Classification of schizophrenic patients and healthy controls using multiple spatially independent components of structural MRI data

Abstract

Several meta-analyses were recently conducted in attempts to identify the core brain regions exhibiting pathological changes in schizophrenia, which could potentially act as disease markers. Based on the findings of these meta-analyses, we developed a multivariate pattern analysis method to classify schizophrenic patients and healthy controls using structural magnetic resonance imaging (sMRI) data. Independent component analysis (ICA) was used to decompose gray matter density images into a set of spatially independent components. Spatial multiple regression of a region of interest (ROI) mask with each of the components was then performed to determine pathological patterns, in which the voxels were taken as features for classification. After dimensionality reduction using principal component analysis (PCA), a nonlinear support vector machine (SVM) classifier was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a 10-fold cross-validation strategy. Experimental results showed that two distinct spatial patterns displayed discriminative power for schizophrenia, which mainly included the prefrontal cortex (PFC) and subcortical regions respectively. It was found that simultaneous usage of these two patterns improved the classification performance compared to using either of them alone. Moreover, the two pathological patterns constitute a prefronto-subcortical network, suggesting that schizophrenia involves abnormalities in networks of brain regions.

Key words： schizophrenia; discriminative analysis; gray matter network; independent component analysis (ICA); support vector machine (SVM)

References

1 Andreasen N C, Nopoulos P, O’Leary D S, Miller D D, Wassink T, Flaum M. Defining the phenotype of schizophrenia: cognitive dysmetria and its neural mechanisms. Biological Psychiatry, 1999, 46(7): 908-920

2 Shenton M E, Dickey C C, Frumin M, McCarley R W. A review of MRI findings in schizophrenia. Schizophrenia Research, 2001, 49(1-2): 1-52

3 Honea R, Crow T J, Passingham D, Mackay C E. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. American Journal of Psychiatry, 2005, 162(12): 2233-2245

4 Ellison-Wright I, Glahn D C, Laird A R, Thelen S M, Bullmore E. The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation metaanalysis. American Journal of Psychiatry, 2008, 165(8): 1015-1023

5 Glahn D C, Laird A R, Ellison-Wright I, Thelen S M, Robinson J L, Lancaster J L, Bullmore E, Fox P T. Metaanalysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biological Psychiatry, 2008, 64(9): 774-781

6 Giuliani N R, Calhoun V D, Pearlson G D, Francis A, Buchanan R W. Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophrenia Research, 2005, 74(2-3): 135-147

7 Fan Y, Shen D G, Gur R C, Gur R E, Davatzikos C. COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Transactions on Medical Imaging, 2007, 26(1): 93-105

8 Pereira F, Mitchell T, Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. NeuroImage, 2009, 45(1): S199-S209

9 McKeown M J, Jung T P, Makeig S, Brown G, Kindermann S S, Lee T W, Sejnowski T J. Spatially independent activity patterns in functional MRI data during the Stroop colornaming task. Proceedings of the National Academy of Sciences of the United States of America, 1998, 95(3): 803-810

10 Calhoun V D, Adali T, Pearlson G D, Pekar J J. Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Human Brain Mapping, 2001, 13(1): 43-53

11 Hu D W, Yan L, Liu Y, Zhou Z, Friston K J, Tan C, Wu D. Unified SPM-ICA for fMRI analysis. NeuroImage, 2005, 25(3): 746-755

12 Xu L, Groth K M, Pearlson G, Schretlen D J, Calhoun V D. Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia. Human Brain Mapping, 2009, 30(3): 711-724

13 Calhoun V D, Maciejewski P K, Pearlson G D, Kiehl K A. Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Human Brain Mapping, 2008, 29(11): 1265-1275

14 Ashburner J, Friston K J. Voxel-based morphometry — the methods. NeuroImage, 2000, 11(6): 805-821

15 Stoica P, Selen Y. Model-order selection: a review of information criterion rules. IEEE Signal Processing Magazine, 2004, 21(4): 36-47

16 Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 1995, 7(6): 1129-1159

17 Rüsch N, Spoletini I, Wilke M, Bria P, Di Paola M, Di Iulio F, Martinotti G, Caltagirone C, Spalletta G. Prefrontalthalamic-cerebellar gray matter networks and executive functioning in schizophrenia. Schizophrenia Research, 2007, 93(1-3): 79-89

18 Nenadic I, Sauer H, Gaser C. Distinct pattern of brain structural deficits in subsyndromes of schizophrenia delineated by psychopathology. NeuroImage, 2010, 49(2): 1153-1160

19 Shen H, Wang L B, Liu Y D, Hu D W. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage, 2010, 49(4): 3110-3121

20 Fan Y, Resnick S M, Wu X Y, Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. NeuroImage, 2008, 41(2): 277-285

21 Raz N, Lindenberger U, Rodrigue K M, Kennedy K M, Head D, Williamson A, Dahle C, Gerstorf D, Acker J D. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cerebral Cortex, 2005, 15(11): 1676-1689

22 Witte A V, Savli M, Holik A, Kasper S, Lanzenberger R. Regional sex differences in grey matter volume are associated with sex hormones in the young adult human brain. NeuroImage, 2010, 49(2): 1205-1212

23 Frangou S, Chitins X, Williams S C R. Mapping IQ and gray matter density in healthy young people. NeuroImage, 2004, 23(3): 800-805