bioRxiv Subject Collection: Neuroscience's Journal
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Sunday, April 21st, 2024
| Time |
Event |
| 12:47a |
Multiscale modelling of neuronal dynamics in hippocampus CA1
The development of biologically realistic models of brain microcircuits and regions is currently a very relevant topic in computational neuroscience. From basic research to clinical applications, there is an increasing demand for accurate models that incorporate local cellular and network specificities, able to capture a broad range of dynamics and functions associated with given brain regions. One of the main challenges of these models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. One novel approach to this problem is the use of mean-field models of neuronal activity to build large-scale simulations. This provides an effective solution to the passage between scales with relatively low computational demands, which is achieved by a drastic reduction in the dimensionality of the system. In this paper we introduce a multiscale modelling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modelling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. To develop the mean-field model we make use of a recently introduced formalism based on a bottom-up approach that is easily applicable to different neuronal models and cell types. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. In addition, we show an example of the implementation of our model to study a stimulus propagation at the macro-scale, and we compare the results obtained from our model with the corresponding spiking network model of the whole CA1 area. | | 12:47a |
Activity based proteome profiling of serum serinehydrolases: application in pediatric abusive head trauma
Purpose: Traumatic brain injury (TBI), including pediatric abusive head trauma (AHT), is the leading cause of death and disability in children and young adults worldwide. The current understanding of trauma-induced molecular changes in the brain of human subjects with intracranial haemorrhage (ICH) remains inadequate and requires further investigation to improve the outcome and management of TBI in the clinic. Calcium-mediated damage at the site of brain injury has been shown to activate several catalytic enzymes. Experimental design: Serine hydrolases (SHs) are major catalytic enzymes involved in the biochemical pathways of blood coagulation, systemic inflammation and neuronal signaling. Here we investigated activity-based protein profiling (ABPP) by measuring the activity status of SH enzymes in the serum of infants with severe ICH as a consequence of AHT or atraumatic infants who died of sudden infant death syndrome (SIDS). Results: Our proof-of-principle study revealed significantly reduced physiological activity of dozens of metabolic SHs in the serum of infants with severe AHT compared to the SIDS group, with some of the enzymes being related to neurodevelopment and basic brain metabolism Conclusions and clinical relevance: To our knowledge, this is the first study to investigate the ABPP of the SHs enzyme family to detect changes in their physiological activity in blood serum in severe TBI. We used antemortem (AM) serum from infants under the age of 2 years who were victims of AHT with a severe form of ICH. The analytical approach used in the proof-of-principle study shows reduced activities of serum serine lipases in AHT cases and could be further investigated in mild forms of AHT, which currently show 30% of misdiagnosed cases in clinics. | | 1:15a |
Connectomes, simultaneous EEG-fMRI resting-state data and brain simulation results from 50 healthy subjects
We present raw and processed multimodal empirical data as well as simulation results from a study with The Virtual Brain (TVB). Simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) resting-state data, diffusion-weighted MRI, and structural MRI were acquired for 50 healthy adult subjects (18 - 80 years of age) at the Charite University Medicine, Berlin, Germany. We constructed personalized models from this multimodal data with TVB by optimizing parameters on an individual basis that predict multiple empirical features in fMRI and EEG, e.g. dynamic functional connectivity and bimodality in the alpha band power. We annotated this large comprehensive empirical and simulated data set according to the openMINDS meta data schema and structured it following Brain Imaging Data Structure (BIDS) standards for EEG and MRI as well as the BIDS Extension Proposal for computational modeling data. This dataset provides ready-to-use data for future research at various levels of processing including the thereof inferred brain simulation results for a large data set of healthy subjects with a wide age range. | | 10:48a |
BNST GluN2D-containing NMDARs contribute to ethanol intake but not negative affective behaviors in female mice
Alcohol use disorder (AUD) is a chronic, relapsing disease, highly comorbid with anxiety and depression. The bed nucleus of the stria terminalis (BNST), and Crh+ neurons in this region are thought to play a key role in chronic ethanol-induced increases in volitional ethanol intake. This role has been hypothesized to be driven by emergent BNST-dependent negative affective behaviors. Indeed, we report here that in female mice undergoing a home cage chronic drinking forced abstinence model (CDFA), excitatory transmission undergoes time-dependent upregulation in BNST Crh+ cells. Excitatory NMDA receptors (NMDARs) are a major target of ethanol, and chronic ethanol exposure has been shown to regulate NMDAR function and expression. GluN2D subunit-containing NMDARs have emerged as a target of interest due to their limited distribution and potential roles in affective behavior. We find that knockdown of dorsal BNST (dBNST) GluN2D expression significantly decreases ethanol intake in female, but not male, mice. While BNST Grin2b expression was significantly increased in protracted abstinence following CDFA, no differences in Grin2d expression were observed in dBNST or specifically in dBNST Crh+ neurons. Finally, to determine the impact of GluN2D expression on negative affective behaviors, open field, elevated zero maze, and forced swim tasks were used to measure anxiety- and depressive-like behaviors in constitutive and conditional BNST GluN2D knockout mice. Surprisingly, we find that deletion of GluN2D fails to alter negative affect in ethanol-naive female mice. Together, these data suggest a role for BNST GluN2D-containing NMDARs in ethanol drinking behaviors but not abstinence from ethanol, highlighting potential sex differences and behavioral specificity in the context of AUD behaviors. Overall, these data further suggest roles for BNST synaptic signaling in volitional ethanol intake that are partially independent of actions on affective behavior. | | 1:31p |
Pinging the Hidden Attentional Priority Map: Suppression Needs Attention
