bioRxiv Subject Collection: Neuroscience's Journal
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Thursday, January 4th, 2024
| Time |
Event |
| 9:45a |
Rapid Motor Adaptation via Population-level Modulation of Cerebellar Error Signals
A core principle of cerebellar learning theories is that climbing fibers from the inferior olive convey error signals about movement execution to Purkinje cells in the cerebellar cortex. These inputs trigger synaptic changes which are purported to drive progressive adjustment of future movements. Individually, binary complex spike signals lack information about the sign and magnitude of errors which presents a problem for cerebellar learning paradigms exhibiting fast adaptation. Using a newly developed behavioral paradigm in mice, we introduced sensorimotor perturbations into a simple joystick pulling behavior and found parasagittal bands of Purkinje cells with reciprocal modulation of complex spike activity. Whereas complex spiking showed little modulation in the unperturbed condition, alternating bands were activated or inhibited when the perturbation was introduced, and this modulation encoded the sign and magnitude of the resulting sensorimotor mismatch. These findings provide an important piece of information for the understanding of cerebellar learning that helps to explain how the cerebellum could use supervised learning to quickly adapt motor behavior in response to perturbations.
One-Sentence SummaryPopulations of Purkinje cells in the cerebellum facilitate trial-by-trial movement refinement by converting binary signals from movement errors into graded signals that encode the errors direction and magnitude. | | 11:45a |
Mitochondrial reverse electron transport in myeloid cells perpetuates neuroinflammation
Sustained smouldering, or low grade, activation of myeloid cells is a common hallmark of several chronic neurological diseases, including multiple sclerosis (MS)1. Distinct metabolic and mitochondrial features guide the activation and the diverse functional states of myeloid cells2. However, how these metabolic features act to perpetuate neuroinflammation is currently unknown. Using a multiomics approach, we identified a new molecular signature that perpetuates the activation of myeloid cells through mitochondrial complex II (CII) and I (CI) activity driving reverse electron transport (RET) and the production of reactive oxygen species (ROS). Blocking RET in pro-inflammatory myeloid cells protected the central nervous system (CNS) against neurotoxic damage and improved functional outcomes in animal disease models in vivo. Our data show that RET in myeloid cells is a potential new therapeutic target to foster neuroprotection in smouldering inflammatory CNS disorders3. | | 4:50p |
NeuroMechanics: Electrophysiological and Computational Methods to Accurately Estimate the Neural Drive to Muscles in Humans In Vivo
The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing. | | 4:50p |
Synaptic Proteomes of Cortical Interneuron Classes Revealed by Antibody Directed Proximity Labeling.
Subtypes of inhibitory interneurons play diverse roles within neural circuits in cerebral cortex. Defining the molecular underpinnings of interneuron functions within cortical circuits will require identification of interneuron synaptic proteomes. In this study, we first combined genetically directed expression of tdTomato-synaptophysin with antibody-directed proximity labeling and tandem mass spectrometry to identify synaptic proteomes of three major interneuron classes in mouse cortex: parvalbumin (PV), somatostatin (SS), and vasoactive intestinal peptide (VIP).
After stringent filtering we identified 581 proteins: 228 identified in all cell classes and 353 in one or two of three classes. The PV class had the largest number of uniquely identified proteins (141), followed by VIP (30) and SST (20). Consistent with previously reported electrophysiological evidence, PV presynaptic proteomes were enriched for NMDA receptor subunits and scaffolding proteins. We used antibodies against synaptotagmin 2 (Syt2), a presynaptic protein present at PV synapses, to confirm NMDAR localization, and to find that the mu-opioid receptor agonist buprenorphine rapidly caused reorganization of the PV presynaptic proteome. Overall, our results reveal proteomes of PV, SST, and VIP interneurons in cortex that likely underlie distinct and dynamic interneuron synaptic properties. | | 6:03p |
Spatiotemporal Mapping and Molecular Basis of Whole-brain Circuit Maturation
Brain development is highly dynamic and asynchronous, marked by the sequential maturation of functional circuits across the brain. The timing and mechanisms driving circuit maturation remain elusive due to an inability to identify and map maturing neuronal populations. Here we create DevATLAS (Developmental Activation Timing-based Longitudinal Acquisition System) to overcome this obstacle. We develop whole-brain mapping methods to construct the first longitudinal, spatiotemporal map of circuit maturation in early postnatal mouse brains. Moreover, we uncover dramatic impairments within the deep cortical layers in a neurodevelopmental disorders (NDDs) model, demonstrating the utility of this resource to pinpoint when and where circuit maturation is disrupted. Using DevATLAS, we reveal that early experiences accelerate the development of hippocampus-dependent learning by increasing the synaptically mature granule cell population in the dentate gyrus. Finally, DevATLAS enables the discovery of molecular mechanisms driving activity-dependent circuit maturation. | | 6:03p |
Hippocampus oxytocin signaling promotes prosocial eating in rats
The hypothalamic neuropeptide oxytocin (OT) influences both food intake and social behavior. Given that food preference and consumption are heavily affected by social factors in mammals, it is critical to understand the extent that OTs role in regulating these two fundamental behaviors is interconnected. Here we evaluated the role of OT signaling in the dentate gyrus of the dorsal hippocampus (HPCd), a brain region recently linked with eating and social memory, on food preference and consumption in rats under conditions that vary with regards to social presence and conspecific familiarity. Results from neuropharmacological and virogenetic knockdown approaches reveal that HPCd OT signaling promotes eating in the presence of a familiar but not an unfamiliar conspecific. Additionally, HPCd OT receptor signaling is required for the social transmission of food preference. These findings collectively identify the HPCd as a novel substrate where oxytocin synergistically influences eating and social behaviors. | | 6:32p |
Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning
The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP. | | 7:45p |
Fluorescence lifetime imaging of sDarken as a tool for the evaluation of serotonin levels
Recent advances in the development of genetically encoded biosensors have resulted in a variety of different neurotransmitter sensors for the precise measurement of the dynamics of neurotransmitters, neuromodulators, peptides and hormones in real time. However, intensity-based measurements of fluorescent biosensors are limited by their dependence on the expression level of the sensor, the intensity of the excitation light, and photobleaching overtime. Here, we show that the FLIM of sDarken (a GPCR-based genetically encoded sensor for serotonin) decreases with increasing serotonin concentrations. Different members of the sDarken family, with different affinities for serotonin, show concentration-dependent changes in fluorescence lifetime according to their dynamic range. We believe that this feature of sDarken is a value-adding complement to intensity-based information and may lead to a better understanding of serotonin dynamics in health and disease. | | 7:45p |
ALS patient-derived motor neuron networks exhibit microscale dysfunction and mesoscale compensation rendering them highly vulnerable to perturbation
Amyotrophic lateral sclerosis affects upper and lower motor neurons, causing progressive neuropathology leading to structural and functional alterations of affected neural networks long prior to development of symptoms. Certain genetic mutations, such as expansions in C9orf72, predispose motor neuron populations to pathological dysfunction. However, it is not known how underlying pathological predisposition affects structural and functional dynamics within vulnerable networks. Here, we studied micro-and mesoscale dynamics of ALS patient derived motor neuron networks over time. We show, for the first time, that ALS patient derived motor neurons with endogenous genetic predisposition develop classical ALS cytopathology in the form of cytoplasmic TDP-43 inclusions and self-organise into computationally efficient networks, albeit with functional hallmarks of higher metabolic cost compared to healthy controls. These hallmarks included microscale impairments and mesoscale compensation including increased centralisation of function. Moreover, we show that these networks are highly susceptible to transient perturbation by exhibiting induced hyperactivity. | | 11:18p |
Selfish behavior requires top-down control of prosocial motivation
Individuals must regularly choose between prosocial and proself behaviors. While past neuroscience research has revealed the neural foundations for prosocial behaviors, many studies have oversimplified proself behaviors, viewing them merely as a reward-maximization process. However, recent behavioral evidence suggests that response times for proself behaviors are often slower than those for prosocial behaviors, suggesting a more complex interdependence between prosocial and proself neural computations. To address this issue, we conducted an fMRI experiment with the ultimatum game, where participants were requested to accept (money distributed as proposed) or reject (both sides receive none) offers of money distribution. In the decisions, the participants could maximize self-interest by accepting the offer (i.e., proself), while by rejecting it, they could punish unfair proposers and promote the "equity" social norm (i.e., prosocial). We constructed a drift diffusion model (DDM) that considers both behavioral choices and response times and used the DDM parameters in our fMRI analysis. We observed that participants who suppressed inequity-driven rejection behaviors displayed heightened dACC activity in response to disadvantageous inequity. Importantly, our functional connectivity analysis demonstrated that the dACC exhibited negative functional connectivity with the amygdala when unfair offers were presented. Furthermore, the PPI connectivity encoded the average reaction time for accepting unfair offers (i.e., proself behaviors). Considering that the amygdala also responded to disadvantageous inequity in these experiments and previous studies, these results show that the top-down control of prosocial motives (i.e., aversion to disadvantageous inequity) plays a key role in implementing proself behaviors. | | 11:18p |
Linking brain-heart interactions to emotional arousal in immersive virtual reality
The subjective experience of emotions is rooted in the contextualized perception of changes in bodily (e.g., heart) activity. Increased emotional arousal (EA) has been related to lower high- frequency heart rate variability (HF-HRV), lower EEG parieto-occipital alpha power, and higher heartbeat-evoked potential (HEP) amplitudes. We studied EA-related brain-heart interactions (BHIs) using immersive virtual reality (VR) for naturalistic yet controlled emotion induction. 29 healthy adults (13 women, age: 26{+/-}3) completed a VR experience that included rollercoasters while EEG and ECG were recorded. Continuous EA ratings were collected during a video replay immediately after. We analyzed EA-related changes in HF-HRV as well as in BHIs using HEPs and directional functional BHI modeling.
Higher EA was associated with lower HEP amplitudes in a left fronto-central electrode cluster. While parasympathetic modulation of the heart (HF-HRV) and parieto-occipital EEG alpha power were reduced during higher EA, there was no evidence for the hypothesized EA-related changes in bidirectional information flow between them. Whole-brain exploratory analyses in additional EEG (delta, theta, alpha, beta and gamma) and HRV (low-frequency, LF, and HF) frequency bands indicated a temporo-occipital cluster, in which higher EA was linked to decreased brain-to-heart (gamma[->]HF-HRV) and increased heart-to-brain (LF-HRV[->]gamma) information flow. Our results confirm previous findings from less naturalistic experiments and suggest EA-related BHI changes in temporo-occipital gamma power. |
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