Non-Stationarity in the “Resting Brain’s” Modular Architecture

作者：David T. Jones, Prashanthi Vemuri, Matthew C. Murphy, Jeffrey L. Gunter, Matthew L. Senjem, Mary M. Machulda, Scott A. Przybelski, Brian E. Gregg, Kejal Kantarci, David S. Knopman, Bradley F. Boeve, Ronald C. Petersen, Clifford R. Jack · 发表于：PLoS ONE · 年份：2012 · DOI：10.1371/journal.pone.0039731 · 被引用次数：467 · 研究领域：Functional Brain Connectivity Studies、Advanced Neuroimaging Techniques and Applications、Neural dynamics and brain function

Task-free functional magnetic resonance imaging (TF-fMRI) has great potential for advancing the understanding and treatment of neurologic illness. However, as with all measures of neural activity, variability is a hallmark of intrinsic connectivity networks (ICNs) identified by TF-fMRI. This variability has hampered efforts to define a robust metric of connectivity suitable as a biomarker for neurologic illness. We hypothesized that some of this variability rather than representing noise in the measurement process, is related to a fundamental feature of connectivity within ICNs, which is their non-stationary nature. To test this hypothesis, we used a large (n = 892) population-based sample of older subjects to construct a well characterized atlas of 68 functional regions, which were categorized based on independent component analysis network of origin, anatomical locations, and a functional meta-analysis. These regions were then used to construct dynamic graphical representations of brain connectivity within a sliding time window for each subject. This allowed us to demonstrate the non-stationary nature of the brain's modular organization and assign each region to a "meta-modular" group. Using this grouping, we then compared dwell time in strong sub-network configurations of the default mode network (DMN) between 28 subjects with Alzheimer's dementia and 56 cognitively normal elderly subjects matched 1:2 on age, gender, and education. We found that differences in connectivity...