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

Abstract

We developed a new flexible approach for a co-analysis of multimodal 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. D

Examining the relationship between brain function and structure on voxel-by-voxel-basis

“…For example, Matsuda et al [3] demonstrated that the functional and structural deficits associated Alzheimer's disease (AD) occur together in some brain areas, but not all the areas. This overlap and dissociation of functional and structural changes have also been observed in other studies [4][5][6]. These two examples demonstrate the benefit of analyzing functional and structural imaging data together in enhanced understanding of the function-structure relationship occurring in many diseases, conditions, and experiment paradigms.…”

“…A permutation test can produce the distribution of voxel values of W from the imaging data directly by random re-labeling. Details of this permutation approach are described elsewhere [5].…”

“…This multi-modal analysis framework based on a combining function and a permutation test is fairly simple to implement, and appropriate selections of combining functions enable assessment of different multi-modal relationships [5]. However, the interpretation of the results from this type of analyses is highly subjective and dependent on the choice of combining function.…”

Biological parametric mapping: A statistical toolbox for multimodality brain image analysis

“…In another univariate approach (Pell, et al 2004), a conjunction analysis (Friston, et al 1999) was applied to voxel-based morphometry (VBM) and T2-relaxometry data to assess the degree of concurrent changes in both data sets. A non-parametric method has recently been proposed based on the use of combining functions and permutation testing that is able to detect not only areas of concurrent changes but also disassociated changes across modalities (Hayasaka, et al 2006). None of these massively univariate approaches, however, can explicitly model changes in one imaging modality as a function of another modality.…”

A dialectical approach for classification of DW-MR Alzheimer’s images

“…Therefore, the precise volumetrical measurement of cerebral structures is very important in the diagnosis and evaluation of the progression of diseases such as Alzheimer's [9], [16], [17], [18], [19], especially the measurement of areas occupied by the sulci and lateral ventriculi, because such measurement enables quantitative information to be added to the qualitative information provided by the magnetic resonance diffusion-weighted images [20].…”