Awake mouse brain photoacoustic and optical imaging through a transparent ultrasound cranial window.

Optical resolution photoacoustic microscopy (OR-PAM) can map the cerebral vasculature at capillary-level resolution. However, the OR-PAM setup's bulky imaging head makes awake mouse brain imaging challenging and inhibits its integration with other optical neuroimaging modalities. Moreover, the glass cranial windows used for optical microscopy are unsuitable for OR-PAM due to the acoustic impedance mismatch between the glass plate and the tissue. To overcome these challenges, we propose a lithium niobate based transparent ultrasound transducer (TUT) as a cranial window on a thinned mouse skull. The TUT cranial window simplifies the imaging head considerably due to its dual functionality as an optical window and ultrasound transducer. The window remains stable for six weeks, with no noticeable inflammation and minimal bone regrowth. The TUT window's potential is demonstrated by imaging the awake mouse cerebral vasculature using OR-PAM, intrinsic optical signal imaging, and two-photon microscopy. The TUT cranial window can potentially also be used for ultrasound stimulation and simultaneous multimodal imaging of the awake mouse brain.

Correction: Personalized glucose forecasting for type 2 diabetes using data assimilation.

[This corrects the article DOI: 10.1371/journal.pcbi.1005232.].

A Brain-Heart Biomarker for Epileptogenesis.

Postinjury epilepsy is an potentially preventable sequela in as many as 20% of patients with brain insults. For these cases biomarkers of epileptogenesis are critical to facilitate identification of patients at high-risk of developing epilepsy and to introduce effective anti-epileptogenic interventions. Here, we demonstrate that delayed brain-heart coincidences serve as a reliable biomarker. In a murine model of post-infection acquired epilepsy, we used long-term simultaneous measurements of the brain activity via electroencephalography and autonomic cardiac activity via electrocardiography, in male mice, to quantitatively track brain-heart interactions during epileptogenesis. We find that abnormal cortical discharges precede abnormal fluctuations in the cardiac rhythm at the resolution of single beat-to-beat intervals. The delayed brain-heart coincidence is detectable as early as the onset of chronic measurements, 2-14 weeks before the first seizure, only in animals that become epileptic, and increases during epileptogenesis. Therefore, delayed brain-heart coincidence serves as a biomarker of epileptogenesis and could be used for phenotyping, diagnostic, and therapeutic purposes.SIGNIFICANCE STATEMENT No biomarker that readily predicts and tracks epileptogenesis currently exists for the wide range of human acquired epilepsies. Here, we used long-term measurements of brain and heart activity in a mouse model of post-infection acquired epilepsy to investigate the potential of brain-heart interaction as a biomarker of epileptogenesis. We found that delayed coincidences from brain to heart can clearly separate the mice that became epileptic from those that did not weeks before development of epilepsy. Our findings allow for phenotyping and tracking of epileptogenesis in this and likely other models of acquired epilepsy. Such capability is critical for efficient adjunctive treatment development and for tracking the efficacy of such treatments.

Personalized glucose forecasting for type 2 diabetes using data assimilation.

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.

Rapid eye movement sleep and hippocampal theta oscillations precede seizure onset in the tetanus toxin model of temporal lobe epilepsy.

Improved understanding of the interaction between state of vigilance (SOV) and seizure onset has therapeutic potential. Six rats received injections of tetanus toxin (TeTX) in the ventral hippocampus that resulted in chronic spontaneous seizures. The distribution of SOV before 486 seizures was analyzed for a total of 19 d of recording. Rapid eye movement sleep (REM) and exploratory wake, both of which express prominent hippocampal theta rhythm, preceded 47 and 34%, for a total of 81%, of all seizures. Nonrapid eye movement sleep (NREM) and nonexploratory wake, neither of which expresses prominent theta, preceded 6.8 and 13% of seizures. We demonstrate that identification of SOV yields significant differentiation of seizure susceptibilities, with the instantaneous seizure rate during REM nearly 10 times higher than baseline and the rate for NREM less than half of baseline. Survival analysis indicated a shorter duration of preseizure REM bouts, with a maximum transition to seizure at ∼90 s after the onset of REM. This study provides the first analysis of a correlation between SOV and seizure onset in the TeTX model of temporal lobe epilepsy, as well as the first demonstration that hippocampal theta rhythms associated with natural behavioral states can serve a seizure-promoting role. Our findings are in contrast with previous studies suggesting that the correlations between SOV and seizures are primarily governed by circadian oscillations and the notion that hippocampal theta rhythms inhibit seizures. The documentation of significant SOV-dependent seizure susceptibilities indicates the potential utility of SOV and its time course in seizure prediction and control.

