Combining anatomical manifold information via diffeomorphic metric mappings for studying cortical thinning of the cingulate gyrus in schizophrenia

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 Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. HHS Public Access

Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: Detecting, quantifying, and predicting

“…Brain warping techniques such as large deformation diffeomorphic metric mapping (LDDMM) have been reported to characterize region-specific variations in numerous neurodegenerative disease studies in terms of either volume or shape analysis [Csernansky et al, 1998, 2000, 2002, 2005; Qiu et al, 2007, 2008, 2009; Wang et al, 2003, 2006, 2007; Younes et al, in press]. To date, most studies have been focused on specific regions such as hippocampus or lateral ventricle [Apostolova et al, 2006; Csernansky et al, 2005; Ferrarini et al, 2006; Wang et al, 2006].…”

The diffeomorphometry of regional shape change rates and its relevance to cognitive deterioration in mild cognitive impairment and Alzheimer's disease

“…We term the morphometric analysis performed in this metric space “diffeomorphometry” [Miller et al, 2014]. Diffeomorphometry has been successfully applied to the study of subcortical and ventricular structures in various neurodegenerative diseases [Csernansky et al, 1998, 2000, 2002, 2005; Miller et al, 2013; Qiu et al, 2007, 2008, 2009; Tang et al, 2014; Wang et al, 2003, 2006, 2007; Younes et al, 2014b]. Most existing diffeomorphometry-based analyses focus on cross-sectional group comparisons in terms of the regional volume or shape computed from a single scan (usually the baseline scan), rather than the longitudinal rates of change computed from multiple sequential scans.…”

Morphometric Differences in Planum Temporale in Schizophrenia and Bipolar Disorder Revealed by Statistical Analysis of Labeled Cortical Depth Maps

“…A second approach is Local LCDM, which condenses to a single distance value at each point on the surface. Local LCDM was used to examine variation in thickness across cingulate (15) and the left PT (16) surfaces in SCZ. In the latter study, the variation in thickness was found to be consistent with post-mortem analysis (17).…”