An MVPA method based on sparse representation for pattern localization in fMRI data analysis

Accepted Manuscript

An MVPA Method Based on Sparse Representation for Pattern Localization in fMRI Data Analysis Fangyi Wang, Yuanqing Li, Zhenghui Gu PII: DOI: Reference:

S0925-2312(17)30997-9 10.1016/j.neucom.2016.12.099 NEUCOM 18527

To appear in:

Neurocomputing

Received date: Revised date: Accepted date:

13 September 2016 15 December 2016 17 December 2016

Please cite this article as: Fangyi Wang, Yuanqing Li, Zhenghui Gu, An MVPA Method Based on Sparse Representation for Pattern Localization in fMRI Data Analysis, Neurocomputing (2017), doi: 10.1016/j.neucom.2016.12.099

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Higlights • A new MVPA method for fMRI Data Analysis.

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• The ability of detecting subtle differences between experimental conditions.

• We localized two category-specific brain activation patterns corresponding to two experimental conditions.

• The two sets consisted of a maximal number of informative features.

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• The wrong selected features (noises) were removed by permutation tests.

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An MVPA Method Based on Sparse Representation for Pattern Localization in fMRI Data Analysis

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Fangyi Wanga,b , Yuanqing Lia,b,∗, Zhenghui Gua,b a Center

for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, 510640, China b Guangzhou Key Laboratory of Brain Computer Interaction and Applications, Guangzhou 510640, China

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Abstract

Multivariate pattern analysis (MVPA) approach applied to neuroimaging data, such as functional magnetic resonance imaging (fMRI) data, has received a great deal of attention because of its sensitivity to distinguishing patterns of neural activities associated with different stimuli or cognitive states. Generally, when

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using MVPA approach to decode the mental states or stimuli, a set of discriminative variables (e.g. voxels) is first selected. However, in most of existing MVPA methods, the selected variables do not contain all informative variables,

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since these selected variables are sufficient for decoding. In this paper, we propose a multivariate pattern analysis method based on sparse representation for decoding the brain states and localizing category-specific brain activation areas

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corresponding to two experimental conditions/tasks at the same time. Unlike traditional MVPA approaches, this method is designed to find informative vari-

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ables as many as possible. We applied the proposed method to two judgement experiments: a gender discrimination and a emotion discrimination task, data analysis results demonstrate its effectiveness and potential applications.

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Keywords: Sparse representation, localizing, decoding, fMRI

∗ Corresponding

author Email addresses: [email protected] (Fangyi Wang), [email protected] (Yuanqing Li), [email protected] (Zhenghui Gu)

Preprint submitted to Neurocomputing

June 1, 2017

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1. Introduction On the any given moment, our brains are accessing vast amount of information about the around environment. How the brain processes this flood of

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information within local and global networks is one fundamental question in neuroscience. Functional magnetic resonance imaging (fMRI) has become one of the

most popular tools for imaging brain function [1]. However, fMRI data yields very complex, high-dimensional data sets including up to hundreds of thousand

voxels. Traditionally, the data have been analyzed with a mass-univariate gen-

eral linear model(GLM) approach to reveal task-related brain areas by treating each voxel separately [2]. One of the limitations about the GLM approach is

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that the interrelationship among voxels of spatially distributed brain areas is not considered because it works on isolated voxels and ignores joint information among them.

In recent years, multivariate pattern analysis (MVPA) approaches have shown promise for the analysis of fMRI data, their ability to localize spatial patterns

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of activity that differentiate across experimental conditions/tasks [3]. These spatial patterns generally are too weak to be detected by GLM [4, 5, 6, 7, 8].

