bioRxiv Subject Collection: Neuroscience's Journal

Discovering plasticity rules that organize and maintain neural circuits
Intrinsic dynamics within the brain can accelerate learning by providing a prior scaffolding for dynamics aligned with task objectives. Such intrinsic dynamics should self-organize and self-sustain in the face of fluctuating inputs and biological noise, including synaptic turnover and cell death. An example of such dynamics is the formation of sequences, a ubiquitous motif in neural activity. The sequence- generating circuit in zebra finch HVC provides a reliable timing scaffold for motor output in song and demonstrates a remarkable capacity for unsupervised recovery following perturbation. Inspired by HVC, we seek a local plasticity rule capable of organizing and maintaining sequence-generating dynamics despite continual network perturbations. We adopt a meta-learning approach introduced by Confavreux et al, which parameterizes a learning rule using basis functions constructed from pre- and postsynaptic activity and synapse size, with tunable time constants. Candidate rules are simulated within initially random networks, and their fitness is evaluated according to a loss function that measures the fidelity with which the resulting dynamics encode time. We use this approach to introduce biological noise, forcing meta-learning to find robust solutions. We first show that, in the absence of perturbation, meta-learning identifies a temporally asymmetric generalization of Oja's rule that reliably organizes sparse sequential activity. When synaptic turnover is introduced, the learned rule incorporates an additional form of homeostasis, better maintaining sequential dynamics relative to other previously proposed rules. Additionally, inspired by recent findings demonstrating plasticity in synapses from inhibitory interneurons in HVC, we explore the role of inhibitory plasticity in sequence-generating circuits. We find that learned plasticity adjusts both excitation and inhibition in response to manipulations, outperforming rules applied only to excitatory connections. We demonstrate how plasticity acting on both excitatory and inhibitory synapses can better shape excitatory cell dynamics to scaffold timing representations.

Development of novel signal and spike velocity analysis tools in peripheral nerve cuffs
Peripheral nerve neurotechnologies hold significant promise as avenues for new closed-loop clinical treatments. However, analysis tools for nerve recordings - a key component of closed-loop nerve technologies - remain underdeveloped compared to brain-focused methods. This study introduces and explores the performance of two novel nerve signal analysis techniques which rely on a defining feature of peripheral nerve signals: the reliable conduction velocity of signals transmitted by axons in nerves. We test the capabilities of the introduced cross-correlation and spike delay velocity analysis techniques both in silico on synthetic nerve signals and on in vivo nerve signals acquired from freely-moving rats. Our findings show that both techniques can be successfully employed to extract transmission direction and velocity information from nerve cuff recordings. Notably, cross-correlation analysis can be employed to detect neural signals of very low signal-to-noise ratio, otherwise undetectable by typical spike detection approaches. Our findings provide new techniques to both enhance detection and extract new information in the form of velocity data from nerve recordings. As axon signal conduction direction and velocity is tightly linked to neural function, these techniques can support new research into peripheral nervous system function and new therapeutic approaches driven by neural interfaces.

