Characterizing the structure of mouse behavior using Motion Sequencing.

Spontaneous mouse behavior is composed from repeatedly used modules of movement (e.g., rearing, running or grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the effect of drugs, genes, context, sensory stimuli and neural activity on natural behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically inspired method that uses three-dimensional machine vision and unsupervised machine learning to decompose spontaneous mouse behavior into a series of elemental modules called 'syllables'. This protocol is based upon a MoSeq pipeline that includes modules for depth video acquisition, data preprocessing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of the video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other. In addition, we provide instructions for analyzing and visualizing the outputs of keypoint-MoSeq, a recently developed variant of MoSeq that can identify behavioral motifs from keypoints identified from standard (rather than depth) video. This protocol and the accompanying pipeline significantly lower the bar for users without extensive computational ethology experience to adopt this unsupervised, data-driven approach to characterize mouse behavior.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics.

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

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.

Spontaneous behaviour is structured by reinforcement without explicit reward.

Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences1,2. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behaviour.

Cerebellum-Specific Deletion of the GABAA Receptor δ Subunit Leads to Sex-Specific Disruption of Behavior.

Granule cells (GCs) of the cerebellar input layer express high-affinity δ GABAA subunit-containing GABAA receptors (δGABAARs) that respond to ambient GABA levels and context-dependent neuromodulators like steroids. We find that GC-specific deletion of δGABAA (cerebellar [cb] δ knockout [KO]) decreases tonic inhibition, makes GCs hyperexcitable, and in turn, leads to differential activation of cb output regions as well as many cortical and subcortical brain areas involved in cognition, anxiety-like behaviors, and the stress response. Cb δ KO mice display deficits in many behaviors, but motor function is normal. Strikingly, δGABAA deletion alters maternal behavior as well as spontaneous, stress-related, and social behaviors specifically in females. Our findings establish that δGABAARs enable the cerebellum to control diverse behaviors not previously associated with the cerebellum in a sex-dependent manner. These insights may contribute to a better understanding of the mechanisms that underlie behavioral abnormalities in psychiatric and neurodevelopmental disorders that display a gender bias.

Revealing the structure of pharmacobehavioral space through motion sequencing.

Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically inspired behavioral analysis method that identifies modular components of three-dimensional mouse body language called 'syllables'. Here, we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuroactive and psychoactive drugs administered to a cohort of nearly 700 mice. MoSeq identifies syllables that are characteristic of individual drugs, a finding we leverage to reveal specific on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior and suggest that behavioral syllables represent a new class of druggable target.

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.

Carbon fiber on polyimide ultra-microelectrodes.

OBJECTIVE: Most preparations for making neural recordings degrade over time and eventually fail due to insertion trauma and reactive tissue response. The magnitudes of these responses are thought to be related to the electrode size (specifically, the cross-sectional area), the relative stiffness of the electrode, and the degree of tissue tolerance for the material. Flexible carbon fiber ultra-microelectrodes have a much smaller cross-section than traditional electrodes and low tissue reactivity, and thus may enable improved longevity of neural recordings in the central and peripheral nervous systems. Only two carbon fiber array designs have been described previously, each with limited channel densities due to limitations of the fabrication processes or interconnect strategies. Here, we describe a method for assembling carbon fiber electrodes on a flexible polyimide substrate that is expected to facilitate the construction of high-density recording and stimulating arrays. APPROACH: Individual carbon fibers were aligned using an alignment tool that was 3D-printed with sub-micron resolution using direct laser writing. Indium deposition on the carbon fibers, followed by low-temperature microsoldering, provided a robust and reliable method of electrical connection to the polyimide interconnect. MAIN RESULTS: Spontaneous multiunit activity and stimulation-evoked compound responses with SNR >10 and >120, respectively, were recorded from a small (125 µm) peripheral nerve. We also improved the typically poor charge injection capacity of small diameter carbon fibers by electrodepositing 100 nm-thick iridium oxide films, making the carbon fiber arrays usable for electrical stimulation as well as recording. SIGNIFICANCE: Our innovations in fabrication technique pave the way for further miniaturization of carbon fiber ultra-microelectrode arrays. We believe these advances to be key steps to enable a shift from labor intensive, manual assembly to a more automated manufacturing process.

Knowing where the nose is.

Improvements in imaging technology and the development of powerful machine learning algorithms are revolutionizing the study of animal behavior in the laboratory. These innovations promise to reveal both global and local features of action relevant to understanding how the brain functions. A study in BMC Biology describes one such tool called OptiMouse, which is an open source platform that uses video to capture key features of mouse behavior, including information relevant to olfactory investigation.See research article: 10.1186/s12915-017-0377-3.

A micro-scale printable nanoclip for electrical stimulation and recording in small nerves.

OBJECTIVE: The vision of bioelectronic medicine is to treat disease by modulating the signaling of visceral nerves near various end organs. In small animal models, the nerves of interest can have small diameters and limited surgical access. New high-resolution methods for building nerve interfaces are desirable. In this study, we present a novel nerve interface and demonstrate its use for stimulation and recording in small nerves. APPROACH: We design and fabricate micro-scale electrode-laden nanoclips capable of interfacing with nerves as small as 50 µm in diameter. The nanoclips are fabricated using a direct laser writing technique with a resolution of 200 nm. The resolution of the printing process allows for incorporation of a number of innovations such as trapdoors to secure the device to the nerve, and quick-release mounts that facilitate keyhole surgery, obviating the need for forceps. The nanoclip can be built around various electrode materials; here we use carbon nanotube fibers for minimally invasive tethering. MAIN RESULTS: We present data from stimulation-evoked responses of the tracheal syringeal (hypoglossal) nerve of the zebra finch, as well as quantification of nerve functionality at various time points post implant, demonstrating that the nanoclip is compatible with healthy nerve activity over sub-chronic timescales. SIGNIFICANCE: Our nerve interface addresses key challenges in interfacing with small nerves in the peripheral nervous system. Its small size, ability to remain on the nerve over sub-chronic timescales, and ease of implantation, make it a promising tool for future use in the treatment of disease.