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Applications of the MVPA have been rapidly developed in fMRI data analysis, such as stimuli reconstruction [9, 10], attention [11, 12, 13], decision making [14], concept representation [15, 16, 17]. Recent MVPA approaches include three

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common forms: regions of interest (ROI) based MVPA[8, 18, 14], whole-brain MVPA [19, 20], local multivariate search approach(e.g. searchlight) [14, 21]. In

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MVPA, the last few years have witnessed a flurry of research activity on algorithms and theory aimed at feature selection and estimation involving sparse

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representation because of its ability to handle high dimensional data with com-

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pressed samples, and discover sparse spatial activity patterns, thus enhancing interpretability. Several methods including Lasso [22], sparse logistic regression [23], Elastic [24], Sparse NMF [25] have been used for this purpose. Moreover, an alternative feature selection approach is to use linear or nonlinear dimen-

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sionality reduction methods, such as PCA [26] and LLE [27].

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Although MVPA approaches have yielded remarkable insights into understanding the types of stimulus attributes that might be represented in distributed spatial activity patterns, they are inherently limited in their ability

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to characterize the underlying feature space. Because the informative voxels/features selected by MVPA approaches are based on their prediction power,

part of informative voxels may be sufficient and the redundancy of information may be useless for decoding, but these redundant information are important to localize category-specific brain activation area[23]. In this paper, we propose a

new MVPA method for fMRI data analysis. The proposed method combine a forward feature selection scheme with a sparse regularization and permutation

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testing for feature selection in multivariate pattern classification settings, we illustrated the application of our approach using an fMRI data set. The remainder of this paper is organized as follows: Section 2 describes the experimental setting and the detail of proposed method, while Section 3 reports 45

and analyzes the experimental results, followed by our paper conclusions in

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Section 4.

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2. Materials and methods 2.1. Participants

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Twelve healthy native male Chinese (aged 21 to 48 years, with normal or corrected-to-normal vision and normal hearing) participated in this study. All subjects provided written informed consent prior to the experiment. The exper-

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imental protocol was approved by the Ethics Committee of Guangdong General Hospital, China.

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2.2. Experimental stimuli and Procedure

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We selected 80 movie clips of faces with audio from internet sources. Af-

ter image processing (Windows movie maker), each edited movie clip was in gray scale, lasted 1400 ms and subtended 10.7◦ × 8.7◦ . Semantically, these 80 movie clips could be partitioned orthogonally into two groups based on either

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gender (40 male vs. 40 female Chinese faces) or facial emotion (40 crying vs. 60

40 laughing faces). The luminance levels of the videos were matched by adjusting the total power value of each video. Similarly, the audio power levels

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were also matched by adjusting the total power value of each audio clip. During the experiment, stimulus presentation and response recording were controlled

with ePrime software. The visual stimuli were projected onto a screen using an 65

LCD projector (SA-9900 fMRI Stimulation System, Shenzhen Sinorad Medical

Electronics, Inc.), and the subjects viewed the visual stimuli through a mirror mounted on a head coil. The auditory stimuli were delivered through a pneu-

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matic headset (SA-9900 fMRI Stimulation System, Shenzhen Sinorad Medical Electronics, Inc.).

Each subject completed two runs, one run for gender discrimination and the

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other for emotion discrimination, each run contained 10 blocks and each block contained 8 trials. For the 10-fold cross-validation, the 80-trial data of each run were equally partitioned into 10 non-overlapping datasets, each corresponding

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to 1 of the 10 blocks. In the Kth fold, the test data was made up by Kth block of each run, the remain formed the train data. During each trial, which lasted 10

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seconds or 5 volumes (TR=2 s), the subjects were asked to focus their attention on either the gender or emotion of faces in the movie clips and recognize whether each face was male/female or crying/laughing. At the beginning of each trial, a

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stimulus was presented to the subject for 1400 ms, followed by a 600-ms blank period. This 2-s (one TR) cycle with the same stimulus was repeated 4 times

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for effectively eliciting a brain activity pattern and was followed by a 6-s blank period. Mean responses of third, fourth and fifth volumes in each trial were used, whereas the other volumes were discarded because of the delay of BOLD

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response. More details were described in previous study [21].

