Real‐time estimation of dynamic functional connectivity networks

Monti, Ricardo Pio; Lorenz, Romy; Braga, Rodrigo M.; Anagnostopoulos, Christoforos; Leech, Robert; Montana, Giovanni

doi:10.1002/hbm.23355

Cited by 24 publications

(21 citation statements)

References 89 publications

(144 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two well-known models, which simulate dynamic correlation in finance literature, have recently drawn a lot of attention from scientists in the field of neuroscience, like real-time fMRI. For instance, [22] proposed to utilize EWMA models as well as Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm to estimate dynamic FC in real time.…”

Section: Introductionmentioning

confidence: 99%

“…When the conditional covariance is time-variant, we have conditional heteroscedasticity which is the main focus of multivariate volatility modeling. The effective estimations derived from volatility models in neuroimaging context [21,22] motivated us to investigate and modify them. In this regard, the EWMA and DCC models introduced in [21] are further developed in this article.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-based estimation of dynamic functional connectivity in resting-state functional magnetic resonance imaging

2017

View full text Add to dashboard Cite

Recently, we have witnessed an increase in scientific interest in understanding the dynamic nature of brain networks by evaluating dynamic functional connectivity (FC) using functional magnetic resonance imaging (fMRI). In this work, we introduce two multivariate volatility models, standardized dynamic conditional correlation, and standardized exponentially weighted moving average, both of which are built upon the framework of dynamic conditional correlation and exponentially weighted moving average models, respectively. In these two models, we use standardized residuals with the goal of determining whether the use of standardized residuals reduces the mean square rate error. Moreover, in traditional simulation studies, time series were considered with zero conditional expectation and static conditional variance which do not capture the nature of the real data. This is because of hemodynamic response function in the brain and dynamic functional connectivity of each brain region with itself during the experiment time, respectively. That is why, next, some new simulation studies are introduced which are more similar to blood-oxygen-level-dependent time series of brain regions. Then, methods' proficiency is analyzed using previous and new simulation studies. Results show that, in both former and latter simulations, the new methods work better. Finally, the best model is utilized to model FC in an Iranian resting-state fMRI data.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based estimation of dynamic functional connectivity in resting-state functional magnetic resonance imaging

2017

View full text Add to dashboard Cite

show abstract

“…One active area of investigation with the HCP data, afforded in part by the relatively long acquisitions and a large amount of data, has been the examination of “dynamic” rsfcMRI or changes in the patterns of connectivity over time within an individual. This work has attempted to identify various “states” or patterns in rsfcMRI that may vary in structured ways over time 84–88 . Further, others have linked variation in such dynamic rsfcMRI to behavior, including executive function 89 .…”

Section: Advances In Our Understanding Of the Nature Of Brain Networkmentioning

confidence: 99%

Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology

Deanna

2017

Harv Rev Psychiatry

View full text Add to dashboard Cite

A key tenet of modern psychiatry is that psychiatric disorders arise from abnormalities in brain circuits that support human behavior. Our ability to examine hypotheses around circuit level abnormalities in psychiatric disorders has been made possible by advances in human neuroimaging technologies. These advances have provided the basis for recent efforts in the field to develop a more complex understanding of the function of brain circuits in health and of their relationship to behavior, which in turns provides a foundation for our understanding of how disruptions in such circuits contribute to the development of psychiatric disorders. The focus of this review will be on the use of resting state functional connectivity (rsfcMRI) to assess brain circuits, the advances in this field generated by the Human Connectome Project, and the relevance of these advances for understanding neural circuit dysfunction in psychopathology. I focus on the methods developed by the Human Connectome Project that may be particularly relevant to studies of psychopathology, outline some of the key findings in terms of what constitutes a brain region, and highlight new information about the nature and stability of brain circuits. I then describe some of the new findings from the Human Connectome Project about neural circuits and their relationships to behavior that may be particularly relevant to psychopathology. Finally, I end on future directions in terms of extensions of the Human Connectome Project methods across the lifespan and into manifest illness, as well as a discussion of potential treatment implications.

show abstract

“…In estimating dFC, the DCC model has minimum error in the estimation of time‐varying correlation, and its performance is as good as that of oracle sliding window (OSW) . Numerous studies have used the DCC and EWMA models to estimate dFC . In fact, some studies have investigated and revised these models.…”

Section: Introductionmentioning

confidence: 99%

Modified models and simulations for estimating dynamic functional connectivity in resting state functional magnetic resonance imaging

Behboudi

Farnoosh

2020

Statistics in Medicine

View full text Add to dashboard Cite

As understanding the nature of brain networks through dynamic functional connectivity (dFC) estimation is of paramount significant, the introduction and revision of blood-oxygen-level dependent (BOLD) signal simulation methods in brain regions and dFC estimation methods have gained significant ground in recent years. Based on the observation of BOLD signals with multivariate nonnormal distribution in functional magnetic resonance imaging (fMRI) images, we first propose a copula-based method for the production of these signals, in which nonnormal data are generated with a selected time-varying covariance matrix. Therefore, we can compare the performance of models in the cases where brain signals have a multivariate nonnormal distribution. Then, two kendallized exponentially weighted moving average (KEWMA) and kendallized dynamic conditional correlation (KDCC) multivariate volatility models are introduced which are based on two well-known and commonly used exponentially weighted moving average (EMWA) and dynamic conditional correlation (DCC) models. The results show that KDCC model can estimate conditional correlation significantly far better than the former ones (ie, DCC, standardized dynamic conditional correlation, EWMA, and standardized exponentially weighted moving average) on both types of data (ie, multivariate normal and nonnormal). In the next step, the bivariate normal distribution in Iranian resting state fMRI data is confirmed by using statistical tests, and it is shown that the dynamic nature of FC is not optimally detected using prevalent methods. Two alternative Portmanteau and rank-based tests are proposed for the examination of conditional heteroscedasticity in data. Finally, dFC in these data is estimated by employing the KDCC model.

show abstract

Real‐time estimation of dynamic functional connectivity networks

Cited by 24 publications

References 89 publications

Model-based estimation of dynamic functional connectivity in resting-state functional magnetic resonance imaging

Model-based estimation of dynamic functional connectivity in resting-state functional magnetic resonance imaging

Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology

Modified models and simulations for estimating dynamic functional connectivity in resting state functional magnetic resonance imaging

Contact Info

Product

Resources

About