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bioRxiv2017

Statistical Mechanics Of Phase Space Partitioning In Large-Scale Spiking Neuron Circuits

Synaptic interactions structure the phase space of the dynamics of neural circuits and constrain neural computation. Understanding how requires methods that handle those discrete interactions, yet few exist. Recently, it was discovered that even random networks exhibit dynamics that partitions the phase space into numerous attractor basins. Here we utilize this phenomenon to develop theory for the geometry of phase space partitioning in spiking neural circuits. We find basin boundaries structuring the phase space are pre-images of spike-time collision events. Formulating a statistical theory of spike-time collision events, we derive expressions for the rate of divergence of neighboring basins and for their size distribution. This theory reveals that the typical basin diameter grows with inhibitory coupling strength and shrinks with the rate of spike events. Our study provides an analytical and generalizable approach for dissecting how connectivity, coupling strength, single neuron dynamics and population activity shape the phase space geometry of spiking circuits.

biophysics↗

bioRxiv2017

Rem2 regulates distinct homeostatic mechanisms in visual circuit plasticity

Activity-regulated genes sculpt neural circuits in response to sensory experience. These calcium-sensitive genes generally fall into two categories: transcription factors and proteins that function at synapses. Yet little is known about activity-regulated, cytosolic proteins that transduce signals between the neuronal membrane and the nucleus. Using the visual system as a model, we investigated the role of the activity-regulated, non-canonical Ras-like GTPase Rem2 in vivo. We demonstrate that Rem2-/- mice fail to exhibit normal ocular dominance plasticity during the critical period. At the circuit level, cortical layer 2/3 neurons in Rem2-/- mice show deficits in both postsynaptic scaling up of excitatory synapses and misregulation of intrinsic excitability. Further, we reveal that Rem2 plays a novel, cell-autonomous role in regulating neuronal intrinsic excitability. Thus, Rem2 is a critical regulator of neural circuit function and distinct homeostatic plasticity mechanisms in vivo.\n\nHIGHLIGHTSO_LIRem2 is required in excitatory cortical neurons for normal ocular dominance plasticity\nC_LIO_LIRem2 regulates postsynaptic homoeostatic synaptic scaling up\nC_LIO_LIRem2 alters the intrinsic excitability of neurons in a cell-autonomous manner\nC_LI

neuroscience↗

bioRxiv2020

A neuromechanical model and kinematic analyses for Drosophila larval crawling based on physical measurements

Animal locomotion requires dynamic interactions between neural circuits, muscles, and surrounding environments. In contrast to intensive studies on neural circuits, the neuromechanical basis for animal behaviour remains unclear due to the lack of information on the physical properties of animals. Here, we proposed an integrated neuromechanical model based on physical measurements by taking Drosophila larvae as a model of soft- bodied animals. The biomechanical parameters of fly larvae were measured by the stress- relaxation test. By optimizing parameters in the neural circuit, our neuromechanical model succeeded in quantitatively reproducing the kinematics of larval locomotion that were obtained experimentally. This model could reproduce the observation of optogenetic studies reported previously. The model predicted that peristaltic locomotion could be exhibited in a low friction condition. Analysis of floating larvae provided results consistent with this prediction. Furthermore, the model predicted a significant contribution of intersegmental connections in the central nervous system, which contrasts with a previous study. This hypothesis allowed us to make a testable prediction for the variability in intersegmental connection in sister species of the genus Drosophila. Our model based on physical measurement provides a new foundation to study locomotion in soft-bodied animals and soft robot engineering.

neuroscience↗

bioRxiv2024

Circuits and mechanisms for TMS-induced corticospinal waves: Connecting sensitivityanalysis to the network graph

Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation. AUTHOR SUMMARYUnderstanding circuit mechanisms underlying the response to transcranial magnetic stimulation remains a significant challenge for translational and clinical research. Computational models can reconstruct network activity in response to stimulation, but basic sensitivity analyses are insufficient to identify the fundamental circuit properties that underly an evoked response. We developed a data-driven neuronal network model of motor cortex, constrained with human recordings, that reproduced the corticospinal response to magnetic stimulation. The model supported several hypotheses, e.g., the importance of stimulating incoming fibers as well as neurons within the cortical column and the relevance of both excitatory and inhibitory neurons. Following a sensitivity analysis, we conducted a secondary structural analysis that linked the results of the sensitivity analysis to the network using machine learning. The structural analysis pointed to anatomical mechanisms that contributed to specific peaks in the response. Generally, given the anatomy and circuit of a neural region, identifying strongly connected paths in the network and the conduction delays of these paths can screen for important contributors to response peaks. This work supports and expands on hypotheses explaining the response to transcranial magnetic stimulation and adds a novel method for identifying generalizable neural circuit mechanisms.

neuroscience↗

bioRxiv2022

TOB is an effector of the hippocampus-mediated acute stress response

Stress affects behavior and involves critical dynamic changes at multiple levels ranging from molecular pathways to neural circuits and behavior. Abnormalities at any of these levels lead to decreased stress resilience and pathological behavior. However, temporal modulation of molecular pathways underlying stress response remains poorly understood. Transducer of ErbB2.1, known as TOB, (TOB1) is involved in different physiological functions, including cellular stress and immediate response to stimulation. In this study, we investigated the role of TOB in the brains stress machinery at molecular, neural circuit, and behavioral levels. Interestingly, TOB protein levels increased after mice were exposed to acute stress. At the neural circuit level, functional magnetic resonance imaging (fMRI) suggested that intra-hippocampal and hippocampal-prefrontal connectivity were dysregulated in Tob knockout (Tob-KO) mice. Electrophysiological recordings in hippocampal slices showed increased postsynaptic AMPAR-mediated neurotransmission, accompanied by decreased GABA neurotransmission and subsequently altered Excitatory/Inhibitory balance after Tob deletion. At the behavioral level, Tob-KO mice show abnormal, hippocampus-dependent, contextual fear conditioning and extinction, and depression-like behaviors. On the other hand, increased anxiety observed in Tob-KO mice is hippocampus-independent. At the molecular level, we observed decreased stress-induced LCN2 expression and ERK phosphorylation, as well as increased MKP-1 expression. This study suggests that TOB serves as an important modulator in hippocampal stress signaling machinery. In summary, we show a molecular pathway and neural circuit mechanism by which TOB deletion contributes to expression of pathological stress-related behavior.

neuroscience↗

bioRxiv2024

RNA-programmable cell type monitoring and manipulation in the human cortex with CellREADR

Reliable and systematic access to diverse cell types is necessary for understanding the organization, function, and pathophysiology of human neural circuits. Methods for targeting human neural populations are scarce and currently center on identifying transcriptional enhancers and engineering viral capsids. Here we demonstrate the utility of CellREADR, a programmable RNA sensor-effector technology that couples cellular RNA sensing to effector protein translation, for accessing, monitoring, and manipulating specific neuron types in human cortex, ex vivo. We designed CellREADRs to target two subpopulations, CALB2 GABAergic interneurons and FOXP2 glutamatergic projection neurons, then validated targeting specificity using histological, electrophysiological, and transcriptomic methods. CellREADR expression of channelrhodopsin and GCamp enabled the manipulation and monitoring of these populations in live cortical microcircuits. By demonstrating specific, reliable, and programmable experimental access to human neuronal subpopulations, our results highlight CellREADRs potential for studying neural circuits and treating brain disorders.

