C. elegans enteric motor neurons fire synchronized action potentials underlying the defecation motor program

The C. elegans nervous system was thought to be strictly analog, constituted solely by graded neurons. We recently discovered neuronal action potentials in the sensory neuron AWA; however, the extent to which the C. elegans nervous system relies on analog or digital neural signaling and coding is unclear. Here we report that the enteric motor neurons AVL and DVB fire all-or-none calcium-mediated action potentials that play essential roles in the rhythmic defecation behavior in C. elegans. Both AVL and DVB synchronously fire giant action potentials to faithfully execute all-or-none expulsion following the intestinal pacemaker. AVL fires unusual compound action potentials with each positive calcium-mediated spike followed by a potassium-mediated negative spike. The depolarizing calcium spikes in AVL are mediated by a CaV2 calcium channel UNC-2, while the negative potassium spikes are mediated by a repolarization-activated potassium channel EXP-2. Whole-body behavior tracking and simultaneous neural imaging in free-moving animals suggest that action potentials initiated in AVL in the head propagate along its axon to the tail and activate DVB through the INX-1 gap junction. Synchronized action potential spikes between AVL and DVB, as well as the negative spike and long-lasting afterhyperpolarization in AVL, play an important function in executing expulsion behavior. This work provides the first evidence that in addition to sensory coding, C. elegans motor neurons also use digital coding scheme to perform specific functions including long-distance communication and temporal synchronization, suggesting further, unforeseen electrophysiological diversity remains to be discovered in the C. elegans nervous system.

Manipulation of the Tyrosinase gene permits improved CRISPR/Cas editing and neural imaging in cichlid fish

Direct tests of gene function have historically been performed in a limited number of model organisms. The CRISPR/Cas system is species-agnostic, offering the ability to manipulate genes in a range of models, enabling insights into evolution, development, and physiology. Astatotilapia burtoni, a cichlid fish from the rivers and shoreline around Lake Tanganyika, has been extensively studied in the laboratory to understand evolution and the neural control of behavior. Here we develop protocols for the creation of CRISPR-edited cichlids and create a broadly useful mutant line. By manipulating the Tyrosinase gene, which is necessary for eumelanin pigment production, we describe a fast and reliable approach to quantify and optimize gene editing efficiency. Tyrosinase mutants also remove a major obstruction to imaging, enabling visualization of subdermal structures and fluorophores in situ. These protocols will facilitate broad application of CRISPR/Cas9 to studies of cichlids as well as other non-traditional model aquatic species.

GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging

Optical imaging of calcium signals in the brain has enabled researchers to observe the activity of hundreds-to-thousands of individual neurons simultaneously. Current methods predominantly focus on matrix factorization and aim at detecting neurons in the imaged field-of-view, and then inferring the corresponding time-traces. The explicit locality constraints on the cell shapes additionally limits the applicability to optical imaging at different scales (i.e., dendritic or widefield data). Here we present a new method that frames the problem of isolating independent fluorescing components as a dictionary learning problem. Specifically, we focus on the time-traces, which are the main quantity used in scientific discovery, and learn the dictionary of time traces with the spatial maps acting as the presence coefficients encoding which pixels the time traces are active in. Furthermore, we present a novel graph filtering model which redefines connectivity between pixels in terms of their shared temporal activity, rather than spatial proximity. This model greatly eases the ability of our method to handle data with complex non-local spatial structure, such as dendritic imaging. We demonstrate important properties of our method, such as robustness to initialization, implicitly inferring number of neurons and simultaneously detecting different neuronal types, on both synthetic data and real data examples. Specifically, we demonstrate applications of our method to calcium imaging both at the dendritic, somatic, and widefield scales.

Possible Therapeutic Options for Complex Regional Pain Syndrome

Complex regional pain syndrome (CRPS) describes an array of painful conditions that are characterized by continuing regional pain. CRPS comprises severe and inappropriate pain in cases of complete recovery after trauma. Research on the pharmacological treatment of CRPS, however, has not been well investigated. In this study, we compared the pain relief effects of different drugs (URB597, pyrrolidine dithiocarbamate, and hydralazine) in a rat model of chronic post-ischemic pain-induced CRPS. After drug injection, CRPS-induced mechanical allodynia was significantly recovered. After three repetitive drug injections, mechanical sensitivity generally improved as hyper-nociception subsided. Reduced Nav1.7 expression at dorsal root ganglions (DRGs) was observed in the drug treatment groups. Neural imaging analysis revealed decreased neural activity for each drug treatment, compared to vehicle. In addition, treatments significantly reduced IL-1β, IL-6, and TNFα expression in DRGs. These results indicated that drugs could reduce the expression of inflammatory factors and alleviate the symptoms of chronic post-ischemic pain-induced CRPS.