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2.3. fMRI data Collection and Preprocessing Functional images were collected using a 3 Tesla GE Signal Excite HD MR

scanner at Guangdong General Hospital, China. A 3D anatomical T1-weighted scan (FOV, 280 mm; matrix, 256 × 256; 128 slices; and slice thickness: 1.8 mm) 5

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was acquired before the functional scan for each subject. During the experi90

ment, gradient-echo echo-planar (EPI) T2*-weighted images (25 slices acquired in an ascending noninterleaved order; TR=2000 ms, TE=35 ms, flip angle= 70◦ ;

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FOV: 280 mm, matrix: 64 × 64, slice thickness: 5.5 mm, no gap) were acquired,

covering the entire brain. The preprocessing were executed with the program

SPM8 software package1 . First five volumes were discarded because the MRI 95

signals were unsteady. The preprocessing procedure includes head motion correction, slice timing, co-registration between the functional scans and the structural scan, normalization to an MNI standard brain, data masking to exclude

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non-brain voxels, time series detrending and normalization of time series of each run to zero mean and unit variance, using custom functions in Matlab 2012a 100

(Matlab Mathwork, Inc., Natick, MA).To reduce the computational burden and remove noise, filtering of the original data by correlation which was calculated voxelwise between the time series and stimulus function, the 6000 voxels with

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high absolute value were selected for later processing. 2.4. Feature selection and Decoding

Feature selection is an important problem in machine learning, pattern recog-

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nition, and statistics. Due to extremely high dimensional features and small number of samples which is known as the curse-of-dimensionality problem in

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fMRI studies. As a result, ideally choosing a small subset of features is necessary to maximize model prediction accuracy. The feature selection ability 110

modeled in the sparse representation can be used to selected subset of relevant

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features in the signal and meanwhile separating it into two sets corresponding to two class labels, according to the signs of the sparse representation weights

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[19, 23, 28]. This sparse representation for regularization is very important for MVPA because feature selection allows for functional localization of cognitive

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processes, with sparser feature selection providing more concise localization [29]. The sparse representation of signal can be described with the following equa1 http://www.fil.ion.ucl.ac.uk/spm/

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tion: y = Aw.

(1)

where,y ∈
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−1 indicates the other class. The data matrix A ∈
are the numbers of samples and features respectively. w ∈
y = Aw.

(2)

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minkwk0

0-norm of w is the sparsest solution of equation (1). Here, we use a greedy 125

algorithm: Orthogonal Matching Pursuit (OMP) [30] to solve this problem, which has the advantages of computationally efficient and easy to implement [31].

The overall scheme for sparse representation based feature selection and

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decoding, as shown in Fig. 1 and described in following steps:

Step 1: A K-fold cross-validation is performed after data partition(K =

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− 10). In each fold, we obtain two sets of informative features, IND+ k and INDk ,

corresponding to gender and emotion recognition task respectively. By taking the union operation across folds, we obtain two sets of informative features

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IND+ and IND− (see Fig. 1 (A)) at the individual subject level. Step 2: Each fold of the cross-validation contains n0 iterations. As an ex-

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ample, Fig. 1 (B) illustrates the kth fold. In the nth iteration (n = 1, . . . , n0 ),

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(n)

(n)

two sets of informative features Ind+ and Ind− are obtained in each iteration.

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− The selected sets of this fold are IND+ k and INDk .

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Step 3: In the nth iteration of this fold (see Fig. 1(B)), we first perform

sparse representations on the train data to obtain a weight vector w(n) . Second, (n)

(n)

we determine two sets of informative features Ind+ and Ind− using this weight (n)

vector. Specifically, Ind+ contains N0 features corresponding to the largest elements(generally positive, gender recognition task in this paper) of the weight (n)

vector w(n) , while Ind+

contains N0 features corresponding to the smallest 7

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Figure 1: Scheme of feature selection by the sparse representation method and decoding of individual subject data. This algorithm contains K folds of cross validation (A) with the

elements (generally negative, emotion recognition task in this paper) of w(n)

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iteration steps (including n0 iterations) of the kth fold listed in (B) as an example.

