bioRxiv Subject Collection: Neuroscience's Journal

Flexible EMG arrays with integrated electronics for scalable electrode density
Recent developments in electrode technology have demonstrated the power of flexible microelectrode arrays (FMEAs) for measuring muscle activity at high resolution. We recently introduced the Myomatrix array, a FMEA optimized for measuring the activity of individual motor units (the collection of muscle fibers innervated by a single motor neuron) [1] in freely behaving animals. Although FMEAs are fundamentally changing the way EMG is acquired, the number of recording channels is limited by the size of the plug that interfaces with the digital amplifier hardware and the density of electrode connections on the array. Increasing EMG channel count and supporting electrophysiological studies in smaller animals depends on two seemingly incompatible goals: reducing device size while increasing the number of recording channels. The solution to this is to increase channel density, which is currently limited by requiring that separate headstage and FMEA components be used simultaneously. In our prior devices [1], each FMEA had a dedicated wire output for every electrode input, creating a channel density is 1 : 1. To improve this channel density, we have developed a novel device integrating a digital amplifier (bare-die RHD2216 chip, Intan, Inc. [6]) directly onto an FMEA. This new design reduces the devices backend footprint by 74% and relocates the intan bare die from the headstage to the FMEA itself, creating a channel density of 1 : 3.2. Our methodology combines standard FMEA microfabrication with wire-bonding and surface-mounted components, enabling direct integration into a Serial Peripheral Interface (SPI) connection into the device itself, without any separate headstage. With this initial device we see a 1 : 3.2 channel density, but our method allows for using other bare die amplifiers (Intan, Inc., USA) for a channel density of 1 : 12.8. Our findings present a robust technique for chip embedding in custom FMEAs, applicable to in-vivo electrophysiology

Modelling determinants of region-specific dopamine dynamics in the striatum
Striatal dopamine (DA) release regulates reward-related learning, motivation, and behavioural activation, and is believed to consist of a short-lived phasic and continuous tonic component. Here, we build a large-scale three-dimensional model of extracellular DA dynamics in the dorsal (DS) and ventral striatum (VS) based on experimentally determined biological parameters. The model predicts rapid dynamics in DS with little-to-no basal DA, whereas lower uptake capacity in VS slows signalling and enables build-up of a tonic DA level. Receptor binding simulations reveals that DA D1 receptor occupancy follows extracellular DA concentration with millisecond delay, while DA D2 receptors integrate DA signal over seconds. Our modelling further predicts that VS extracellular levels are highly sensitive to DA transporter (DAT) uptake capacity and posit prevalent nanoclustering of DAT in VS as a regulator of DA uptake. Our simulations substantiate regional differences in extracellular DA dynamics and challenge prevailing paradigms of striatal DA signalling.

Medial prefrontal cortex and nucleus reuniens are critical for working memory in an operant delayed nonmatch task
Working memory refers to the temporary retention of a small amount of information used in the execution of a cognitive task. The prefrontal cortex and its connections with thalamic subregions are thought to mediate specific aspects of working memory, including engaging with the hippocampus to mediate memory retrieval. We used an operant delayed-non match to position task, which does not require the hippocampus, to determine roles of the rodent medial prefrontal cortex (mPFC), the nucleus reuniens thalamic region (RE), and their connection. We found that transient inactivation of the mPFC and RE using the GABA-A agonist muscimol led to a delay-independent reduction in behavioral performance in the delayed non-match to position paradigm. Critically, we used a chemogenetic approach to determine the directionality of the necessary circuitry for behavioral performance reliant on working memory. Specifically, when we targeted mPFC neurons that project to the RE (mPFC-RE) we found a delay-independent reduction in the delayed non-match to position task, but not when we targeted RE neurons that project to the mPFC (RE-mPFC). Our results suggest a broader role for the mPFC-RE circuit in mediating working memory beyond the connection with the hippocampus.

Thalamic Control Over Laminar Cortical Dynamics Across Conscious States
The human brain must support both stable and flexible neural dynamics in order to adapt to changing contexts. This paper investigates the role of the thalamus, a crucial subcortical structure, in orchestrating these opposing dynamics in the cerebral cortex. Through two distinct classes of cortical projections, the thalamus is able to support distinct dynamics modes: some cells relay precise information between cortical regions, whereas others diffusely modulate ongoing cortical dynamics. Traditional approaches to analysing neural data struggle to capture the moment-to-moment intricacies of brain dynamics, akin to mapping a rivers topography without understanding its flow, or laminarity. Inspired by the field of fluid dynamics, we show that spontaneous fMRI data exhibits non-trivial fluctuations in laminarity. Propofol-induced anesthesia selectively disrupts the non-laminar aspects of cortical dynamics while preserving laminar flow, which we validate with a large-scale biophysical model of the thalamocortical system. Finally, we confirmed theoretical predictions from the biophysical model using multielectrode electrophysiological recordings from the cerebral cortex of an anesthetized macaque - direct stimulation of the diffusely-projecting thalamus restored non-laminar cortical fluctuations and the waking state. We conclude that the thalamus provides versatile control over the cortical laminar and non-laminar flows that characterize conscious states.

