Alzheimer’s Disease Neuroimaging Inniative. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, School of Medicine, Philadelphia, PA 19104, USA.
NeuroImage (Impact Factor: 6.36). 03/2008; 39(4):1731-43. DOI: 10.1016/j.neuroimage.2007.10.031
Source: PubMed


Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses.

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Available from: Kayhan Batmanghelich, Jan 09, 2015
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    • "For specific problem domains, such as disease determinant retrieval, Elastic Net is used to select features supervisely according to the pre-defined diagnostic labels[21]with only the normal cognitive (NC) and AD patients. It is reasonable to assume that affected brain ROIs of MCI patients are in a subset of the selected regions[7]. "

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    • "However, most high-dimensional survival models focus on the identification of a small set of covariates and their overall effect on time-to-event outcomes[Biswas et al. (2008), Huang et al. (2013,Li and Ma (2013)]. These approaches can be suboptimal for high-dimensional imaging data, since the effect of imaging data on clinical data and other imaging data is often nonsparse, which makes it notoriously difficult for many existing regularization methods [Fan and Lv (2010),Tibshirani (1996)]. In Section 2 we introduce BFLCRM and its associated Bayesian estimation procedure . "

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    • "Effective and accurate diagnosis of Alzheimer's disease and its prodromal stage, MCI, are crucial for drug trials, given the urgent need for treatments to resist or slow disease progression. Many neuroimaging studies have used anatomical measures derived from T1-weighted brain MRI, such as cortical thickness, and volumetric or shape measures of subregions of the brain, to differentiate AD or MCI from NC (Fan et al., 2008; Hua et al., 2008a,b; Gerardin et al., 2009; Magnin et al., 2009; Hua et al., 2010; Cuingnet et al., 2011; Westman et al., 2011; Hua et al., 2013; Gutman et al., 2015). Moreover, measures derived from functional imaging or cerebrospinal fluid (CSF) assays have also been used to help classify individuals with cognitive impairment vs. healthy controls (De Santi et al., 2001; Morris et al., 2001; Bouwman et al., 2007; Mattsson et al., 2009; Shaw et al., 2009; Fjell et al., 2010). "
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    ABSTRACT: Alzheimer's disease (AD) is a progressive brain disease. Accurate detection of AD and its prodromal stage, mild cognitive impairment (MCI), are crucial. There is also a growing interest in identifying brain imaging biomarkers that help to automatically differentiate stages of Alzheimer's disease. Here, we focused on brain structural networks computed from diffusion MRI and proposed a new feature extraction and classification framework based on higher order singular value decomposition and sparse logistic regression. In tests on publicly available data from the Alzheimer's Disease Neuroimaging Initiative, our proposed framework showed promise in detecting brain network differences that help in classifying different stages of Alzheimer's disease.
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