Neuromorphometry in Schizophrenia by Computer Algorithm

点击翻译按钮获取中文摘要

DESCRIPTION: (provided by applicant) Precisely defining the characteristics of neuroanatomical abnormalities in schizophrenia (i.e., volume losses, shape deformations and altered asymmetries) can provide critical evidence for determining the pathogenesis of schizophrenia. More recently, there has been substantial interest in determining whether such neuroanatomical abnormalities show progressive changes over time. However, improved methodologies for neuromorphometiy are needed for such experiments. During the first funding period of this project, we made further substantial progress in the development of high dimensional brain mapping (HDBM). In this renewal application, we show that HDBM can be used to measure brain structure volumes, shapes and asymmetries with excellent reliability, and that H1)BM can be used to detect abnormalities of the hippocampus and cingulate gyrus that discriminate matched groups of schizophrenia and control subjects. We now propose to collect and analyze high resolution magnetic resonance (MR) scans at three time points two years apart in matched schizophrenia and control subjects. Subjects recruited during the initial funding period will return for these additional assessments, and will be supplemented by new subjects as needed. Because other researchers have suggested that neurodegeneration may occur early in the course of schizophrenia, we propose to enrich our population of subjects by adding additional subjects who are in their first episode of schizophrenia. Clinical data in the subjects with schizophrenia will be collected quarterly throughout the study, and the impact of factors other than schizophrenia that might cause changes in brain structure (e.g., alcohol) will be carefully assessed and considered in the data analysis. Selected brain structure volumes, composite shapes derived from transformation vector fields, and hemispheric symmetries derived from symmetry group transformations will be compared across the selected time points. In addition, we propose to determine whether there are relationships between changes in clinical variables and changes in brain structure volume, shape and asymmetry.