Frequency dependence of behavioral modulation by hippocampal electrical stimulation.

Electrical stimulation offers the potential to develop novel strategies for the treatment of refractory medial temporal lobe epilepsy. In particular, direct electrical stimulation of the hippocampus presents the opportunity to modulate pathological dynamics at the ictal focus, although the neuroanatomical substrate of this region renders it susceptible to altering cognition and affective processing as a side effect. We investigated the effects of three electrical stimulation paradigms on separate groups of freely moving rats (sham, 8-Hz and 40-Hz sine-wave stimulation of the ventral/intermediate hippocampus, where 8- and 40-Hz stimulation were chosen to mimic naturally occurring hippocampal oscillations). Animals exhibited attenuated locomotor and exploratory activity upon stimulation at 40 Hz, but not at sham or 8-Hz stimulation. Such behavioral modifications were characterized by a significant reduction in rearing frequency, together with increased freezing behavior. Logistic regression analysis linked the observed changes in animal locomotion to 40-Hz electrical stimulation independently of time-related variables occurring during testing. Spectral analysis, conducted to monitor the electrophysiological profile in the CA1 area of the dorsal hippocampus, showed a significant reduction in peak theta frequency, together with reduced theta power in the 40-Hz vs. the sham stimulation animal group, independent of locomotion speed (theta range: 4-12 Hz). These findings contribute to the development of novel and safe medical protocols by indicating a strategy to constrain or optimize parameters in direct hippocampal electrical stimulation.

Multiparametric Brain Hemodynamics Imaging Using a Combined Ultrafast Ultrasound and Photoacoustic System.

Studying brain-wide hemodynamic responses to different stimuli at high spatiotemporal resolutions can help gain new insights into the mechanisms of neuro- diseases and -disorders. Nonetheless, this task is challenging, primarily due to the complexity of neurovascular coupling, which encompasses interdependent hemodynamic parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), and cerebral oxygen saturation (SO2). The current brain imaging technologies exhibit inherent limitations in resolution, sensitivity, and imaging depth, restricting their capacity to comprehensively capture the intricacies of cerebral functions. To address this, a multimodal functional ultrasound and photoacoustic (fUSPA) imaging platform is reported, which integrates ultrafast ultrasound and multispectral photoacoustic imaging methods in a compact head-mountable device, to quantitatively map individual dynamics of CBV, CBF, and SO2 as well as contrast agent enhanced brain imaging at high spatiotemporal resolutions. Following systematic characterization, the fUSPA system is applied to study brain-wide cerebrovascular reactivity (CVR) at single-vessel resolution via relative changes in CBV, CBF, and SO2 in response to hypercapnia stimulation. These results show that cortical veins and arteries exhibit differences in CVR in the stimulated state and consistent anti-correlation in CBV oscillations during the resting state, demonstrating the multiparametric fUSPA system's unique capabilities in investigating complex mechanisms of brain functions.

Modulation of hippocampal rhythms by subthreshold electric fields and network topology.

Theta (4-12 Hz) and gamma (30-80 Hz) rhythms are considered important for cortical and hippocampal function. Although several neuron types are implicated in rhythmogenesis, the exact cellular mechanisms remain unknown. Subthreshold electric fields provide a flexible, area-specific tool to modulate neural activity and directly test functional hypotheses. Here we present experimental and computational evidence of the interplay among hippocampal synaptic circuitry, neuronal morphology, external electric fields, and network activity. Electrophysiological data are used to constrain and validate an anatomically and biophysically realistic model of area CA1 containing pyramidal cells and two interneuron types: dendritic- and perisomatic-targeting. We report two lines of results: addressing the network structure capable of generating theta-modulated gamma rhythms, and demonstrating electric field effects on those rhythms. First, theta-modulated gamma rhythms require specific inhibitory connectivity. In one configuration, GABAergic axo-dendritic feedback on pyramidal cells is only effective in proximal but not distal layers. An alternative configuration requires two distinct perisomatic interneuron classes, one exclusively receiving excitatory contacts, the other additionally targeted by inhibition. These observations suggest novel roles for particular classes of oriens and basket cells. The second major finding is that subthreshold electric fields robustly alter the balance between different rhythms. Independent of network configuration, positive electric fields decrease, while negative fields increase the theta/gamma ratio. Moreover, electric fields differentially affect average theta frequency depending on specific synaptic connectivity. These results support the testable prediction that subthreshold electric fields can alter hippocampal rhythms, suggesting new approaches to explore their cognitive functions and underlying circuitry.