(n)

(n)

[19, 23]. Third, we remove these features in Ind+ and Ind− from the data set in this iteration, an updated data set with remaining features is obtained for next

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iteration. Finally, we perform another 9-fold cross-validation procedure based on the updated train data set using Support vector machine (SVM), the prediction 150

accuracy of labels is denoted as r(n+1) , r1 was performed by using the initial

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train data set. Meanwhile, we also perform a decoding based on all the selected features set of the test data of this fold, the prediction model was trained by the

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train data with same features of this fold, and the prediction accuracy of labels is denoted as Gn . To assess statistical significance of decoding accuracy, we also

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employed nonparametric permutation test [32]. The null hypothesis assumes that the relationship between the data and the labels cannot be learned reliably by the family of classifiers used in the training step. In permutation testing, we randomly permuted the class labels of the training data (all the selected 8

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features used here) 10000 times and calculated corresponding pseudo decoding 160

accuracies. Remark : In this scheme of feature selection, the number of features with

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the largest positive/the smallest negative weights selected in each iteration, 15 was assigned to this parameter according to previous studies [14, 28]. 2.5. Localization

After feature selection, we can obtain two sets of selected features IND+ and

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IND− by 10-fold of cross-validation for each subject, which corresponding to

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gender and emotion discrimination task respectively. However, part of selected features may represent noise. In order to remove these features representing noise, we perform permutation test on the set of selected features IND+ and 170

IND− as below.

Step 1: (probability maps): Two probability maps were constructed using the two sets of features selected across all the K folds of cross-validation for

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each subject. For example, using the sets IND+ , we assign scores to features based on selection frequency, which by counting the times that the features in IND+ , because of features that are repeatedly selected among folds of training

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data sets could be important, so high quantitative values should be assigned. If this features does not appear in IND+ , the frequency is set to zero. Thus, we

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obtain a probability map corresponding to gender recognition task. Similarly, we obtain a probability map corresponding to emotion recognition task using 180

IND− . Finally, we averaged these probability maps across all subjects to obtain

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two probability maps at the group level. Step 2 (permutation): By permuting the class labels 300 times randomly

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and repeated the above procedure of feature selection in each permutation, and obtained 300 pairs probability maps. Based on these probability distributions,

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it is possible to test the null hypothesis at the voxel level. Step 3 (multiple comparison correction): For multiple comparison correction,

a null distribution for each class was constructed by pooling all probability values of the 300 average probability maps corresponding to this class, which were 9

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70 MACG MACr

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Decoding accuracy (%)

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Figure 2: Iterative decoding accuracy curves. MACr and MACG are abbreviations for mean accuracy curves of r(n+1) and Gn at the group level respectively.

obtained through the 300 permutations. The p value of each voxel is calculated 190

as the proportion of values in the null distribution that is greater or equal to

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the value obtained by using the real label (i.e. non-permutated) data. A critical threshold was determined by False Discovery Rate (FDR)<0.05 for each class, we remove those features with their values greater than the critical threshold

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[19, 32]. The remaining features are those informative ones to localize spatial activity pattern with respect to the corresponding label (e.g., class).

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3. Results

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3.1. Decoding accuracy Each discrimination task (gender or emotion) contained 10 blocks and each

block contained 8 trials. For each subject, we applied 10-fold cross-validation scheme, the dataset of each discrimination task was divided into 10 equal sub-

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sets by blocks. Test data of each fold included 1 block of gender discrimination task and 1 block of emotion discrimination task. The remain data of two discrimination tasks was used as train data for this fold. There were two decoding results r(n+1) and Gn of each fold that correspond

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to classification based on train data and test data respectively (see Materials 10

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2500 2000 1500 1000 500 0

0

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50 60 Accuracy (%)

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Permutation count

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Figure 3: The distribution of permutation test (10000 repetitions). The vertical red line indicates the real decoding accuracy without permutation.

and Methods). Increasing n (number of iterations) will result in increasing number of features for Gn calculation. By contrast, number of features for r(n+1) calculation was decreased(With the increasing of iterations, more and

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more informative features were removed). The two average decoding accuracy curves across folds and subjects are shown in Fig. 2, where MACr and MACG

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are abbreviations for mean accuracy curves of r(n+1) and Gn at the group level respectively. We can see that after 15 iterations, the mean accuracy curve of Gn has reached 90% and keeps stable after then, because most of latter selected

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features are informative but highly related with earlier ones, which matches our expectation. Meanwhile, informative features were removed from the train data leads to decline of the mean accuracy curve of r(n+1) .

