bioRxiv Subject Collection: Neuroscience's Journal
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Friday, November 29th, 2024
| Time |
Event |
| 12:31a |
Bidirectionally responsive thermoreceptors encode cool and warm
Thermal sensation is a fundamental sense initiated by the activity of primary afferent thermoreceptors. While considerable attention has been paid to the encoding of noxious temperatures by thermoreceptors, it is far less clear how they encode innocuous cool and warm which are more commonly encountered in the environment. To address this, we sampled the entire thermoreceptor population using in vivo two-photon calcium imaging in the lumbar dorsal root ganglia of awake and anesthetized mice. We found that the vast majority of thermoreceptors respond bidirectionally, with an enhanced response to cool and a suppressed response to warm. Using in vivo pharmacology and computational modelling, we demonstrate that conductance changes in the cool-sensitive TRPM8 channel are sufficient to explain this bidirectional response type. Our comprehensive dataset reveals the fundamental principles of the peripheral encoding of innocuous temperatures and suggests that the same population of thermoreceptors underlie the distinct sensations of cool and warm. | | 12:31a |
Sex differences in the rates and association of cerebral blood flow and glucose metabolism in normative ageing.
Although dysfunction in cerebral blood flow and glucose metabolism is linked to neurodegeneration, it is currently unclear if there are sex and age differences in their rates and association. Seventeen younger males (mean age 27.5 years), 20 younger females (28.4), 22 older males (76.6) and 20 older females (75.3) completed a MR/PET scan and cognitive battery. Females had higher CBF and CMRGLC than males, regardless of age. CBF was lower in ageing. As CMRGLC increased, the positive effect of higher CBF on working memory, cognitive flexibility, and processing speed also increased. Individuals with the highest CBF had the highest CMRGLC. However, this association was moderated by sex and age, with significant negative associations across older females, possibly reflecting a compensatory response to a loss of blood flow and metabolism. We conclude that there are sex differences in the rates and association of cerebral blood flow and glucose metabolism in normative ageing and that high rates of blood flow and metabolism combine to support cognition. | | 12:31a |
Decoupling of GABA and Glutamine-Glutamate Dynamics and their role in tactile perception: An fMRS Study
Tactile processing is fundamental for our daily lives. In particular, adaptation, the mechanism by which neural (and behavioural) responses change due to repeated stimulation, is key in adjusting our responses to the environment and is often affected in neurodevelopmental conditions such as autism and ADHD. While GABA and glutamate--the main inhibitory and excitatory neurotransmitters-- are known to be fundamental for encoding sensory input, we know little regarding the dynamic responses of the GABA and glutamatergic systems during tactile processing. Here, we examine how GABA and glutamine+glutamate (Glx) in vivo dynamics change during repetitive tactile stimulation and how these changes relate to tactile perception in a healthy population, using functional magnetic resonance spectroscopy (fMRS). Our study showed that repetitive tactile stimulation induced a decoupling between GABA and Glx during the first stimulation blocks as suggested by a negative correlation between GABA and Glx, which changed from a positive correlation at baseline. Subsequently, a multivariate time series analysis showed a predictive temporal relationship between Glx and GABA, showing that changes in these metabolites are temporally linked with an estimated lag of 6 seconds informing on a complex metabolite response function. The absence of gross metabolite change suggests that Glx and GABA adjust in relation to each other in response to repeated tactile stimulation. Furthermore, individual differences in the changed GABA and Glx levels correlated with perceptual measures of touch. Together, our study highlights the complex relationship between GABA and glutamate in tactile processing and demonstrates that experience-dependence plasticity induces a decoupling between these key metabolites. Further study into their dynamic interplay may be key to understanding adaptation as meso-levels in the brain and how these mechanisms differ in neurodevelopmental and neurological conditions. | | 12:31a |
More or less latent variables in the high-dimensional data space? That is the question
Dimensionality reduction is widely used in modern Neuroscience to process massive neural recordings data. Despite the development of complex non-linear techniques, linear algorithms, in particular Principal Component Analysis (PCA), are still the gold standard. However, there is no consensus on how to estimate the optimal number of latent variables to retain. In this study, we addressed this issue by testing different criteria on simulated data. Parallel analysis and cross validation proved to be the best methods, being largely unaffected by the number of units and the amount of noise. Parallel analysis was quite conservative and tended to underestimate the number of dimensions especially in low-noise regimes, whereas in these conditions cross validation provided slightly better estimates. Both criteria consistently estimate the ground truth when 100+ units were available. As an exemplary application to real data, we estimated the dimensionality of the spiking activity in two macaque parietal areas during different phases of a delayed reaching task. We show that different criteria can lead to different trends in the estimated dimensionality. These apparently contrasting results are reconciled when the implicit definition of dimensionality underlying the different criteria is considered. Our findings suggest that the term dimensionality needs to be defined carefully and, more importantly, that the most robust criteria for choosing the number of dimensions should be adopted in future works. To help other researchers with the implementation of such an approach on their data, we provide a simple software package, and we present the results of our simulations through a simple Web based app to guide the choice of latent variables in a variety of new studies.