Reconstructing mammalian sleep dynamics with data assimilation.

Data assimilation is a valuable tool in the study of any complex system, where measurements are incomplete, uncertain, or both. It enables the user to take advantage of all available information including experimental measurements and short-term model forecasts of a system. Although data assimilation has been used to study other biological systems, the study of the sleep-wake regulatory network has yet to benefit from this toolset. We present a data assimilation framework based on the unscented Kalman filter (UKF) for combining sparse measurements together with a relatively high-dimensional nonlinear computational model to estimate the state of a model of the sleep-wake regulatory system. We demonstrate with simulation studies that a few noisy variables can be used to accurately reconstruct the remaining hidden variables. We introduce a metric for ranking relative partial observability of computational models, within the UKF framework, that allows us to choose the optimal variables for measurement and also provides a methodology for optimizing framework parameters such as UKF covariance inflation. In addition, we demonstrate a parameter estimation method that allows us to track non-stationary model parameters and accommodate slow dynamics not included in the UKF filter model. Finally, we show that we can even use observed discretized sleep-state, which is not one of the model variables, to reconstruct model state and estimate unknown parameters. Sleep is implicated in many neurological disorders from epilepsy to schizophrenia, but simultaneous observation of the many brain components that regulate this behavior is difficult. We anticipate that this data assimilation framework will enable better understanding of the detailed interactions governing sleep and wake behavior and provide for better, more targeted, therapies.

Dissecting Multiparametric Cerebral Hemodynamics using Integrated Ultrafast Ultrasound and Multispectral Photoacoustic Imaging.

Understanding brain-wide hemodynamic responses to different stimuli at high spatiotemporal resolutions can help study neuro-disorders and brain functions. However, the existing brain imaging technologies have limited resolution, sensitivity, imaging depth and provide information about only one or two hemodynamic parameters. To address this, we propose a multimodal functional ultrasound and photoacoustic (fUSPA) imaging platform, which integrates ultrafast ultrasound and multispectral photoacoustic imaging methods in a compact head-mountable device, to quantitatively map cerebral blood volume (CBV), cerebral blood flow (CBF), oxygen saturation (SO2) dynamics as well as contrast agent enhanced brain imaging with high spatiotemporal resolutions. After systematic characterization, the fUSPA system was applied to quantitatively study the changes in brain hemodynamics and vascular reactivity at single vessel resolution in response to hypercapnia stimulation. Our results show an overall increase in brain-wide CBV, CBF, and SO2, but regional differences in singular cortical veins and arteries and a reproducible anti-correlation pattern between venous and cortical hemodynamics, demonstrating the capabilities of the fUSPA system for providing multiparametric cerebrovascular information at high-resolution and sensitivity, that can bring insights into the complex mechanisms of neurodiseases.

The role of inhibition in oscillatory wave dynamics in the cortex.

Cortical oscillations arise during behavioral and mental tasks, and all temporal oscillations have particular spatial patterns. Studying the mechanisms that generate and modulate the spatiotemporal characteristics of oscillations is important for understanding neural information processing and the signs and symptoms of dynamical diseases of the brain. Nevertheless, it remains unclear how GABAergic inhibition modulates these oscillation dynamics. Using voltage-sensitive dye imaging, pharmacological methods, and tangentially cut occipital neocortical brain slices (including layers 3-5) of Sprague-Dawley rat, we found that GABAa disinhibition with bicuculline can progressively simplify oscillation dynamics in the presence of carbachol in a concentration-dependent manner. Additionally, GABAb disinhibition can further simplify oscillation dynamics after GABAa receptors are blocked. Both GABAa and GABAb disinhibition increase the synchronization of the neural network. Theta frequency (5-15-Hz) oscillations are reliably generated by using a combination of GABAa and GABAb antagonists alone. These theta oscillations have basic spatiotemporal patterns similar to those generated by carbachol/bicuculline. These results are illustrative of how GABAergic inhibition increases the complexity of patterns of activity and contributes to the regulation of the cortex.

Development of Mechanistic Neural Mass (mNM) Models that Link Physiology to Mean-Field Dynamics.

