An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

Abstract

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

“…Such problems with image registration may be exacerbated in cross‐cohort comparisons by warping T1w anatomical scans to a template image with varying degrees of success due to differences in motion‐related artifacts. The implication of this naturally extends to other measurements based on accurate estimates of brain anatomy including analysis of structural covariance networks [e.g., Zielinski et al, 2010; Montembeault et al, 2012], volume based morphometry [e.g., Schmitter et al, 2015], tract‐based spatial statistics [e.g., fractional anisotropy, diffusivity; Smith et al, 2006, 2007], and brain lesions imaged with FLAIR [e.g., white matter hyper‐intensities, infarctions; Hajnal et al, 1992; Brant‐Zawadzki et al, 1996], among many others.…”

Utility of real-time prospective motion correction (PROMO) on 3D T1-weighted imaging in automated brain structure measurements

“…Further, automated brain structure measurements have been applied in clinical practice to supplement assessment of brain atrophy and diagnosis of degenerative diseases3456. However, the comparatively long acquisition time of 3D-T1WI frequently leads to the formation of motion artefacts, which can lead to mischaracterisation of the size and tissue properties of brain structures78.…”

Temporal Correlation Structure Learning for MCI Conversion Prediction

“…To evaluate our Temporal-GAN model, we compare with the following methods: SVM-Linear (support vector machine with linear kernel), which has been widely applied in MCI conversion prediction [6,15]; SVM-RBF (SVM with RBF kernel), as employed in [10,21]; and SVM-Polynomial (SVM with polynomial kernel) as used in [10]. Also, to validate the improvement by learning the temporal correlation structure, we compare with the Neural Network with exactly the same structure in our classification network (network C in Fig.…”