diagnosis of schizophrenia using multivariate analysis of ... · diagnosis of schizophrenia using...

International Journal of Engineering Trends and Technology (IJETT) – Volume 37 Number 4 - July 2016

ISSN: 2231-5381 http://www.ijettjournal.org Page 221

Diagnosis of Schizophrenia using Multivariate

Analysis of Brain MR Images Sushrutha Bharadwaj. M

1, Anand Prem Rajan

2

1&2School of Biosciences and Technology, VIT University, Vellore - 632014, India

1Department of Medical Electronics, Dayananda Sagar College of Engineering, Bengaluru, India - 560078

Abstract- Current laboratory procedures for the diagnosis of

Schizophrenia are mere subjective methods i.e. based on

symptom profiles. Neural correlates do not provide sufficiently

useful criteria. The existing univariate image analysis

procedures are sub-optimal for the identification of specific

neural correlates of schizophrenia. This paper aims at

developing a mathematical tool for the diagnosis of

schizophrenia using multivariable analysis of brain MR

images. To identify the correlative pattern of gray matter

differences between schizophrenics and healthy controls,

Multivariate Linear Model (MLM) and Voxel based

Morphometry (VBM) in Statistical Parametric Mapping (SPM)

are used. Five images each of patients and controls are

acquired and subject to multivariate analysis. The perceivable

differences between the brain anatomies of the two groups are

extracted and the regions of interest are recorded using the

Talairach and Tournoux atlas. The pattern of the Eigen image

is characterized by positive loadings indicating gray matter

decline in the patients as well as the negative loadings

reflecting gray matter increase in the patients. A learning

algorithm using Support Vector Machines (SVM) is proposed

and a classifier model is to be built. The differences extracted

from the multivariate results have to be input to the model.

When a random image is then input to the model, it has to

accurately classify the incoming image as schizophrenic or a

healthy one. The accuracy is predicted to be around 80%.

These findings suggest that the multivariable analysis of brain

images could be an effective method for the diagnosis of

schizophrenia.

Keywords: Multivariate analysis, Schizophrenia, Statistical

Parametric Mapping, Support Vector Mapping, Voxel Based

Morphometry

I. INTRODUCTION

Current operational diagnostic systems for major

psychiatric disorders such as schizophrenia are based

solely on clinical manifestations and associated psycho-

social impairments [1-3]. There is no single objective

approach for the diagnosis of schizophrenia. No

laboratory test for schizophrenia currently exists.

Current image analysis approaches based on univariate

methods have been found to be sub-optimal to identify

potential specific neural correlates for schizophrenia.

Researchers are now looking towards multi-variable

analysis methods for an efficient objective analysis of

the disease. Current research focuses on developing a

clinical diagnosis procedure for schizophrenia. In

univariate analysis the time series of each voxel in the

brain is separately modeled and tested statistically for a

condition of interest, usually in the framework of the

General Linear Model [4-5].

In contrast, multivariate analysis considers the

measurement of signals from many locations

simultaneously. It is based on finding those patterns of

the brain across many voxels which are characteristic of

Schizophrenics versus controls to find perceivable

characteristics of schizophrenics using MR images.

Support vector machine (SVM) is a data classification

algorithm that has been increasingly employed as a

multivariate method for brain response classification [6].

SVM procedures can reveal coherent brain activations

for individual subjects. The research intends to build a

classifier using SVM that mathematically classifies a

random image as a schizophrenic or a control.

II. METHODS

The block diagram of figure 1 shows a series of

processes that are employed in statistical analysis that

are used in this research. MR images of the subjects

were acquired and pre-processing algorithms were

applied on the images. Statistical analysis of the images

was performed using Statistical Parametric Mapping

software. The Multivariate Methods toolbox was used to

process and extract multiple features from the images.

These features were used by the Support Vector

Machine tool to design a learning algorithm which

learns about the differences in the images of

schizophrenics and controls. Finally, a random image

input to the algorithm will be classified as a

schizophrenic or a normal image.

IMAGE ACQUISITION AND ANALYSIS

For experimental purpose, five images each of

schizophrenic patients and healthy controls were

acquired using MRI technique. The patients were

recruited from National Institute of Mental Health and

Neuro Sciences (NIMHANS), Bangalore, India. All the

subjects were of same age, sex, education and had

similar socio-economic status. Most importantly, all the

patients were right handed and anti-psychotic naïve.

