Analysis of spatio‐temporal brain imaging patterns by hidden markov models and serial MRI images
Year: 2014
Type: article
Abstract: Brain changes due to development and maturation, normal aging, or degenerative disease are continuous, gradual, and variable across individuals. To quantify the individual progression of brain changes, we propose a spatio-temporal methodology based on Hidden Markov Models (HMM), and apply it on four-dimensional structural brain magnetic resonance imaging series of older individuals. First, regional brain features are extracted in order to reduce image dimensionality. This process is guided by the objective of the study or the specific imaging patterns whose progression is of interest, for example, the evaluation of Alzheimer-like patterns of brain change in normal individuals. These regional features are used in conjunction with HMMs, which aim to measure the dynamic association between brain structure changes and progressive stages of disease over time. A bagging framework is used to obtain models with good generalization capability, since in practice the number of serial scans is limited. An application of the proposed methodology was to detect individuals with the risk of developing MCI, and therefore it was tested on modeling the progression of brain atrophy patterns in older adults. With HMM models, the state-transition paths corresponding to longitudinal brain changes were constructed from two completely independent datasets, the Alzheimer Disease Neuroimaging Initiative and the Baltimore Longitudinal Study of Aging. The statistical analysis of HMM-state paths among the normal, progressive MCI, and MCI groups indicates that, HMM-state index 1 is likely to be a predictor of the conversion from cognitively normal to MCI, potentially many years before clinical symptoms become measurable.
Source: Human Brain Mapping
DOI: 10.1002/hbm.22511
Authors: Ying Wang, Susan M. Resnick, Christos Davatzikos, the Baltimore Longitudinal Study of Aging and the Alzheimer's Disease Neuroimaging Initiative
Institutions: University of Pennsylvania, National Institute on Aging
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G104586243, G1333555762, UC Davis Alzheimer's Disease Core Center, "MR Morphometrics and Cognitive Decline Rate in Large-Scale Aging Studies", EARLY MARKERS OF ALZHEIMERS DISEASE, G558582310, Alzheimer's Disease Neuroimaging Initiative, G596962434, Computational neuroanatomy of aging and AD via pattern analysis, Alzheimer's Disease Cooperative Study
OpenAlex ID: W1486708136