

Z=d*n (1)

r is the desired number of principal components.

Z_pca =r*n (2)

Z_pca matrix containing r principal components scaled to variance 1 of the input samples.

Eigenvectors is a (d*r) matrix containing the scaled eigenvectors of the sample covariance of Z.

In a typical use the top r components with the largest eigenvalues are selected.

Fig.1 fMRI decomposition using Principal Component Analysis

IV.VOXEL SELECTION FRAMEWORK FOR BRAINACTIVITY CLASSIFICATION

A. Decomposed Voxel Selection Framework

Depending on the mutual information between the voxels this paper proposed a voxel selection framework for the decomposed signal [35]. fMRI signal suffers due to noise and signal redundancy if conventional MI-based voxel selection process is operated on fMRI signal. In our methodology we applied PCA algorithm to get decomposed signal which is robust against noise and redundancy [33].In the proposed methodology voxels are selected from this decomposed signal, based on their mutual information these voxels are selected.

B. MI – based Voxel Selection:

Mutual Information (MI) is characterizes a quantity that measures relationship between two random variables that are sampled simultaneously. In this algorithm, we used MI which will calculate mutual information between features or between class label (stimulus condition) and a feature. Consider MI between the class random variable X and the j^th feature Y_j The entropy of Y is defined as ^H(Y)



and the MI between X and Y is defined as the Kullback-Leibler divergence between the distributions P(X,Y) and P(X) * P(Y):

Vol. 28, No. 7, (2019), pp. 33-37 y dxdy

P x P

y x y P

x P Y

X MI

j j y

x

j

j ( ) ( )

) , log ( ) , ( )

, (

,



 (9)

Where P(x,yj) is a joint distribution of continuous variables X and Y_jwhich can be calculated more conveniently using the chain rule P(x,y_j)=P(x)P(y_j|x). For greater MI value it gives higher statistical relevancy between the stimulus condition and the voxel. In this methodology we have used three classification methods which are widely used for MVPA.

1) Gaussian Naïve Bayes (GNB).For each feature this classifier learns a class conditional Gaussian generative model. Naïve Bayes classifiers x apply Bayes theorem with intense individual assumptions between the features.

2) Multilayer Perceptron Neural Network Classifier (NN): It is a class of feedforward artificial neural network.MLP is a supervised learning technique. NN extracts features that are fed to other algorithms for clustering and classification.

3) Sparse Multinomial Logistic Regression Classifier (SMLR). SMLR classifier gives the solution for binary or multi class classification problem .Weight parameters of classifier are learned in sparse way. Because of this unique feature SMLR is used to solve high dimensional classification problems. Due to this feature selection which is very time consuming becomes easy to user.

V. EXPERIMENTAL RESULTS A. fMRI Data

The fMRI dataset used were downloaded from http://data.pymvpa.org/datasets/haxby2001. Data is acquired for six subjects. The data procurement process was divided into trials/ intervals. In some intervals, the subject was at rest or directed to look on the screen. In half of the trials the picture was presented first, followed by the sentence while in the remaining trials, the sentence was presented first, followed by the picture, subject pressed the mouse button to indicate whether the sentence correctly described the picture. Total 80 trials performed for every subject in the first forty trials, picture was shown first followed by the sentence and in the remaining trials sentence was shown first, followed by the picture. In this trial, pictures are like geometric arrangement of symbols like _,*, /, $. The first stimulus (sentence or picture) was presented for 4 seconds, succeeded by a blank screen for 4 seconds. The second stimulus was then presented for up to 4 seconds, after that subject pressed the button to specify if according to picture the sentence is correctly narrate. A rest period of 15 seconds was placed before the next trial.

Sentences were illustrations like ―It is true that the plus is below the dollar.‖ Half of the sentences were negated (e.g., ―It is not true that the star is above the plus.‖) and the other half were validated sentences. fMRI images were captured for every 500 msec. Our aim is to train a classifier to determine, the subject is viewing either a sentence or picture during the 8 second interval of fMRI data. In eight second of interval classifier contains 16 images which contain 160,000 voxels before feature selection. Here fMRI time series consider

region of interest (ROI) rather that complete brain which is defined as 25-30 ROI.

B. Adopted Methodologies

In this paper we have explored four methodologies for the analysis of fMRI data and accuracy is predicted. First, without any decomposition and voxel selection framework, simple classifier model has been used. Second, the MI – based voxel selection framework [9] has been developed for which shows enhancement in accuracy than traditional classification model.

Third on fMRI signal PCA applied and get decomposed signal that has been used for training to classifier and last the proposed methodology has been developed, voxels are selected using MI on fMRI signal after feature extraction using PCA.

The proposed methodology is compared with other three methodologies in terms of its performance. MATLAB is used to develop these methodologies.

Subject Classifier Methodologies

Without MI

With

MI PCA PCA +MI

(Proposed) Subject1 MLP

Neural 0.88 0.83 0.84 0.90

Subject2 0.84 0.90 0.90 0.90

Subject3 0.76 0.88 0.76 0.86

Subject4 0.76 0.7 0.70 0.79

Subject5 0.79 0.75 0.73 0.81

Subject6 0.80 0.76 0.76 0.86

Subject1 NBayes 0.80 0.80 0.78 0.80

Subject2 0.80 0.80 0.78 0.81

Subject3 0.80 0.80 0.79 0.79

Subject4 0.80 0.80 0.79 0.81

Subject5 0.83 0.80 0.80 0.81

Subject6 0.80 0.80 0.84 0.86

Subject1 SMLR 0.88 0.88 0.85 0.87

Subject2 0.82 0.84 0.89 0.89

Subject3 0.84 0.84 0.89 0.89

Subject4 0.84 0.84 0.84 0.86

Subject5 0.95 0.95 0.88 0.91

Subject6 0.80 0.80 0.79 0.82

Fig.2. Classification accuracies from classifiers for six subjects.

C. Discussion

After applying PCA algorithm we get decomposed signal and redundant voxels get removed and the voxels who are having values greater than threshold are kept. Feature selection of fMRI signal improved by using decomposition framework. We tested relative performance of three classifiers Fig. 2 presents the performance of six subjects in terms of accuracies using three classifiers. The performance of these classifiers is varying with respect to subjects. SMLR gives better accuracy while GNB yields worst accuracy, MLP Neural classifier gives better results. These results show that by using informative voxels using MI improved overall classification performance.

Vol. 28, No. 7, (2019), pp. 33-37 VI. CONCLUSION

In this paper, we used multi voxel pattern analysis (MVPA) to discriminate and classify fMRI cognitive states across multiple human subjects. To extract features from raw fMRI signal we have used PCA. To identify informative voxels from feature extracted decomposed fMRI signal a voxel selection framework has been introduced. Using decomposition framework and active feature selection, classification accuracy gets improved. In the future we can use this methodology for identification of healthy brain and disorder brain [31]. For task based fMRI this methodology can apply to detect facial expressions with improved accuracy.

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