In this paper, we propose a new method that utilizes a novel spatially adaptive scheme for detection of multivariate neuroimaging patterns relating to a continuous subject-level variable, aiming to effectively determine the optimal spatially adaptive filtering of neuroimaging data from the persepective of finding relationships between imaging and continues (e.g. clinical and cognitive) variables. Analyses employ local pattern analysis using regularized least square regression with nonnegativity constraints within a spatial neighborhood around each voxel. Within each neighborhood, we determine the optimal regression coefficients that relate local patterns to the continuous variable of interest. As each voxel belongs to multiple overlapping neighborhoods, the statistic for a given voxel is determined by combining weights from all neighborhoods to which the voxel participates. Finally, non-parametric permutation testing is used to obtain a voxelwise significance map. Using both simulated and real fMRI data, we demonstrate the effectiveness of the proposed method.
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