Attentional capture by an irrelevant salient distractor is attenuated when the distractor is presented more frequently in one location compared to other locations, suggesting that people learn to suppress an irrelevant salient location. However, to date it is unclear whether this suppression is proactive, applied before attention has been directed to the distractor location, or reactive, occurring after attention has been directed to that specific location. The aim of the present study is to investigate how suppression is accomplished by using the pinging technique which allows one to probe how attention is distributed across the visual field prior to the presentation of the search display. In an EEG experiment, participants performed a visual search task wherein they were tasked with identifying a shape singleton in the presence of an irrelevant color singleton. Compared to all other locations, this color singleton appeared more frequently at a specific location, which was termed the high-probability location. Prior to the search task, we introduced a continuous recall spatial memory task to reveal the hidden attentional priority map. Participants had to memorize the location of a memory cue continuously and report this location after the visual search task. Critically, after the presentation of the memory cue but before the onset of the search display, a neutral placeholder display was presented to probe how hidden priority map is reconfigured by the learned distractor suppression. Behaviorally, there was clear evidence that the high-probability location was suppressed, as search was more efficient when the distractor appeared at this location. To examine the priority map prior to search, we adopted an inverted encoding approach to reconstruct the tuning profile of the memorized position in the spatial memory task. Inverted modeling resulted in reliable tuning profiles during memory maintenance that gradually decayed and that were revived again by the onset of a neutral placeholder display preceding search. After the onset of the placeholders, the tuning profile observed was characterized by a spatial gradient centered over the high-probability location, with tuning being most pronounced at the-to-be suppressed location. This finding suggests that while learned suppression is initiated prior to search display onset, it is preceded by an initial phase of spatial selection, which is in line with a reactive suppression account. Together these results further our understanding of the mechanism of spatial distractor suppression. | | 2:45p |
Neural and computational mechanisms of effort under the pressure of a deadline
Deadlines fundamentally shape the motivation for effort. Research examining effort-based choices finds high effort is an avoided cost. However, this work overlooks the fact that effort can be valuable when it makes progress on long-term goals before deadlines. We test a new framework where motivation depends on deadline pressure (work remaining / time remaining). Across three studies we use computational modelling on novel tasks examining effort-based decisions when effort makes progress on goals with deadlines. In support of hypotheses, deadline pressure significantly impacts decision-making, shifting people from avoiding effort, to seeking and valuing it. Using ultra-high-field fMRI, we show that functionally connected putamen and midcingulate cortex (MCC) sub-regions process and update estimates of deadline pressure, with distinct anterior cingulate and putamen sub-regions processing the costs or added value of effort. We show the neurocomputational mechanisms for how deadline pressure shapes motivation, and that keep us "on track" for our goals. | | 4:49p |
Elevated DNA Damage without signs of aging in the short-sleeping Mexican Cavefish
Dysregulation of sleep has widespread health consequences and represents an enormous health burden. Short-sleeping individuals are predisposed to the effects of neurodegeneration, suggesting a critical role for sleep in the maintenance of neuronal health. While the effects of sleep on cellular function are not completely understood, growing evidence has identified an association between sleep loss and DNA damage, raising the possibility that sleep facilitates efficient DNA repair. The Mexican tetra fish, Astyanax mexicanus provides a model to investigate the evolutionary basis for changes in sleep and the consequences of sleep loss. Multiple cave-adapted populations of these fish have evolved to sleep for substantially less time compared to surface populations of the same species without identifiable impacts on healthspan or longevity. To investigate whether the evolved sleep loss is associated with DNA damage and cellular stress, we compared the DNA Damage Response (DDR) and oxidative stress levels between A. mexicanus populations. We measured markers of chronic sleep loss and discovered elevated levels of the DNA damage marker gammaH2AX in the brain, and increased oxidative stress in the gut of cavefish, consistent with chronic sleep deprivation. Notably, we found that acute UV-induced DNA damage elicited an increase in sleep in surface fish but not in cavefish. On a transcriptional level, only the surface fish activated the photoreactivation repair pathway following UV damage. These findings suggest a reduction of the DDR in cavefish compared to surface fish that coincides with elevated DNA damage in cavefish. To examine DDR pathways at a cellular level, we created an embryonic fibroblast cell line from the two populations of A. mexicanus. We observed that both the DDR and DNA repair were diminished in the cavefish cells, corroborating the in vivo findings and suggesting that the acute response to DNA damage is lost in cavefish. To investigate the long-term impact of these changes, we compared the transcriptome in the brain and gut of aged surface fish and cavefish. Strikingly, many genes that are differentially expressed between young and old surface fish do not transcriptionally vary by age in cavefish. Taken together, these findings suggest that have developed resilience to sleep loss, despite possessing cellular hallmarks of chronic sleep deprivation. | | 5:15p |
What and where in electromagnetic brain imaging
To understand the brain, we need to observe both the nature and dynamics of its activity (the "what"), and the location or distribution of its sources (the "where"). This paper proposes a new approach based on standard data-driven linear analysis, in which these two elements are derived in parallel from separate columns of the analysis matrix. A subset of columns enhances the activity of interest, and its complement defines spatial filters that suppress that activity (null filters). Each null filter is combined with an anatomy-dependent source model to estimate a subset of the sources space (zero set) in which the source might reside, and the final estimate is derived from the intersection of zero sets. There is no theoretical limit to the accuracy with which the location of a source can be estimated, but practical limits may arise from noise in the data, imperfect calibration, or an incomplete or inaccurate source model. | | 5:15p |
Expected reward value and reward prediction errors reinforce but also interfere with human time perception.
