Probabilistic models of individual and collective animal behavior

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.Comment: 26 pages, 11 figure

Integrating Behavioral Health & Primary Care in New Hampshire: A Path Forward to Sustainable Practice & Payment Transformation

New Hampshire residents face challenges with behavioral and physical health conditions and the interplay between them. National studies show the costs and the burden of illness from behavioral health conditions and co-occurring chronic health conditions that are not adequately treated in either primary care or behavioral health settings. Bringing primary health and behavioral health care together in integrated care settings can improve outcomes for both behavioral and physical health conditions. Primary care integrated behavioral health works in conjunction with specialty behavioral health providers, expanding capacity, improving access, and jointly managing the care of patients with higher levels of acuity In its work to improve the health of NH residents and create effective and cost-effective systems of care, the NH Citizens Health Initiative (Initiative) created the NH Behavioral Health Integration Learning Collaborative (BHI Learning Collaborative) in November of 2015, as a project of its Accountable Care Learning Network (NHACLN). Bringing together more than 60 organizations, including providers of all types and sizes, all of the state’s community mental health centers, all of the major private and public insurers, and government and other stakeholders, the BHI Learning Collaborative built on earlier work of a NHACLN Workgroup focused on improving care for depression and co-occurring chronic illness. The BHI Learning Collaborative design is based on the core NHACLN philosophy of “shared data and shared learning” and the importance of transparency and open conversation across all stakeholder groups. The first year of the BHI Learning Collaborative programming included shared learning on evidence-based practice for integrated behavioral health in primary care, shared data from the NH Comprehensive Healthcare Information System (NHCHIS), and work to develop sustainable payment models to replace inadequate Fee-for-Service (FFS) revenues. Provider members joined either a Project Implementation Track working on quality improvement projects to improve their levels of integration or a Listen and Learn Track for those just learning about Behavioral Health Integration (BHI). Providers in the Project Implementation Track completed a self-assessment of levels of BHI in their practice settings and committed to submit EHR-based clinical process and outcomes data to track performance on specified measures. All providers received access to unblinded NHACLN Primary Care and Behavioral Health attributed claims data from the NHCHIS for provider organizations in the NH BHI Learning Collaborative. Following up on prior work focused on developing a sustainable model for integrating care for depression and co-occurring chronic illness in primary care settings, the BHI Learning Collaborative engaged consulting experts and participants in understanding challenges in Health Information Technology and Exchange (HIT/HIE), privacy and confidentiality, and workforce adequacy. The BHI Learning Collaborative identified a sustainable payment model for integrated care of depression in primary care. In the process of vetting the payment model, the BHI Learning Collaborative also identified and explored challenges in payment for Substance Use Disorder Screening, Brief Intervention and Referral to Treatment (SBIRT). New Hampshire’s residents will benefit from a health care system where primary care and behavioral health are integrated to support the care of the whole person. New Hampshire’s current opiate epidemic accentuates the need for better screening for behavioral health issues, prevention, and treatment referral integrated into primary care. New Hampshire providers and payers are poised to move towards greater integration of behavioral health and primary care and the Initiative looks forward to continuing to support progress in supporting a path to sustainable integrated behavioral and primary care

