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

Abstract

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.

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

“…may be a more sensitive marker for mental conditions than metrics about stable characteristics of the brain (16). Preliminary research has already revealed differences in dwell time between controls and both Alzheimer's disease patients (42) and schizophrenics (43). In two recent reviews on resting state dynamics (4,16), it was acknowledged that a better understanding of the relationship between BOLD dynamics and behavior was still needed.…”

Hyperconnectivity is a fundamental response to neurological disruption.

“…These findings are an important reminder that the typical measures of connectivity isolated in this literature capture only the most robust effects leaving unknown transient shifts in network connectivity. By modeling stochasticity in time series data (e.g., via sliding time windows) one can determine the timing and variability of observable connections to better understand how neural networks adapt to challenge and disruption (Gates & Molenaar, 2012;Hillary et al, 2011a;Jones et al, 2012;Kiviniemi et al, 2011;Leonardi et al, 2013).…”

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

“…Some studies have reported that 30 s of data suffices to discriminate between cognitive states and to estimate reliable modular graph metrics (Jones et al, 2012;Shirer et al, 2012). We therefore chose a sliding window length of 30 TRs to capture changes in FC over time.…”