Neural correlates of kinematic features of passive finger movement revealed by univariate and multivariate fMRI analyses
Finger movements are associated with a relatively large neural representation. Passive finger movement - which involves refraining from actively performing or resisting movement - is a robust approach to investigate the neural representation of kinesthesia and proprioception in the brain. While some studies have characterized the neural correlates of passive finger movement, they have relied solely on mass univariate analysis, potentially affecting result sensitivity. Additionally, limited consideration has been given to stimulus duration, a factor closely tied to some kinematic features (amplitude and velocity), which recently proposed modeling approaches now take into account. Here, we reanalyzed previously published data using univariate and multivariate analysis to understand how kinesthesia is neurally encoded in neurotypical subjects in two separate experiments. Systematic passive stimulation of the fingers was provided using an MR-compatible robot while functional magnetic resonance imaging data was recorded. Our analyses consisted of univariate and multivariate approaches, conducted separately for each kinematic feature and adjusted for stimulus duration, regardless of whether brain activation scales with it. We provide a detailed mapping of brain areas related to amplitude, velocity, and direction of passive finger movement, including sensorimotor, subcortical, and cerebellar areas. In general, multivariate pattern analysis was more sensitive than the univariate approach in identifying brain regions associated with passive finger movement. Our univariate analysis demonstrated that activity in sensorimotor and subcortical areas was higher for larger amplitudes and slower velocities, which opposes to the original study's results, likely due to our treatment of stimulus duration as a confounder specified as a parametric modulator. A novel result, we also demonstrated that brain activity in sensorimotor areas was higher for extension compared to flexion of passive finger movement. In terms of kinematic features, a larger neural representation was found for amplitude and direction compared to velocity of passive finger movement. This indicates that kinesthesia and proprioception may be more reliant on displacement than kinematic aspects of passive finger movement. While univariate analyses are limited in addressing spatial heterogeneity and subject-level variability, our multivariate analyses showed increased sensitivity in identifying brain regions encoding passive movement. Our findings may extend the knowledge of how the brain encodes physical movements and may help design neurorehabilitation strategies.

Comamonas aquatica inhibits TIR-1/SARM1 induced axon degeneration
Emerging evidence suggests the microbiome critically influences the onset and progression of neurodegenerative diseases; however, the identity of neuroprotective bacteria and the molecular mechanisms that respond within the host remain largely unknown. We took advantage of Caenorhabditis elegans' well characterized nervous system and ability to eat uni-bacterial diets to determine how metabolites and neuroprotective molecules from single species of bacteria suppress degeneration of motor neurons. We found Comamonas aquatica significantly protects against degeneration induced by overexpressing a key regulator of axon degeneration, TIR-1/SARM1. Genetic analyses and metabolomics reveal Comamonas protects against neurodegeneration by providing sufficient Vitamin B12 to activate METR-1/MTR methionine synthase in the intestine, which then lowers toxic levels of homocysteine in TIR-1-expressing animals. Defining a molecular pathway between Comamonas and neurodegeneration adds significantly to our understanding of gut-brain interactions and, given the prominent role of homocysteine in neurodegenerative disorders, reveals how such a bacterium could protect against disease.

Strikingly different neurotransmitter release strategies in dopaminergic subclasses
Neuronal function is intimately tied to axodendritic polarity. Neurotransmitter release, for example, is usually the role of the axon. There are widespread exceptions to this rule, however, including many mammalian neuronal types that can release neurotransmitter from their dendrites. In the mouse olfactory bulb, closely related subclasses of dopaminergic interneuron differ markedly in their polarity, with one subtype lacking an axon entirely. These axon-bearing and anaxonic dopaminergic subclasses have distinct developmental profiles and sensory responses, but how their fundamental polarity differences translate to functional outputs remains entirely unknown. Here, we provide anatomical evidence for distinct neurotransmitter release strategies among these closely related dopaminergic subtypes: anaxonic cells release from their dendrites, while axon-bearing neurons release exclusively from their intermittently myelinated axon. These structural differences are linked to a clear functional distinction: anaxonic, but not axon-bearing dopaminergic neurons are capable of self-inhibition. Our findings suggest that variations in polarity can produce striking distinctions in neuronal outputs, and that even closely related neuronal subclasses may play entirely separate roles in sensory information processing.

Long-lasting, subtype-specific regulation of somatostatin interneurons during sensory learning
Somatostatin (SST)-expressing inhibitory neurons are a major class of neocortical gamma-amino butyric acid (GABA) neurons, where morphological, electrophysiological, and transcriptomic analyses indicate more than a dozen different subtypes. However, whether this diversity is related to specific roles in cortical computations and plasticity remains unclear. Here we identify learning-dependent, subtype-specific plasticity in layer 2/3 SST neurons of the mouse somatosensory cortex. Martinotti-type, SST neurons expressing calbindin-2 show a selective decrease in excitatory synaptic input and stimulus-evoked calcium responses as mice learn a stimulus-reward association. Using these insights, we develop a label-free classifier using basal activity from in vivo imaging that accurately predicts learning-associated response plasticity. Our data indicate that molecularly-defined SST neuron subtypes play specific and highly-regulated roles in sensory information processing and learning.

