Showing papers by "Emery N. Brown published in 2019"

Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile.

Abstract: Background: Glaucoma is the leading cause of irreversible blindness worldwide. It is a heterogeneous group of conditions with a common optic neuropathy and associated loss of peripheral vision. Both over and under-diagnosis carry high costs in terms of healthcare spending and preventable blindness. The characteristic clinical feature of glaucoma is asymmetrical optic nerve rim narrowing, which is difficult for humans to quantify reliably. Strategies to improve and automate optic disc assessment are therefore needed to prevent sight loss. Methods: We developed a novel glaucoma detection algorithm that segments and analyses colour photographs to quantify optic nerve rim consistency around the whole disc at 15-degree intervals. This provides a profile of the cup/disc ratio, in contrast to the vertical cup/disc ratio in common use. We introduce a spatial probabilistic model, to account for the optic nerve shape, we then use this model to derive a disc deformation index and a decision rule for glaucoma. We tested our algorithm on two separate image datasets (ORIGA and RIM-ONE). Results: The spatial algorithm accurately distinguished glaucomatous and healthy discs on internal and external validation (AUROC 99.6% and 91.0% respectively). It achieves this using a dataset 100-times smaller than that required for deep learning algorithms, is flexible to the type of cup and disc segmentation (automated or semi-automated), utilises images with missing data, and is correlated with the disc size (p = 0.02) and the rim-to-disc at the narrowest rim (p<0.001, in external validation). Discussion: The spatial probabilistic algorithm is highly accurate, highly data efficient and it extends to any imaging hardware in which the boundaries of cup and disc can be segmented, thus making the algorithm particularly applicable to research into disease mechanisms, and also glaucoma screening in low resource settings.

The Role of Glutamatergic and Dopaminergic Neurons in the Periaqueductal Gray/Dorsal Raphe: Separating Analgesia and Anxiety

Abstract: The periaqueductal gray (PAG) is a significant modulator of both analgesic and fear behaviors in both humans and rodents, but the underlying circuitry responsible for these two phenotypes is incompletely understood. Importantly, it is not known if there is a way to produce analgesia without anxiety by targeting the PAG, as modulation of glutamate or GABA neurons in this area initiates both antinociceptive and anxiogenic behavior. While dopamine (DA) neurons in the ventrolateral PAG (vlPAG)/dorsal raphe display a supraspinal antinociceptive effect, their influence on anxiety and fear are unknown. Using DAT-cre and Vglut2-cre male mice, we introduced designer receptors exclusively activated by designer drugs (DREADD) to DA and glutamate neurons within the vlPAG using viral-mediated delivery and found that levels of analgesia were significant and quantitatively similar when DA and glutamate neurons were selectively stimulated. Activation of glutamatergic neurons, however, reliably produced higher indices of anxiety, with increased freezing time and more time spent in the safety of a dark enclosure. In contrast, animals in which PAG/dorsal raphe DA neurons were stimulated failed to show fear behaviors. DA-mediated antinociception was inhibitable by haloperidol and was sufficient to prevent persistent inflammatory pain induced by carrageenan. In summary, only activation of DA neurons in the PAG/dorsal raphe produced profound analgesia without signs of anxiety, indicating that PAG/dorsal raphe DA neurons are an important target involved in analgesia that may lead to new treatments for pain.

Developing a personalized closed-loop controller of medically-induced coma in a rodent model