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With the decoding accuracy as the statistic, the distribution of permutation

is shown in Fig. 3. As demonstrated by Fig. 3, the classifier learned the rela-

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tionship between the data and the labels with a probability of being wrong of

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<0.0001. 3.2. Localization of informative features Using two sets of selected features, we perform permutation test with p < 0.05 FDR-corrected and cluster size of 15 voxels to construct the corresponding

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z=-55

z=-50

z=-45

z=-40

z=-35

z=-30

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z=-15

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z=-60

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Figure 4: Voxels selected by our method with p < 0.05 FDR-corrected cluster size of 15

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voxels. The blue clusters corresponded to the gender discrimination task, and the red clusters corresponded to emotion discrimination task.

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activation map (see Materials and Methods), the distribution of informative patterns (clusters) are shown in Fig. 4. As we observe, although the two informative

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patterns share some common brain areas,such as left cuneus, left lingual gyrus, bilateral inferior occipital gyrus. Meanwhile, the pattern with non-overlapped which means the task-specific pattern that was separated successfully. For in-

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stance, brain regions including left precentral gyrus, left middle frontal gyrus

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and bilateral postcentral gyrus for emotion discrimination task, and left insula,

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right hippocampus and right thalamus for emotion discrimination task.

4. Conclusions In fMRI data analysis, there are hundreds of thousands of voxels, which is

much larger than the number of samples, resulting in overfitting. To address 235

this issue, the number of features needs to be significantly reduced, and infor12

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mative features have to be wisely selected in order to make the classification task efficiently. In this paper, we proposed an MVPA methods based on sparse representation for decoding the brain states and localizing task-specific brain

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activation areas at the same time. Experimental results using two discrimination tasks confirmed that such a method is capable of finding two corresponding

semantic categories (gender and emotion) sets of informative features and decoding the two tasks with significantly high accuracy.

Acknowledgements

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This work was supported by the National Key Basic Research Program of

China (973 Program) under Grant 2015CB351703, the National Natural Science Foundation of China under Grants 61633010, 91420302 and 61573150, and Guangdong Natural Science Foundation under Grant 2014A030312005.

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biography

Fangyi Wang received the M.S. degree in signal and information processing from Jiangxi Science and Technology Normal University,

Nanchang, China, in 2012. He is currently working toward the Ph.D. degree in pattern recognition and intelligent systems at the South China University of

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Technology, Guangzhou, China. His current research interests include the fields of sparse representation, fMRI data analysis, pattern recognition and braincom-

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puter interface.

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Yuanqing Li was born in Hunan Province, China, in 1966.

He received the B.S. degree in applied mathematics from Wuhan University, Wuhan, China, in 1988, the M.S. degree in applied mathematics from South

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China Normal University, Guangzhou, China, in 1994, and the Ph.D. degree in control theory and applications from South China University of Technology,

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Guangzhou, China, in 1997. Since 1997, he has been with South China Uni355

versity of Technology, where he became a full professor in 2004. In 200204, he worked at the Laboratory for Advanced Brain Signal Processing, RIKEN Brain

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Science Institute, Saitama, Japan, as a researcher. In 200408, he worked at the Laboratory for Neural Signal Processing, Institute for Infocomm Research, Singapore, as a research scientist. His research interests include, blind signal

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processing, sparse representation, machine learning, brain-computer interface, EEG and fMRI data analysis. He is the author or coauthor of more than 60

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scientific papers in journals and conference proceedings.

Zhenghui Gu received the Ph.D. degree from Nanyang

Technological University, Singapore, in 2003. From 2002 to 2008, she was with 365

the Institute for Infocomm Research, Singapore. In 2008, she joined the College

of Automation Science and Engineering, South China University of Technology,

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Guangzhou, as an associate professor. Her current research interests include the

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fields of brain signal processing and pattern recognition.

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