neuroscience↗

bioRxiv2016

NeuroGFX: a graphical functional explorer for fruit fly brain circuits

Recently, multiple focused efforts have resulted in substantial increase in the availability of connectome data in the fruit fly brain. Elucidating neural circuit function from such structural data calls for a scalable computational modeling methodology. We propose such a methodology that includes i) a brain emulation engine, with an architecture that can tackle the complexity of whole brain modeling, ii) a database that supports tight integration of biological and modeling data along with support for domain specific queries and circuit transformations, and iii) a graphical interface that allows for total flexibility in configuring neural circuits and visualizing run-time results, both anchored on model abstractions closely reflecting biological structure. Towards the realization of such a methodology, we have developed NeuroGFX and integrated it into the architecture of the Fruit Fly Brain Observatory (http://fruitflybrain.org). The computational infrastructure in NeuroGFX is provided by Neurokernel, an open source platform for the emulation of the fruit fly brain, and NeuroArch, a database for querying and executing fruit fly brain circuits. The integration of the two enables the algorithmic construction/manipulation/revision of executable circuits on multiple levels of abstraction of the same model organism. The power of this computational infrastructure can be leveraged through an intuitive graphical interface that allows visualizing execution results in the context of biological structure. This provides an environment where computational researchers can present configurable, executable neural circuits, and experimental scientists can easily explore circuit structure and function ultimately leading to biological validation. With these capabilities, NeuroGFX enables the exploration of function from circuit structure at whole brain, neuropil, and local circuit level of abstraction. By allowing for independently developed models to be integrated at the architectural level, NeuroGFX provides an open plug and play, collaborative environment for whole brain computational modeling of the fruit fly.

neuroscience↗

bioRxiv2020

Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque

A major effort is now underway across the brain sciences to identify, characterize and manipulate mesoscale neural circuits in order to elucidate the mechanisms underlying sensory perception, cognition and behavior. Optical imaging technologies, in conjunction with genetically encoded sensors and actuators, serve as important tools toward these goals, allowing access to large-scale genetically defined neuronal populations. In particular, one-photon miniature microscopes, coupled with genetically encoded calcium indicators and microendoscopic gradient-refractive index (GRIN) lenses, enable unprecedented readout of neural circuit dynamics in cortical and deep subcortical brain regions during active behavior in rodents. This has already led to breakthrough discoveries across a wide array of rodent brain regions and behaviors. However, in order to study the neural circuit mechanisms underlying more complex and clinically relevant human behaviors and cognitive functions, it is crucial to translate this technology to non-human primates. Here, we describe the first successful application of this technology in the rhesus macaque. We identified a viral strategy for robust expression of GCaMP, optimized a surgical protocol for microendoscope GRIN lens insertion, and created a chronic cranial chamber and lens mounting system for imaging in gyral cortex. Using these methods, we demonstrate the ability to perform plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from over 100 genetically-targeted neurons simultaneously in dorsal premotor cortex while the macaque performs a naturalistic motor reach task with the head unrestrained and freely moving. The recorded population of neurons exhibited calcium dynamics selective to the direction of reach, which we show can be used to decode the animals trial-by-trial motor behavior. Recordings were stable over several months, allowing us to longitudinally track large populations of individual neurons and monitor their relationship to motor behavior over time. Finally, we demonstrate the ability to conduct simultaneous, multi-site imaging in bilateral dorsal premotor cortices, offering an opportunity to study distributed networks underlying complex behavior and cognition. Together, this work establishes head-mounted microendoscopic calcium imaging in macaque as a powerful new approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease. HighlightsFirst demonstration of head-mounted microendoscopic calcium imaging in behaving macaque. Surgical protocols developed for preparing the animal for calcium imaging, including virus injections to express GCaMP and chronic implantation of a GRIN lens to enable optical access to gyral cortex. Proof of concept plug-and-play calcium imaging in behaving macaques with months long stable recording capability allowing populations of individual neurons to be tracked longitudinally. Bilateral calcium imaging from dorsal premotor cortex exhibited dynamics selective to the animals direction of reach and allowed decoding of the animals motor behavior

neuroscience↗

bioRxiv2021

Adaptation in cone photoreceptors contributes to an unexpected insensitivity of On parasol retinal ganglion cells to spatial structure in natural images

Neural circuits are constructed from nonlinear building blocks, and not surprisingly overall circuit behavior is often strongly nonlinear. But neural circuits can also behave near linearly, and some circuits shift from linear to nonlinear behavior depending on stimulus conditions. Such control of the linearity or nonlinearity of circuit behavior is fundamental to neural computation. Here we study a surprising stimulus dependence of the responses of On (but not Off) parasol retinal ganglion cells: these cells respond nonlinearly to spatial structure in temporally-modulated grating stimuli but linearly to spatial structure in flashed gratings and natural visual inputs. We show that this unexpected response linearity can be explained by a shift in the balance of excitatory and inhibitory synaptic inputs that originates at least in part from adaptation in the cone photoreceptors. More generally, this highlights how subtle asymmetries in signaling - here in the cone signals - can qualitatively alter circuit computation.

