Matthew J. Johnson

Mapping Sub-Second Structure in Mouse Behavior

Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.

A touch of TRALI.

Transfusion‐related acute lung injury (TRALI) is a leading cause of transfusion‐associated morbidity and mortality. The National Heart, Lung, and Blood Institute (NHLBI) and Canadian Consensus Conference definitions of TRALI exclude cases of mild TRALI. As a result, many cases of mild TRALI are likely to be missed. Three cases are reported in which patients experienced the acute onset of breathlessness in association with transfusion of blood components containing human leukocyte antigen (HLA) antibodies reactive with recipient HLA antigens. Despite the sudden onset of a pulmonary syndrome in association with transfusion, clinicians caring for these patients did not consider TRALI, and no case would meet recent consensus definitions. Nevertheless, supporting clinical and serologic evidence for TRALI was found in each case. Benefits in recognizing mild cases of TRALI include quantifying the true incidence of TRALI, understanding the physiology of mild versus severe TRALI, and preventing subsequent cases of TRALI due to donors found to have HLA antibodies.

Cardiac Sarcoidosis Imitating Arrhythmogenic Right Ventricular Dysplasia

A 59-year-old male was admitted to Massachusetts General Hospital, Boston, Mass, with a 2-month history of exertional dyspnea (New York Heart Association class II to III). The patient denied dyspnea at rest, chest pain, palpitations, or syncope. There was no history of fevers or recent weight loss. An outpatient echocardiogram (Figure 1), performed as part of the workup of the patient’s dyspnea, demonstrated normal left ventricular size and function. The right ventricle (RV) was normal in size but diffusely hypokinetic. There was evidence of segmental RV dysfunction, with 2 discrete aneurysmal areas in the RV free wall at the base and apex, which measured 1.5 and 3.0 cm in width. Both areas appeared thinned and dyskinetic. The echocardiographic appearance was suggestive of arrhythmogenic RV dysplasia/cardiomyopathy (ARVD/C).1 A CT scan ruled out the presence of pulmonary embolism but was notable for marked mediastinal lymphadenopathy (Figure 2 …

A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation.

BACKGROUND Rodent hippocampal population codes represent important spatial information about the environment during navigation. Computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. NEW METHOD We extend our previous work and propose a novel Bayesian nonparametric approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a Bayesian nonparametric model. Specifically, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational Bayes (VB). RESULTS The effectiveness of our Bayesian approaches is demonstrated on recordings from a freely behaving rat navigating in an open field environment. COMPARISON WITH EXISTING METHODS The HDP-HMM outperforms the finite-state HMM in both simulated and experimental data. For HPD-HMM, the MCMC-based inference with Hamiltonian Monte Carlo (HMC) hyperparameter sampling is flexible and efficient, and outperforms VB and MCMC approaches with hyperparameters set by empirical Bayes. CONCLUSION The Bayesian nonparametric HDP-HMM method can efficiently perform model selection and identify model parameters, which can used for modeling latent-state neuronal population dynamics.

Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward.