Stimulant-Induced Spatial Temporal Patterns in Pet Data : Illustrations Using SPM,Manacova-CVA and PLS

Abstract

Summary

Functional brain images arc extraordinarily rich data sets that reflect brain function such as cerebral blood flow. These images are widely applied to study brain activity after drug administration or in response to perceptual and cognitive stimuli. Positron Emission Tomography (PET) constitutes one important modality that provides functional brain images. Spatial and temporal patterns in brain activities captured from these images have helped to better understand brain dysfunction in disease and aid in diagnosis and treatment of diseases including ncurogenerative disorders. As PET provides maps consisting of average levels of activity for cuboidal voxels of tissue, it is a substantial analytic challenge to integrate the temporal, spatial and statistical signals making up these data. This paper presents a comparative analysis of three voxel-based methods for characterization of spatial-temporal patterns provided by PET images of cerebral blood flow . The methods are: (i) statistical parametric mapping (SPM) providing a voxel-wise brain map of a selected statistic; (ii) multivariate analysis of covariance jointly with canonical variate analysis (MANCOVA-CVA) identifying regions of the brain that are most responsible for global statistical significance; and (iii) partial least squares (PLS) that uses a path modeling technique with latent variables for dimension reduction. The methods are illustrated using data from sequential $H_2^{15}\hspace{0.17em}O$ PET studies in baboons to examine whether amphetamine affects relative cerebral blood flow (rCBF) differentially over time and over different regions of brain. The results are found to be consistent across the three methods.