neuroscience↗

bioRxiv2023

Knowing what you don't know: Estimating the uncertainty of feedforward and feedback inputs with prediction-error circuits

At any moment, our brains receive a stream of sensory stimuli arising from the world we interact with. Simultaneously, neural circuits are shaped by feedback signals carrying predictions about the same inputs we experience. Those feedforward and feedback inputs often do not perfectly match. Thus, our brains have the challenging task of integrating these conflicting streams of information according to their reliabilities. However, how neural circuits keep track of both the stimulus and prediction uncertainty is not well understood. Here, we propose a network model whose core is a hierarchical prediction-error circuit. We show that our network can estimate the variance of the sensory stimuli and the uncertainty of the prediction using the activity of negative and positive prediction-error neurons. In line with previous hypotheses, we demonstrate that neural circuits rely strongly on feedback predictions if the perceived stimuli are noisy and the underlying generative process, that is, the environment is stable. Moreover, we show that predictions modulate neural activity at the onset of a new stimulus, even if this sensory information is reliable. In our network, the uncertainty estimation, and, hence, how much we rely on predictions, can be influenced by perturbing the intricate interplay of different inhibitory interneurons. We, therefore, investigate the contribution of those inhibitory interneurons to the weighting of feedforward and feedback inputs. Finally, we show that our network can be linked to biased perception and unravel how stimulus and prediction uncertainty contribute to the contraction bias.

neuroscience↗

bioRxiv2019

Trans-synaptic Fish-lips Signaling Prevents Misconnections between Non-synaptic Partner Olfactory Neurons

Our understanding of the mechanisms of neural circuit assembly is far from complete. Identification of new wiring molecules with novel mechanisms of action will provide new insights into how complex and heterogeneous neural circuits assemble during development. Here, we performed an RNAi screen for cell-surface molecules and identified the leucine-rich-repeat containing transmembrane protein, Fish-lips (Fili), as a novel wiring molecule in the assembly of the Drosophila olfactory circuit. Fili contributes to the precise targeting of both olfactory receptor neuron (ORN) axons as well as projection neuron (PN) dendrites. Cell-type-specific expression and genetic analyses suggest that Fili sends a trans-synaptic repulsive signal to neurites of non-partner classes that prevent their targeting to inappropriate glomeruli in the antennal lobe.\n\nSignificance StatementIn the fruit fly olfactory system, 50 classes of olfactory receptor neurons (ORNs) make precise synaptic connections with 50 classes of corresponding projection neurons (PNs). Identification of wiring molecules in this circuit can provide insight into understanding neural circuit assembly. This paper reports the role of a transmembrane protein, Fish-lips (Fili), in forming specific connections in this circuit. We found that some ORN axons are repelled by Fili, which is present on dendrites of non-matching PN class, preventing them from targeting inappropriate glomeruli. Similarly, some PN dendrites are repelled by Fili expressed by non-matching ORN class for their correct targeting. Together, these results suggest that Fili mediates repulsion between axons and dendrites of non-synaptic partners to ensure precise wiring patterns.

neuroscience↗

bioRxiv2024

Brain-like neural dynamics for behavioral control develop through reinforcement learning

During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produce neural dynamics that enable the rich repertoire of behaviors we perform. What begins as a series of "babbles" coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories--supervised learning (SL) and reinforcement learning (RL)--that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that this similarity with biological brains depends critically on matching biomechanical properties of the limb. Dynamical analysis on network activity revealed that our RL-trained networks operate at the "edge of chaos", a dynamical regime known for its computational richness, greater memory capacity, and robust plasticity properties. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.

neuroscience↗

bioRxiv2021

Disparate energy consumption despite similar network activity

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here, we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We use a novel machine learning method to identify a range of network models that can generate activity patterns matching experimental data, and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures, but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that, despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.

