A non-parametric approach for co-analysis of multi-modal brain imaging data: Application to Alzheimer's disease

被引:38
作者
Hayasaka, S
Du, AT
Duarte, A
Kornak, J
Jahng, GH
Weiner, MW
Schuff, N
机构
[1] VA Med Ctr, CIND, San Francisco, CA 94121 USA
[2] Univ Calif San Francisco, Dept Radiol, San Francisco, CA 94143 USA
关键词
permutation; combining function; multiple modalities; conjunction;
D O I
10.1016/j.neuroimage.2005.10.052
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We developed a new flexible approach for a co-analysis of multi-modal brain imaging data using a non-parametric framework. In this approach, results from separate analyses on different modalities are combined using a combining function and assessed with a permutation test. This approach identifies several cross-modality relationships, such as concordance and dissociation, without explicitly modeling the correlation between modalities. We applied our approach to structural and perfusion MRI data from an Alzheimer's disease (AD) study. Our approach identified areas of concordance, where both gray matter (GM) density and perfusion decreased together, and areas of dissociation, where GM density and perfusion did not decrease together. In conclusion, these results demonstrate the utility of this new non-parametric method to quantitatively assess the relationships between multiple modalities. (c) 2005 Elsevier Inc. All rights reserved.
引用
收藏
页码:768 / 779
页数:12
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