Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occurs over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods, have various limitations due to their inherent non-adaptive nature and high-dimensionality including an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multi- modal functional imaging datasets. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns to detect dynamic state transitions in data and a low-dimensional manifold of dynamic RSFC. TVDN is generalizable to handle multimodal functional neuroimaging data (fMRI and MEG/EEG). The resulting estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

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Differential Contributions of Static and Time-Varying Functional Connectivity to Human Behavior

10.1101/2020.07.14.203273 ◽

2020 ◽

Author(s):

Adam Eichenbaum ◽

Ioannis Pappas ◽

Daniel Lurie ◽

Jessica R. Cohen ◽

Mark D’Esposito

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Cognitive Ability ◽

Human Behavior ◽

Resting State ◽

Behavioral Variability ◽

Static Case ◽

Time Varying ◽

Resting State Functional Connectivity ◽

Brain Functional Connectivity

AbstractMeasures of human brain functional connectivity acquired during the resting-state track critical aspects of behavior. Recently, fluctuations in resting-state functional connectivity patterns – typically averaged across in traditional analyses – have been considered for their potential neuroscientific relevance. There exists a lack of research on the differences between traditional “static” measures of functional connectivity and newly-considered “time-varying” measures as they relate to human behavior. Using functional magnetic resonance imagining (fMRI) data collected at rest, and a battery of behavioral measures collected outside the scanner, we determined the degree to which each modality captures aspects of personality and cognitive ability. Measures of time-varying functional connectivity were derived by fitting a Hidden Markov Model. To determine behavioral relationships, static and time-varying connectivity measures were submitted separately to canonical correlation analysis. A single relationship between static functional connectivity and behavior existed, defined by measures of personality and stable behavioral features. However, two relationships were found when using time-varying measures. The first relationship was similar to the static case. The second relationship was unique, defined by measures reflecting trialwise behavioral variability. Our findings suggest that time-varying measures of functional connectivity are capable of capturing unique aspects of behavior to which static measures are insensitive.Author SummaryCorrelated patterns of brain activity measured in the absence of any prescribed task show meaningful temporal fluctuations. However, the manner by which such fluctuations track aspects of human behavior remains unresolved. The current report takes a data-driven approach to characterize how time-varying patterns of human brain functional connectivity differ from traditional static measures in their ability to track aspects of personality and cognitive ability. We determine that time-varying patterns of functional connectivity not only track similar aspects of behavior as do static measures, but also unique behavioral qualities as well, specifically those that reflect behavioral variability. These results highlight the importance and relevance of examining time-varying measures of functional connectivity.

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The Neural Basis of Time-Varying Resting-State Functional Connectivity

Brain Connectivity ◽

10.1089/brain.2014.0250 ◽

2014 ◽

Vol 4 (10) ◽

pp. 769-779 ◽

Cited By ~ 78

Author(s):

Shella Dawn Keilholz

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Time Varying ◽

Resting State Functional Connectivity ◽

Neural Basis

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Differential contributions of static and time-varying functional connectivity to human behavior

Network Neuroscience ◽

10.1162/netn_a_00172 ◽

2020 ◽

pp. 1-21

Author(s):

Adam Eichenbaum ◽

Ioannis Pappas ◽

Daniel Lurie ◽

Jessica R. Cohen ◽

Mark D’Esposito

Keyword(s):

Functional Connectivity ◽

Human Behavior ◽

Resting State ◽

Behavioral Variability ◽

Static Case ◽

Time Varying ◽

Resting State Functional Connectivity ◽

Behavioral Measures ◽

And Behavior ◽

Brain Functional Connectivity

Measures of human brain functional connectivity acquired during the resting-state track critical aspects of behavior. Recently, fluctuations in resting-state functional connectivity patterns—typically averaged across in traditional analyses—have been considered for their potential neuroscientific relevance. There exists a lack of research on the differences between traditional “static” measures of functional connectivity and newly considered “time-varying” measures as they relate to human behavior. Using functional magnetic resonance imagining (fMRI) data collected at rest, and a battery of behavioral measures collected outside the scanner, we determined the degree to which each modality captures aspects of personality and cognitive ability. Measures of time-varying functional connectivity were derived by fitting a hidden Markov model. To determine behavioral relationships, static and time-varying connectivity measures were submitted separately to canonical correlation analysis. A single relationship between static functional connectivity and behavior existed, defined by measures of personality and stable behavioral features. However, two relationships were found when using time-varying measures. The first relationship was similar to the static case. The second relationship was unique, defined by measures reflecting trialwise behavioral variability. Our findings suggest that time-varying measures of functional connectivity are capable of capturing unique aspects of behavior to which static measures are insensitive.

Download Full-text