Identification of imaging biomarkers in schizophrenia: a coefficient-constrained independent component analysis of the mind multi-site schizophrenia study - PubMed (original) (raw)

Multicenter Study

Identification of imaging biomarkers in schizophrenia: a coefficient-constrained independent component analysis of the mind multi-site schizophrenia study

Dae Il Kim et al. Neuroinformatics. 2010 Dec.

Abstract

A number of recent studies have combined multiple experimental paradigms and modalities to find relevant biological markers for schizophrenia. In this study, we extracted fMRI features maps from the analysis of three experimental paradigms (auditory oddball, Sternberg item recognition, sensorimotor) for a large number (n=154) of patients with schizophrenia and matched healthy controls. We used the general linear model (GLM) and independent component analysis (ICA) to extract feature maps (i.e. ICA component maps and GLM contrast maps), which were then subjected to a coefficient-constrained independent component analysis (CCICA) to identify potential neurobiological markers. A total of 29 different feature maps were extracted for each subject. Our results show a number of optimal feature combinations that reflect a set of brain regions that significantly discriminate between patients and controls in the spatial heterogeneity and amplitude of their feature signals. Spatial heterogeneity was seen in regions such as the superior/middle temporal and frontal gyri, bilateral parietal lobules, and regions of the thalamus. Most strikingly, an ICA feature representing a bilateral frontal pole network was consistently seen in the ten highest feature results when ranked on differences found in the amplitude of their feature signals. The implication of this frontal pole network and the spatial variability which spans regions comprising of bilateral frontal/temporal lobes and parietal lobules suggests that they might play a significant role in the pathophysiology of schizophrenia.

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Figures

Fig. 1

Overlay of 29 features from ICA and GLM approaches. The first three rows represent ICA features from different tasks where the rows from top to bottom are AOD, SIRP, and SM task respectively. The ICA features have all been thresholded at the same t-threshold (FDR, p<1 × 10−12), while the SPM overlays (fourth row) have been thresholded at various p-values beyond FDR, p<1 × 10−4 for display purposes

Fig. 2

An overview of the CCICA analysis starting from the preprocessed fMRI data and ending with the ranked CCICA components by J-divergence and p-values. fMRI data is first preprocessed to undergo analysis via ICA and GLM. This results in a set of features that are no longer time-dependent and reshaped into a matrix of subjects by voxels. A further data reduction step takes place via PCA-R which allows us to prepare our data for CCICA. The CCICA then extracts its own set of independent components that might contain single or joint feature components based off of the individual features themselves and all possible pairwise comparisons of these features. Finally an automated artifact removal tool allows us to find components related to areas of the brain only and we rank these components based off of their j-divergence and p-value results

Fig. 3

Results from the top 2 components ranked by their p-value metrics. All maps have been thresholded at z-score>2.5 and the highest ranked components are shown from top to bottom. A high p-value ranking reflects a significant difference in the amplitude or modulation of their fMRI signal within the context of their associated feature component

Fig. 4

Results from the top 2 components ranked by their J-divergence metrics. All maps have been thresholded at z-score>2.5 and the highest ranked components are shown from top to bottom. The J-divergence metric determines the degree of spatial heterogeneity between patients and controls, where a high J-divergence score reflects significant differences in the distribution of their feature component signals

Fig. 5

A closer look at the J-divergence metric for the highest scoring CCICA component. The regions reflect z-scores greater than 2.5 for patients, controls, and both. The region in green shows where both participant groups had z-scores greater than 2.5 and the degree of spatial variability can be seen in the number of regions that are not shared between both groups

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Identification of imaging biomarkers in schizophrenia: a coefficient-constrained independent component analysis of the mind multi-site schizophrenia study - PubMed (original) (raw)