Time perception is often investigated in animal models and in humans using instrumental paradigms where reinforcement learning (RL) and associated dopaminergic processes have modulatory effects. For example, interval timing, which includes the judgment of relatively short intervals of time (ranging from milliseconds to minutes), has been shown to be modulated by manipulations of striatal dopamine. The expected value of reward (EV) and reward prediction errors (RPEs) are key variables described in RL-theory that explain dopaminergic signals during reward processing during instrumental learning. Notably, the underlying connection between RL-processes and time perception in humans is relatively underexplored. Herein, we investigated the impact of EV and RPEs on interval timing in humans. We tested the hypotheses that EV and RPEs modulate the experience of short time intervals. We demonstrate that expectations of monetary gains or losses increases the initial performance error for 1000ms intervals. Temporal learning over repeated trials is observed with accelerated learning of non-reinforced 1000ms intervals; however, RPEs, specifically about rewards and not punishments, appear to reinforce performance errors, which effectively interferes with the rate at which (reinforced) 1000ms intervals were learned. These effects were not significant for 3000ms and 5000ms intervals. Our results demonstrate that EV and RPEs influence human behavior about 1000ms time intervals. We discuss our results considering model-free temporal difference RL-theory, which suggests the hypothesis that interval timing may be mediated by dopaminergic signals that reinforce the learning and prediction of dynamic state- transitions which could be encoded without an explicit reference to "time" intervals. | | 5:15p |
DOES EXPERIENCE MODULATE AUTOMATIC IMITATION? A NEW LOOK
Automatic imitation is a stimulus-response compatibility effect wherein observing an action automatically influences motor performance. However, the mechanism underlying this effect remains controversial. Associative Sequence Learning suggests that automatic imitation arises from contingent visual and motor activity associations. Prior studies have shown that exposure to counter-imitative training can alter these visuomotor associations, suggesting that automatic imitation can be modulated by experience. Here, we aim to bring new insight into how this modulation occurs by exploring the time course of automatic imitation before and after counter-imitative training. If automatic imitation is merely a result of contingent associations, as previously suggested, the effect should consistently be modulated following such training. However, if this is not the case, automatic imitation is not, or not only, a matter of contingent association, at least not as currently understood. | | 5:47p |
A Libra in the Brain: Neural Correlates of Error Awareness Predict Moral Wrongness and Guilt Proneness
Error awareness is a fundamental mechanism in humans. Through traditional psychological tasks, certain neural activities that represent errors in objective manners have been identified. However, there is limited knowledge on how humans subjectively determine right from wrong in moral contexts. In this study, participants (N=39) mentally simulated themselves as the agents of moral and immoral behaviors, while viewing a series of actions with EEG recording and MRI scanning, respectively. A significant difference in error-related negativity (ERN) was observed among morally wrong scenarios, accompanied by higher wrongfulness ratings. Additionally, individual differences in guilt-proneness could predict the subjects' ERN amplitude. The ERN amplitude was correlated with the BOLD activity in the anterior mid-cingulate cortex and anterior insula to immoral scenarios, reflecting error awareness toward moral wrongfulness. The late potential component displayed greater negativity to immoral scenarios and was correlated with BOLD activities in the amygdala, ventromedial prefrontal cortex, and temporoparietal junction, indicating cognitive and affective evaluation in moral judgment. In line with the moral dynamic framework, our results demonstrated individual variability in moral judgments, as indicated by dispersed and overlapping cognitive neural networks. This suggests that subjective evaluations of wrongfulness are underpinned by neural mechanisms, associated with those involved in objective error awareness. | | 5:47p |
Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow
In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. We measured border ownership modulation within large populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that interactions between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, the magnitude of border ownership modulation was predicted by greater information flow from feedback/horizontal to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF. |
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