Vasopressin-to-Oxytocin Receptor Crosstalk in the Preoptic Area Underlying Parental Behaviors in Male Mice
The transition to parenthood brings significant changes in behavior toward offspring. For instance, in anticipation of their offspring, male mice shift from infanticidal to caregiving behaviors. While the release of oxytocin from the paraventricular hypothalamus (PVH) plays a critical role in paternal caregiving, it does not fully account for the entire behavioral shift. The specific downstream neurons and signaling mechanisms involved in this process remain obscure. Here, we demonstrate that PVH vasopressin neurons also essentially contribute to a paternal behavioral shift. This vasopressin signal is partially transmitted through oxytocin receptors (OTRs) expressed in the anterior commissure and medial nuclei of the preoptic area. These OTR-expressing neurons receive inputs from both PVH oxytocin and vasopressin neurons and are responsible for expressing paternal caregiving behaviors. Collectively, this non-canonical vasopressin-to-OTR crosstalk within specific limbic circuits acts as a pivotal regulator of paternal behavioral changes in mice.

Regulation of proteostasis by sleep through autophagy in Drosophila models of Alzheimer's Disease
Sleep and circadian rhythm dysfunctions are common clinical features of Alzheimer 's Disease (AD). Increasing evidence suggests that in addition to being a symptom, sleep disturbances can also drive the progression of neurodegeneration. Protein aggregation is a pathological hallmark of AD, however the molecular pathways behind how sleep affects protein homeostasis remain elusive. Here we demonstrate that sleep modulation influences proteostasis and the progression of neurodegeneration in Drosophila models of Tauopathy. We show that sleep deprivation enhanced Tau aggregational toxicity resulting in exacerbated synaptic degeneration. In contrast, sleep induction using gaboxadol led to reduced hyperphosphorylated Tau accumulation in neurons as a result of modulated autophagic flux and enhanced clearance of ubiquitinated Tau, suggesting altered protein processing and clearance that resulted in improved synaptic integrity and function. These findings highlight the complex relationship between sleep and autophagy, in regulating protein homeostasis, and the neuroprotective potential of sleep-enhancing therapeutics to slow the progression or delay the onset of neurodegeneration.

Immune Checkpoint Inhibition-related Neuroinflammation Disrupts Cognitive Function
Combinatorial blockade of Cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and Programmed Cell Death Protein 1 (PD-1) significantly improve the progression-free survival of individuals with metastatic cancers, including melanoma. In addition to unleashing anti-tumor immunity, combination immune checkpoint inhibition (ICI) disrupts immune-regulatory networks critical for maintaining homeostasis in various tissues, including the central nervous system (CNS). Although ICI- and cancer-related cognitive impairments (CRCI) in survivors are increasingly becoming evident, our understanding of ICI-induced immune-related adverse effects (IREA) in the CNS remains incomplete. Here, our murine melanoma model reveals that combination ICI impairs hippocampal-dependent learning and memory, as well as memory consolidation processes. Mechanistically, combination ICI disrupted synaptic integrity, and neuronal plasticity, reduced myelin, and further predisposed CNS for exaggerated experimental autoimmune encephalomyelitis. Combination ICI substantially altered both lymphoid and myeloid cells in the CNS. Neurogenesis was unaffected, however, microglial activation persisted for two-months post-ICI, concurrently with cognitive deficits, which parallels clinical observations in survivors. Overall, our results demonstrate that blockade of CTLA-4 and PD-1 alters neuro-immune homeostasis and activates microglia, promoting long-term neurodegeneration and driving cognitive impairments. Therefore, limiting microglial activation is a potential avenue to mitigate CNS IRAE while maintaining the therapeutic benefits of rapidly evolving ICIs and their combinations.