Alcohol Withdrawal Alters the Inhibitory Landscape of the Prelimbic Cortex in an Interneuron- and Sex-specific Manner
Alcohol use disorder (AUD) is highly prevalent and associated with substantial morbidity and high mortality among substance use disorders. While there are currently three FDA-approved medications for treating AUDs, none specifically target the withdrawal/negative affect stage of AUD, underscoring the need to understand the underlying neurobiology during this critical stage of the addiction cycle. One key region involved in alcohol withdrawal and negative affect is the prelimbic cortex, a subregion of the medial prefrontal cortex. While previous studies have examined alcohol-related adaptations in prefrontal cortical principal glutamatergic neurons, here we used male and female PV:Ai14, SOM:Ai14 and VIP:Ai14 mice to examine synaptic adaptations in all three major classes of prelimbic cortex interneurons following 72 hour withdrawal from a continuous access to two bottle choice model of EtOH drinking in male and female mice. We found that alcohol withdrawal increased excitability of prelimbic PV interneurons in males, but decreased excitability in prelimbic VIP interneurons in females. Additionally, alcohol withdrawal reduced GABA release onto PV interneurons in males while increasing glutamate release onto VIP interneurons in females. In SOM interneurons, alcohol withdrawal had no effect on excitability, but decreased glutamate release onto SOM interneurons in males. Together, our studies identified sex-specific alcohol withdrawal-induced synaptic plasticity in three different types of interneurons and could provide insight into the cellular substrates of negative affective states associated with alcohol withdrawal.

Deficits in forelimb reach learning in a mouse model of Fragile X syndrome
Fragile X syndrome is a leading cause of intellectual disability and autism spectrum disorder, for which therapies are limited. A mouse model of Fragile X syndrome, the Fmr1 knockout (KO) mouse, has been particularly valuable for interrogating the molecular, cellular, and circuit mechanisms that underlie the neurological deficits seen in this syndrome. Key deficits in Fragile X syndrome include impairments in social behaviors, cognition, and motor learning. Given the difficulties in extrapolating more complex human behaviors to mouse models, simple motor behaviors are a particularly tractable form of learning to study in the mouse. We investigated a form of forelimb reach learning in Fmr1 KO mice, precisely quantifying different parameters of the task using both manual analysis and DeepLabCut-based tracking of reach trajectories. While Fmr1 KO mice show impaired learning overall, our results demonstrated that the presence or absence of a cue that signals reward alleviates some of the deficits. In addition to a single metric of success in learning, we determined the specific parameters of the motor behavior that were responsible for that success or failure. In particular, our results suggested that Fmr1 KO mice showed impaired improvement in the trajectory of the reach, reflected by a greater likelihood of completely missing the target, and in a lower learning index for the optimal reach trajectory. In addition, we fully described the features underlying learning, including categorizing the first attempt during trials, failed reaches where mice make contact with the reward, the number of trials where no attempts were made, as well as how the pattern of these different behaviors varies in Fmr1 KO mice. Our findings provide an essential framework for linking specific behavioral impairments in motor learning to the cellular and circuit mechanisms that support them.

