TY - JOUR
T1 - Joint source based morphometry identifies linked gray and white matter group differences
AU - Xu, Lai
AU - Pearlson, Godfrey
AU - Calhoun, Vince D.
N1 - Funding Information:
We would like to thank Arvind Caprihan for his helpful comments and suggestions. This study was funded by the National Institutes of Health grants: 1 R01 EB 000840 and 1 R01 EB 005846 (to VC), NSF-IIS Award no: 0612076 (to VC), and 2 RO1 MH43775 MERIT Award, 5 RO1 MH52886 and a NARSAD Distinguished Investigator Award (to GP).
PY - 2009/2/1
Y1 - 2009/2/1
N2 - We present a multivariate approach called joint source based morphometry (jSBM), to identify linked gray and white matter regions which differ between groups. In jSBM, joint independent component analysis (jICA) is used to decompose preprocessed gray and white matter images into joint sources and statistical analysis is used to determine the significant joint sources showing group differences and their relationship to other variables of interest (e.g. age or sex). The identified joint sources are groupings of linked gray and white matter regions with common covariation among subjects. In this study, we first provide a simulation to validate the jSBM approach. To illustrate our method on real data, jSBM is then applied to structural magnetic resonance imaging (sMRI) data obtained from 120 chronic schizophrenia patients and 120 healthy controls to identify group differences. JSBM identified four joint sources as significantly associated with schizophrenia. Linked gray-white matter regions identified in each of the joint sources included: 1) temporal - corpus callosum, 2) occipital/frontal - inferior fronto-occipital fasciculus, 3) frontal/parietal/occipital/temporal - superior longitudinal fasciculus and 4) parietal/frontal - thalamus. Age effects on all four joint sources were significant, but sex effects were significant only for the third joint source. Our findings demonstrate that jSBM can exploit the natural linkage between gray and white matter by incorporating them into a unified framework. This approach is applicable to a wide variety of problems to study linked gray and white matter group differences.
AB - We present a multivariate approach called joint source based morphometry (jSBM), to identify linked gray and white matter regions which differ between groups. In jSBM, joint independent component analysis (jICA) is used to decompose preprocessed gray and white matter images into joint sources and statistical analysis is used to determine the significant joint sources showing group differences and their relationship to other variables of interest (e.g. age or sex). The identified joint sources are groupings of linked gray and white matter regions with common covariation among subjects. In this study, we first provide a simulation to validate the jSBM approach. To illustrate our method on real data, jSBM is then applied to structural magnetic resonance imaging (sMRI) data obtained from 120 chronic schizophrenia patients and 120 healthy controls to identify group differences. JSBM identified four joint sources as significantly associated with schizophrenia. Linked gray-white matter regions identified in each of the joint sources included: 1) temporal - corpus callosum, 2) occipital/frontal - inferior fronto-occipital fasciculus, 3) frontal/parietal/occipital/temporal - superior longitudinal fasciculus and 4) parietal/frontal - thalamus. Age effects on all four joint sources were significant, but sex effects were significant only for the third joint source. Our findings demonstrate that jSBM can exploit the natural linkage between gray and white matter by incorporating them into a unified framework. This approach is applicable to a wide variety of problems to study linked gray and white matter group differences.
KW - Gray matter and white matter
KW - Group differences
KW - Joint independent component analysis
KW - Joint source based morphometry
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U2 - 10.1016/j.neuroimage.2008.09.051
DO - 10.1016/j.neuroimage.2008.09.051
M3 - Article
C2 - 18992825
AN - SCOPUS:57649213849
SN - 1053-8119
VL - 44
SP - 777
EP - 789
JO - NeuroImage
JF - NeuroImage
IS - 3
ER -