Parallel Synapses with Transmission Nonlinearities Enhance Neuronal Classification Capacity
Cortical neurons often establish multiple synaptic contacts with the same postsynaptic neuron. To avoid functional redundancy of these parallel synapses, it is crucial that each synapse exhibits distinct computational properties. Here we model the current to the soma contributed by each synapse as a sigmoidal transmission function of its presynaptic input, with learnable parameters such as amplitude, slope, and threshold. We evaluate the classification capacity of a neuron equipped with such nonlinear parallel synapses, and show that with a small number of parallel synapses per axon, it substantially exceeds that of the Perceptron. Furthermore, the number of correctly classified data points can increase superlinearly as the number of presynaptic axons grows. When training with an unrestricted number of parallel synapses, our model neuron can effectively implement an arbitrary aggregate transmission function for each axon, constrained only by monotonicity. Nevertheless, successful learning in the model neuron often requires only a small number of parallel synapses. We also apply these parallel synapses in a feedforward neural network trained to classify MNIST images, and show that they can increase the test accuracy. This demonstrates that multiple nonlinear synapses per input axon can substantially enhance a neuron's computational power.

Exploring Comorbidity Networks in Mild Traumatic Brain Injury Subjects through Graph Theory: A Traumatic Brain Injury Model Systems Study
Traumatic brain injuries (TBIs) are characterized by myriad comorbidities that affect the functioning of the affected individuals. The comorbidities that TBI subjects experience span a wide range, ranging from psychiatric diseases to those that affect the various systems of the body. This is compounded by the fact that the problems that TBI subjects face could span over an extended period post-primary injury. Further, no drug exists to prevent the spread of secondary injuries after a primary impact. In this study, we employed graph theory to understand the patterns of comorbidities after mild TBIs. Upon application of network analysis and a novel clustering algorithm, we discovered interesting associations between comorbidities in young and old subjects with the condition. Specifically, bipolar disorder was seen as related to cardiovascular comorbidities, a pattern that was observed only in the young subjects. Similar associations between obsessive-compulsive disorder and rheumatoid arthritis were observed in young subjects. Psychiatric comorbidities exhibited differential associations with non-psychiatric comorbidities depending on the age of the cohort. The study results might have implications for effective surveillance and the management of comorbidities post mild TBIs.

Load-dependent and load-independent effects on longitudinal motor training in human continuous hand movements
Motor learning involves complex interactions between the cognitive and sensorimotor systems, which is susceptible to different levels of task load. While the mechanism underlying load-dependent regulations in cognitive functions has been extensively investigated, their influence on downstream execution in motor skill learning remains less understood. The current study extends the understanding of how load levels affect motor learning by a longitudinal functional near-infrared spectroscopy (fNIRS) study in which 30 participants (15 females) engaged in extensive practice on a two-dimensional continuous hand tracking task with varying task difficulties. We propose the index of difficulty (ID) as a quantitative estimate of task difficulty, which is positively correlated with psychometric measure of subjective workload level. Results shows that as behavioral performance improved over time, participants adopted a direction-specific and load-independent (i.e., consistent across different load levels) control strategy, shifting from feedback-dominant to feedforward-dominant control in the vertical direction as training progressed. Crucially, we provide robust evidence of the learning-induced alteration in load-dependent cortical activation patterns, suggesting that effective motor skill learning may lead to shift towards an inverted-U relationship between activation and load level in the pre-motor and supplementary motor areas. In addition, brain-behavior relationship in the frontoparietal network was strengthened after training. Taken together, our findings provide new insights into the learning-induced plasticity in brain and behavior associated with load-dependent and load-independent contributions to motor skill learning.

Sleep homeostasis in a naturalistic setting
Sleep, especially NREM sleep depth is homeostatically regulated, as sleep pressure builds up during wakefulness and diminishes during deep sleep. Previous evidence from this phenomenon, however, mainly stems from experimental studies which may not generalize to an ecologically valid setting. In the current study, we used a dataset of 246 individuals sleeping for at least seven nights each with a mobile EEG headband according to their ordinary daily schedule to investigate the effect of time spent in wakefulness on sleep characteristics. Increased time in wakefulness prior to sleep was associated with decreased sleep onset latency, increased sleep efficiency, a larger percentage of N3 sleep, and higher delta activity. Moreover, increased sleep pressure resulted in an increase in both the slope and the intercept of the sleep EEG spectrum. As predicted, PSD effects were most prominent in the earliest hours of sleep. Our results demonstrate that experimental findings showing increased sleep depth after extended wakefulness generalize to ecologically valid settings, and that time spent awake is an important determinant of sleep characteristics on the subsequent night. Our findings are evidence for the efficacy of sleep restriction, a behavioral technique already widely used in clinical settings, as a simple but powerful method to improve the objective quality of sleep in those with sleep problems.

