Imputation techniques for the detection of microstructural changes in schizophrenia, with an application to magnetization transfer imaging.

Neuroimaging techniques such as magnetization transfer imaging allow the detection of microstructural alterations of tissue, and for this reason have been applied to the study of disorders such as schizophrenia. However, they are also sensitive to partial volume effects arising from mixed compartments, such as those comprising cerebral spinal fluid, which makes separate evaluation of volumetric and structural alterations difficult. Ensuing regional differences in the distribution of data and signal-to-noise ratio add further potential bias to their assessment. In the present study we simultaneously applied tissue segmentation, statistical imputation, and nonparametric inference to address these issues and improve the validity of statistical inference. In a case study of N=32 schizophrenic patients matched to the same number of controls, we compared a standard voxel-based analysis with one supplemented by the imputation technique. We were able to replicate significant results in the imputed analysis and even extend them in the areas not excluded by excessive partial volume effects. Application of segmentation algorithms in this dataset also suggested that partial volume effects from spinal fluid potentially affect inference in most cortical gray matter, unless remedial steps are undertaken. Refined imputation methods may be particularly attractive in future research settings characterized by large samples and the availability of adequate computational resources.