An intein-split transactivator for intersectional neural imaging and optogenetic manipulation

The complexity of brain circuitry is manifested by numerous cell types based on genetic marker, location and neural connectivity. Cell-type specific recording and manipulation is essential to disentangle causal neural mechanisms in physiology and behavior; however, many current approaches are largely limited by number of intersectional features, incompatibility of common effectors and insufficient gene expression. To tackle these limitations, we devise an intein-based intersectional synthesis of transactivator (IBIST) to selectively control gene expression of common effectors in specific cell types defined by a combination of multiple features. We validate the specificity and sufficiency of IBIST to control common effectors including fluorophores, optogenetic opsins and Ca2+ indicators in various intersectional conditions in vivo. Using IBIST-based Ca2+ imaging, we show that the IBIST can intersect up to five features, and that hippocampal cells tune differently to distinct emotional valences depending on the pattern of projection targets. Collectively, the IBIST multiplexes the capability to intersect cell-type features and is compatible with common effectors to effectively control gene expression, monitor and manipulate neural activities.

Comparison of Human Social Brain Activity During Eye-Contact With Another Human and a Humanoid Robot

Robot design to simulate interpersonal social interaction is an active area of research with applications in therapy and companionship. Neural responses to eye-to-eye contact in humans have recently been employed to determine the neural systems that are active during social interactions. Whether eye-contact with a social robot engages the same neural system remains to be seen. Here, we employ a similar approach to compare human-human and human-robot social interactions. We assume that if human-human and human-robot eye-contact elicit similar neural activity in the human, then the perceptual and cognitive processing is also the same for human and robot. That is, the robot is processed similar to the human. However, if neural effects are different, then perceptual and cognitive processing is assumed to be different. In this study neural activity was compared for human-to-human and human-to-robot conditions using near infrared spectroscopy for neural imaging, and a robot (Maki) with eyes that blink and move right and left. Eye-contact was confirmed by eye-tracking for both conditions. Increased neural activity was observed in human social systems including the right temporal parietal junction and the dorsolateral prefrontal cortex during human-human eye contact but not human-robot eye-contact. This suggests that the type of human-robot eye-contact used here is not sufficient to engage the right temporoparietal junction in the human. This study establishes a foundation for future research into human-robot eye-contact to determine how elements of robot design and behavior impact human social processing within this type of interaction and may offer a method for capturing difficult to quantify components of human-robot interaction, such as social engagement.

Reforming Sectarian Beliefs in Iraq: Winning the Peace

Sectarian violence continues in Iraq affecting regional and world security. Neuroscience techniques are used to assess the mentalizing process and counter-arguing in response to videos designed to prevent extremist radicalization. Measurement of neural activity in brain Regions of Interest (ROI) assists identification of messages which can promote favorable behavior. Activation of the Medial Prefrontal Cortex (MPFC) is associated with message adoption and behavior change. Public Service Announcements (PSAs) have not been effective in reducing violence in Iraq. This study demonstrates that the four PSAs investigated in this study do not activate the MPFC. The RLPFC is a brain ROI associated with counter-arguing and message resistance. This study demonstrates that reduction in activity in the Right Lateral Prefrontal Cortex (RLPFC) is associated with decreased sectarianism. Engagement was measured and is associated with activity in the frontal pole regions.We introduce Functional Near-infrared Spectroscopy (fNIRS) to measure the neural activity of highly sectarian Iraqis in response to these anti-sectarian messages. Neural activity was measured while viewing three PSAs and a fourth unpublished video. All four videos are intended to reduce sectarianism. A novel sectarianism scale is introduced to measure sectarian beliefs before and after the messages. This sectarian scale has high internal consistency as measured by Cronbach’s alpha. Measured activation of brain ROIs are correlated with changes in the sectarian scale. Twelve Sunni and twelve Shi’a Iraqis participated in the study. Subjects were shown the four videos in randomized order, while equipped with a fNIRS neural imaging device. All four videos produced significant engagement. None of the videos reduced sectarianism nor caused brain activation of adoption. This is consistent with the widely held Iraqi public perception that the PSAs are ineffective. Only one video, which was un-published, caused reduced sectarian beliefs. This un-published fourth video was associated with decreased counter-arguing. Counter-arguing is associated with message resistance.

Online learning for orientation estimation during translation in an insect ring attractor network

Insect neural systems are a promising source of inspiration for new algorithms for navigation, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing of orientation estimation error. Overall, our results here may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.