neuroscience↗

bioRxiv2024

A toolbox for ablating excitatory and inhibitory synapses

Recombinant optogenetic and chemogenetic proteins are potent tools for manipulating neuronal activity and controlling neural circuit function. However, there are few analogous tools for manipulating the structure of neural circuits. Here, we introduce three rationally designed genetically encoded tools that use E3 ligase-dependent mechanisms to trigger the degradation of synaptic scaffolding proteins, leading to functional ablation of synapses. First, we developed a constitutive excitatory synapse ablator, PFE3, analogous to the inhibitory synapse ablator GFE3. PFE3 targets the RING domain of the E3 ligase Mdm2 and the proteasome-interacting region of Protocadherin 10 to the scaffolding protein PSD-95, leading to efficient ablation of excitatory synapses. In addition, we developed a light-inducible version of GFE3, paGFE3, using a novel photoactivatable complex based on the photocleavable protein PhoCl2c. paGFE3 degrades Gephyrin and ablates inhibitory synapses in response to 400 nm light. Finally, we developed a chemically inducible version of GFE3, chGFE3, which degrades inhibitory synapses when combined with the bio-orthogonal dimerizer HaloTag ligand-trimethoprim. Each tool is specific, reversible, and capable of breaking neural circuits at precise locations.

neuroscience↗

bioRxiv2023

Mapping Sleep's Oscillatory Events as a Biomarker of Alzheimer's Disease

ObjectiveMemory-associated neural circuits produce oscillatory events within single-channel sleep electroencephalography (EEG), including theta bursts (TBs), sleep spindles (SPs) and multiple subtypes of slow waves (SWs). Changes in the temporal "coupling" of these events are proposed to serve as a biomarker for early stages of Alzheimers disease (AD) pathogenesis. MethodsWe analyzed data from 205 aging adults, including single-channel sleep EEG, cerebrospinal fluid (CSF) AD-associated biomarkers, and Clinical Dementia Rating(R) (CDR(R)) scale. Individual SW events were sorted into high and low transition frequencies (TF) subtypes. We utilized time-frequency spectrogram locations within sleep EEG to "map" the precision of SW-TB and SW-SP neural circuit coupling in relation to amyloid positivity (by CSF A{beta}42/A{beta}40 threshold), cognitive impairment (by CDR), and CSF levels of AD-associated biomarkers. ResultsCognitive impairment was associated with lower TB spectral power in both high and low TF SW-TB coupling (p<0.001, p=0.001). Cognitively unimpaired, amyloid positive aging adults demonstrated lower precision of the neural circuits propagating high TF SW-TB (p<0.05) and low TF SW-SP (p<0.005) event coupling, compared to cognitively unimpaired amyloid negative individuals. Biomarker correlations were significant for high TF SW-TB coupling with CSF A{beta}42/A{beta}40 (p=0.005), phosphorylated-tau181 (p<0.005), and total-tau (p<0.05). Low TF SW-SP coupling was also correlated with CSF A{beta}42/A{beta}40 (p<0.01). InterpretationLoss of integrity in neural circuits underlying sleep-dependent memory processing can be measured for both SW-TB and SW-SP coupling in spectral time-frequency space. Breakdown of sleeps memory circuit integrity is associated with amyloid positivity, higher levels of AD-associated pathology, and cognitive impairment.

neuroscience↗

bioRxiv2023

Muscarinic Signaling Pathway Controls Cortical Circuit Regulation of Lateral Ventricle Quiescent Neural Stem Cells

Neurogenesis and proliferation of neural stem cells (NSCs) in the subventricular zone (SVZ) are controlled by both intrinsic molecular pathways and extrinsic signaling cues, including neural circuits. One such circuit, the ACC-subep-ChAT+ circuit, has been identified as a regulator of ventral SVZ neurogenesis by modulating the proliferation of LV NSCs. However, the specific neural signals that promote the proliferation activity of LV NSCs have remained largely unknown. In this study, we uncover a molecular mechanism underlying the cellular activation and proliferation of quiescent NSCs (qNSCs) in the lateral ventricle SVZ (LV-SVZ) mediated by the cortical circuit. Our findings demonstrate that postnatal and adult LV qNSCs are triggered by the cortical circuit through ChAT+ neuron stimulation, consequently resulting in the activation of muscarinic 3 receptors (M3) expressed on LV qNSCs. This, in turn, triggers inositol 1,4,5-trisphosphate receptor type 1 (IP3R1) activation, causing intracellular calcium release. Subsequently, the proliferation of LV qNSCs occurs through the downstream regulation of the calcium/calmodulin dependent protein kinase II delta (CAMK2D) and the MAPK10 signaling pathway. These findings shed light on the molecular regulatory mechanisms that govern LV qNSCs and emphasize the significant role of the cortical circuit in promoting their proliferative activation within the ventral LV-SVZ.

