Behavioral weight management use in the Veterans Health Administration: Sociodemographic and health correlates.

INTRODUCTION: Over 40 % of United States Veterans Health Administration (VHA) primary care patients have obesity. Few patients use VHA's flagship weight management program, MOVE! and there is little information on other behavioral weight management program use. METHODS: The national United States cohort included over 1.5 million primary care patients with obesity, age 18-79, based on VHA administrative data. Gender stratified multivariable logistic regression identified correlates of weight management use in the year after a patient's first primary care appointment (alpha of 0.05). Weight management use was defined as MOVE! or nutrition clinic visits. RESULTS: The cohort included 121,235 women and 1,521,547 men with 13 % and 7 % using weight management, respectively. Point estimates for specific correlates of use were similar between women and men, and across programs. Black patients were more likely to use weight management than White patients. Several physical and mental health diagnoses were also associated with increased use, such as sleep apnea and eating disorders. Age and distance from VHA were negatively associated with weight management use. CONCLUSIONS: When assessing multiple types of weight management visits, weight management care in VHA appears to be used more often by some populations at higher risk for obesity. Other groups may need additional outreach, such as those living far from VHA. Future work should focus on outreach and prevention efforts to increase overall use rates. This work could also examine the benefits of tailoring care for populations in greatest need.

The EMBER trial for weight management engagement: A hybrid type 1 randomized controlled trial protocol.

BACKGROUND: Almost 40% of Veterans Health Administration (VHA) users have obesity. VHA's national weight management program is associated with weight loss and improved health. However, while 94% of eligible VHA users are offered weight management programs, <8% use them. We developed EMBER - a novel, Motivational Interviewing-based, self-help tool - with the goal of Enhancing Motivation for Better Engagement and Reach for weight management. EMBER is not a weight management program; instead it engages people in existing programs by informing and guiding choices about weight management. METHODS: The EMBER Trial is a randomized hybrid type 1 effectiveness implementation trial. Participants are Palo Alto or Houston VA Health Care System users with obesity who have not used a VHA weight management program in the past two years (target N = 470). Participants are randomly assigned to EMBER or an information-only control condition, after which they receive materials on paper or digitally, per their preference. The trial's primary goal is to determine whether participants randomized to EMBER are more likely to have any weight management engagement at two-month follow-up compared to those in the control condition. Secondary outcomes include 6-month retention in weight management, weight management behaviors, weight loss, quality of life, and implementation outcomes (e.g., reach, appropriateness). CONCLUSION: EMBER is the first self-directed, Motivational Interviewing-based intervention designed to increase weight management program engagement. The study takes a low-touch, population health approach that could be modified for other programs if effective. The Hybrid Type 1 design will ensure results can be scaled and sustained.

Hidden behavioral fingerprints in epilepsy.

Epilepsy is a major disorder affecting millions of people. Although modern electrophysiological and imaging approaches provide high-resolution access to the multi-scale brain circuit malfunctions in epilepsy, our understanding of how behavior changes with epilepsy has remained rudimentary. As a result, screening for new therapies for children and adults with devastating epilepsies still relies on the inherently subjective, semi-quantitative assessment of a handful of pre-selected behavioral signs of epilepsy in animal models. Here, we use machine learning-assisted 3D video analysis to reveal hidden behavioral phenotypes in mice with acquired and genetic epilepsies and track their alterations during post-insult epileptogenesis and in response to anti-epileptic drugs. These results show the persistent reconfiguration of behavioral fingerprints in epilepsy and indicate that they can be employed for rapid, automated anti-epileptic drug testing at scale.

Peer support and whole health coaching to address the healthcare needs of homeless veterans: a pilot study.