Metabolic Divergence Between Healthy and Conditional Presenilin-1/2 knockout Dementia Model Mice under Chronic Nicotine exposure
Nicotine, a primary bioactive compound in tobacco, influences a wide range of metabolic pathways, with potential implications for neurodegenerative diseases such as Alzheimer's disease (AD). This study investigates the metabolic effects of chronic nicotine administration in presenilin-1/2 double knockout (DKO) mice and wild-type (WT) control mice. Eight-month-old DKO and WT mice underwent a three-month oral nicotine administration which did not significantly affect body weight or spontaneous locomotor activity in either DKO or WT mice, as measured by weekly body weight measurement and open field test. The metabolomic analyses were performed using untargeted LC-MS and GC-MS techniques. The results revealed distinct metabolic profiles between DKO and WT mice under nicotine exposure. In DKO mice, nicotine exacerbated deficits in purine and nicotinamide metabolism, further highlighting dysregulation in energy homeostasis. Key findings include reduced adenosine and elevated dopamine levels in DKO serum, alongside increased cotinine concentrations, suggesting altered nicotine metabolism. WT mice showed significant changes in energy and lipid metabolism pathways, though with less pronounced alterations compared to DKO mice. Enrichment analyses identified significant pathway disruptions, particularly in choline metabolism, amino acid metabolism, and central carbon metabolism, with genotype-dependent responses to nicotine. This study highlights the intrinsic metabolic alterations in DKO mice, such as disruptions in purine and nicotinamide metabolism, which contribute to their neurodegenerative pathology and unique responses to nicotine. By revealing the differential metabolic impacts of nicotine on healthy and neurodegenerative states, the findings provide valuable insights into its dual therapeutic and pathological roles. These results may guide future research into the metabolic underpinnings of neurodegeneration and the potential for nicotine as a targeted therapeutic intervention.

Beyond the Surface: Revealing the Depths of Brain Activity by Predicting fMRI from EEG with Deep Learning
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the two most commonlyusednon-invasive methods for studying brain function, having different but complementary strengths: high temporal resolution of the former and high spatial resolution of the latter. Crucially, fMRI is vital for studying subcortical areas, as those are practically out of reach for conventional EEG. At the same time, EEG is cost-effective and, thus, often preferable to fMRI if comparable information could be extracted which is not the case then the deep subcortical brain activity is of interest. Here we explore the possibility of recovering subcortical hemodynamics from the non-invasively recorded scalp EEG signals. To this end, we have developed a lightweight EEG-to-fMRI neural network and using an extended and publicly available dataset with concurrently recorded EEG-fMRI data show that our model allows for the prediction of the detailed Blood Oxygenation Level Dependent (BOLD) activity of 7 bilaterally symmetric subcortical structures solely from multichannel EEG data. We report the performance significantly above chance and exceeding the scores achieved for a single subcortical structure and obtained on the proprietary datasets. In contrast to the studies focusing on a single subcortical region in our approach we were able to decode multichannel EEG into 14 + 4 region-specific variations of BOLD signals measured relative to their mean hemodynamic activity. The use of relative BOLD signals allowed us to exert control over the artificial inflation of decoding accuracy scores when the decoder predicts the common mode component that is likely to have a non-neuronal origin (heartbeat, movement, etc). Finally, we interpreted our model. The electrical activity of the sensorimotor cortex appeared to contribute most to the prediction of the subcortical hemodynamics. Also, the hemodynamics of the thalamus has the smallest delay with respect to the EEG signals. Both observations are physiologically plausible and ensure the potential reliability of the decoder. Taken together, these findings pave the road towards the creation of low-cost AI-powered EEG-based fMRI digital twin technology capable of tracking subcortical activity in an ecological setting. The technology, once mature, will find numerous applications from fundamental neuroscience through diagnostics to neurorehabilitation and affective neurointerfaces.