The MRI images were acquired using a 3.0 Tesla

Philips (Achieva R2 release) MRI scanner. T1 weighted

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three-dimensional magnetization prepared rapid

acquisition gradient echo sequence was performed

yielding a total of 165 slices in the saggital plane. The

imaging parameters were: TR= 8.1 ms; TE= 3.7 ms;

Nutation angle= ; Field of view= 256mm; Slice

thickness= 1mm without any inter slice gap; NEX= 1;

Matrix size= 256*256; Voxel size= 1mm X 1mm X

1mm.

Figure 1: Basic flow of processes for multivariate

analysis

Image analysis was performed using the Statistical

Parametric Mapping (SPM) 5 software developed by

Wellcome Department of Cognitive Neurology, Institute

of Neurology, London, UK

(http://www.fil.ion.ucl.ac.uk/spm). This software was

implemented in MATLAB 7.01 (Mathworks Inc.,

Sherborn, MA, USA).

Image processing and analysis was performed

according to the standard VBM protocol [7]. The first

step is to realign the data to 'undo' the effects of subject

movement during the scanning session. The spatial

normalization involved transforming MRI images of all

the subjects to a template that approximated the

stereotactic space of Talairach and Tournoux [8]. The

spatially normalized images were resliced to a final

voxel size of 1×1×1 mm3 and partitioned into gray

matter, white matter, cerebrospinal fluid, and other

compartments. Modulated segments of gray matter were

finally smoothed with a FWHM Gaussian kernel.

Statistical parametric mapping entails the

construction of spatially extended statistical processes to

test hypotheses about regionally specific effects [9].

Statistical parametric maps (SPMs) are image processes

with voxel values that are, under the null hypothesis,

distributed according to a known probability density

function, usually the Student's T or F distributions. We

analyze each and every voxel using any standard

(univariate) statistical test. The resulting statistical

parameters are assembled into an image - the SPM.

SPMs are interpreted as spatially extended statistical

processes by referring to the probabilistic behavior of

Gaussian fields [10-13]. In the present research,

statistical t-test was conducted on the two groups of data

and the results were estimated. These results were then

used for multivariate analysis. The regional differences

in gray matter volume were preserved in these results.

MULTIVARIATE ANALYSIS

Next, we extracted the gray matter distribution that

differed most between schizophrenics and healthy

controls. We used the MLM (Multivariate Linear

Model) Package for this analysis (MMtoolbox, SHFJ-

CEA, Orsay, France,

http://www.madic.org/download/MMTBx/). The general

format of the multivariate protocol is shown in (1)

[Ds, u, Nvox] = MM (argfile, typeAnal) (1)

where Ds – Eigen values; u – Eigen vector; Nvox –

Total number of voxels considered; TypeAnal – Type of

analysis.

The standard way of using the MM package is to first

perform a standard spm analysis that will provide a first

apriori model, the corresponding estimated parameters

and residual sum of square images. The temporal filter

or the normalization chosen is of great importance for

meaningful interpretation of the MM results. The MLM

method is based on singular value decomposition of the

matrix Z, where Y is the data, X is the linear model, and

Σ represents the temporal covariance matrix of the data

[14].

(2)

The MM toolbox analyses the results of SPM

analysis and computes the dispersion matrix using the

beta and Eigen images as shown in figure 2. By default,

the package computes up to five Eigen images. Since

IMAGE ACQUISITION

PRE PROCESSING

STATISTICAL ANALYSIS

MULTIVARIATE PROCESSING

EXTRACTION OF FEATURES

LEARNING ALGORITHM

RESULT




there is no temporal covariance for VBM data, the

matrix was simplified to . This matrix was

then decomposed into an Eigen image (Figure 3) that

represents the variance between the data and a set of

regressors.

Figure 2: Flowchart of MM analysis

MM embeds the concept of several spatial spaces such

that if the regression model is performed separately on

several subjects (1...N) or regions of interest having the

same temporal space, MM allows us to consider the data

as a matrix with dimension as in (3):

Common-time-dim X (subject1-space-dim + subject2-

space-dim + ... + subjectN-space-dim)

(3)

In such a case, the result of the analysis for the first

component is one time dimension Eigen vector and one

space dimension vector with size given in (4):

(Subject1-space-dim + subject2-space-dim + ... +

subjectN-space-dim) (4)

This can also be considered as one Eigen image per

subject (or region of interest). More often, the MM is

performed on a matrix with dimension given in (5):

(Subject1-time-dim + subject2-time-dim + ... +

subjectN-time-dim) X common-space-dim (5)

III. RESULTS AND DISCUSSION

The results obtained are particularly important for

characterizing the difference between a group of patients

and a group of healthy controls. The gray matter

increase in figure (3) is indicated by the positive

loadings which can also be noted in the Eigen values in

figure 4 indicating an increase in gray matter in the

corresponding regions of schizophrenics’ brains.