Key pointsO_LIParallel analysis and cross-validation are the most effective criteria for principal components retention, with parallel analysis being slightly more conservative in low-noise conditions, but being more robust with larger noise.
C_LIO_LIThe size of data matrix as well as the decay rate of the explained variance decreasing curve strongly limit the number of latent components that should be considered.
C_LIO_LIWhen analyzing real spiking data, the estimated dimensionality depends dramatically on the criterion used, leading to apparently different results. However, these differences stem, in large part, from the implicit definitions of dimensionality underlying each criterion.
C_LIO_LIThis study emphasizes the need for careful definition of dimensionality in population spiking activity and suggests the use of parallel analysis and cross-validation methods for future research.
C_LI | | 12:31a |
How the layer-dependent ratio of excitatory to inhibitory cells shapes cortical coding in balanced networks
The cerebral cortex exhibits a sophisticated neural architecture across its six layers. Recently, it was found that these layers exhibit different ratios of excitatory to inhibitory (EI) neurons, ranging from 4 to 9. This ratio is a key factor for achieving the often reported balance of excitation and inhibition, a hallmark of cortical computation. However, neither previous theoretical nor simulation studies have addressed how these differences in EI ratio will affect layer-specific dynamics and computational properties. We investigate this question using a sparsely connected network model of excitatory and inhibitory neurons. To keep the network in a physiological range of firing rates, we varied the inhibitory firing threshold or the synaptic strength between excitatory and inhibitory neurons. We find that decreasing the EI ratio allows the network to explore a higher-dimensional space and enhance its capacity to represent complex input. By comparing the empirical EI ratios of layer 2/3 and layer 4 in the rodent barrel cortex, we predict that layer 2/3 has a higher dimensionality and coding capacity than layer 4. Furthermore, our analysis of primary visual cortex data from the Allen Brain Institute corroborates these modelling results, also demonstrating increased dimensionality and coding capabilities of layer 2/3.
Author summaryExperimental studies indicate that the ratio of excitatory to inhibitory neurons varies across different cortical layers. In this study, we investigate how these varying excitatory-to-inhibitory (EI) ratios affect the layer-specific dynamics and computational capacity of cortical networks. We modeled a randomly connected network of spiking neurons, incorporating different EI ratios based on experimental observations. Our findings reveal that as the influence of inhibition increases, corresponding to lower EI ratios, the network explores a higher dimensionality in its activity, thereby enhancing its capacity to encode high-dimensional inputs. These results align with our analysis of experimental data recorded from layers 2/3 and layer 4 of the rodent primary visual cortex. Specifically, our findings support the hypothesis that layer 2/3, which has a lower EI ratio compared to layer 4, possesses a greater computational capacity. | | 12:31a |
Hippocampal CA2 neurons disproportionately express AAV-delivered genetic cargo
Hippocampal area CA2 is unique in many ways, largely based on the complement of genes expressed there. We and others have observed that CA2 neurons exhibit a uniquely robust tropism for adeno-associated viruses (AAVs) of multiple serotypes and variants. In this study, we aimed to systematically investigate the propensity for AAV tropism toward CA2 across a wide range of AAV serotypes and variants, injected either intrahippocampally or systemically, including AAV1, 2, 5, 6, 8, 9, DJ, PHP.B, PHP.eB, and CAP-B10. We found that most serotypes and variants produced disproportionally high expression of AAV-delivered genetic material in hippocampal area CA2, although two serotypes (AAV6 and DJ) did not. In an effort to understand the mechanism(s) behind