Brain rhythms emerge from the mean-field activity of networks of neurons. There have been many efforts to build mathematical and computational embodiments in the form of discrete cell-group activities-termed neural masses-to understand in particular the origins of evoked potentials, intrinsic patterns of activities such as theta, regulation of sleep, Parkinson's disease related dynamics, and mimic seizure dynamics. As originally utilized, standard neural masses convert input through a sigmoidal function to a firing rate, and firing rate through a synaptic alpha function to other masses. Here we define a process to build mechanistic neural masses (mNMs) as mean-field models of microscopic membrane-type (Hodgkin Huxley type) models of different neuron types that duplicate the stability, firing rate, and associated bifurcations as function of relevant slow variables - such as extracellular potassium - and synaptic current; and whose output is both firing rate and impact on the slow variables - such as transmembrane potassium flux. Small networks composed of just excitatory and inhibitory mNMs demonstrate expected dynamical states including firing, runaway excitation and depolarization block, and these transitions change in biologically observed ways with changes in extracellular potassium and excitatory-inhibitory balance.

Optimization of an unscented Kalman filter for an embedded platform.

The unscented Kalman filter (UKF) is finding increased application in biological fields. While realizing a complex UKF system in a low-power embedded platform offers many potential benefits including wearability, it also poses significant design challenges. Here we present a method for optimizing a UKF system for realization in an embedded platform. The method seeks to minimize both computation time and error in UKF state reconstruction and forecasting. As a case study, we applied the method to a model for the rat sleep-wake regulatory system in which 432 variants of the UKF over six different variables are considered. The optimization method is divided into three stages that assess computation time, state forecast error, and state reconstruction error. We apply a cost function to variants that pass all three stages to identify a variant that computes 27 times faster than the reference variant and maintains required levels of state estimation and forecasting accuracy. We draw the following insights: 1) process noise provides leeway for simplifying the model and its integration in ways that speed computation time while maintaining state forecasting accuracy, 2) the assimilation of observed data during the UKF correction step provides leeway for simplifying the UKF structure in ways that speed computation time while maintaining state reconstruction accuracy, and 3) the optimization process can be accelerated by decoupling variables that directly impact the underlying model from variables that impact the UKF structure.

Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures.

The insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. If the ISR can be reliably inferred from measurements, it could be used for understanding and clinically diagnosing problems with the glucose regulation system. Objective: This study aims to develop a model-based method for inferring a parametrization of the ISR and related physiological information among people with different glycemic conditions in a robust manner. The developed algorithm is applicable for both dense or sparsely sampled plasma glucose/insulin measurements, where sparseness is defined in terms of sampling time with respect to the fastest time scale of the dynamics. Methods: An algorithm for parametrizing and validating a functional form of the ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin accumulation was developed. The method and modeling applies equally to c-peptide secretion rate (CSR) when c-peptide is measured. Accuracy of fit is reliant on reconstruction error of the measured trajectories, and when c-peptide is measured the relationship between CSR and ISR. The algorithm was applied to data from 17 subjects with normal glucose regulatory systems and 9 subjects with cystic fibrosis related diabetes (CFRD) in which glucose, insulin and c-peptide were measured in course of oral glucose tolerance tests (OGTT). Results: This model-based algorithm inferred parametrization of the ISR and CSR functional with relatively low reconstruction error for 12 of 17 control and 7 of 9 CFRD subjects. We demonstrate that when there are suspect measurements points, the validity of excluding them may be interrogated with this method. Significance: A new estimation method is available to infer the ISR and CSR functional profile along with plasma insulin and c-peptide absorption rates from sparse measurements of insulin, c-peptide, and plasma glucose concentrations. We propose a method to interrogate and exclude potentially erroneous OGTT measurement points based on reconstruction errors.

Proceedings of the Sleep and Epilepsy Workshop: Section 3 Mortality: Sleep, Night, and SUDEP.

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. Likely pathophysiological mechanisms include seizure-induced cardiac and respiratory dysregulation. A frequently identified feature in SUDEP cases is that they occur at night. This raises the question of a role for sleep state in regulating of SUDEP. An association with sleep has been identified in a number of studies with patients and in animal models. The focus of this section of the Sleep and Epilepsy Workshop was on identifying and understanding the role for sleep and time of day in the pathophysiology of SUDEP.

Proceedings of the Sleep and Epilepsy Workshop: Section 1 Decreasing Seizures-Improving Sleep and Seizures, Themes for Future Research.