Alternatively, the negative loadings in Eigen images

indicate a decrease in gray matter in schizophrenics.

This observation is very useful in differentiating

between a schizophrenic brain and a normal brain.

Figure 3: Eigen images resulting from MM analysis.

Figure 4: Comparison b/w observed and predicted Eigen

values.

MULTIVARIATE METHODS

DESCRIPTION OF INPUT

ARGUMENTS

GET INPUT ARGUMENTS

SET MODEL SUB SPACE OF

INTEREST

COMPUTE TIME x TIME

DISPERSION MATRIX

COMPUTE DISPERSION MATRIX

WITH BETA IMAGES

WRITE EIGEN IMAGES




IV. CONCLUSION

In the current paper, SPM and MM toolbox have

been used to differentiate schizophrenia from healthy

control subjects using multivariate analysis methods

applied on MR images. Perceivable differences between

the two groups of images are extracted. The positive and

negative loadings in the Eigen images obtained using

multivariate analysis algorithm relate to the gray matter

changes in the brain that are particular to the

Schizophrenia images.

The future work focuses on extracting the

perceivable differences from the results of multivariate

analysis and to build a classifier model using Support

Vector Machines (SVM). When a random image is input

to the model, it is intended to mathematically classify it

as either schizophrenic or a healthy control.

LIBSVM, a library of support vector machines has

been found to be a powerful tool for building a classifier

model. It can be found at

http://www.csie.ntu.edu.tw/~cjlin/libsvm. The accuracy

of classification is also expected to be high compared to

other classification models. Further elaboration of these

methods may contribute for effective clinical diagnosis

of schizophrenia.

ACKNOWLEDGEMENTS

The authors thank Dr. G. Venkat Subramanian, Dept

of Psychiatry, NIMHANS for providing suitable data

and communicating about SPM & VBM methods; Dr.

Ferath Kherif, UCL, London for guidance with the MM

toolbox; Dr. Fumiko Hoeft, Stanford University for her

communications about SVM.

REFERENCES

[1] World Health Organization, 1993. The ICD-10 Classification of

Mental and Behavioral Disorders: Diagnostic Criteria for Research

[2] American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, 4th ed. American Psychiatric Press,

Washington, DC.

[3] Yasuhiro Kawasaki, Michio Suzuki, Ferath Kherif, Tsutomu Takahashi, Shi-Yu Zhou, Kazue Nakamura, Mie Matsui, Tomiki

Sumiyoshi, Hikaru Seto, Masayoshi Kurachi., 2007. Multivariate

voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls; Neuroimage 34 (2007) 235-242

[4] Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams,

S. C., Frackowiak, R. S., and Turner, R. (1995b). Analysis of fMRI time-series revisited. Neuroimage 2, 45–53.

[5] Worsley, K. J., Liao, C. H., Aston, J., Petre, V., Duncan, G. H.,

Morales, F., and Evans, A. C. (2002). A general statistical analysis for fMRI data. Neuroimage 15, 1–15.

[6] Ze Wang, Anna R. Childress, Jiongjiong Wang, and John A.

Detre., 2007. Support vector machine learning-based fMRI data group analysis, NeuroImage 36, 1139-1151.

[7] Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston,

K.J., Frackowiak, R.S., 2001a. A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14, 21–36.

[8] Talairach, J., Tournox, P., 1988. Co-Planar Stereotaxi Atlas of the

Human Brain: Three-dimensional Proportional System. Thieme, New York.

[9] Friston KJ Frith CD Liddle PF and Frackowiak RSJ. (1991)

Comparing functional (PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab. 11:690-699

[10] Adler RJ. (1981) In “The geometry of random fields”. Wiley New

York [11] Worsley KJ Evans AC Marrett S and Neelin P. (1992) A three-

dimensional statistical analysis for rCBF activation studies in human

brain. J Cereb. Blood Flow Metab. 12:900-918 [12] Friston KJ Worsley KJ Frackowiak RSJ Mazziotta JC and Evans

AC. (1994a) Assessing the significance of focal activations using their

spatial extent. Hum. Brain Mapp. 1:214-220 [13] Worsley KJ Marrett S Neelin P Vandal AC Friston KJ and Evans

AC. (1996) A unified statistical approach or determining significant

signals in images of cerebral activation. Hum. Brain Mapp. 4:58-73 [14] Worsley, K.J., Poline, J.B., Friston, K.J., Evans, A.C., 1997.

Characterizing the response of PET and fMRI data using multivariate

linear models. NeuroImage 6, 305–319.


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