Two-dimensional perisaccadic visual mislocalization in rhesus macaque monkeys
Perceptual localization of brief, high contrast perisaccadic visual probes is grossly erroneous. While this phenomenon has been extensively studied in humans, more needs to be learned about its underlying neural mechanisms. This ideally requires running similar behavioral paradigms in animals. However, during neurophysiology, neurons encountered in the relevant sensory and sensory-motor brain areas for visual mislocalization can have arbitrary, non-cardinal response field locations. This necessitates using mislocalization paradigms that can work with any saccade direction. Here, we first established such a paradigm in three male rhesus macaque monkeys. In every trial, the monkeys generated a visually-guided saccade towards an eccentric target. Once a saccade onset was detected, we presented a brief flash at one of three possible locations ahead of the saccade target location. After an experimentally-imposed delay period, we removed the saccade target, and the monkeys were then required to generate a memory-guided saccade towards the remembered flash location. All three monkeys readily learned the task, and, like humans, they all showed strong backward mislocalization towards the saccade target, which recovered for later flashes from the saccade time. Importantly, we then replicated a well-known property of human perisaccadic mislocalization, as revealed by two-dimensional mislocalization paradigms: that mislocalization is strongest for upward saccades. For horizontal saccades, we additionally found stronger mislocalization for upper visual field flashes, again consistent with humans. Our results establish a robust two-dimensional mislocalization paradigm in monkeys, and they pave the way for exploring the neural mechanisms underlying the dependence of perisaccadic mislocalization strength on saccade direction.

Mapping object space dimensions: new insights from temporal dynamics
How is object information organized in high-level visual cortex? Recently, a comprehensive model of object space in macaques was proposed, defined by the orthogonal axes of animacy and aspect ratio (Bao et al., 2020). However, when using stimuli that dissociated category, animacy, and aspect ratio in humans, no tuning of aspect ratio was observed in fMRI data (Yargholi & Op de Beeck, 2023). This difference could be a result of different stimuli, the limited temporal resolution of fMRI, or information available about the presented stimuli. Here, we asked if and when information about aspect ratio, animacy, and category is available over time. We collected whole-brain electroencephalography (EEG) data while participants (N = 20) viewed the stimulus set used by Yargholi & Op de Beeck (2023). To mask object details and increase reliance on shape information, we also presented silhouette versions of the stimuli. Stimuli were presented in 5Hz streams using rapid serial visual presentation, with intact and silhouette stimuli sets were shown in separate streams. Using standard multivariate decoding pipelines and representational similarity analysis, we found that aspect ratio, category, and animacy were represented during visual object processing. The dominant dimension was modulated by stimulus type, demonstrating that the observable dimensions of object space depend on the nature of the stimuli presented. Taken together, these findings demonstrate that aspect ratio is represented during object processing, however earlier and more transiently than categorical dimensions, such as animacy. By focusing on underlying temporal dynamics, our results provide clear new insights into the contradicting findings reported in previous work and reveal a more nuanced understanding of how object space evolves over time.

Cingulate and striatal hubs are linked to early skill learning
Early skill learning develops in the context of activity changes in distributed cortico-subcortical regions. Here, we investigated network hubs, centers of information integration and transmission, within the brain network supporting early skill learning. We recorded magnetoencephalographic (MEG) brain activity in healthy human subjects who learned a moderately difficult sequence skill with their non-dominant left hand. We then computed network hub strength by summing top 10% functional connectivity over 86 parcellated brain regions (AAL3 atlas) and five brain oscillatory frequency bands (alpha, low-, high-beta, low- and high-gamma). Virtually all skill gains developed during rest intervals of early learning (micro-offline gains). MEG hub strength in the alpha band (8-13Hz) in bilateral anterior cingulate (ACC) and caudate and in the low-beta band (13-16Hz) in bilateral caudate and right putamen correlated with micro-offline gains. These regions linked strongly with the hippocampus, parahippocampal cortex, and lingual and fusiform gyri. Thus, alpha and low-beta brain oscillatory activity in cingulate and striatal regions appear to contribute as hubs of information integration and transmission during early skill learning.

