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

• Published in: PloS one Vol. 7; no. 6; p. e39731
• Main Authors: Jones, David T., Vemuri, Prashanthi, Murphy, Matthew C., Gunter, Jeffrey L., Senjem, Matthew L., Machulda, Mary M., Przybelski, Scott A., Gregg, Brian E., Kantarci, Kejal, Knopman, David S., Boeve, Bradley F., Petersen, Ronald C., Jack, Clifford R.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.06.2012; Public Library of Science (PLoS)
• Subjects: Alzheimer Disease - physiopathology; Alzheimer's disease; Biology; Biomarkers; Brain; Brain - physiology; Brain architecture; Brain mapping; Care and treatment; Cognitive ability; Cohort Studies; Configurations; Dementia disorders; Dwell time; Female; Functional magnetic resonance imaging; Geriatrics; Graphical representations; Humans; Independent component analysis; Information processing; Magnetic resonance; Magnetic Resonance Imaging; Male; Medicine; Meta-analysis; Neural networks; Neuroimaging; Neurology; NMR; Noise measurement; Nuclear magnetic resonance; Older people; Population; Studies; Trends; Variability; Well construction; Windows (intervals); United States > US; Minnesota
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0039731

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 we and others have previously observed in Alzheimer's disease can be explained by differences in dwell time in DMN sub-network configurations, rather than steady state connectivity magnitude. DMN dwell time in specific modular configurations may also underlie the TF-fMRI findings that have been described in mild cognitive impairment and cognitively normal subjects who are at risk for Alzheimer's dementia.