Article

Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study

Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, PA 19104, USA.
NeuroImage (Impact Factor: 6.36). 07/2008; 41(2):277-85. DOI: 10.1016/j.neuroimage.2008.02.043
Source: PubMed

ABSTRACT This work builds upon previous studies that reported high sensitivity and specificity in classifying individuals with mild cognitive impairment (MCI), which is often a prodromal phase of Alzheimer's disease (AD), via pattern classification of MRI scans. The current study integrates MRI and PET (15)O water scans from 30 participants in the Baltimore Longitudinal Study of Aging, and tests the hypothesis that joint evaluation of structure and function can yield higher classification accuracy than either alone. Classification rates of up to 100% accuracy were achieved via leave-one-out cross-validation, whereas conservative estimates of generalization performance in new scans, evaluated via bagging cross-validation, yielded an area under the receiver operating characteristic (ROC) curve equal to 0.978 (97.8%), indicating excellent diagnostic accuracy. Spatial maps of regions determined to contribute the most to the classification implicated many temporal, prefrontal, orbitofrontal, and parietal regions. Detecting complex patterns of brain abnormality in early stages of cognitive impairment has pivotal importance for the detection and management of AD.

0 Followers
 · 
96 Views
    • "The proposed study, which relates well to these two studies, uses instead a fully automated approach to rank the neurobiological variables and volumetric measures. Thus, a more global approach is provided for constructing patterns of structural and physiological abnormalities in their entirety [5], with statistical proofs in support of the choice of the different variables and measures considered. Other studies have focused their research efforts on determining the distinctive features that could delineate early MCI (EMCI) from late MCI (LMCI) [19] [20]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Brain atrophy in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.
    Computational Intelligence and Neuroscience 01/2015; 2015:14. DOI:10.1155/2015/865265
  • Source
    • "It is noted that in both of these studies, which focus more on the conversion from MCI to AD, the volumetric measures of the different brain regions were selected manually, and both studies relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) public database. As such, the proposed study which provides an automated approach at ranking the neurobiological variables will augment and complement such findings, as reported in both of these studies, to reflect more globally patterns of structural and physiological abnormalities in their entirety [5], and with statistical context for a more meaningful choice of the different variables. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes to combine MRI data with a neuropsychological test, mini-mental state examination (MMSE), as input to a multi-dimensional space for the classification of Alzheimer's disease (AD) and it's prodromal stages—mild cognitive impairment (MCI) including amnestic MCI (aMCI) and nonamnestic MCI (naMCI). The decisional space is constructed using those features deemed statistically significant through an elaborate feature selection and ranking mechanism. FreeSurfer was used to calculate 55 volumetric variables, which were then adjusted for intracranial volume, age and education. The classification results obtained using support vector machines are based on twofold cross validation of 50 independent and randomized runs. The study included 59 AD, 67 aMCI, 56 naMCI, and 127 cognitively normal (CN) subjects. The study shows that MMSE scores contain the most discriminative power of AD, aMCI, and naMCI. For AD versus CN, the two most discriminative volumetric variables (right hippocampus and left inferior lateral ventricle), when combined with MMSE scores, provided an average accuracy of 92.4% (sensitivity: 84.0%; specificity: 96.1%). MMSE scores are found to improve all classifications with accuracy increments of 8.2% and 12% for aMCI versus CN and naMCI versus CN, respectively. Results also show that brain atrophy is almost evenly seen on both sides of the brain for AD subjects, which is different from right-side dominance for aMCI and left-side dominance for naMCI. Furthermore, hippocampal atrophy is seen to be the most significant for aMCI, while Accumbens area and ventricle are most significant for naMCI.
    IEEE Transactions on Biomedical Engineering 08/2014; 61(8):2245-2253. DOI:10.1109/TBME.2014.2310709 · 2.23 Impact Factor
  • Source
    • "As MRI is routinely performed in the clinical workup of cognitive decline, advanced data analysis techniques as those by pattern recognition tools make use of already existing data, which is thus cost effective and without additional discomfort for the patient. It is possible to predict future cognitive decline in MCI using baseline MRI based on grey matter voxel based morphometry (VBM) (Plant et al. 2010; Misra et al. 2009; Fan et al. 2008c), white matter DTI (Haller et al. 2010a, b; O'Dwyer et al. 2012) or iron deposition (Haller et al. 2010a, b). Another potential application for pattern recognition is in patient follow-up by a surrogate marker of patients' cognitive function based on imaging data; e.g., MVPA has been proposed as one way to overcome the clinico-radiological paradox in multiple sclerosis (Hackmack et al. 2012b), and resting-state fMRI appears as a promising candidate to provide relevant features of MCI to AD progression (Damoiseaux et al. 2012). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.
    Brain Topography 03/2014; 27(3). DOI:10.1007/s10548-014-0360-z · 2.52 Impact Factor
Show more

Preview

Download
3 Downloads
Available from