Complex regulation of Cav2.2 N-type Ca2+ channels by Ca2+ and G-proteins
G-protein coupled receptors inhibit Cav2.2 N-type Ca2+ channels by a fast, voltage-dependent pathway mediated by Gai/Gbg and a slow, voltage-independent pathway mediated by Gaq-dependent reductions in phosphatidylinositol 4,5-bisphosphate (PIP2) or increases in arachidonic acid. Studies of these forms of regulation generally employ Ba2+ as the permeant ion, despite that Ca2+-dependent pathways may impinge upon G-protein modulation. To address this possibility, we compared tonic G-protein inhibition of currents carried by Ba2+ (IBa) and Ca2+ (ICa) in HEK293T cells transfected with Cav2.2. Both IBa and ICa exhibited voltage-dependent facilitation (VDF), consistent with Gbg unbinding from the channel. Compared to that for IBa, VDF of ICa was less sensitive to an inhibitor of Ga proteins (GDP-beta-S) and an inhibitor of Gbg (C-terminal construct of G-protein coupled receptor kinase 2). While insensitive to high intracellular Ca2+ buffering, VDF of ICa that remained in GDP-beta-S was blunted by reductions in PIP2. We propose that when G-proteins are inhibited, Ca2+ influx through Cav2.2 promotes a form of VDF that involves PIP2. Our results highlight the complexity whereby Cav2.2 channels integrate G-protein signaling pathways, which may enrich the information encoding potential of chemical synapses in the nervous system.

Broca's area responsible for speech production is regulated by lung functions
For more than one and a half centuries, Broca's area, specifically, the pars opercularis and pars triangularis in the left inferior frontal gyrus, has been known to be crucial for human speech production. However, it remains unanswered why this region is recruited for speaking. Speech production involves not only conceptualization and motor planning, but also respiration that provides the airflow necessary for creating sounds. Thus, the role of Broca's area in speech may be shaped by the functionality and the related brain regions associated with lungs. To test this hypothesis, we recruited the patients suffering from chronic obstructive pulmonary disease (COPD) and required them to speak out words while scanning their brains with functional and quantitative magnetic resonance imaging (fMRI and qMRI). We found that the COPD patients exhibited altered cortical responses during speaking in left inferior prefrontal and other regions, and the cortical sites governing breathing functioned abnormally in this task. In addition, using the qMRI to generate longitudinal relaxation time (T1) maps as an index of brain microstructural changes including dendritic maturation and myelination, we discovered that the patients showed significantly higher T1 values than the control group in Broca's area, suggesting reduced myelination and impaired microstructural integrity. Crucially, the data indicated that more severe dyspnea was associated with less well-developed microstructure in Broca's area and its weaker activation. Our study has demonstrated for the first time that lungs may function to shape Broca's area as the speaking center, and this is consistent with the recent work of a lung-brain axis.

Severe traumatic brain injury temporally affects cerebral blood flow, endothelial cell phenotype, and cilia
Background. Previous clinical work suggested that altered cerebral blood flow (CBF) in severe traumatic brain injury (sTBI) correlates with poor executive function and clinical outcome. However, the molecular consequences of altered CBF on endothelial cells (ECs) and their blood flow-sensor organelle called cilia are not known. Methods. We performed laser speckle contrast imaging, single cell isolation, and single cell RNA sequencing (scRNAseq) after sTBI in a closed skull, linear impact mouse model. Validation of select ciliary target protein changes was performed using flow cytometry. Additionally, in vitro experiments modeled the post-injury hypoxic environment to evaluate the effect on cilia protein ARL13B in human brain microvascular ECs. Results. We detected immediate reductions in CBF that were sustained for at least 100 minutes in both impacted and non-impacted sides of the brain. Our scRNAseq data detected heterogeneity in the brain cortex-derived EC cluster and demonstrated that two of five unique EC sub-clusters changed their relative proportions post-sTBI. Consistent with flow changes, we identified multiple genes associated with the fluid shear stress pathway that were significantly differentially expressed in brain ECs post-injury. Also, ECs displayed activation of ischemic pathway as early as day 1 post-injury, and enrichment of hypoxia pathway at day 7 and 28 post-injury. Arl13b ciliary gene expression was lost on day 1 in ECs cluster and remained lost for the entire course of the injury. We validated the loss of cilia protein ARL13B specifically from brain ECs as early as day 1 post-injury and detected the protein in the peripheral blood of the injured mice. We also determined that hypoxia could induce loss of ARL13B protein from cultured ECs. Conclusions. In severe TBI, blood flow is disrupted in both impacted and non-impacted regions of the brain, creating a hypoxic environment that may influence ciliary gene and protein expression on ECs.

