Article

Fisher statistics for analysis of diffusion tensor directional information

Department of Neurology, University of Wisconsin, UW Medical Foundation Centennial Building, Madison, WI 53705, USA.
Journal of Neuroscience Methods (Impact Factor: 2.05). 02/2012; 206(1):40-5. DOI: 10.1016/j.jneumeth.2012.02.004
Source: PubMed

ABSTRACT

A statistical approach is presented for the quantitative analysis of diffusion tensor imaging (DTI) directional information using Fisher statistics, which were originally developed for the analysis of vectors in the field of paleomagnetism. In this framework, descriptive and inferential statistics have been formulated based on the Fisher probability density function, a spherical analogue of the normal distribution. The Fisher approach was evaluated for investigation of rat brain DTI maps to characterize tissue orientation in the corpus callosum, fornix, and hilus of the dorsal hippocampal dentate gyrus, and to compare directional properties in these regions following status epilepticus (SE) or traumatic brain injury (TBI) with values in healthy brains. Direction vectors were determined for each region of interest (ROI) for each brain sample and Fisher statistics were applied to calculate the mean direction vector and variance parameters in the corpus callosum, fornix, and dentate gyrus of normal rats and rats that experienced TBI or SE. Hypothesis testing was performed by calculation of Watson's F-statistic and associated p-value giving the likelihood that grouped observations were from the same directional distribution. In the fornix and midline corpus callosum, no directional differences were detected between groups, however in the hilus, significant (p<0.0005) differences were found that robustly confirmed observations that were suggested by visual inspection of directionally encoded color DTI maps. The Fisher approach is a potentially useful analysis tool that may extend the current capabilities of DTI investigation by providing a means of statistical comparison of tissue structural orientation.

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Available from: Paul Rutecki, Dec 30, 2013
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    • "Many aspects of DTI have been studied extensively, from the diffusion tensor model itself (Basser, 2002; Basser et al., 1994b; Basser and Pajevic, 2003; Koay and Özarslan, 2013; Stejskal, 1965) and its higher-order generalizations (Anderson, 2005; Descoteaux et al., 2007; Descoteaux et al., 2011; Jian et al., 2007; Liu et al., 2004; Özarslan and Mareci, 2003), to optimal experimental designs (Cook et al., 2007; Deriche et al., 2009; Dubois et al., 2006; Jones et al., 1999; Koay et al., 2011; Koay et al., 2012) and its inverse problem at various levels of complexity (Andersson, 2008; Basser et al., 1994a; Chang et al., 2005; Chang et al., 2012; Koay et al., 2006; Mangin et al., 2002; Maximov et al., 2011; Veraart et al., 2013; Wang et al., 2004). DTI continues to inspire new analyses, developments, refinements and extensions (Caruyer et al., 2013; Hutchinson et al., 2012; Koay, 2009; Koay, 2014; Koay et al., 2011; Koay et al., 2009a; Koay et al., 2012; Koay et al., 2009b; Wu et al., 2004). Uncertainty quantification (Anderson, 2001; Behrens et al., 2003; Beltrachini et al., 2013; Chang et al., 2007; Jeong and Anderson, 2008; Jones, 2003; Jones and Pierpaoli, 2005; Koay et al., 2007; Koay et al., 2008; Lazar and Alexander, 2003, 2005; Lazar et al., 2005; Poonawalla and Zhou, 2004) in DTI is another important area of research with wide-ranging implications to tractography (Barbieri et al., 2011; Basser et al., 2000; Conturo et al., 1999; Mori et al., 1999; Pajevic et al., 2002; Poupon et al., 2000; Poupon et al., 2001) and data analyses (longitudinal, single-subject, group or graph-theoretic, e.g., (Rubinov and Sporns, 2010)). "
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    • "Regardless, DTI appears to be a promising technique for detecting and tracking structural correlates of repetitive brain injury (Chappell et al., 2008; Bazarian et al., 2012; Bennett et al., 2012; Hutchinson et al., 2012; Li et al., 2012), including those believed to be specific to CTE. "
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