Planning a Better Future for Dual Eligible Elderly in Montgomery County

Older adults who are dual eligible (who qualify for both Medicare and Medicaid) face a daunting gauntlet of challenges in healthcare. Despite comprehensive coverage through Medicare and Medicaid, the lack of coordination between the two systems creates often insurmountable problems of access and delivery. Federally-funded Medicare lacks coordination and integration with federal-state funded Medicaid. Ironically, it is these dual eligible individuals who so desperately need healthcare since they have a higher incidence of cognitive impairment (including Alzheimer's Disease), mental disorders, diabetes, pulmonary disease and strokes. Further, they are more vulnerable and frail, have lower incomes, and are more isolated than are non-dual eligible elderly. These problems, in turn, contribute to significant challenges with housing, food and transportation. The challenges with access to care are tragic, expensive and avoidable.The high care needs of dual eligible individuals and the associated costs have driven states and the federalgovernment to seek ways to better integrate and coordinate their care. The Affordable Care Act (2010) is teemingwith initiatives, demonstrations, and new opportunities premised on finding a way to better meet dual eligibleindividuals' healthcare needs at a cost-effective rate. While little has yet been done at the state level, localproviders are starting to test innovative approaches to delivering better care to dual eligible individuals.This report summarizes state and federal initiatives and opportunities for delivering better care to dual eligible elderly. It also presents the efforts underway at the County level and by local providers. Following the informational section of the report, the Workgroup presents nine systems change recommendations to better improve the care provided to Montgomery County's dual eligible elderly. The recommendations may stand alone, each reflecting their own systems change, or may be combined in a more encompassing effort at service delivery system overhaul.There are numerous federal opportunities for delivering better care to frail populations. Some of them are specifically targeted towards the dual eligible population and others are targeted towards other populations, but include a considerable number of dual eligible individuals. In the report, we describe five different types ofapproaches and describe examples of each

Innovative Medicaid Initiatives to Improve Service Delivery and Quality of Care: A Look at Five State Initiatives

Outlines initiatives in Alabama, Oklahoma, Oregon, Pennsylvania, and Washington to implement patient-centered medical home models designed to coordinate and improve quality of care; strategies; key lessons; and new opportunities under healthcare reform

Examining Medicaid Managed Long-Term Service and Support Programs: Key Issues to Consider

Examines key considerations for states in shifting to Medicaid managed long-term services and support programs, including limited evidence of cost savings and issues for program design, the role of community-based organizations, and state oversight

1st INCF Workshop on Genetic Animal Models for Brain Diseases

The INCF Secretariat organized a workshop to focus on the “role of neuroinformatics in the processes of building, evaluating, and using genetic animal models for brain diseases” in Stockholm, December 13–14, 2009. Eight scientists specialized in the fields of neuroinformatics, database, ontologies, and brain disease participated together with two representatives of the National Institutes of Health and the European Union, as well as three observers of the national INCF nodes of Norway, Poland, and the United Kingdom

Exploratory Mediation Analysis with Many Potential Mediators

Social and behavioral scientists are increasingly employing technologies such as fMRI, smartphones, and gene sequencing, which yield 'high-dimensional' datasets with more columns than rows. There is increasing interest, but little substantive theory, in the role the variables in these data play in known processes. This necessitates exploratory mediation analysis, for which structural equation modeling is the benchmark method. However, this method cannot perform mediation analysis with more variables than observations. One option is to run a series of univariate mediation models, which incorrectly assumes independence of the mediators. Another option is regularization, but the available implementations may lead to high false positive rates. In this paper, we develop a hybrid approach which uses components of both filter and regularization: the 'Coordinate-wise Mediation Filter'. It performs filtering conditional on the other selected mediators. We show through simulation that it improves performance over existing methods. Finally, we provide an empirical example, showing how our method may be used for epigenetic research.Comment: R code and package are available online as supplementary material at https://github.com/vankesteren/cmfilter and https://github.com/vankesteren/ema_simulation

State Variables of the Arm May Be Encoded by Single Neuron Activity in the Monkey Motor Cortex

Revealing the type of information encoded by neurons activity in the motor cortex is essential not only for understanding the mechanism of motion control but also for developing a brain-machine interface. Thus far, the concept of preferred direction vector (PD) has dominated the discussion regarding how neural activity encodes information; however, a unified view of exactly what information is encoded has not yet been established. In the present study, a model was constructed to describe temporal neuron activity by a dot product of the PD and the movement variables vector consisting of joint torque and angular velocity. The plausibility of this model was tested by comparing estimated neural activity with that recorded from the monkey motor cortex, and it was found that this model was able to explain the temporal pattern of neuron activity irrespective of its passive responsiveness. The mean determination coefficients of neurons that responded to proprioceptive stimuli and that responded to visual stimuli were relatively high values of 0.57 and 0.58, respectively. These results suggest that neurons in the monkey motor cortex encode state variables of the arm in a framework of modern control theory and that this information could be decoded for controlling a brain-machine interface