Lactate potentiates NMDA receptor currents via an intracellular redox mechanism targeting cysteines in the C-terminal domain of GluN2B subunits: implications for synaptic plasticity
Through the Astrocyte Neuron Lactate Shuttle, astrocyte-derived lactate fuels the high-energy demands of neurons and acts as a signaling molecule, promoting synaptic plasticity and memory consolidation. Lactate regulates neuronal excitability and modulates the expression of genes related to synaptic plasticity and neuroprotection, but the molecular mode for these signaling actions is uncertain. Using patch-clamp recordings in cultured cortical neurons, we found that lactate enhances both the amplitude and the inactivation time constant of NMDA receptor currents (INMDAR) evoked by brief applications of glutamate and glycine. Not reproduced by HCAR1 agonists, this modulation depends on monocarboxylate transporters and lactate dehydrogenase, indicating the requirement for lactate entry and metabolic conversion into pyruvate and NADH formation within neurons. Disruption of intracellular calcium dynamics or inhibition of Ca2+/calmodulin-dependent protein kinase II (CaMKII), a NMDAR-associated kinase linking Ca2+ signal to long-term potentiation (LTP), significantly diminishes the effects of lactate on INMDAR. We identified two redox-sensitive cysteine-containing sequences in the intrinsically disordered intracellular C-terminal domain of the GluN2B subunit that play a role in the potentiation of NMDAR by lactate. In a compelling set of experiments using HEK cells, we observed that the presence of functional CaMKII and GluN2B-containing NMDARs is necessary for the lactate-enhancing effects. Mutations in GluN2B that prevent CaMKII binding or redox regulation via cysteines abrogate the modulatory action of lactate. Immunoprecipitation experiments in neurons attest that lactate increases the association between CaMKII and GluN2B. This interaction is crucial for the potentiation of INMDAR amplitude by lactate. Proximity ligation assays between GluN2B and the postsynaptic density marker PSD-95 revealed that lactate induced an accumulation of GluN2B in dendritic spines, an effect that was prevented by a CaMKII peptide inhibitor. These results highlight a mechanistic pathway whereby lactate boosts NMDAR function through intracellular metabolic conversion and redox-sensitive interactions requiring CaMKII, establishing a link between astrocyte metabolism and synaptic modulation in neurons.

Visuoinertial and visual feedback in online steering control
Multisensory integration has primarily been studied in static environments, where optimal integration relies on the precision of the respective sensory modalities. However, in numerous situations, sensory information is dynamic and changes over time, due to changes in our bodily state and the surrounding environment. Given that different sensory modalities have different delays, this suggests that optimal integration may not solely depend on sensory precision but may also be affected by the delays associated with each sensory system. To investigate this hypothesis, participants (n = 22, 16 female) engaged in a continuous steering task. Participants sat on a motion platform facing a screen that displayed a cartoonish traffic scene, featuring a car traveling along a road. In the visuoinertial condition, where vestibular and somatosensory feedback were available, they were tasked with counteracting an external multi-frequency perturbation signal, which laterally perturbed the platform and the car, such that the car was kept within the center of the road. In the visual condition, the visual car was perturbed, while the motion platform remained stationary. We show that participants compensate better for the perturbation in the visuoinertial than the visual condition, particularly in the high frequency range of the perturbation. Using computational modelling, we demonstrate that this enhanced performance is partially due to the shorter delay of the vestibular modality. In this condition, participants rely more on the vestibular information, which is less delayed than the more precise but longer delayed, visual information.

Mora-ERP-based RNN-Transformer for decoding single-trial EEGs during silent Japanese speeches
We developed a method for decoding single-trial electroencephalography (EEG) during silent Japanese speeches. In order to cope with problems that there would be always noises in single-trial EEGs, a recurrent neural network (RNN) was used which could reproduce signals under noises. Each of silent-mora-related potentials and the single-trial EEG minus the event-related potential (ERP) were assigned to the signal and the noise, respectively, with reference to the averaging principle. Next, in our Transformer, dot product between the RNN output and the single-trial EEG after positional encoding then Softmax with Loss yielded probabilities of moras, each of which consists of silent Japanese words, phrases or part of sentences. The present decoding was completed by tracing the maximal probability at each block representing time. Average mora error rates (MERs) on pretrained and validated performances for the patient was as low as 1.5 % and 0 %, respectively. The performance for the testing would be refined by many single-trial EEGs during silent Japanese speeches obtained by EEG Web interfaces. This method might be applied to other mora languages.

The balance stabilising benefit of social touch: influence of an individual's age and the partner's relative body characteristics
Interpersonal touch (IPT) is a successful strategy to support the stability of and individual's body balance during a multitude of activities in daily life, including physical education and therapy. Despite common practice, however, the influence of an individual's anthropometry and other personal characteristics, such as age, balancing skills, motor experience, and sex as well as interindividual differences in these characteristics between interaction partners on the balance stabilising benefit of social touch is unknown. We assessed an individual's balance stability and change due to IPT provision during single-legged stance in 72 pairs (age range 4 to 63 years) under four sensory conditions: with or without vision in combination with IPT or without. Two participant subgroups were created: one of more vulnerable with low stability and one of more stable, mature participants. Best fitting multiple linear regression models, including moderating variables, for explaining the benefit of IPT in each visual condition indicated that without vision, an individual's benefit of IPT was determined by their balancing skill and the partner-related difference in balancing skill but not by any other factors or partner-related differences. Especially vulnerable individuals improved considerably with IPT when vision was unavailable. With vision complementing IPT, however, an individual's age-dependent motor developmental potential became an additional moderating factor. These findings indicate that the extent to which IPT is benefitting mutual balance stabilisation does not depend on biomechanical factors. Instead, the IPT benefit emerges as a product of both partner's sensorimotor capabilities moderated by a person's motor developmental potential when visual feedback could be utilised. We discuss a theoretical framework that accounts for the observed dependencies of the effect of haptic social support on balance control.

Expectation Violations as an Effective Alternative to Complex Mentalizing in Novel Communication
Effective communication in the absence of a shared language is a fundamental challenge, often addressed through complex cognitive mechanisms such as Theory of Mind, which allows individuals to infer others' intentions and beliefs. However, this process is cognitively demanding and may not always be necessary. In this study, we propose that a more parsimonious cognitive mechanism-expectancy violations-can serve as an efficient alternative for communication in novel interactions. We tested this in the Tacit Communication Game, where we simulated Sender behavior using four computational models: the Surprise model based on expectancy violations and three levels of Theory of mind. After human Receivers interacted with these simulated Senders, we assessed the effectiveness of communication by analyzing accuracy and reaction times. Our results revealed that Receivers paired with the Surprise model achieved accuracy rates comparable to those interacting with the most complex Theory of mind model and exhibited more human-like message patterns. Additionally, models associated with higher accuracies also resulted in faster reaction times, indicating a reduced cognitive load. These findings challenge the necessity of complex mentalizing strategies in novel human interactions and suggest that an intuitive mechanism of expectancy violation may be a more plausible cognitive mechanism, while also providing quick responses.