BACKGROUND: Homelessness is a robust social determinant of acute care service utilization among veterans. Although intensive outpatient programs have been developed for homeless veterans who are high utilizers of acute care ("super utilizers"), few scalable programs have been implemented to address their needs. OBJECTIVE: Describe the development and pilot testing of a novel intervention that integrates the roles of a peer and whole health coach ("Peer-WHC") in coordination with primary care teams to reduce homeless veterans' frequent use of acute care. DESIGN: Single-arm trial in three outpatient primary care clinics at a Veterans Health Administration (VHA) medical center; pre/post design using mixed-methods. PARTICIPANTS: Twenty veterans from VHA's homeless registry who were super-utilizers of acute care and enrolled in primary care. INTERVENTION: Weekly health coaching sessions with a peer over 12 weeks, including discussions of patients' health care utilization patterns and coordination with primary care. MAIN MEASURES: Rates of session attendance and intervention fidelity, patient-reported satisfaction and changes in patient engagement and perceptions of health, pre/post utilization of acute and supportive care services, and qualitative interviews with multiple stakeholders to identify barriers and facilitators to implementation. KEY RESULTS: On average, patients attended 6.35 sessions (SD = 3.5, Median = 7). Satisfaction scores (M = 28.75 out of 32; SD = 2.79) exceeded a priori benchmarks. Patients' perceptions of health improved from pre to post [t(df)=-2.26(14), p = 0.04]. In the 3-months pre/post, 45% (n = 9) and 15% (n = 3) of patients, respectively, were hospitalized. Qualitative feedback from patients, providers, and peers and fidelity metrics suggested value in increasing the length of the intervention to facilitate goal-setting with patients and coordination with primary care. CONCLUSION: Findings support the feasibility, acceptability, and utility of Peer-WHC to address the healthcare needs of homeless veterans. A future trial is warranted to test the impact of Peer-WHC on reducing these patients' frequent use of acute care.

Maximally selective single-cell target for circuit control in epilepsy models.

Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.

Erratum: Entorhinal mismatch: A model of self-supervised learning in the hippocampus.

[This corrects the article DOI: 10.1016/j.isci.2021.102364.].

Entorhinal mismatch: A model of self-supervised learning in the hippocampus.

The hippocampal formation displays a wide range of physiological responses to different spatial manipulations of the environment. However, very few attempts have been made to identify core computational principles underlying those hippocampal responses. Here, we capitalize on the observation that the entorhinal-hippocampal complex (EHC) forms a closed loop and projects inhibitory signals "countercurrent" to the trisynaptic pathway to build a self-supervised model that learns to reconstruct its own inputs by error backpropagation. The EHC is then abstracted as an autoencoder, with the hidden layers acting as an information bottleneck. With the inputs mimicking the firing activity of lateral and medial entorhinal cells, our model is shown to generate place cells and to respond to environmental manipulations as observed in rodent experiments. Altogether, we propose that the hippocampus builds conjunctive compressed representations of the environment by learning to reconstruct its own entorhinal inputs via gradient descent.

Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus.

Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training ANN using error backpropagation has created the current revolution in artificial intelligence, raising the question of whether the epistemic autonomy displayed in biological cognition can be achieved with error backpropagation-based learning. We present evidence suggesting that the entorhinal-hippocampal complex combines epistemic autonomy with error backpropagation. Specifically, we propose that the hippocampus minimizes the error between its input and output signals through a modulatory counter-current inhibitory network. We further discuss the computational emulation of this principle and analyze it in the context of autonomous cognitive systems.

Correction: Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer.

[This corrects the article DOI: 10.1371/journal.pcbi.1005754.].

Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer.

The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. Patterned mossy fiber activity induces rhythmic Golgi cell activity that is synchronized by shared parallel fiber input and by gap junctions. This leads to long distance synchrony of Golgi cells along the transverse axis, powerfully regulating granule cell firing by imposing inhibition during a specific time window. The essential network mechanisms, including tunable Golgi cell oscillations, on-beam inhibition and NMDA receptors causing first winner keeps winning of granule cells, illustrate how fundamental properties of the granule layer operate in tandem to produce (1) well timed and spatially bound output, (2) a wide dynamic range of granule cell firing and (3) transient and coherent gating oscillations. These results substantially enrich our understanding of granule cell layer processing, which seems to promote spatial group selection of granule cell activity as a function of timing of mossy fiber input.

Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit.

The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.

The layer-oriented approach to declarative languages for biological modeling.

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

MRCI: a flexible real-time dynamic clamp system for electrophysiology experiments.

We present a real-time simulation system that enables modeled dynamical systems to interact with physical experimental systems, and is specifically aimed towards execution of the dynamic clamp protocol. Model reference current injection (MRCI) operates under Real-Time Linux (RT-Linux or RTL) and provides a simple equation-oriented language for describing dynamical system models. Features include scripting of commands to implement repeatable protocols, the ability to output pre-computed waveforms through any variable or parameter of the model, the means to conduct time measurements and assess the computational performance of the real-time system, and an installation program that installs the software and accompanying device drivers with minimal input from the user. Tested models operate as fast as 30 kHz, with actual maximum rates dependent on model complexity. We present sample models that exhibit the main features of the modeling language. Experiments demonstrate the abilities of the system by creating a hybrid network of real and simulated neurons, and playing a pre-defined synaptic waveform into a synaptic conductance variable. We conclude by introducing a waveform reconstruction technique that is useful for establishing the presence of significant experimental error in implementations of the dynamic clamp protocol.