bioRxiv Subject Collection: Neuroscience's Journal

Interplay of Long- and Short-term Synaptic Plasticity in a Spiking Network Model of Rat's Episodic Memory
We investigated the interaction of long-term episodic processes with effects of short-term dynamics of recency. This work takes inspiration from a seminal experimental work involving an odor-in-context association task conducted on rats (Panoz-Brown et al., 2016). In the experimental task, rats were presented with odor pairs in two arenas serving as old or new contexts for specific odors-items. Rats were rewarded for selecting the odor that was new to the current context. New odor items were deliberately presented with higher recency relative to old items, so that episodic memory was put in conflict with non-episodic recency effects. To study our hypothesis about the major role of synaptic interplay of long- and short-term plasticity phenomena in explaining rats' performance in such episodic memory tasks, we built a computational spiking model consisting of two reciprocally connected networks that stored contextual and odor information as consolidated and distributed memory patterns (cell assemblies). We induced context-item coupling between the two networks using Bayesian-Hebbian plasticity with eligibility traces to account for reward based learning. We first reproduced quantitatively and explained mechanistically the findings of the experimental study, and further simulated alternative tasks, e.g. where old odor items were instead encoded with higher recency, thus synergistically confounding episodic memory with effects of recency. Our model predicted that higher recency of old items enhances item-in-context memory by boosting the activations of old items resulting in further enhancement of memory performance. We argue that the model offers a computational framework for studying behavioral implications of the synaptic underpinning of different memory effects in experimental episodic memory paradigms.

Highly effective verified lucid dream induction using combined cognitive-sensory training and wearable EEG: a multi-centre study
The state of becoming aware that one is dreaming within an ongoing dream, referred to as lucid dreaming (LD), can occur spontaneously. Yet, since the occurrence of spontaneous LD is relatively rare, various methods have been proposed to induce LD. Existing scientific literature, however, has been constrained by either small sample sizes with limited generalizability, or by reliance on subjective measures without physiological signals. To address these limitations, we recorded verifiable LD using 2-channel EEG and an open-source dream engineering toolbox (Dreamento) in a large sample size of 60 participants collected across a multi-center study in the Netherlands (NL), Italy (IT), and Canada (CA). We employed a novel combination of the senses-initiated lucid dreaming (SSILD) method and a targeted lucidity reactivation (TLR) protocol. Our final sample consists of 60 participants who came twice to the lab for morning naps with a pre-sleep lucidity training paired with multimodal sensory cues (visual, auditory, tactile). Cues were presented again in REM sleep in one of the two naps (stimulation and sham conditions counterbalanced). This preprint reports results from NL and IT in 40 participants: we successfully induced signal-verified lucid dreams (SVLD) in 65% and 45% of NL and IT participants, respectively. Among these, 45% and 35% (NL and IT) of REM cueing and 35% and 15% (NL and IT) of REM sham sessions resulted in at least one SVLD. In NL, the REM cueing sessions yielded 37 predefined eye signals with an average continuously verified lucidity duration of 78.75 +- 54.85 s. The REM sham sessions resulted in 15 eye signals in the presence of LD report (i.e, SVLD) and had an average duration of 47.80 +- 22.49 s. In IT, 48 predefined eye signals were identified within REM cueing sessions, with an average overall duration (i.e., from the first to the last predefined eye signal) of 506.33 +- 643.73 s and an average continuously verified (consecutive eye signals) duration of 91.13 +- 70.87 s. In contrast, 10 predefined eye signals were identified during REM sham sessions, with an average overall duration of 546.5 +- 744.58 s and a single continuously verified episode that lasted 20 s. Preliminary findings suggest that REM cueing aids the initiation and maintenance of lucidity, facilitates objective estimation of LD duration, and increases dream control. Future research should focus on automating the tools we provided and conducting larger-scale fully automatised studies at home to further explore factors contributing to such high success rates.

Forelimb motor recovery by modulating extrinsic and intrinsic signaling as well as neuronal activity after the cervical spinal cord injury
Singular strategies for promoting axon regeneration and motor recovery after spinal cord injury (SCI) have been attempted with limited success. Here, we propose the combinatorial approach of deleting extrinsic and intrinsic factors paired with neural stimulation, will enhance adaptive axonal growth and motor recovery after SCI. We previously showed the deletion of RhoA and Pten in corticospinal neurons inhibits axon dieback and promotes axon sprouting after lumbar SCI. Here, we examined the effects of RhoA;Pten deletion coupled with neural stimulation after cervical SCI. This combinatorial approach promoted more boutons on injured corticospinal neurons in the spinal cord compared to sole RhoA;Pten deletion. Although RhoA;Pten deletion does not promote motor recovery in the forelimb after SCI, stimulating corticospinal neurons in those mice results in partial motor recovery. These results demonstrate that a combinatorial approach that pairs genetic modifications with neuronal stimulation can promote axon sprouting and motor recovery following SCI.

ComBatLS: A location- and scale-preserving method for multi-site image harmonization
Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

Regulator of G Protein Signaling 14 protein expression profile in the adult mouse brain
Regulator of G protein signaling 14 (RGS14) is a multifunctional signaling protein that serves as a natural suppressor of synaptic plasticity in the mouse brain. Our previous studies showed that RGS14 is highly expressed in postsynaptic dendrites and spines of pyramidal neurons in hippocampal area CA2 of the developing mouse brain. However, our more recent work with adult rhesus macaque brain shows that RGS14 is found in multiple neuron populations throughout hippocampal area CA1 and CA2, caudate nucleus, putamen, globus pallidus, substantia nigra, and amygdala in the adult rhesus monkey brain. In the mouse brain, we also have observed RGS14 protein in discrete limbic regions linked to reward behavior and addiction, including the central amygdala and the nucleus accumbens, but a comprehensive mapping of RGS14 protein expression in the adult mouse brain is lacking. Here, we report that RGS14 is more broadly expressed in mouse brain than previously known. Intense RGS14 staining is observed in specific neuron populations of the hippocampal formation, amygdala, septum, bed nucleus of stria terminalis and ventral striatum/nucleus accumbens. RGS14 is also observed in axon fiber tracts including the dorsal fornix, fimbria, stria terminalis, and the ventrohippocampal commissure. Moderate RGS14 staining is observed in various other adjacent regions not previously reported. These findings show that RGS14 is expressed in brain regions that govern aspects of core cognitive functions such as sensory perception, emotion, memory, motivation, and execution of actions, and suggests that RGS14 may serve to suppress plasticity and filter inputs in these brain regions to set the overall tone on experience-to-action processes.

Food and water uptake are regulated by distinct central amygdala circuits revealed using intersectional genetics
The central amygdala (CeA) plays a crucial role in defensive and appetitive behaviours. It contains genetically defined GABAergic neuron subpopulations distributed over three anatomical subregions, capsular (CeC), lateral (CeL), and medial (CeM). The roles that these molecularly- and anatomically- defined CeA neurons play in appetitive behavior remain unclear. Using intersectional genetics, we found that neurons driving food or water consumption are confined to the CeM. Separate CeM subpopulations exist for water only versus water or food consumption. In vivo calcium imaging revealed that CeMHtr2a neurons promoting feeding are responsive towards appetitive cues with little regard for their physical attributes. CeMSst neurons involved in drinking are sensitive to the physical properties of salient stimuli. Both CeM subtypes receive inhibitory input from CeL and send projections to the parabrachial nucleus to promote appetitive behavior. These results suggest that distinct CeM microcircuits evaluate liquid and solid appetitive stimuli to drive the appropriate behavioral responses.

Phylogenetically-Preserved Multiscale Neuronal Activity: Iterative Coarse-Graining Reconciles Scale-Dependent Theories of Brain Function
Brain recordings collected at different resolutions support statistically distinct signatures of information processing, leading to scale-dependent theories of brain function. Here, we demonstrate that these disparate neural-coding signatures emerge from the same multiscale functional organisation of neuronal activity across calcium-imaging recordings collected from the whole brains of zebrafish and nematode, as well as sensory regions of the fly, mouse, and macaque brain. Network simulations show that hierarchical-modular structural connectivity facilitates multiscale functional coordination, enhancing information processing benefits such as a maximal dynamic range. Finally, we demonstrate that this cross-scale organisation supports distinct behavioural states across species by reconfiguring functional affiliation and temporal dynamics at the mesoscale. Our findings suggest that self-similar scaling of neuronal activity is a universal principle that reconciles scale-dependent theories of brain function, facilitating both efficiency and resiliency while enabling significant reconfiguration of mesoscale cellular ensembles to accommodate behavioural demands.

Hypothalamic prostaglandins facilitate recovery from hypoglycemia but exacerbate recurrent hypoglycemia in mice
The hypothalamus regulates systemic glucose metabolism by monitoring glucose levels. In response to hypoglycemia, glucose-inhibited (GI) neurons promote counter-regulatory responses (CRRs) stimulating glucagon, epinephrine, and cortisol secretions. Recurrent hypoglycemia (RH) attenuates CRRs. Here, we show that prostaglandins are produced in the hypothalamus during hypoglycemia to activate GI neurons and thus increase glucagon secretion. RH attenuated glucose production by decreasing glucagon secretion. RH caused a metabolic adaptation and preserved intermediates of glycolysis and amino acids in the hypothalamus during hypoglycemia. Inhibition of prostaglandin production by using short-hairpin RNA (shRNA) against cytosolic phospholipase A2 (cPLA2) in the hypothalamus decreased the attenuation of CRRs by RH. CRR hormones and the activity of GI neurons were not changed in the shRNA-treated group. Our data suggest that hypothalamic prostaglandins are critical for recovering from acute hypoglycemia by affecting glucose-sensing neurons. Hypothalamic prostaglandins are also essential to develop an attenuation of CRRs during RH.

A functional network model for body column neural connectivity in Hydra
Hydra is a non-senescent animal with a relatively small number of cell types and overall low structural complexity, but a surprisingly rich behavioral repertoire. The main drivers of Hydra's behavior are neurons that are arranged in two nerve nets comprising several distinct neuronal populations. Among these populations is the ectodermal nerve net N3 which is located throughout the animal. It has been shown that N3 is necessary and sufficient for the complex behavior of somersaulting and is also involved in Hydra feeding behavior. Despite being a behavioral jack-of-all-trades, there is insufficient knowledge on the coupling structure of neurons in N3, its connectome, and its role in activity propagation and function. We construct a model connectome for the part of N3 located on the body column. Using experimental data on the placement of neuronal somata and the spatial dimensions of the body column, we show that a generative network model combining non-random placement of neuronal somata and the preferred orientation of primary neurites yields good agreement with experimentally observed distributions of connection distances, connection angles, and the number of primary neurites per neuron. Having validated the N3 connectome model in this fashion, we place a simple excitable dynamical model on each node of the body column network and show that it generates directed, short-lived, fast propagating patterns of activity. In addition, by slightly changing the parameters of the dynamical model, the same structural network can also generate persistent activity. Finally, we use a neuromorphic circuit based on the Morris-Lecar model to show that the same structural connectome can, in addition to through-conductance with biologically plausible time scales, also host a dynamical pattern related to the complex behavioral pattern of somersaulting. We speculate that such different dynamical regimes act as dynamical substrates for the different functional roles of N3, allowing Hydra to exhibit behavioral complexity with a relatively simple nervous system that does not possess modules or hubs.

The ventral hippocampal-nucleus accumbens shell circuit drives approach decisions under social novelty and learned cue approach-avoidance conflict
Successful resolution of approach-avoidance conflict (AAC) is fundamentally important for survival, and its dysregulation is a hallmark of many neuropsychiatric disorders, and yet the underlying neural circuit mechanisms are not well elucidated. Converging human and animal research has implicated the anterior/ventral hippocampus (vHPC) as a key node in arbitrating AAC in a region-specific manner. In this study, we sought to target the vHPC CA1 projection pathway to the nucleus accumbens (NAc) to delineate its contribution to AAC decision-making, particularly in the arbitration of learned reward- and punishment signals, as well as innate signals. To this end, we used pathway-specific chemogenetics in male and female Long Evans rats to inhibit the NAc shell projecting vHPC CA1 neurons while rats underwent a test in which cues of positive and negative valence were presented concurrently to elicit AAC. Further behavioral assays of social preference and memory, reward and punishment cue processing, anxiety, and novelty processing were administered to further interrogate the conditions under which the vCA1-NAc shell pathway is recruited. Chemogenetic inhibition of the vCA1-NAc shell circuit resulted in animals exhibiting increased decision-making time and avoidance bias specifically in the face of motivational conflict, as the same behavioral phenotype was absent in separate conditioned cue preference and avoidance tests. vCA1-NAc shell inhibition also led to a reduction in seeking social interaction with a novel rat but did not alter anxiety-like behaviors. The vCA1-NAc shell circuit is therefore critically engaged in biasing decisions to approach in the face of social novelty and approach-avoidance conflict. Dysregulation of this circuit could lead to the precipitation of addictive behaviours in substance abuse, or potentiation of avoidance in situations of approach-avoidance conflict.

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles
Various mathematical models have been formulated to describe the changes in synaptic strengths resulting from spike-timing-dependent plasticity (STDP). A subset of these models include a third factor, dopamine, which interacts with the timing of pre- and postsynaptic spiking to contribute to plasticity at specific synapses, notably those from cortex to striatum at the input layer of the basal ganglia. Theoretical work to analyze these plasticity models has largely focused on abstract issues, such as the conditions under which they may promote synchronization and the weight distributions induced by inputs with simple correlation structures, rather than on scenarios associated with specific tasks, and has generally not considered dopamine-dependent forms of STDP. In this paper, we analyze, mathematically and with simulations, three forms of dopamine-modulated STDP in three scenarios that are relevant to corticostriatal synapses. Two of the models considered comprise previously proposed STDP rules with modifications to incorporate dopamine, while the third is a corticostriatal dopamine-dependent STDP rule adapted from a similar one already in the literature. We test the ability of each of the three models to maintain its weights in the face of noise and to complete simple reward prediction and action selection tasks, studying the learned weight distributions and corresponding task performance in each setting. Interestingly, we find that each of the three plasticity rules is well suited to a subset of the scenarios studied but falls short in others. These results show that different tasks may require different forms of synaptic plasticity, yielding the prediction that the precise form of the STDP mechanism may vary across regions of the striatum, and other brain areas impacted by dopamine, that are involved in distinct computational functions.

Providing context: Extracting non-linear and dynamic temporal motifs from brain activity
Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional connectivity (tr-FC). While many approaches have been proposed, these typically focus on linear approaches like computing the linear correlation at a timestep or within a window. In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. This has the advantage of allowing our model to capture differences at multiple temporal scales. For the timestep-specific scale, which has higher temporal precision, we find significant differences between schizophrenia patients and control subjects in their temporal step distance through our model's latent space. We also find that window-specific embeddings, or as we refer to them, context embeddings, more accurately separate windows from schizophrenia patients and control subjects than the standard tr-FC approach. Moreover, we find that for individuals with schizophrenia, our model's context embedding space is significantly correlated with both age and symptom severity. Interestingly, patients appear to spend more time in three clusters, one closer to controls which shows increased visual-sensorimotor, cerebellar-subcortical, and reduced cerebellar-sensorimotor functional network connectivity (FNC), an intermediate station showing increased subcortical-sensorimotor FNC, and one that shows decreased visual-sensorimotor, decreased subcortical-sensorimotor, and increased visual-subcortical domains. We verify that our model captures features that are complementary to - but not the same as - standard tr-FC features. Our model can thus help broaden the neuroimaging toolset in analyzing fMRI dynamics and shows potential as an approach for finding psychiatric links that are more sensitive to individual and group characteristics.

Highly synchronized inhibition from Purkinje cells entrains cerebellar output in zebrafish
Cerebellar function, known to be important for motor learning and motor coordination, is mediated by efferent neurons that project to diverse motor areas. To understand cerebellar function, it is imperative to study how these efferent neurons integrate inputs from the principal neurons of the cerebellar cortex, the inhibitory Purkinje neurons (PNs). In zebrafish, PNs are bistable and we show here that bistability influences spike synchrony among PNs. Bistability also alters spike correlation with motor bouts. We asked how PN population synchrony influences Eurydendroid cells (ECs), which are postsynaptic targets of PNs and are the cerebellar efferent cells in zebrafish. Using optogenetics, we artificially modulated population synchrony of PNs over millisecond time scales and showed that under conditions of high synchrony, EC firing is briefly suppressed and entrained by PN spiking. However, the magnitude of such modulation is relatively small and indicates a strong combined influence of other synaptic inputs on EC spiking.

Learning from imagined experiences via an endogenous prediction error
Experiences shape preferences. This is particularly the case when they deviate from our expectations and thus elicit prediction errors. Here we show that prediction errors do not only occur in response to actual events - they also arise endogenously in response to merely imagined events. Specifically, we show that people acquire a preference for acquaintances as they imagine interacting with them in unexpectedly pleasant situations. This learning can best be accounted for by a computational model that calculates prediction errors based on these rewarding experiences. Using functional MRI, we show that the prediction error is mediated via striatal activity. This activity, in turn, seems to update preferences about the individuals by updating their cortical representations. Our findings demonstrate that imaginings can violate our own expectations and thus drive endogenous learning by coopting a neural system that implements reinforcement learning. They reveal fundamental principles how we acquire knowledge devoid of actual experiences.

Reduced SH3RF3 may protect against Alzheimer disease by lowering microglial pro-inflammatory responses via modulation of JNK and NFkB signaling
Understanding how high-risk individuals are protected from Alzheimer disease (AD) may illuminate potential therapeutic targets. A previously identified non-coding SNP in SH3RF3/POSH2 significantly delayed disease onset in a Caribbean Hispanic cohort carrying the PSEN1 G206A mutation sufficient to cause early-onset AD and microglial expression of SH3RF3 has been reported to be a key driver of late-onset AD. SH3RF3 acts as a JNK pathway scaffold and can activate NFkB signaling. While effects of SH3RF3 knockdown in human neurons were subtle, including decreased phospho-tau S422, knockdown in human microglia significantly reduced inflammatory cytokines in response to either a viral mimic or oligomeric ABeta42. This was associated with reduced activation of JNK and NFkB pathways in response to these stimuli. Pharmacological inhibition of JNK or NFkB signaling phenocopied SH3RF3 knockdown. We also found PSEN1 G206A microglia have reduced inflammatory responses to oABeta42. Thus, further reduction of microglial inflammatory responses in PSEN1 mutant carriers by protective SNPs in SH3RF3 might reduce the link between amyloid and neuroinflammation to subsequently delay the onset of AD.

A database of digital line drawings that depict expected and unexpected action-place relationships
In the present study, we introduce an image database created with a simple digital line-drawing tool to represent the expected and unexpected action-place relationships. This database consists of 70 drawings. We validated the dataset with 207 participants. They evaluated the actions, places, and the probability of an action taking place in the respective location. The comprehensibility of each drawing was evaluated using three measures: H-statistics (entropy), which is a measure of the uncertainty of the definition; the definition similarity percentages which is a measure of the naming agreement that is consistent among participants; and the mean probability of the depicted action taking place in the depicted location. Each drawing includes different agents and environments, providing researchers with the opportunity to use this dataset in various fields of cognitive neuroscience, including visual recognition, memory, predictive coding, and novelty detection.

Decoding dynamic visual scenes across the brain hierarchy
Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a paramount investigation within the domain of neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding dataset and utilize the capabilities of deep learning neural network models to study the question of neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. We find that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within both the visual cortex and subcortical nuclei, in contrast to a relatively diminished encoding activity within hippocampal neurons. Strikingly, our results reveal a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings not only corroborate existing knowledge in visual coding using artificial visual stimuli but illuminate the functional role of these deeper brain regions using dynamic natural scenes. Consequently, our results proffer a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding of dynamic natural visual scenes, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

Tracing neurodiverse disruptions underlying emotional episodic memory to diagnosis-specific network of emotional regulation in psychiatric disorders
Objective: Emotional dysfunctions are prevalent across various psychiatric disorders, leading to diverse emotional problems. Disrupted emotional episodic memory is a prominent deficit and may underlie various affective symptoms in clinical phenotypes. However, diagnosis-specific of neurodiverse disruptions remain elusive. Methods: We used task-based functional magnetic resonance imaging (fMRI) and a normative modelling framework to establish a reference for functional activation during emotional episodic memory, drawing from a large dataset of healthy individuals (n = 409). Individualized deviations from this reference were evaluated using a clinical dataset of 328 participants, which included 168 healthy controls and patients with major depressive disorder (MDD, n = 56), bipolar disorder (BD, n = 31), and schizophrenia (SZ, n = 73). Regional deviations were mapped to four large-scale emotional regulation networks and used to predict affective symptoms across different mental disorders. Results: We constructed a verifiable normative model of functional activation during emotional episodic memory to parse clinical heterogeneity. Diagnosis-specific regional deviations were enriched in the non-overlapping large-scale emotional regulation networks: MDD showed enrichment in emotion regulation network related to emotion perception and generation, BD in cognitive appraisal and emotional reactivity, and SZ in working memory and response inhibition. Individualized deviations significantly predicted affective symptom in distinct disorder, and specific emotional regulation network showed maximum feature weight. Conclusions: These findings have potential implications for the understanding of dissociable neuropathological patterns of affective symptoms and improving individualized clinical diagnosis and treatment in psychiatric disorders.

Bilingual language processing relies on shared semantic representations that are modulated by each language
Billions of people throughout the world are bilingual and can understand semantic concepts in multiple languages. However, there is little agreement about how the brains of bilinguals represent semantic information from different languages. Some theories suggest that bilingual speakers' brains contain separate representations for semantic information from different languages, while others suggest that different languages evoke the same semantic representations in the brain. To determine how the brains of bilinguals represent semantic information from different languages, we used functional magnetic resonance imaging (fMRI) to record brain responses while participants who are fluent in both English and Chinese read several hours of natural narratives in each language. We then used this data to specifically and comprehensively compare semantic representations between the two languages. We show that while semantic representations are largely shared between languages, these representations undergo fine-grained shifts between languages. These shifts systematically alter how different concept categories are represented in each language. Our results suggest that for bilinguals, semantic brain representations are shared across languages but modulated by each language. These results reconcile competing theories of bilingual language processing.