Abstract: OBJECTIVE Personalized automatic control of medically-induced coma, a critical multi-day therapy in the intensive care unit, could greatly benefit clinical care and further provide a novel scientific tool for investigating how the brain response to anesthetic infusion rate changes during therapy. Personalized control would require real-time tracking of inter- and intra-subject variabilities in the brain response to anesthetic infusion rate while simultaneously delivering the therapy, which has not been achieved. Current control systems for medically-induced coma require a separate offline model fitting experiment to deal with inter-subject variabilities, which would lead to therapy interruption. Removing the need for these offline interruptions could help facilitate clinical feasbility. In addition, current systems do not track intra-subject variabilities. Tracking intra-subject variabilities is essential for studying whether or how the brain response to anesthetic infusion rate changes during therapy. Further, such tracking could enhance control precison and thus help facilitate clinical feasibility. APPROACH Here we develop a personalized closed-loop anesthetic delivery (CLAD) system in a rodent model that tracks both inter- and intra-subject variabilities in real time while simultaneously controlling the anesthetic in closed loop. We tested the CLAD in rats by administrating propofol to control the electroencephalogram (EEG) burst suppression. We first examined whether the CLAD can remove the need for offline model fitting interruption. We then used the CLAD as a tool to study whether and how the brain response to anesthetic infusion rate changes as a function of changes in the depth of medically-induced coma. Finally, we studied whether the CLAD can enhance control compared with prior systems by tracking intra-subject variabilities. MAIN RESULTS The CLAD precisely controlled the EEG burst suppression in each rat without performing offline model fitting experiments. Further, using the CLAD, we discovered that the brain response to anesthetic infusion rate varied during control, and that these variations correlated with the depth of medically-induced coma in a consistent manner across individual rats. Finally, tracking these variations reduced control bias and error by more than 70% compared with prior systems. SIGNIFICANCE This personalized CLAD provides a new tool to study the dynamics of brain response to anesthetic infusion rate and has significant implications for enabling clinically-feasible automatic control of medically-induced coma.

Disruption of the ascending arousal network in acute traumatic disorders of consciousness

Abstract: Objective To determine whether ascending arousal network (AAn) connectivity is reduced in patients presenting with traumatic coma. Methods We performed high-angular-resolution diffusion imaging in 16 patients with acute severe traumatic brain injury who were comatose on admission and in 16 matched controls. We used probabilistic tractography to measure the connectivity probability (CP) of AAn axonal pathways linking the brainstem tegmentum to the hypothalamus, thalamus, and basal forebrain. To assess the spatial specificity of CP differences between patients and controls, we also measured CP within 4 subcortical pathways outside the AAn. Results Compared to controls, patients showed a reduction in AAn pathways connecting the brainstem tegmentum to a region of interest encompassing the hypothalamus, thalamus, and basal forebrain. When each pathway was examined individually, brainstem-hypothalamus and brainstem-thalamus CPs, but not brainstem-forebrain CP, were significantly reduced in patients. Only 1 subcortical pathway outside the AAn showed reduced CP in patients. Conclusions We provide initial evidence for the reduced integrity of axonal pathways linking the brainstem tegmentum to the hypothalamus and thalamus in patients presenting with traumatic coma. Our findings support current conceptual models of coma as being caused by subcortical AAn injury. AAn connectivity mapping provides an opportunity to advance the study of human coma and consciousness.

Delta oscillations phase limit neural activity during sevoflurane anesthesia.

Abstract: Understanding anesthetic mechanisms with the goal of producing anesthetic states with limited systemic side effects is a major objective of neuroscience research in anesthesiology. Coherent frontal alpha oscillations have been postulated as a mechanism of sevoflurane general anesthesia. This postulate remains unproven. Therefore, we performed a single-site, randomized, cross-over, high-density electroencephalogram study of sevoflurane and sevoflurane-plus-ketamine general anesthesia in 12 healthy subjects. Data were analyzed with multitaper spectral, global coherence, cross-frequency coupling, and phase-dependent methods. Our results suggest that coherent alpha oscillations are not fundamental for maintaining sevoflurane general anesthesia. Taken together, our results suggest that subanesthetic and general anesthetic sevoflurane brain states emerge from impaired information processing instantiated by a delta-higher frequency phase-amplitude coupling syntax. These results provide fundamental new insights into the neural circuit mechanisms of sevoflurane anesthesia and suggest that anesthetic states may be produced by extracranial perturbations that cause delta-higher frequency phase-amplitude interactions. Chamadia et al. show that a delta-higher frequency phase-amplitude coupling syntax, rather than coherent frontal alpha oscillations, is fundamental to sevoflurane anesthesia. Their conclusion is based on analyzing different EEG measures across different anesthetic states.

Cognitive Impairment Is Associated with Absolute Intraoperative Frontal α-Band Power but Not with Baseline α-Band Power: A Pilot Study.

