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1. Molecular characterization of frog vocal neurons using constellation pharmacology

   https://doi.org/10.1152/jn.00105.2020  

   Inagaki, Ryota Thomas ; Raghuraman, Shrinivasan ; Chase, Kevin ; Steele, Theresa ; Zornik, Erik ; Olivera, Baldomero M ; Yamaguchi, Ayako ( June 2020 , Journal of Neurophysiology)

   null (Ed.)

   Identification and characterization of neuronal cell classes in motor circuits are essential for understanding the neural basis of behavior. It is a challenging task, especially in a non-genetic model organism, to identify cell-specific expression of functional macromolecules. Here, we performed constellation pharmacology, calcium imaging of dissociated neurons to pharmacologically identify functional receptors expressed by vocal neurons in adult male and female African clawed frogs, Xenopus laevis. Previously we identified a population of vocal neurons called fast trill neurons (FTNs) in the amphibian parabrachial nucleus (PB) that express NMDA receptors and GABA and/or glycine receptors. Using constellation pharmacology, we identified four cell classes of putative fast trill neurons (pFTNs, responsive to both NMDA and GABA/glycine applications). We discovered that some pFTNs responded to the application of substance P (SP), acetylcholine (ACh), or both. Electrophysiological recordings obtained from FTNs using an ex vivo preparation verified that SP and/or ACh depolarize FTNs. Bilateral injection of ACh, SP, or their antagonists into PBs showed that ACh receptors are not sufficient but necessary for vocal production, and SP receptors play a role in shaping the morphology of vocalizations. Additionally, we discovered that the PB of adult female X. laevis also contains all the subclasses of neurons at a similar frequency as in males, despite their sexually distinct vocalizations. These results reveal novel neuromodulators that regulate X. laevis vocal production, and demonstrate the power of constellation pharmacology in identifying the neuronal subtypes marked by functional expression of cell-specific receptors in non-genetic model organisms. 

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2. Two conserved vocal central pattern generators broadly tuned for fast and slow rates generate species-specific vocalizations in Xenopus clawed frogs

   https://doi.org/10.7554/eLife.86299  

   Yamaguchi, Ayako ; Peltier, Manon ( May 2023 , eLife)

   Across phyla, males often produce species-specific vocalizations to attract females. Although understanding the neural mechanisms underlying behavior has been challenging in vertebrates, we previously identified two anatomically distinct central pattern generators (CPGs) that drive the fast and slow clicks of maleXenopus laevis,using an ex vivo preparation that produces fictive vocalizations.Here, we extended this approach to four additional species,X. amieti, X. cliivi, X. petersii, and X. tropicalis,by developing ex vivo brain preparation from which fictive vocalizations are elicited in response to a chemical or electrical stimulus. We found that even though the courtship calls are species-specific, the CPGs used to generate clicks are conserved across species. The fast CPGs, which critically rely on reciprocal connections between the parabrachial nucleus and the nucleus ambiguus, are conserved among fast-click species, and slow CPGs are shared among slow-click species. In addition, our results suggest that testosterone plays a role in organizing fast CPGs in fast-click species, but not in slow-click species. Moreover, fast CPGs are not inherited by all species but monopolized by fast-click species. The results suggest that species-specific calls of the genusXenopushave evolved by utilizing conserved slow and/or fast CPGs inherited by each species.

    

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3. To hum or not to hum: Neural transcriptome signature of male courtship vocalization in a teleost fish

   https://doi.org/10.1111/gbb.12740  

   Tripp, Joel A. ; Feng, Ni Y. ; Bass, Andrew H. ( June 2021 , Genes, Brain and Behavior)

   For many animal species, vocal communication is a critical social behavior and often a necessary component of reproductive success. Additionally, vocalizations are often demanding motor acts. Wanting to know whether a specific molecular toolkit might be required for vocalization, we used RNA‐sequencing to investigate neural gene expression underlying the performance of an extreme vocal behavior, the courtship hum of the plainfin midshipman fish (Porichthys notatus). Single hums can last up to 2 h and may be repeated throughout an evening of courtship activity. We asked whether vocal behavioral states are associated with specific gene expression signatures in key brain regions that regulate vocalization by comparing transcript expression levels in humming versus non‐humming males. We find that the circadian‐related genesperiod3andClockare significantly upregulated in the vocal motor nucleus and preoptic area‐anterior hypothalamus, respectively, in humming compared with non‐humming males, indicating that internal circadian clocks may differ between these divergent behavioral states. In addition, we identify suites of differentially expressed genes related to synaptic transmission, ion channels and transport, neuropeptide and hormone signaling, and metabolism and antioxidant activity that together may support the neural and energetic demands of humming behavior. Comparisons of transcript expression across regions stress regional differences in brain gene expression, while also showing coordinated gene regulation in the vocal motor circuit in preparation for courtship behavior. These results underscore the role of differential gene expression in shifts between behavioral states, in this case neuroendocrine, motor and circadian control of courtship vocalization.

    

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4. Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries

   https://doi.org/10.7554/eLife.68192  

   Hernández, Damián G ; Sober, Samuel J ; Nemenman, Ilya ( March 2022 , eLife)

   The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein’s function based on its sequence, we still do not understand how to accurately predict an organism’s behavior based on neural activity. Here, we introduce the unsupervised Bayesian Ising Approximation (uBIA) for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. In data recorded during singing from neurons in a vocal control region, our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, we used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets. 

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5. Error detection and correction in intracortical brain–machine interfaces controlling two finger groups

   https://doi.org/10.1088/1741-2552/acef95  

   Wallace, Dylan M. ; Benyamini, Miri ; Nason-Tomaszewski, Samuel R. ; Costello, Joseph T. ; Cubillos, Luis H. ; Mender, Matthew J. ; Temmar, Hisham ; Willsey, Matthew S. ; Patil, Parag G. ; Chestek, Cynthia A. ; et al ( August 2023 , Journal of Neural Engineering)

   Objective.While brain–machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e. consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.Mainresults.First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below$5\mathrm{\%}$, it was possible to achieve mean true positive rate of$28.1\mathrm{\%}$online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.Significance.Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements may be obtained by enhancing classification and correction strategies.

    

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