Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce 'Lightning Pose', an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We released a cloud application that allows users to label data, train networks and process new videos directly from the browser.

A modular architecture for organizing, processing and sharing neurophysiology data.

We describe an architecture for organizing, integrating and sharing neurophysiology data within a single laboratory or across a group of collaborators. It comprises a database linking data files to metadata and electronic laboratory notes; a module collecting data from multiple laboratories into one location; a protocol for searching and sharing data and a module for automatic analyses that populates a website. These modules can be used together or individually, by single laboratories or worldwide collaborations.

Neural network models for DMT-induced visual hallucinations.

The regulatory role of the serotonergic system on conscious perception can be investigated perturbatorily with psychedelic drugs such as N,N-Dimethyltryptamine. There is increasing evidence that the serotonergic system gates prior (endogenous) and sensory (exogenous) information in the construction of a conscious experience. Using two generative deep neural networks as examples, we discuss how such models have the potential to be, firstly, an important medium to illustrate phenomenological visual effects of psychedelics-besides paintings, verbal reports and psychometric testing-and, secondly, their utility to conceptualize biological mechanisms of gating the influence of exogenous and endogenous information on visual perception.

Projection-specific deficits in synaptic transmission in adult Sapap3-knockout mice.

Obsessive-compulsive disorder (OCD) is a circuit disorder involving corticostriatal projections, which play a role in motor control. The Sapap3-knockout (KO) mouse is a mouse model to study OCD and recapitulates OCD-like compulsion through excessive grooming behavior, with skin lesions appearing at advanced age. Deficits in corticostriatal control provide a link to the pathophysiology of OCD. However, there remain significant gaps in the characterization of the Sapap3-KO mouse, with respect to age, specificity of synaptic dysfunction, and locomotor phenotype. We therefore investigated the corticostriatal synaptic phenotype of Sapap3-KO mice using patch-clamp slice electrophysiology, in adult mice and with projection specificity. We also analyzed grooming across age and locomotor phenotype with a novel, unsupervised machine learning technique (MoSeq). Increased grooming in Sapap3-KO mice without skin lesions was age independent. Synaptic deficits persisted in adulthood and involved the projections from the motor cortices and cingulate cortex to the dorsolateral and dorsomedial striatum. Decreased synaptic strength was evident at the input from the primary motor cortex by reduction in AMPA receptor function. Hypolocomotion, i.e., slowness of movement, was consistently observed in Sapap3-KO mice. Our findings emphasize the utility of young adult Sapap3-KO mice to investigate corticostriatal synaptic dysfunction in motor control.

Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin.

What is the level of consciousness of the psychedelic state? Empirically, measures of neural signal diversity such as entropy and Lempel-Ziv (LZ) complexity score higher for wakeful rest than for states with lower conscious level like propofol-induced anesthesia. Here we compute these measures for spontaneous magnetoencephalographic (MEG) signals from humans during altered states of consciousness induced by three psychedelic substances: psilocybin, ketamine and LSD. For all three, we find reliably higher spontaneous signal diversity, even when controlling for spectral changes. This increase is most pronounced for the single-channel LZ complexity measure, and hence for temporal, as opposed to spatial, signal diversity. We also uncover selective correlations between changes in signal diversity and phenomenological reports of the intensity of psychedelic experience. This is the first time that these measures have been applied to the psychedelic state and, crucially, that they have yielded values exceeding those of normal waking consciousness. These findings suggest that the sustained occurrence of psychedelic phenomenology constitutes an elevated level of consciousness - as measured by neural signal diversity.

Global and local complexity of intracranial EEG decreases during NREM sleep.

Key to understanding the neuronal basis of consciousness is the characterization of the neural signatures of changes in level of consciousness during sleep. Here we analysed three measures of dynamical complexity on spontaneous depth electrode recordings from 10 epilepsy patients during wakeful rest (WR) and different stages of sleep: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability over time of the set of channels active above a threshold; (iii) synchrony coalition entropy, which measures the variability over time of the set of synchronous channels. When computed across sets of channels that are broadly distributed across multiple brain regions, all three measures decreased substantially in all participants during early-night non-rapid eye movement (NREM) sleep. This decrease was partially reversed during late-night NREM sleep, while the measures scored similar to WR during rapid eye movement (REM) sleep. This global pattern was in almost all cases mirrored at the local level by groups of channels located in a single region. In testing for differences between regions, we found elevated signal complexity in the frontal lobe. These differences could not be attributed solely to changes in spectral power between conditions. Our results provide further evidence that the level of consciousness correlates with neural dynamical complexity.