Functional MRI and multivariate autoregressive models

Rogers, Baxter P.; Katwal, Santosh B.; Morgan, Victoria L.; Asplund, Christopher L.; Gore, John C.

doi:10.1016/j.mri.2010.03.002

Cited by 59 publications

(50 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3A) and/or when the CROSS network functional connectivity begins to increase. This is similar to our previous findings of an increase in influence of the contralateral hippocampus' fMRI signal on the ipsilateral hippocampus' fMRI signal as the duration of disease increased determined by Granger causality measures (Deshpande et al, 2009;Rogers et al, 2010), which we interpreted as evidence of a weakening of the ipsilateral hippocampus function over time.…”

Section: Midline Cingulate Networksupporting

confidence: 91%

Evolution of Functional Connectivity of Brain Networks and Their Dynamic Interaction in Temporal Lobe Epilepsy

Morgan

Abou‐Khalil²,

Rogers³

2015

Brain Connectivity

Self Cite

View full text Add to dashboard Cite

This study presents a cross-sectional investigation of functional networks in temporal lobe epilepsy (TLE) as they evolve over years of disease. Networks of interest were identified based on a priori hypotheses: the network of seizure propagation ipsilateral to the seizure focus, the same regions contralateral to seizure focus, the cross hemisphere network of the same regions, and a cingulate midline network. Resting functional magnetic resonance imaging data were acquired for 20 min in 12 unilateral TLE patients, and 12 age- and gender-matched healthy controls. Functional changes within and between the four networks as they evolve over years of disease were quantified by standard measures of static functional connectivity and novel measures of dynamic functional connectivity. The results suggest an initial disruption of cross-hemispheric networks and an increase in static functional connectivity in the ipsilateral temporal network accompanying the onset of TLE seizures. As seizures progress over years, the static functional connectivity across the ipsilateral network diminishes, while dynamic functional connectivity measures show the functional independence of this ipsilateral network from the network of midline regions of the cingulate declines. This implies a gradual breakdown of the seizure onset and early propagation network involving the ipsilateral hippocampus and temporal lobe as it becomes more synchronous with the network of regions responsible for secondary generalization of the seizures, a process that may facilitate the spread of seizures across the brain. Ultimately, the significance of this evolution may be realized in relating it to symptoms and treatment outcomes of TLE.

show abstract

Section: Midline Cingulate Networksupporting

confidence: 91%

Evolution of Functional Connectivity of Brain Networks and Their Dynamic Interaction in Temporal Lobe Epilepsy

Morgan

Abou‐Khalil²,

Rogers³

2015

Brain Connectivity

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, David et al (2008) show that lag-based causality can give reasonable results after deconvolving the HRF, with the timing of the HRF estimated through the use of electrophysiological data acquired simultaneously. In another example, Rogers et al (2010) show that neural lags as short as 100 ms should be estimable between two areas from the BOLD timecourses in the case when the two areas in question have (presumably) identical HRFs (left and right V1), and using high field strength (7 T) and low TR (250 ms).…”

Section: Discussionmentioning

confidence: 99%

Network modelling methods for FMRI

et al. 2011

View full text Add to dashboard Cite

“…Evolutionary models are another family of techniques for modeling time-series in which observed data at a given time depend on the previous data via a linear or nonlinear transformation function, (Harrison et al, 2003; Rogers et al, 2010; Smith et al, 2010; Samdin et al, 2016; Fiecas and Ombao, 2016; Ting et al, 2015). …”

Section: Introductionmentioning

confidence: 99%

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI

et al. 2017

View full text Add to dashboard Cite

There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks—three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity.

show abstract

Functional MRI and multivariate autoregressive models

Cited by 59 publications

References 70 publications

Evolution of Functional Connectivity of Brain Networks and Their Dynamic Interaction in Temporal Lobe Epilepsy

Evolution of Functional Connectivity of Brain Networks and Their Dynamic Interaction in Temporal Lobe Epilepsy

Network modelling methods for FMRI

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI

Contact Info

Product

Resources

About