Epileptic seizures, sleep, and circadian timing share bilateral interactions, but concerted work to characterize these interactions and to leverage them to the advantage of patients with epilepsy remains in beginning stages. To further the field, a multidisciplinary group of sleep physicians, epileptologists, circadian timing experts, and others met to outline the state of the art, gaps of knowledge, and suggest ways forward in clinical, translational, and basic research. A multidisciplinary panel of experts discussed these interactions, centered on whether improvements in sleep or circadian rhythms improve decrease seizure frequency. In addition, education about sleep was lacking in among patients, their families, and physicians, and that focus on education was an extremely important "low hanging fruit" to harvest. Improvements in monitoring technology, experimental designs sensitive to the rigor required to dissect sleep versus circadian influences, and clinical trials in seizure reduction with sleep improvements were appropriate.

Toward rational design of electrical stimulation strategies for epilepsy control.

Electrical stimulation is emerging as a viable alternative for patients with epilepsy whose seizures are not alleviated by drugs or surgery. Its attractions are temporal and spatial specificity of action, flexibility of waveform parameters and timing, and the perception that its effects are reversible unlike resective surgery. However, despite significant advances in our understanding of mechanisms of neural electrical stimulation, clinical electrotherapy for seizures relies heavily on empirical tuning of parameters and protocols. We highlight concurrent treatment goals with potentially conflicting design constraints that must be resolved when formulating rational strategies for epilepsy electrotherapy, namely, seizure reduction versus cognitive impairment, stimulation efficacy versus tissue safety, and mechanistic insight versus clinical pragmatism. First, treatment markers, objectives, and metrics relevant to electrical stimulation for epilepsy are discussed from a clinical perspective. Then the experimental perspective is presented, with the biophysical mechanisms and modalities of open-loop electrical stimulation, and the potential benefits of closed-loop control for epilepsy.

Functional hyperemia drives fluid exchange in the paravascular space.

The brain lacks a conventional lymphatic system to remove metabolic waste. It has been proposed that directional fluid movement through the arteriolar paravascular space (PVS) promotes metabolite clearance. We performed simulations to examine if arteriolar pulsations and dilations can drive directional CSF flow in the PVS and found that arteriolar wall movements do not drive directional CSF flow. We propose an alternative method of metabolite clearance from the PVS, namely fluid exchange between the PVS and the subarachnoid space (SAS). In simulations with compliant brain tissue, arteriolar pulsations did not drive appreciable fluid exchange between the PVS and the SAS. However, when the arteriole dilated, as seen during functional hyperemia, there was a marked exchange of fluid. Simulations suggest that functional hyperemia may serve to increase metabolite clearance from the PVS. We measured blood vessels and brain tissue displacement simultaneously in awake, head-fixed mice using two-photon microscopy. These measurements showed that brain deforms in response to pressure changes in PVS, consistent with our simulations. Our results show that the deformability of the brain tissue needs to be accounted for when studying fluid flow and metabolite transport.

The systemDrive: a Multisite, Multiregion Microdrive with Independent Drive Axis Angling for Chronic Multimodal Systems Neuroscience Recordings in Freely Behaving Animals.

A multielectrode system that can address widely separated targets at multiple sites across multiple brain regions with independent implant angling is needed to investigate neural function and signaling in systems and circuits of small animals. Here, we present the systemDrive, a novel multisite, multiregion microdrive that is capable of moving microwire electrode bundles into targets along independent and nonparallel drive trajectories. Our design decouples the stereotaxic surgical placement of individual guide cannulas for each trajectory from the placement of a flexible drive structure. This separation enables placement of many microwire multitrodes along widely spaced and independent drive axes with user-set electrode trajectories and depths from a single microdrive body, and achieves stereotaxic precision with each. The system leverages tight tube-cannula tolerances and geometric constraints on flexible drive axes to ensure concentric alignment of electrode bundles within guide cannulas. Additionally, the headmount and microdrive both have an open-center design to allow for the placement of additional sensing modalities. This design is the first, in the context of small rodent chronic research, to provide the capability to finely position microwires through multiple widely distributed cell groups, each with stereotaxic precision, along arbitrary and nonparallel trajectories that are not restricted to emanate from a single source. We demonstrate the use of the systemDrive in male Long-Evans rats to observe simultaneous single-unit and multiunit activity from multiple widely separated sleep-wake regulatory brainstem cell groups, along with cortical and hippocampal activity, during free behavior over multiple many-day continuous recording periods.

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition's effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data.