PROJECT SUMMARY Abnormalities of white matter are important in schizophrenia. A preponderance of studies have found decreased levels of transcripts for myelin-related proteins in autopsy brains. Some have found decrease in the proteins themselves, and some have not. Hundreds of diffusion tensor imaging (DTI) studies have found reduced fractional anisotropy (FA) in the brains of many people with schizophrenia (SCH). Decreased FA is interpreted as disruption of normal architecture. However, postmortem examination has failed to identify characteristic abnormalities. This suggests that abnormalities are subtle, and perhaps postmortem examinations have not used the right tools to find them. We have therefore been developing, as part of a collaboration supported by our concluding Fogarty project, two new methods to characterize white matter at high resolution. The first is a machine learning protocol to measure axonal diameters and myelin sheath thickness in electron microscope (EM) images of prefrontal white matter, recognizing and avoiding artifacts in EM of autopsy tissue. This will enable us to measure thousands of fibers in EM images produced as part of our concluding Fogarty project, from individuals with SCH, major depressive disorder (MDD), or no psychiatric illness (NPI). The second method, suggested by the DTI findings, is to analyze the arrangement of the axons themselves. We will use 3-dimensional (3D) reconstructions of high-resolution images of the axons themselves, identified by Bielschowsky silver stain or immunohistochemistry for phosphorylated neurofilament protein. To obtain high-resolution images of Bielschowsy stains, we will take advantage of the recent observation by Dr. Mark Sonders, co-investigator on this project, that these and other heavy metal stains luminesce under 2-photon infrared excitation. This yields clear and measurable images of individual axons. We will perform these procedures on sections from existing paraffin blocks that comprise a complete left prefrontal coronal section from 36 triads containing 1 case each of SCH, MDD, or NPI, matched for sex and age. These brains were included in earlier studies that yielded data on protein composition, mRNA for myelin-related proteins, DNA methylation, microglial activation, and semiquantitative myelin histology. In a third, exploratory aim, we will employ graphical models to combine these various types of data with known properties of CNS white matter and myelin to build a model of what is disturbed in schizophrenia. We expect that novel techniques for data fusion will reveal associations based on multidimensional correlations that could not be detected by modeling the single-domain datasets separately. In the process of completing these scientific aims, we will pursue the pedagogic goals of training the first two professional biostatisticians in Macedonia, and an academic pathologist. We will also hold a seminar course for biological researchers to build awareness and understanding of the power of biostatistical and other computational methods to enrich their research.