Abstract: Background Cognitive abilities decline with aging, leading to a higher risk for the development of postoperative delirium or postoperative neurocognitive disorders after general anesthesia. Since frontal α-band power is known to be highly correlated with cognitive function in general, we hypothesized that preoperative cognitive impairment is associated with lower baseline and intraoperative frontal α-band power in older adults. Methods Patients aged ≥65 years undergoing elective surgery were included in this prospective observational study. Cognitive function was assessed on the day before surgery using six age-sensitive cognitive tests. Scores on those tests were entered into a principal component analysis to calculate a composite "g score" of global cognitive ability. Patient groups were dichotomized into a lower cognitive group (LC) reaching the lower 1/3 of "g scores" and a normal cognitive group (NC) consisting of the upper 2/3 of "g scores." Continuous pre- and intraoperative frontal electroencephalograms (EEGs) were recorded. EEG spectra were analyzed at baseline, before start of anesthesia medication, and during a stable intraoperative period. Significant differences in band power between the NC and LC groups were computed by using a frequency domain (δ 0.5-3 Hz, θ 4-7 Hz, α 8-12 Hz, β 13-30 Hz)-based bootstrapping algorithm. Results Of 38 included patients (mean age 72 years), 24 patients were in the NC group, and 14 patients had lower cognitive abilities (LC). Intraoperative α-band power was significantly reduced in the LC group compared to the NC group (NC -1.6 [-4.48/1.17] dB vs. LC -6.0 [-9.02/-2.64] dB), and intraoperative α-band power was positively correlated with "g score" (Spearman correlation: r = 0.381; p = 0.018). Baseline EEG power did not show any associations with "g." Conclusions Preoperative cognitive impairment in older adults is associated with intraoperative absolute frontal α-band power, but not baseline α-band power.

A Neurologic Examination for AnesthesiologistsAssessing Arousal Level during Induction, Maintenance, and Emergence

Abstract: Anesthetics have profound effects on the brain and central nervous system. Vital signs, along with the electroencephalogram and electroencephalogram-based indices, are commonly used to assess the brain states of patients receiving general anesthesia and sedation. Important information about the patient's arousal state during general anesthesia can also be obtained through use of the neurologic examination. This article reviews the main components of the neurologic examination focusing primarily on the brainstem examination. It details the components of the brainstem examination that are most relevant for patient management during induction, maintenance, and emergence from general anesthesia. The examination is easy to apply and provides important complementary information about the patient's arousal level that cannot be discerned from vital signs and electroencephalogram measures.

Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile (vol 14, e0209409, 2019)

Abstract: BACKGROUND Glaucoma is the leading cause of irreversible blindness worldwide. It is a heterogeneous group of conditions with a common optic neuropathy and associated loss of peripheral vision. Both over and under-diagnosis carry high costs in terms of healthcare spending and preventable blindness. The characteristic clinical feature of glaucoma is asymmetrical optic nerve rim narrowing, which is difficult for humans to quantify reliably. Strategies to improve and automate optic disc assessment are therefore needed to prevent sight loss. METHODS We developed a novel glaucoma detection algorithm that segments and analyses colour photographs to quantify optic nerve rim consistency around the whole disc at 15-degree intervals. This provides a profile of the cup/disc ratio, in contrast to the vertical cup/disc ratio in common use. We introduce a spatial probabilistic model, to account for the optic nerve shape, we then use this model to derive a disc deformation index and a decision rule for glaucoma. We tested our algorithm on two separate image datasets (ORIGA and RIM-ONE). RESULTS The spatial algorithm accurately distinguished glaucomatous and healthy discs on internal and external validation (AUROC 99.6% and 91.0% respectively). It achieves this using a dataset 100-times smaller than that required for deep learning algorithms, is flexible to the type of cup and disc segmentation (automated or semi-automated), utilises images with missing data, and is correlated with the disc size (p = 0.02) and the rim-to-disc at the narrowest rim (p<0.001, in external validation). DISCUSSION The spatial probabilistic algorithm is highly accurate, highly data efficient and it extends to any imaging hardware in which the boundaries of cup and disc can be segmented, thus making the algorithm particularly applicable to research into disease mechanisms, and also glaucoma screening in low resource settings.

Quantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy

Abstract: Objectives:Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroenc

A Systematic Method for Preprocessing and Analyzing Electrodermal Activity

Abstract: Electrodermal activity (EDA) is a measure of sympathetic tone using sweat gland activity that has applications in research and clinical medicine. We previously identified never-before-seen statistical structure in EDA. However, there is no systematic method to preprocess and analyze EDA data to capture such statistical structure. Therefore, in this study, we analyzed the data of two healthy volunteers while awake and at rest. We used a systematic process that takes advantage of the tail behavior of various statistical distributions to ensure capturing the point process structure in EDA. We verified the presence of this temporal structure in a new dataset of subjects. Our results demonstrate for the first time that point process structure of EDA pulses can be identified across multiple datasets using a systematic method that is still rooted in the underlying physiology.

State-Space Global Coherence to Estimate the Spatio-Temporal Dynamics of the Coordinated Brain Activity

Abstract: Characterizing coordinated brain dynamics present in high-density neural recordings is critical for understanding the neurophysiology of healthy and pathological brain states and to develop principled strategies for therapeutic interventions. In this research, we propose a new modeling framework called State Space Global Coherence (SSGC), which allows us to estimate neural synchrony across distributed brain activity with fine temporal resolution. In this modeling framework, the cross-spectral matrix of neural activity at a specific frequency is defined as a function of a dynamical state variable representing a measure of Global Coherence (GC); we then combine filter-smoother and Expectation-Maximization (EM) algorithms to estimate GC and the model parameters. We demonstrate a SSGC analysis in a 64-channel EEG recording of a human subject under general anesthesia and compare the modeling result with empirical measures of GC. We show that SSGC not only attains a finer time resolution but also provides more accurate estimation of GC.

Neural signatures of loss of consciousness and its recovery by thalamic stimulation.

Abstract: We know that general anesthesia produces unconsciousness but not quite how. We recorded neural activity from the frontal, parietal, and temporal cortices and thalamus while maintaining unconsciousness in non-human primates (NHPs) with propofol. Unconsciousness was marked by slow frequency (∼1 Hz) oscillations in local field potentials, entraining local spiking to Up states alternating with Down states of little spiking, and decreased higher frequency (>4 Hz) coherence. The thalamus contributed to cortical rhythms. Its stimulation “awakened” anesthetized NHPs and reversed the electrophysiologic features of unconsciousness. Unconsciousness thus resulted from slow frequency hypersynchrony and loss of high-frequency dynamics, partly mediated by the thalamus, that disrupts cortical communication/integration.

A hidden semi-Markov model for estimating burst suppression EEG

Abstract: Burst suppression is an electroencephalogram (EEG) pattern associated with profoundly inactivated brain states characterized by cerebral metabolic depression. This pattern is distinguished by short-duration band-limited electrical activity (bursts) interspersed between relatively near-isoelectric periods (suppressions). Prior work in neurophysiology suggests that burst and suppression segments are respectively associated with consumption and regeneration of adenosine triphosphate resource in cortical networks. This indicates that once a suppression (or, burst) segment begins, the propensity to switch out of the state gradually increases with duration spent in the state. Prior EEG monitoring frameworks that track the brain state during burst suppression by tracking the estimated fraction of time spent in suppression, relative to bursts, do not incorporate this information. In this work, we incorporate this information within a hidden semi-Markov model (HSMM) wherein two states (burst & suppression) stochastically switch between each other using sojourn-time dependent transition probabilities. We demonstrate the HSMM’s utility in analyzing clinical data by estimating the state probabilities, the optimal state sequence, and the brain’s metabolic activation level characterized by parameters governing sojourn-time dependence in transition probabilities. The HSMM-based approach proposed here provides a novel statistical framework that advances the state-of-the-art in analyzing burst suppression EEG.

