Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

Ting, Chee-Ming; Seghouane, Abd‐Krim; Khalid, Muhammad Usman; Salleh, Shaharuddin

doi:10.1162/neco_a_00765

Cited by 17 publications

(10 citation statements)

References 26 publications

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“…In order to mine interference data more accurately, a threshold of mining accuracy can be set. When the mining accuracy is less than χ , ( 1 ) should be used for secondary mining [ 28 ]. The function expression of mining accuracy in the process of interference data mining can be given as follows:

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Section: Technical Elements Of Big Data Environment Platform Designmentioning

confidence: 99%

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

Shi

2022

Journal of Environmental and Public Health

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The pivotal and most important technology point of the application of big data in modern network teaching is the establishment of teaching platform. Based on Hadoop big data technology, this paper establishes a big data platform designed by Chinese teaching institutions in colleges and universities. The teaching system not only realizes the diversified development of network teaching but also realizes the discovery of teaching platform users’ needs and the push of resources. In addition, it is found that a small number of people do not have a high understanding of the teaching mode of big data through extensive studies, which also shows that traditional teaching is still lagging behind in big data education. However, on the whole, the Chinese teaching model in universities based on the big data environment deserves further promotion and application in universities.

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Section: Technical Elements Of Big Data Environment Platform Designmentioning

confidence: 99%

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

Shi

2022

Journal of Environmental and Public Health

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“…The use of AR models to explore the multivariate interactions within fMRI time series dates back to more than a decade (Harrison et al, 2003;Valdes-Sosa, 2004;Valdés-Sosa et al, 2005;Rogers et al, 2010). The optimal order of these models decreases with the number of ROIs, and was in general found to be one in whole brain analyses considering more than a hundred ROIs (Valdes-Sosa, 2004;Ting et al, 2015). More recently, the AR-1 model was also shown to be a promising representation of FC dynamics (Liégeois et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Dynamic mode decomposition of resting-state and task fMRI

et al. 2019

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Component analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain networks shaping functional connectivity (FC). Importantly, these approaches are static in the sense that they ignore the temporal information contained in fMRI time series. Therefore, the corresponding components provide a static characterization of FC. Building upon recent findings suggesting that FC dynamics encode richer information about brain functional organization, we use a dynamic extension of component analysis to identify dynamic modes (DMs) of fMRI time series. We demonstrate the feasibility and relevance of this approach using resting-state and motor-task fMRI data of 730 healthy subjects of the Human Connectome Project (HCP). In resting-state, dominant DMs have strong resemblance with classical resting-state networks, with an additional temporal characterization of the networks in terms of oscillatory periods and damping times. In motor-task conditions, dominant DMs reveal interactions between several brain areas, including but not limited to the posterior parietal cortex and primary motor areas, that are not found with classical activation maps. Finally, we identify two canonical components linking the temporal properties of the resting-state DMs with 158 behavioral and demographic HCP measures. Altogether, these findings illustrate the benefits of the proposed dynamic component analysis framework, making it a promising tool to characterize the spatio-temporal organization of brain activity.

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“…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

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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.

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Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

Cited by 17 publications

References 26 publications

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

[Retracted] Research on Organization Design of College Chinese Teaching under Big Data Environment

Dynamic mode decomposition of resting-state and task fMRI

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

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