Automated preclinical detection of mechanical pain hypersensitivity and analgesia.

ABSTRACT: The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for high-throughput preclinical analgesic efficacy assessment.

Mice exhibit stochastic and efficient action switching during probabilistic decision making.

In probabilistic and nonstationary environments, individuals must use internal and external cues to flexibly make decisions that lead to desirable outcomes. To gain insight into the process by which animals choose between actions, we trained mice in a task with time-varying reward probabilities. In our implementation of such a two-armed bandit task, thirsty mice use information about recent action and action­outcome histories to choose between two ports that deliver water probabilistically. Here we comprehensively modeled choice behavior in this task, including the trial-to-trial changes in port selection, i.e., action switching behavior. We find that mouse behavior is, at times, deterministic and, at others, apparently stochastic. The behavior deviates from that of a theoretically optimal agent performing Bayesian inference in a hidden Markov model (HMM). We formulate a set of models based on logistic regression, reinforcement learning, and sticky Bayesian inference that we demonstrate are mathematically equivalent and that accurately describe mouse behavior. The switching behavior of mice in the task is captured in each model by a stochastic action policy, a history-dependent representation of action value, and a tendency to repeat actions despite incoming evidence. The models parsimoniously capture behavior across different environmental conditionals by varying the stickiness parameter, and like the mice, they achieve nearly maximal reward rates. These results indicate that mouse behavior reaches near-maximal performance with reduced action switching and can be described by a set of equivalent models with a small number of relatively fixed parameters.

Miniature microscopes for manipulating and recording in vivo brain activity.

Here we describe the development and application of miniature integrated microscopes (miniscopes) paired with microendoscopes that allow for the visualization and manipulation of neural circuits in superficial and subcortical brain regions in freely behaving animals. Over the past decade the miniscope platform has expanded to include simultaneous optogenetic capabilities, electrically-tunable lenses that enable multi-plane imaging, color-corrected optics, and an integrated data acquisition platform that streamlines multimodal experiments. Miniscopes have given researchers an unprecedented ability to monitor hundreds to thousands of genetically-defined neurons from weeks to months in both healthy and diseased animal brains. Sophisticated algorithms that take advantage of constrained matrix factorization allow for background estimation and reliable cell identification, greatly improving the reliability and scalability of source extraction for large imaging datasets. Data generated from miniscopes have empowered researchers to investigate the neural circuit underpinnings of a wide array of behaviors that cannot be studied under head-fixed conditions, such as sleep, reward seeking, learning and memory, social behaviors, and feeding. Importantly, the miniscope has broadened our understanding of how neural circuits can go awry in animal models of progressive neurological disorders, such as Parkinson's disease. Continued miniscope development, including the ability to record from multiple populations of cells simultaneously, along with continued multimodal integration of techniques such as electrophysiology, will allow for deeper understanding into the neural circuits that underlie complex and naturalistic behavior.

The Striatum Organizes 3D Behavior via Moment-to-Moment Action Selection.

Many naturalistic behaviors are built from modular components that are expressed sequentially. Although striatal circuits have been implicated in action selection and implementation, the neural mechanisms that compose behavior in unrestrained animals are not well understood. Here, we record bulk and cellular neural activity in the direct and indirect pathways of dorsolateral striatum (DLS) as mice spontaneously express action sequences. These experiments reveal that DLS neurons systematically encode information about the identity and ordering of sub-second 3D behavioral motifs; this encoding is facilitated by fast-timescale decorrelations between the direct and indirect pathways. Furthermore, lesioning the DLS prevents appropriate sequence assembly during exploratory or odor-evoked behaviors. By characterizing naturalistic behavior at neural timescales, these experiments identify a code for elemental 3D pose dynamics built from complementary pathway dynamics, support a role for DLS in constructing meaningful behavioral sequences, and suggest models for how actions are sculpted over time.

Silk Fibroin Films Facilitate Single-Step Targeted Expression of Optogenetic Proteins.

Optical methods of interrogating neural circuits have emerged as powerful tools for understanding how the brain drives behaviors. Optogenetic proteins are widely used to control neuronal activity, while genetically encoded fluorescent reporters are used to monitor activity. These proteins are often expressed by injecting viruses, which frequently leads to inconsistent experiments due to misalignment of expression and optical components. Here, we describe how silk fibroin films simplify optogenetic experiments by providing targeted delivery of viruses. Films composed of silk fibroin and virus are applied to the surface of implantable optical components. After surgery, silk releases the virus to transduce nearby cells and provide localized expression around optical fibers and endoscopes. Silk films can also be used to express genetically encoded sensors in large cortical regions by using cranial windows coated with a silk/virus mixture. The ease of use and improved performance provided by silk make this a promising approach for optogenetic studies.

Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data.

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.

Opposing roles of PlexinA and PlexinB in axonal branch and varicosity formation.

Establishing precise synaptic connectivity during development is crucial for neural circuit function. However, very few molecules have been identified that are involved in determining where and how many synapses form. The Plexin cell-surface molecules are a conserved family of axon guidance receptors that mediate axon fasciculation and repulsion during neural development, and later in development PlexinA receptors are involved in eliminating axonal branches and synapse numbers. Here we investigate the roles of PlexinA and PlexinB receptors in axonal branch and varicosity formation in Drosophila. We knocked down PlexinA or PlexinB expression using RNAi in identified mechanosensory neurons and analyzed axonal branching patterns and varicosity formations. Reducing PlexinA expression increased the axonal arbor complexity by increasing the number of branches and varicosities along the axon. In contrast, knocking down PlexinB expression decreased morphological complexity by decreasing the number of branches and the overall size of the axonal arbor, but did not reduce the number of varicosities. Our results demonstrate opposing roles for PlexinA and PlexinB in local wiring within a target region, where PlexinA functions to suppress excessive axonal branches and synapses and PlexinB facilitates axonal growth.