Rhythmic modulation of subthalamo-pallidal interactions depends on synaptic rewiring through inhibitory plasticity
Rhythmic stimulation offers a paradigm to modulate brain oscillations and, therefore, influence brain function. A growing body of evidence indicates that reciprocal interactions between the neurons of the subthalamic nucleus (STN) and globus pallidus externus (GPe) play a central role in the emergence of abnormal synchronous beta (15-30 Hz) oscillations in Parkinson's disease (PD). The proliferation of inhibitory GPe-to-STN synapses following dopamine loss exacerbates this pathological activity. Rhythmic modulation of the STN and/or GPe, for example, by deep brain stimulation (DBS), can restore physiological patterns of activity and connectivity. Here, we tested whether dual targeting of STN-GPe by rhythmic stimulation can modulate pathologically strong GPe-to-STN synapses through inhibitory spike-timing-dependent plasticity (iSTDP). More specifically, we examined how time-shifted paired stimuli delivered to the STN and GPe can lead to inter-population synaptic rewiring. To that end, we first theoretically analysed the optimal range of stimulation time shift and frequency for effective synaptic rewiring. Then, as a minimal model for generating subthalamo-pallidal oscillations in healthy and PD conditions, we considered a biologically inspired STN-GPe loop comprised of conductance-based spiking neurons. Consistent with the theoretical predictions, rhythmic stimulation with appropriate time shift and frequency modified GPe-to-STN interactions through iSTDP, i.e., by long-lasting rewiring of pathologically strong synaptic connectivity. This ultimately caused desynchronising after-effects within each population such that excessively synchronous beta activity in the PD state was suppressed, resulting in a decoupling of the STN-GPe network and restoration of healthy dynamics in the model. Decoupling effects of the dual STN-GPe stimulation can be realised by time-shifted continuous and intermittent stimuli, as well as monopolar and bipolar simulation waveforms. Our findings demonstrate the critical role of neuroplasticity in shaping long-lasting stimulation effects and may contribute to the optimisation of a variety of multi-site stimulation paradigms aimed at reshaping dysfunctional brain networks by targeting plasticity.

Microglia-astrocyte interplay mitigates Aβ toxicity in a novel human 3D neurosphere model of Alzheimer's Disease
Microgliosis and astrogliosis characteristically occur at A{beta} plaques in the brains of Alzheimer's disease (AD) patients although the impact of gliosis in AD is poorly understood. We studied the impact of A{beta}-induced gliosis using human induced pluripotent stem cell (hiPSC)-derived 3D neurospheres (hiNS), containing only astrocytes and neurons versus hiNS with the addition of iPSC-derived microglia (hiMG). Applying A{beta} to hiNS containing only astrocytes and neurons triggered pathological features of AD including plaque-like aggregates, reactive astrocytes, oxidative stress, neuronal dysfunction and cell death. In contrast, when hiMG were combined with hiNS, infiltrating hiMG effectively phagocytose A{beta} and facilitate neuroprotection. Our findings further support a pivotal role for microglia in modulating astrocyte A{beta} responses by inducing AD-associated gene expression in astrocytes, including the upregulation of APOE, crucial for A{beta} clearance. This model, highlighting the neuroprotective potential of microglia-astrocyte interactions, offers a novel platform for exploring AD mechanisms and novel therapeutic strategies.

iTome Volumetric Serial Sectioning Apparatus for TEM
An automated ultra-microtome capable of sectioning thousands of ultrathin sections onto standard TEM slot grids was developed and used to section: a complete Drosophila melanogaster first-instar larva, three sections per grid, into 4,866 34-nm-thick sections with a cutting and pickup success rate of 99.74%; 30 microns of mouse cortex measuring roughly 400 um x 2000 um at 40 nm per slice; and a full adult Drosophila brain and ventral nerve column into 9,300 sections with a pickup success rate of 99.95%. The apparatus uses optical interferometers to monitor a reference distance between the cutting knife and multiple sample blocks. Cut sections are picked up from the knife-boat water surface while they are still anchored to the cutting knife. Blocks without embedded tissue are used to displace tissue-containing sections away from the knife edge so that the tissue regions end up in the grid slot instead of on the grid rim.

Gene expression asymmetry in Parkinson's Disease; variation of CCT and BEX gene expression levels are correlated with hemisphere specific severity
Parkinson's Disease (PD) develops unilaterally, which may be related to brain hemispheric differences in gene expression. Here we measured bulk RNA-seq levels in neuronal nuclei obtained from prefrontal cortex postmortem brain samples from males and females with PD and from healthy controls. Left and right hemispheres from each brain were related the side of symptom onset and compared. We employed two a priori approaches; first we identified genes differentially expressed between PD and controls and between left vs right PD brain hemispheres. Second, we examined the presence of, and correlates to, variable asymmetry seen in candidate PD differentially expressed genes. We found large variation among individuals with PD, and PD stratification by gene expression similarity was required for patterns of genetic asymmetry to emerge. For a subset of PD brains, hemispherical variation of CCT and BEX gene levels correlated with the side of PD symptom onset.

Building a small brain with a simple stochastic generative model
The architectures of biological neural networks result from developmental processes shaped by genetically encoded rules, biophysical constraints, stochasticity, and learning. Understanding these processes is crucial for comprehending neural circuits' structure and function. The ability to reconstruct neural circuits, and even entire nervous systems, at the neuron and synapse level, facilitates the study of the design principles of neural systems and their developmental plan. Here, we investigate the developing connectome of C. elegans using statistical generative models based on simple biological features: neuronal cell type, neuron birth time, cell body distance, reciprocity, and synaptic pruning. Our models accurately predict synapse existence, degree profiles of individual neurons, and statistics of small network motifs. Importantly, these models require a surprisingly small number of neuronal cell types, which we infer and characterize. We further show that to replicate the experimentally-observed developmental path, multiple developmental epochs are necessary. Validation of our model's predictions of the synaptic connections using multiple reconstructions of adult worms suggests that our model identified the fundamental"backbone" of the connectivity graph. The accuracy of the generative statistical models we use here offers a general framework for studying how connectomes develop and the underlying principles of their design.

Dynamic functional connectivity correlates of trait mindfulness in early adolescence
Background: Trait mindfulness, the tendency to attend to present-moment experiences without judgement, is negatively correlated with adolescent anxiety and depression. Understanding the neural mechanisms underlying trait mindfulness may inform the neural basis of psychiatric disorders. However, few studies have identified brain connectivity states that correlate with trait mindfulness in adolescence, nor have they assessed the reliability of such states. Methods: To address this gap in knowledge, we rigorously assessed the reliability of brain states across 2 functional magnetic resonance imaging (fMRI) scan from 106 adolescents aged 12 to 15 (50% female). We performed both static and dynamic functional connectivity analyses and evaluated the test-retest reliability of how much time adolescents spent in each state. For the reliable states, we assessed associations with self-reported trait mindfulness. Results: Higher trait mindfulness correlated with lower anxiety and depression symptoms. Static functional connectivity (ICCs from 0.31-0.53) was unrelated to trait mindfulness. Among the dynamic brains states we identified, most were unreliable within individuals across scans. However, one state, an hyperconnected state of elevated positive connectivity between networks, showed good reliability (ICC=0.65). We found that the amount of time that adolescents spent in this hyperconnected state positively correlated with trait mindfulness. Conclusions: By applying dynamic functional connectivity analysis on over 100 resting-state fMRI scans, we identified a highly reliable brain state that correlated with trait mindfulness. The brain state may reflect a state of mindfulness, or awareness and arousal more generally, which may be more pronounced in those who are higher in trait mindfulness.

Adult-neurogenesis allows for representational stability and flexibility in early olfactory system
In the early olfactory system, adult-neurogenesis, a process of neuronal replacement results in the continuous reorganization of synaptic connections and network architecture throughout the animal's life. This poses a critical challenge: How does the olfactory system maintain stable representations of odors and therefore allow for stable sensory perceptions amidst this ongoing circuit instability? Utilizing a detailed spiking network model of early olfactory circuits, we uncovered dual roles for adult-neurogenesis: one that both supports representational stability to faithfully encode odor information and also one that facilitates plasticity to allow for learning and adaptation. In the main olfactory bulb, adult-neurogenesis affects neural codes in individual mitral and tufted cells but preserves odor representations at the neuronal population level. By contrast, in the olfactory piriform cortex, both individual cell responses and overall population dynamics undergo progressive changes due to adult-neurogenesis. This leads to representational drift, a gradual alteration in sensory perception. Both processes are dynamic and depend on experience such that repeated exposure to specific odors reduces the drift due to adult-neurogenesis; thus, when the odor environment is stable over the course of adult-neurogenesis, it is neurogenesis that actually allows the representations to remain stable in piriform cortex; when those olfactory environments change, adult-neurogenesis allows the cortical representations to track environmental change. Whereas perceptual stability and plasticity due to learning are often thought of as two distinct, often contradictory processing in neuronal coding, we find that adult-neurogenesis serves as a shared mechanism for both. In this regard, the quixotic presence of adult-neurogenesis in the mammalian olfactory bulb that has been the focus of considerable debate in chemosensory neuroscience may be the mechanistic underpinning behind an array of complex computations.