Personalized Connectome Mapping to Guide Targeted Therapy and Promote Recovery of Consciousness in the Intensive Care Unit

Abstract: There are currently no therapies proven to promote early recovery of consciousness in patients with severe brain injuries in the intensive care unit (ICU). Early recovery of consciousness would benefit patients and families by reducing the likelihood of premature withdrawal of life-sustaining therapy and may decrease ICU complications related to immobility, facilitate self-expression, enable autonomous decision-making, and increase access to rehabilitative care. Here, we present the connectome-based clinical trial platform (CCTP), a new mechanistic paradigm for developing and testing targeted therapies that promote early recovery of consciousness in the ICU. The scientific premise of the CCTP is that personalized brain connectome maps can be used to select patients for targeted therapies that promote recovery of consciousness. Structural and functional MRI connectome maps will identify circuits that may be amenable to neuromodulation. Patients will be selected for clinical trials in the CCTP paradigm based on connectomes that are likely to respond to targeted therapies. To demonstrate the utility of this precision approach, we describe STIMPACT (Stimulant Therapy Targeted to Individualized Connectivity Maps to Promote ReACTivation of Consciousness), a CCTP-based clinical trial in which intravenous methylphenidate will be used to promote early recovery of consciousness in the ICU (ClinicalTrials.gov NCT03814356). We propose that the CCTP has the potential to transform the therapeutic landscape in the ICU and improve outcomes for patients with severe brain injuries.

Correction:Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile (PLoS ONE (2019) 14:1 (e0209409) DOI: 10.1371/journal.pone.0209409)

Abstract: The second author, Bryan M. Williams, should also be listed as a corresponding author. Dr. Williams’ email address is: bryan.williams@Liverpool.ac.uk.

System and method for automated ovarian follicular monitoring

Abstract: Methods and products for automated real-time ovarian follicular detection, monitoring and analysis are provided. The devices and methods allow for remote or local analysis, while minimizing or eliminating the need for technician review of the output images. The methods are useful in human and non-human subjects including companion animals and other animals such as endangered species.

Constructing a control-ready model of EEG signal during general anesthesia in humans.

Abstract: Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work.

A state-space model for dynamic functional connectivity

Abstract: Dynamic functional connectivity (DFC) analysis involves measuring correlated neural activity over time across multiple brain regions. Significant regional correlations among neural signals, such as those obtained from resting-state functional magnetic resonance imaging (fMRI), may represent neural circuits associated with rest. The conventional approach of estimating the correlation dynamics as a sequence of static correlations from sliding time-windows has statistical limitations. To address this issue, we propose a multivariate stochastic volatility model for estimating DFC inspired by recent work in econometrics research. This model assumes a state-space framework where the correlation dynamics of a multivariate normal observation sequence is governed by a positive-definite matrix-variate latent process. Using this statistical model within a sequential Bayesian estimation framework, we use blood oxygenation level dependent activity from multiple brain regions to estimate posterior distributions on the correlation trajectory. We demonstrate the utility of this DFC estimation framework by analyzing its performance on simulated data, and by estimating correlation dynamics in resting state fMRI data from a patient with a disorder of consciousness (DoC). Our work advances the state-of-the-art in DFC analysis and its principled use in DoC biomarker exploration.

Methods and devices for providing a stimulus to a subject to induce gamma oscillations

Abstract: A method includes administering a non-invasive stimulus to a subject having a frequency of about 35 Hz to about 45 Hz to induce synchronized gamma oscillations in at least one brain region of the subject.

Smoother Adaptive Parametric Spectrograms: An Application to EEG Under General Anesthesia

Abstract: High quality spectra are crucial in anesthesia related procedures where Electroencephalogram (EEG) frequency content can drastically help track different brain states. To this end, an adaptive autoregressive model framework to fit non-stationary EEG data using hybrid Kalman Filtering (HKF) is developed. In this setup, a state-space formulation is adopted. Hybridity arises from the fact that the state vector, which includes the autoregressive parameters, evolves in continuous time, while the observation equation is discrete, to account for the fact that the observations, i.e. EEG data, are discrete points in time. As a smoothing constraint, the parameters are modeled to follow a continuous multivariate random walk. As shown in this work, their adaptive estimation by means of HKF and expectation-maximization (EM) algorithm yields smoother estimation of frequency spectra, outperforming other current purely discrete parametric methods as well as various non-parametric approaches. Examples of dynamic EEG data were taken from patients under gradually varying doses of propofol. The suitability of our method for online use in combination with its ability to smoothly track frequency changes in human EEG signals suggests that it can be used for real time brain state tracking under general anesthesia, facilitating the design of closed loop systems for automatic and precise control of brain states.

A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery

Abstract: Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists efficiently achieve and maintain desired anesthetic depth over an extended period of time. A typical CLAD system would use an anesthetic marker, calculated from physiological signals, as real-time feedback to adjust anesthetic dosage towards achieving a desired set-point of the marker. Since control strategies for CLAD vary across the systems reported in recent literature, a comparative analysis of common control strategies can be useful. For a nonlinear plant model based on well-established models of compartmental pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the set-point tracking performance of three output-feedback linear control strategies: proportional-integral-derivative (PID) control, linear quadratic Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically, we numerically simulate multiple CLAD sessions for the scenario where the plant model parameters are unavailable for a patient and the controller is designed based on a nominal model and controller gains are held constant throughout a session. Based on the numerical analyses performed here, conditioned on our choice of model and controllers, we infer that in terms of accuracy and bias PID control performs better than ILQG which in turn performs better than LQG. In the case of noisy observations, ILQG can be tuned to provide a smoother infusion rate while achieving comparable steady-state response with respect to PID. The numerical analyses framework and findings, reported here, can help CLAD developers in their choice of control strategies. This paper may also serve as a tutorial paper for teaching control theory for CLAD.

Systems and methods for monitoring a subject under the influence of drugs

Abstract: Systems and methods for monitoring a subject under, or suspected to be under, the influence of one or more drugs are provided In one aspect, a method comprises controlling one or more sensors of a monitoring device to acquire physiological signals from a subject suspected to be under the influence of one or more drugs, and assembling a set of physiological data using the acquired physiological signals The method also includes generating, using a processor of the monitoring device, physiological markers characteristic of the influence of the one or more drugs by analyzing the set of physiological data, and correlating the physiological markers with a drug profile characterizing the one or more drugs affecting the subject The method further includes proving to a user a report indicating the drug profile characterizing the one or more drugs affecting the subject

Broadband slow-wave modulation in posterior and anterior cortex tracks distinct states of propofol-induced unconsciousness

Abstract: A controversy exists over the roles of frontal and posterior cortices in mediating consciousness and unconsciousness. Disruption of posterior cortex during sleep appears to suppress the contents of dreaming, yet activation of frontal cortex appears necessary for perception and can reverse unconsciousness under anesthesia. We used anesthesia to study how regional cortical disruption, mediated by slow wave modulation of broadband activity, changes during un-consciousness in humans. We found that broadband slow-wave modulation enveloped posterior cortex when subjects initially became unconscious, but later encompassed both frontal and posterior cortex when subjects were more deeply anesthetized and likely unarousable. Our results suggest that unconsciousness under anesthesia comprises several distinct shifts in brain state that disrupt the contents of consciousness distinct from arousal and awareness of those contents.

A state-space model for dynamic functional connectivity

Abstract: Dynamic functional connectivity (DFC) analysis involves measuring correlated neural activity over time across multiple brain regions. Significant regional correlations among neural signals, such as those obtained from resting-state functional magnetic resonance imaging (fMRI), may represent neural circuits associated with rest. The conventional approach of estimating the correlation dynamics as a sequence of static correlations from sliding time-windows has statistical limitations. To address this issue, we propose a multivariate stochastic volatility model for estimating DFC inspired by recent work in econometrics research. This model assumes a state-space framework where the correlation dynamics of a multivariate normal observation sequence is governed by a positive-definite matrix-variate latent process. Using this statistical model within a sequential Bayesian estimation framework, we use blood oxygenation level dependent activity from multiple brain regions to estimate posterior distributions on the correlation trajectory. We demonstrate the utility of this DFC estimation framework by analyzing its performance on simulated data, and by estimating correlation dynamics in resting state fMRI data from a patient with a disorder of consciousness (DoC). Our work advances the state-of-the-art in DFC analysis and its principled use in DoC biomarker exploration.