Combining Boundary-Based Methods With Tensor-Based Morphometry in the Measurement of Longitudinal Brain Change

Abstract

Tensor-based morphometry is a powerful tool for automatically computing longitudinal change in brain structure. Because of bias in images and in the algorithm itself, however, a penalty term and inverse consistency are needed to control the over-reporting of nonbiological change. These may force a tradeoff between the intrinsic sensitivity and specificity, potentially leading to an under-reporting of authentic biological change with time. We propose a new method incorporating prior information about tissue boundaries (where biological change is likely to exist) that aims to keep the robustness and specificity contributed by the penalty term and inverse consistency while maintaining localization and sensitivity. Results indicate that this method has improved sensitivity without increased noise. Thus it will have enhanced power to detect differences within normal aging and along the spectrum of cognitive impairment.

β-amyloid, hippocampal atrophy and their relation to longitudinal brain change in cognitively normal individuals

“…Voxelwise longitudinal change between same-participant T1 structural MRIs was computed using a tensor-based morphometry (TBM) method designed to enhance sensitivity and specificity by incorporating knowledge of likely tissue boundary locations(Fletcher, 2014, E Fletcher, et al, 2013). Prior to TBM registration, the two scans of each participant were denoised using a non-local means algorithm optimized for structural brain T1 images(Coupé, et al, 2008).…”

“…Determinants equal to 1.0 indicate no volume change; determinants between 0 and 1 signify volume shrinkage, while determinants greater than 1.0 indicate expansion. Performing a logarithmic transform gives a symmetric distribution about 0, with negative logs indicating contraction and positive logs indicating expansion(E Fletcher, et al, 2013, Hua, et al, 2008). These will be referred to as log-jacobians.…”

“…The Brodmann ROIs were generated in-house by an experienced neurologist and have been used in previous publications from our laboratory(Evan Fletcher, et al, 2013, Lee, et al, 2012). Hippocampal ROIs were computed in native space by a multi-atlas based automatic segmentation method(Aljabar, et al, 2009).…”

Shape analysis of the human brain.

“…Both VBM and TBM are commonly used for calculating cortical thickness measurements as well. [152] 2002 A 3D 28 C Leow et al [153] 2006 A 3D 17 C Lepore et al [154] 2007 A 3D 30 C Afzali et al [151] 2010 A 3D 31 C Wang et al [155] 2012 A 3D 2 C Yang et al [156] 2012 A 3D 60 C Fletcher et al [157] 2013 A 3D 285 C Shi et al [158] 2013 Leow et al [153] proposed using TBM to identify changes in the brains of aging subjects.…”

“…Leow also illustrated several methods for correcting distortion in TBM techniques. Fletcher et al [157] combined TBM and boundarybased methods to track longitudinal brain changes in subjects. This method was compared to those that do not involve boundary detection, and demonstrated how the inclusion of boundary parameters helped to correct for noise at the tissue boundaries.…”