State and trait characteristics of anterior insula time-varying functional connectivity

AbstractThe human anterior insula (aINS) is a topographically organized brain region, in which ventral portions contribute to socio-emotional function through limbic and autonomic connections, whereas the dorsal aINS contributes to cognitive processes through frontal and parietal connections. Open questions remain, however, regarding how aINS connectivity varies over time. We implemented a novel approach combining seed-to-whole-brain sliding-window functional connectivity MRI and k-means clustering to assess time-varying functional connectivity of aINS subregions. We studied three independent large samples of healthy participants and longitudinal datasets to assess inter- and intra-subject stability, and related aINS time-varying functional connectivity profiles to dispositional empathy. We identified four robust aINS time-varying functional connectivity modes that displayed both “state” and “trait” characteristics: while modes featuring connectivity to sensory regions were modulated by eye closure, modes featuring connectivity to higher cognitive and emotional processing regions were stable over time and related to empathy measures.

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State and trait characteristics of anterior insula time-varying functional connectivity

NeuroImage ◽

10.1016/j.neuroimage.2019.116425 ◽

2020 ◽

Vol 208 ◽

pp. 116425 ◽

Cited By ~ 4

Author(s):

Lorenzo Pasquini ◽

Gianina Toller ◽

Adam Staffaroni ◽

Jesse A. Brown ◽

Jersey Deng ◽

...

Keyword(s):

Functional Connectivity ◽

Anterior Insula ◽

Time Varying

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Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

10.1101/2021.08.15.456390 ◽

2021 ◽

Author(s):

Hadas Benisty ◽

Andrew H Moberly ◽

Sweyta Lohani ◽

Daniel Barson ◽

Ronald R Coifman ◽

...

Keyword(s):

Functional Connectivity ◽

Neural Activity ◽

Large Scale ◽

Analytical Framework ◽

Network Activity ◽

Wide Field ◽

Time Varying ◽

Cortical Networks ◽

Novel Approach ◽

Spontaneous Behavior

Experimental work across a variety of species has demonstrated that spontaneously generated behaviors are robustly coupled to variation in neural activity within the cerebral cortex. Indeed, functional magnetic resonance imaging (fMRI) data suggest that functional connectivity in cortical networks varies across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these studies generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior typically observed in awake animals. Here, we took advantage of recent developments in wide-field, mesoscopic calcium imaging to monitor neural activity across the neocortex of awake mice. Applying a novel approach to quantifying time-varying functional connectivity, we show that spontaneous behaviors are more accurately represented by fast changes in the correlational structure versus the magnitude of large-scale network activity. Moreover, dynamic functional connectivity reveals subnetworks that are not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide insight into how behavioral information is represented across the mammalian neocortex and demonstrate a new analytical framework for investigating time-varying functional connectivity in neural networks.

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On the nature of time-varying functional connectivity in resting fMRI

10.31234/osf.io/xtzre ◽

2018 ◽

Cited By ~ 12

Author(s):

Daniel J Lurie ◽

Daniel Kessler ◽

Danielle S Bassett ◽

Richard F. Betzel ◽

Michael Breakspear ◽

...

Keyword(s):

Functional Connectivity ◽

Research Area ◽

Time Varying ◽

Neural Basis ◽

Open Questions ◽

Wide Range ◽

Neuroimaging Data ◽

Core Concepts ◽

And Behavior ◽

Key Terms

The brain is a complex dynamical system composed of many interacting sub-regions. Knowledge of how these interactions reconfigure over time is critical to a full understanding of the brain’s functional architecture, the neural basis of flexible cognition and behavior, and how neural systems are disrupted in psychiatric and neurological illness. The idea that we might be able to study neural and cognitive dynamics through analysis of neuroimaging data has catalyzed substantial interest in methods which seek to estimate moment-to-moment fluctuations in functional connectivity (often referred to as “dynamic” or time-varying connectivity; TVC). At the same time, debates have emerged regarding the application of TVC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive relevance of resting TVC. These and other unresolved issues complicate the interpretation of resting TVC findings and limit the insights which can be gained from this otherwise promising research area. This article reviews the current resting TVC literature in light of these issues. We introduce core concepts, define key terms, summarize current controversies and open questions, and present a forward-looking perspective on how resting TVC analyses can be rigorously applied to investigate a wide range of questions in cognitive and systems neuroscience.

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Questions and controversies in the study of time-varying functional connectivity in resting fMRI

Network Neuroscience ◽

10.1162/netn_a_00116 ◽

2020 ◽

Vol 4 (1) ◽

pp. 30-69 ◽

Cited By ~ 48

Author(s):

Daniel J. Lurie ◽

Daniel Kessler ◽

Danielle S. Bassett ◽

Richard F. Betzel ◽

Michael Breakspear ◽

...

Keyword(s):

Functional Connectivity ◽

Careful Analysis ◽

Research Area ◽

Time Varying ◽

Time Resolved ◽

Open Questions ◽

Wide Range ◽

Neuroimaging Data ◽

Core Concepts ◽

New Research

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain’s functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as “dynamic” or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

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Multi-dynamic Modelling Reveals Strongly Time-varying Resting fMRI Correlations

10.1101/2021.06.23.449584 ◽

2021 ◽

Author(s):

Usama Pervaiz ◽

Diego Vidaurre ◽

Chetan Gohil ◽

Stephen M. Smith ◽

Mark W Woolrich

Keyword(s):

Functional Connectivity ◽

Brain Activity ◽

Dynamic Modelling ◽

Fmri Data ◽

Generative Adversarial Networks ◽

Activity Levels ◽

Time Varying ◽

Human Connectome Project ◽

The Mean ◽

Over Time

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time-varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variability

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Faculty Opinions recommendation of Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.14264057.15777175 ◽

2012 ◽

Cited By ~ 1

Author(s):

Ahmad Hariri ◽

Caroline Davis

Keyword(s):

Functional Connectivity ◽

Functional Connectivity Mri ◽

Subject Motion

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Robustness Over Time-Varying Channels in DNN-HMM ASR Based Human-Robot Interaction

10.21437/interspeech.2017-1308 ◽

2017 ◽

Cited By ~ 1

Author(s):

José Novoa ◽

Jorge Wuth ◽

Juan Pablo Escudero ◽

Josué Fredes ◽

Rodrigo Mahu ◽

...

Keyword(s):

Human Robot Interaction ◽

Time Varying ◽

Robot Interaction ◽

Time Varying Channels ◽

Over Time

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Forecasting Interconnections in International Housing Markets: Evidence from the Dynamic Model Averaging Approach

Journal of Real Estate Research ◽

10.22300/0896-5803.42.1.37 ◽

2020 ◽

Vol 42 (1) ◽

pp. 37-103

Author(s):

Hardik A. Marfatia

Keyword(s):

Dynamic Model ◽

Model Averaging ◽

Housing Markets ◽

The United States ◽

Forecasting Model ◽

Change Over Time ◽

The United Kingdom ◽

Novel Approach ◽

Dynamic Model Averaging ◽

Over Time

In this paper, I undertake a novel approach to uncover the forecasting interconnections in the international housing markets. Using a dynamic model averaging framework that allows both the coefficients and the entire forecasting model to dynamically change over time, I uncover the intertwined forecasting relationships in 23 leading international housing markets. The evidence suggests significant forecasting interconnections in these markets. However, no country holds a constant forecasting advantage, including the United States and the United Kingdom, although the U.S. housing market's predictive power has increased over time. Evidence also suggests that allowing the forecasting model to change is more important than allowing the coefficients to change over time.

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