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Real-time estimation of dynamic functional connectivity networks.

ABSTRACT: Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time. Hum Brain Mapp 38:202-220, 2017. © 2016 Wiley Periodicals, Inc.

SUBMITTER: Monti RP

PROVIDER: S-EPMC6639120 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Real-time estimation of dynamic functional connectivity networks.

Monti Ricardo Pio RP Lorenz Romy R Braga Rodrigo M RM Anagnostopoulos Christoforos C Leech Robert R Montana Giovanni G

Human brain mapping 20160907 1

Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many c ...[more]

PMID: 27600689

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Project description:Dynamic functional connectivity (DFC) analysis has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal the time-varying functional interactions between brain regions. Although the sliding window (SW) method is popular for DFC analysis, the selection of window length is hard, and the temporal resolution is limited by the window length. The hidden Markov model (HMM) without the limitation of window length has been proven to be able to estimate time-varying brain states from fMRI data. However, HMM tends to be overfitted in DFC analysis of fMRI data because of the high spatial dimension and the limited sample size of fMRI data. In this study, we proposed an alternating HMM (aHMM) method that used the functional connectivity estimation of SW to initialize the covariance matrix of HMM and adopted an alternating HMM procedure to reduce the number of parameters during each optimization. The simulated and real fMRI resting data from the Human Connectome Projects showed that aHMM produced better robustness to noise, parameter number and sample size in DFC estimation than SW and HMM. For the real fMRI resting data of cerebral small vessel disease (CSVD), results of aHMM revealed that amnesia and mild cognitive impairment (aMCI) caused the CSVD with aMCI (CSVD-aMCI) group tended to spend more time on the brain state with overall weak connections and less time on the state with overall strong connections than the CSVD-controls. Moreover, CSVD-aMCI showed significantly lower connectivity amplitude and higher connectivity fluctuation than CSVD-control. In contrast, HMM did not detect intergroup differences of the connectivity amplitude and fluctuations and SW did not detect intergroup differences of connectivity fluctuations and fraction of time. The results further indicated that aHMM outperformed HMM and SW in detecting inter-group differences of temporal properties of DFC and connectivity fluctuations.Supplementary informationThe online version contains supplementary material available at 10.1007/s11571-022-09874-3.

Project description:Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels-group-, individual-, and task-specific, utilizing a combination of well-established statistical methods.

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Real-time estimation of dynamic functional connectivity networks.

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Real-time estimation of dynamic functional connectivity networks.

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