Abstract
Spatial normalization is a crucial step in assessing patterns of neuroanatomical structure and function associated with health and disease. Errors that occur during spatial normalization can influence hypothesis testing due to the dimensionalities of mapping algorithms and anatomical manifolds (landmarks, curves, surfaces, volumes) used to drive the mapping algorithms. The primary aim of this paper is to improve statistical inference using multiple anatomical manifolds and large deformation diffeomorphic metric mapping (LDDMM) algorithms. We propose that combining information generated by the various manifolds and algorithms improves the reliability of hypothesis testing. We used this unified approach to assess variation in the thickness of the cingulate gyrus in subjects with schizophrenia and healthy comparison subjects. Three different LDDMM algorithms for mapping landmarks, curves and triangulated meshes were used to transform thickness maps of the cingulate surfaces into an atlas coordinate system. We then tested for group differences by combining the information from the three types of anatomical manifolds and LDDMM mapping algorithms. The unified approach provided reliable statistical results and eliminated ambiguous results due to surface mismatches. Subjects with schizophrenia had non-uniform cortical thinning over the left and right cingulate gyri, especially in the anterior portion, as compared to healthy comparison subjects.
Original language | English (US) |
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Pages (from-to) | 821-833 |
Number of pages | 13 |
Journal | Neuroimage |
Volume | 37 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1 2007 |
Funding
The work reported here was supported by grants: NIH R01 MH064838, NIH R01 EB00975, NIH P50 MH071616, NIH R01 MH56584, NIH P41 RR15241 and NSF DMS 0456253.
Keywords
- Cingulate gyrus
- Cortical thickness
- Gaussian random field
- Large deformation diffeomorphic metric mapping (LDDMM)
- Schizophrenia
- The Laplace-Beltrami operator
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience