Computational Simulation Model to Predict Behavior Changes in Inflammatory Bowel Disease Patients during the COVID-19 Pandemic: Analysis of Two Regional Japanese Populations.

Managing inflammatory bowel disease (IBD) is a major challenge for physicians and patients during the COVID-19 pandemic. To understand the impact of the pandemic on patient behaviors and disruptions in medical care, we used a combination of population-based modeling, system dynamics simulation, and linear optimization. Synthetic IBD populations in Tokyo and Hokkaido were created by localizing an existing US-based synthetic IBD population using data from the Ministry of Health, Labor, and Welfare in Japan. A clinical pathway of IBD-specific disease progression was constructed and calibrated using longitudinal claims data from JMDC Inc for patients with IBD before and during the COVID-19 pandemic. Key points considered for disruptions in patient behavior (demand) and medical care (supply) were diagnosis of new patients, clinic visits for new patients seeking care and diagnosed patients receiving continuous care, number of procedures, and the interval between procedures or biologic prescriptions. COVID-19 had a large initial impact and subsequent smaller impacts on demand and supply despite higher infection rates. Our population model (Behavior Predictor) and patient treatment simulation model (Demand Simulator) represent the dynamics of clinical care demand among patients with IBD in Japan, both in recapitulating historical demand curves and simulating future demand during disruption scenarios, such as pandemic, earthquake, and economic crisis.

A long-term mechanistic computational model of physiological factors driving the onset of type 2 diabetes in an individual.

A computational model of the physiological mechanisms driving an individual's health towards onset of type 2 diabetes (T2D) is described, calibrated and validated using data from the Diabetes Prevention Program (DPP). The objective of this model is to quantify the factors that can be used for prevention of T2D. The model is energy and mass balanced and continuously simulates trajectories of variables including body weight components, fasting plasma glucose, insulin, and glycosylated hemoglobin among others on the time-scale of years. Modeled mechanisms include dynamic representations of intracellular insulin resistance, pancreatic beta-cell insulin production, oxidation of macronutrients, ketogenesis, effects of inflammation and reactive oxygen species, and conversion between stored and activated metabolic species, with body-weight connected to mass and energy balance. The model was calibrated to 331 placebo and 315 lifestyle-intervention DPP subjects, and one year forecasts of all individuals were generated. Predicted population mean errors were less than or of the same magnitude as clinical measurement error; mean forecast errors for weight and HbA1c were ~5%, supporting predictive capabilities of the model. Validation of lifestyle-intervention prediction is demonstrated by synthetically imposing diet and physical activity changes on DPP placebo subjects. Using subject level parameters, comparisons were made between exogenous and endogenous characteristics of subjects who progressed toward T2D (HbA1c > 6.5) over the course of the DPP study to those who did not. The comparison revealed significant differences in diets and pancreatic sensitivity to hyperglycemia but not in propensity to develop insulin resistance. A computational experiment was performed to explore relative contributions of exogenous versus endogenous factors between these groups. Translational uses to applications in public health and personalized healthcare are discussed.

Network dynamics of the brain and influence of the epileptic seizure onset zone.

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

A network analysis of the dynamics of seizure.

Seizures are events that spread through the brain's network of connections and create pathological activity. To understand what is occurring in the brain during seizure we investigated the time progression of the brain's state from seizure onset to seizure suppression. Knowledge of a seizure's dynamics and the associated spatial structure is important for localizing the seizure foci and determining the optimal location and timing of electrical stimulation to mitigate seizure development. In this study, we analyzed intracranial EEG data recorded in 2 human patients with drug-resistant epilepsy prior to undergoing resection surgery using network analyses. Specifically, we computed a time sequence of connectivity matrices from iEEG (intracranial electroencephalography) recordings that represent network structure over time. For each patient, connectivity between electrodes was measured using the coherence in the band of frequencies with the strongest modulation during seizure. The connectivity matrices' structure was analyzed using an eigen-decomposition. The leading eigenvector was used to estimate each electrode's time dependent centrality (importance to the network's connectivity). The electrode centralities were clustered over the course of each seizure and the cluster centroids were compared across seizures. We found, for each patient, there was a consistent set of centroids that occurred during each seizure. Further, the brain reliably evolved through the same progression of states across multiple seizures including characteristic onset and suppression states.

Quickest detection of drug-resistant seizures: an optimal control approach.

Epilepsy affects 50 million people worldwide, and seizures in 30% of the cases remain drug resistant. This has increased interest in responsive neurostimulation, which is most effective when administered during seizure onset. We propose a novel framework for seizure onset detection that involves (i) constructing statistics from multichannel intracranial EEG (iEEG) to distinguish nonictal versus ictal states; (ii) modeling the dynamics of these statistics in each state and the state transitions; you can remove this word if there is no room. (iii) developing an optimal control-based "quickest detection" (QD) strategy to estimate the transition times from nonictal to ictal states from sequential iEEG measurements. The QD strategy minimizes a cost function of detection delay and false positive probability. The solution is a threshold that non-monotonically decreases over time and avoids responding to rare events that normally trigger false positives. We applied QD to four drug resistant epileptic patients (168 hour continuous recordings, 26-44 electrodes, 33 seizures) and achieved 100% sensitivity with low false positive rates (0.16 false positive/hour). This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Is gamma-band activity in the local field potential of V1 cortex a "clock" or filtered noise?

Gamma-band (25-90 Hz) peaks in local field potential (LFP) power spectra are present throughout the cerebral cortex and have been related to perception, attention, memory, and disorders (e.g., schizophrenia and autism). It has been theorized that gamma oscillations provide a "clock" for precise temporal encoding and "binding" of signals about stimulus features across brain regions. For gamma to function as a clock, it must be autocoherent: phase and frequency conserved over a period of time. We computed phase and frequency trajectories of gamma-band bursts, using time-frequency analysis of LFPs recorded in macaque primary visual cortex (V1) during visual stimulation. The data were compared with simulations of random networks and clock signals in noise. Gamma-band bursts in LFP data were statistically indistinguishable from those found in filtered broadband noise. Therefore, V1 LFP data did not contain clock-like gamma-band signals. We consider possible functions for stochastic gamma-band activity, such as a synchronizing pulse signal.

Multivariate analysis of SEEG signals during seizure.

Epilepsy is a neurological disorder that affects tens of millions of people every year and is characterized by sudden-onset seizures which are often associated with physical convulsions. Effective treatment and management of epilepsy would be greatly improved if convulsions could be caught quickly through early seizure detection. However, this is still a largely open problem due to the challenge of finding a robust statistic from the neural measurements. This paper suggests a new multivariate statistic by combining spectral techniques with matrix theory. Specifically, stereoelectroencephalography (SEEG) data was used to generate a series of coherence connectivity matrices which were then examined using singular value decomposition. Tracking the relative angles of the first singular vectors generated from this data provides an effective way of defining the most dominant characteristics of the SEEG during the normal, the pre-ictal, and the ictal states. This paper indicates that the first singular vector has a characteristic direction indicative of the seizure state and illustrates a data analysis method that incorporates all neural data as opposed to a small selection of channels.

Comparisons of the dynamics of local field potential and multiunit activity signals in macaque visual cortex.

The local field potential (LFP) and multiunit activity (MUA) are extracellularly recorded signals that describe local neuronal network dynamics. In our experiments, the LFP and MUA, recorded from the same electrode in macaque primary visual cortex V1 in response to drifting grating visual stimuli, were evaluated on coarse timescales (∼1-5 s) and fine timescales (<0.1 s). On coarse timescales, MUA and the LFP both produced sustained visual responses to optimal and non-optimal oriented visual stimuli. The sustainedness of the two signals across the population of recording sites was correlated (correlation coefficient, ∼0.4). At most recording sites, the MUA was at least as sustained as the LFP and significantly more sustained for optimal orientations. In previous literature, the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging studies was found to be more strongly correlated with the LFP than with the MUA as a result of the lack of sustained response in the MUA signal. Because we found that MUA was as sustained as the LFP, MUA may also be correlated with BOLD. On fine timescales, we computed the coherence between the LFP and MUA over the frequency range 10-150 Hz. The LFP and MUA were weakly but significantly coherent (∼0.14) in the gamma band (20-90 Hz). The amount of gamma-band coherence was correlated with the power in the gamma band of the LFP. The data were consistent with the proposal that the LFP and MUA are generated in a noisy, resonant cortical network.

Searching for autocoherence in the cortical network with a time-frequency analysis of the local field potential.

Gamma-band peaks in the power spectrum of local field potentials (LFP) are found in multiple brain regions. It has been theorized that gamma oscillations may serve as a 'clock' signal for the purposes of precise temporal encoding of information and 'binding' of stimulus features across regions of the brain. Neurons in model networks may exhibit periodic spike firing or synchronized membrane potentials that give rise to a gamma-band oscillation that could operate as a 'clock'. The phase of the oscillation in such models is conserved over the length of the stimulus. We define these types of oscillations to be 'autocoherent'. We investigated the hypothesis that autocoherent oscillations are the basis of the experimentally observed gamma-band peaks: the autocoherent oscillator (ACO) hypothesis. To test the ACO hypothesis, we developed a new technique to analyze the autocoherence of a time-varying signal. This analysis used the continuous Gabor transform to examine the time evolution of the phase of each frequency component in the power spectrum. Using this analysis method, we formulated a statistical test to compare the ACO hypothesis with measurements of the LFP in macaque primary visual cortex, V1. The experimental data were not consistent with the ACO hypothesis. Gamma-band activity recorded in V1 did not have the properties of a 'clock' signal during visual stimulation. We propose instead that the source of the gamma-band spectral peak is the resonant V1 network driven by random inputs.