The purpose of the study is to propose a new framework for surface-based statistical parametric mapping of PET images using MRI-based cortical surface analysis, including partial volume correction, intensity normalization and spatial normalization on the cortical surface. Maximum PET intensities along the path between inner and outer layer of the cortical gray matter are mapped onto the cortical surface to generate a metabolic activity surface map. For the partial volume correction, the metabolic activity surface map was divided by the partial volume effect map. The regional metabolic activity was normalized by the global activity iteratively calculated at the surface nodes, statistically independent of the group, as measured by F statistics. After surface-based spatial normalization, a statistical evaluation of both cortical thickness and cortical metabolic activity was conducted on the normalized surfaces of 16 patients with schizophrenia and 16 age- and gender-matched healthy controls. The patients with schizophrenia were found to have significant cortical thinning in the temporal and inferior frontal cortices. Accordingly, their PET imaging was significantly affected by the partial volume effect, indicating that partial volume correction could change the statistical results. After correction of the partial volume effects, the patients showed hyperactivity in the temporal cortex, whereas hypoactivity in the prefrontal cortex, predominantly in the left hemisphere. Our results demonstrate that anatomical factors affect an analysis for functional data from the PET, and therefore the importance of combining anatomy and function in the analysis of imaging data for schizophrenia should be considered.
|Number of pages||11|
|Publication status||Published - 2006 Jul 15|
Bibliographical noteFunding Information:
This study was supported by a grant from the Korea Health 21 R&D Project, Ministry of Health and Welfare, Republic of Korea (02-PJ3-PG6-EV07-0002 and A040042).
All Science Journal Classification (ASJC) codes
- Cognitive Neuroscience