Behavioral Relevance of the Dynamics of the Functional Brain Connectome

Abstract

While many previous studies assumed the functional connectivity (FC) between brain regions to be stationary, recent studies have demonstrated that FC dynamically varies across time. However, two challenges have limited the interpretability of dynamic FC information. First, a principled framework for selecting the temporal extent of the window used to examine the dynamics is lacking and this has resulted in ad-hoc selections of window lengths and subsequent divergent results. Second, it is unclear whether there is any behavioral relevance to the dynamics of the functional connectome in addition to that obtained from conventional static FC (SFC). In this work, we address these challenges by first proposing a principled framework for selecting the extent of the temporal windows in a dynamic and data-driven fashion based on statistical tests of the stationarity of time series. Further, we propose a method involving three levels of clustering-across space, time, and subjects-which allow for group-level inferences of the dynamics. Next, using a large resting-state functional magnetic resonance imaging and behavioral dataset from the Human Connectome Project, we demonstrate that metrics derived from dynamic FC can explain more than twice the variance in 75 behaviors across different domains (alertness, cognition, emotion, and personality traits) as compared with SFC in healthy individuals. Further, we found that individuals with brain networks exhibiting greater dynamics performed more favorably in behavioral tasks. This indicates that the ease with which brain regions engage or disengage may provide potential biomarkers for disorders involving altered neural circuitry.

Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data

“…Commonly, an ‘FC change point’ is identified if the distribution of particular FC metrics exhibits salient deviation compared to a preceding interval 69,87–90 .. Such an analysis directs attention to those time instances that are likely most relevant for characterizing brain dynamics.…”

Functional Magnetic Resonance Imaging of the Domestic Dog: Research, Methodology, and Conceptual Issues

“…The establishment of long-range connectivity between anterior and posterior parts of the DMN is thought to facilitate large-scale information integration required for higher cognitive processes. Further, in humans, connectivity magnitude and associated network structure for various resting state networks, specifically the DMN, have been shown to be more informative in predicting behavior as well as traits compared to activation alone (Cole, Yarkoni, Repovš, Anticevic, & Braver, 2012; Jia, Hu, & Deshpande, 2014). Therefore, investigation of resting state networks in awake dogs is a promising area of research.…”

“…Building on the rise of cognitive neuroscience in human cognition, the merger of behavioral and neural responses by dog subjects will provide researchers with comprehensive and expansive data sets, and questions that were previously left to speculation may be explained in terms of neural structure and activation. Finally, the gamut of advanced analysis methods in human fMRI research, such as connectivity models (Jia, Hu, et al, 2014) and multivariate pattern analysis and learning models (Deshpande, Libero, Sreenivasan, Deshpande, & Kana, 2013) can be employed on dog fMRI data, potentially alleviating some of the issues with traditional activation models and giving us new insights into underlying neural mechanisms.…”

“…The precise shape of the HRF has not been determined in canines, and this is an important limitation for future research to address to further validate canine fMRI. Until such advances have been made, it is reasonable to use time and dispersion derivatives in the general linear activation analysis so as to explicitly model and regress out the variability of the HRF (with respect to the canonical HRF) in experimental data (Jia, Hu, & Deshpande, 2014). …”

“…When training dogs to lie still for fMRI, researchers have used a variety of techniques including chaining (e.g., Berns, Brooks, & Spivak, 2013), target stick (e.g., Jia, Pustovyy, et al, 2014), and model-rival (e.g., Andics, Gácsi, Faragó, Kis, & Miklósi, 2014) methods. In most cases, training builds incrementally from basic contingencies outside the scanner room (e.g., head on chin rest in mock coil, touching nose to target) to inside the scanner room (e.g., prone position on scanner bed) to inside the scanner bore (e.g., head still in coil for scan).…”