Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging

Jiang, Fei; Jin, Huaqing; Gao, Yijing; Xie, Xihe; Cummings, Jennifer A.; Raj, Ashish; Nagarajan, Srikantan S.

doi:10.1016/j.neuroimage.2022.119131

Cited by 10 publications

(10 citation statements)

References 90 publications

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“…Dynamics on faster timescales will require finer Morlet wavelet time-frequency decomposition. New approaches that can detect non-evenly-spaced state switching ( Baker et al, 2014 ; Jiang et al, 2022 ) may also be adapted in future iterations of our inference procedure.…”

Section: Discussionmentioning

confidence: 99%

“…We investigate the properties of SGM and extend it to capture the temporal fluctuations in MEG activity, an emerging marker of brain function ( Baker et al, 2014 ; Jiang et al, 2022 ; Liuzzi et al, 2019 ; Quinn et al, 2018 ; Sorrentino et al, 2021 ; Tait & Zhang, 2022 ; Tewarie et al, 2019a , 2019b ; Vidaurre et al, 2018 ). We focus on SGM for various reasons.…”

Section: Introductionmentioning

confidence: 99%

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Stability and dynamics of a spectral graph model of brain oscillations

Verma

Hinkley

Raj

2023

Network Neuroscience

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We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structuring wiring of the brain. Previously we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stability and dynamics of a spectral graph model of brain oscillations

Verma

Hinkley

Raj

2023

Network Neuroscience

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“…One critical caveat of data censoring is the loss of data continuity. This could limit the types of analyses applied to the data, e.g., auto‐regression (Garg et al, 2011 ), phase synchrony (Pedersen et al, 2018 ; Weaver et al, 2016 ; Zhang et al, 2019 ), or dynamic FC analysis (Jiang et al, 2022 ; Rashid et al, 2014 ). Data censoring also brings up the issue of consecutive frame requirements for resting state studies.…”

Section: Discussionmentioning

confidence: 99%

Systematic evaluation of head motion on resting‐state functional connectivity MRI in the neonate

Kim

Asis-Cruz

Kapse

et al. 2022

Human Brain Mapping

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Reliability and robustness of resting state functional connectivity MRI (rs‐fcMRI) relies, in part, on minimizing the influence of head motion on measured brain signals. The confounding effects of head motion on functional connectivity have been extensively studied in adults, but its impact on newborn brain connectivity remains unexplored. Here, using a large newborn data set consisting of 159 rs‐fcMRI scans acquired in the Developing Brain Institute at Children's National Hospital and 416 scans from The Developing Human Connectome Project (dHCP), we systematically investigated associations between head motion and rs‐fcMRI. Head motion during the scan significantly affected connectivity at sensory‐related networks and default mode networks, and at the whole brain scale; the direction of motion effects varied across the whole brain. Comparing high‐ versus low‐head motion groups suggested that head motion can impact connectivity estimates across the whole brain. Censoring of high‐motion volumes using frame‐wise displacement significantly reduced the confounding effects of head motion on neonatal rs‐fcMRI. Lastly, in the dHCP data set, we demonstrated similar persistent associations between head motion and network connectivity despite implementing a standard denoising strategy. Collectively, our results highlight the importance of using rigorous head motion correction in preprocessing neonatal rs‐fcMRI to yield reliable estimates of brain activity.

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“…In the current study, we address these challenges by adopting recent advances in model-based analysis of time-varying FC, and apply them to interrogate the role of dynamic FC in the AD context. We utilize the time-varying dynamic network approach (TVDN) proposed by Jiang et al (2022) to extract these dynamic FCs from magnetoencephalography (MEG) resting state data in a well characterized cohort of patients with AD and an age-matched control cohort study. MEG has been shown to have good sensitivity to detect early functional changes associated with AD pathophysiology (López-Sanz et al, 2018; Khan and Usman, 2015; Mandal et al, 2018; Maestú et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Dynamic functional connectivity MEG features of Alzheimer’s disease

Jin

Ranasinghe

Prahu

et al. 2023

Preprint

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Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have high sensitivity and specificity in distinguishing AD from healthy subjects. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

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Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging

Cited by 10 publications

References 90 publications

Stability and dynamics of a spectral graph model of brain oscillations

Stability and dynamics of a spectral graph model of brain oscillations

Systematic evaluation of head motion on resting‐state functional connectivity MRI in the neonate

Dynamic functional connectivity MEG features of Alzheimer’s disease

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