cell biology↗

bioRxiv2026

A circuit-to-muscle signaling axis controls locomotor gait transitions in C. elegans

Animals switch between locomotor gaits to adapt to changing environments, yet how defined neural circuits and genes coordinate gait transitions remains poorly understood. Here, we show that the head motor neuron SMB acts as a critical gating node that enables the transition from crawling to swimming in Caenorhabditis elegans. SMB ablation disrupts head-body synchronization, prolongs muscle activation, and impairs curvature switching during swimming, indicating that the SMB neurons function to constrain motor output to stabilize swimming-specific oscillations. Through an unbiased forward genetic screen, we identify the NALCN channel complex component UNC-79 and the muscle nicotinic acetylcholine receptor subunit UNC-29 as key molecular determinants of gait transition. UNC-79 functions across multiple head interneuron modules to stabilize circuit-wide excitability required for sustained swimming, whereas UNC-29 acts cell-autonomously in body wall muscles to refine calcium amplitude and propagation dynamics. Genetic epistasis analyses position the SMB neurons upstream of UNC-29-mediated muscle activation, establishing a circuit-to-muscle signaling axis that links defined head motor neurons to downstream molecular effectors. Together, our findings show that gait transition emerges from coordinated modulation of neuronal excitability and muscle calcium dynamics, providing a mechanistic framework for how neural circuits reconfigure motor states to adapt locomotor behavior. HIGHLIGHTSO_LIThe SMB head motor neurons govern the crawl-to-swim transition C_LIO_LIUNC-79/NALCN stabilizes circuit excitability for sustained swimming C_LIO_LIMuscle nAChR subunit UNC-29 tunes calcium gain during gait transition C_LIO_LIA circuit-to-muscle pathway coordinates locomotor state transition C_LI IN BRIEFGait transitions require coordinated changes in neural circuit activity and muscle output. Moon et al. show that the SMB head motor neurons regulate the switch from crawling to swimming in C. elegans by coupling distributed circuit excitability to muscle calcium gain control, defining a circuit-to-muscle pathway that stabilizes locomotor state transitions.

neuroscience↗

bioRxiv2022

The vigor paradox: saccade velocity during deliberation encodes utility of effortful actions

During deliberation, as the brain considers its options, the neural activity representing the goodness of each option rises toward a threshold, and the choice is often dictated by the option for which the rise is fastest. Here we report a surprising correlate of these activities: saccade vigor. We engaged human subjects in a decision-making task in which they considered effortful options, each requiring walking various durations and inclines. As they deliberated, they made saccades between the symbolic representations of those options. These saccades had no bearing on the effort that they would later expend, yet as they deliberated, saccade velocities increased. The rate of rise in vigor was faster for saccades toward the option that they later indicated as their choice, and encoded the difference in the subjective value of the two effortful options. Once deliberation ended, following a brief delay the subjects indicated their choice by making another saccade. Remarkably, vigor for this saccade dropped to baseline and no longer encoded subjective value. These results are consistent with an urgency model of decision-making in which a global signal in the brain drives both the neural circuits that make decisions, and the neural circuits that make movements. Paradoxically, this common drive is shared between the oculomotor circuits and the decision-making circuits, even when the decision involves effortful expenditure during a future event. SignificanceThere is a link between the decisions we make and the movements that follow. Not only do we prefer options of greater value, but we also move faster to acquire them. When deliberating between options, neural activity rises to a threshold and the option that wins this race is the one chosen. We report a potential correlate of this in the motor control circuits; during deliberation, saccade vigor to both options rise, but faster for the option ultimately chosen. Thus, our movements appear to mirror the neural activity conducting the decision-making process. Paradoxically, this is true even when the movements have no direct bearing on the decision at hand.

neuroscience↗