this observation, we considered perineuronal nets (PNNs) that ensheathe CA2 pyramidal cells and, among other functions, buffer diffusion of ions and molecules. We hypothesized that PNNs might attract AAV particles and maintain them in close proximity to CA2 neurons, thereby increasing exposure to AAV particles. However, genetic deletion of PNNs from CA2 had no effect on AAV transduction. Next, we next considered the AAV binding factors and receptors known to contribute to AAV transduction. We found that the AAV receptor (AAVR), which is critical to transduction, is abundantly expressed in CA2, and knockout of AAVR nearly abolished expression of AAV-delivered material by all serotypes tested. Additionally, we found CA2 enrichment of several cell-surface glycan receptors that AAV particles attach to before interacting with AAVR, including heparan sulfate proteoglycans, N-linked sialic acid and N-linked galactose. Indeed, CA2 showed the highest expression of AAVR and the investigated glycan receptors within the hippocampus. We conclude that CA2 neurons are endowed with multiple factors that make it highly susceptible to AAV transduction, particularly to the systemically available PHP variants, including CAP-B10. Given the curved structure of hippocampus and the relatively small size of CA2, systemic delivery of engineered PHP or CAP variants could all but eliminate the need for intrahippocampal AAV injections, particularly when injecting recombinase-dependent AAVs into animals that express recombinases in CA2. | | 12:31a |
Cycles in Seizure Duration and Their Underlying Dynamics in the Tetanus Toxin Rat Model
Seizure duration, a characteristic of epilepsy that is understudied in relation to its relationship with rhythmic cycles, provides critical insights into the severity and temporal dynamics of seizures. This study investigates the rhythmic patterns of seizure duration in the Tetanus Toxin (TT) rat model of epilepsy. Our analysis shows significant cyclical patterns in seizure durations, with periods ranging from 4 to 8 days across rats. The synchronization index and circular-linear correlations revealed phase-locked relationships between seizure durations and cycles, suggesting non-random, predictable temporal dynamics. Further analyses examined the relationship between seizure durations, inter-seizure intervals, and dominant EEG power. The findings highlight that seizure durations exhibit predictable rhythms, which could transform seizure prediction and enable time-based intervention strategies, ultimately improving epilepsy management and patient outcomes. These insights lay the groundwork for personalized, rhythm-aware therapeutic approaches. | | 12:31a |
Isoliquiritigenin attenuated cognitive impairment, cerebral tau phosphorylation and oxidative stress in a streptozotocin-induced mouse model of Alzheimers disease
IntroductionTau hyperphosphorylation, mitochondrial dysfunction and oxidative stress play important roles in Alzheimers disease (AD). Isoliquiritigenin, a natural flavonoid isolated from the root of liquorice, has been shown to exert inhibitory effects on oxidative stress. Here, we assessed the neuroprotective effects of isoliquiritigenin on a streptozotocin-injected mouse model.
MethodMolecular docking analysis performed for isoliquiritigenin with mTOR and ERK2. The mice (n = 27, male) were intracerebroventricularly injected with streptozotocin, treated with isoliquiritigenin (intraperitoneal, 2 days) and assessed using the Morris water maze. Oxidative stress, tau phosphorylation, mitochondrial dysfunction and synaptic impairment were evaluated in the cortex and hippocampal tissues of the mice by using biochemical assays and immunofluorescence staining.
ResultsIsoliquiritigenin treatment mitigated the spatial memory capacity of streptozotocin-injected mice and alleviated tau phosphorylation at Ser396; the production of reactive oxygen species; the mitochondrial fission proteins Mfn1 and Mfn2; neuronal loss; and synaptic impairment (PSD95, SNAP25). Isoliquiritigenin treatment reduced the levels of mTOR Ser2448 and ERK1/2 T202/Y204 and upregulated the level of GSK-3{beta}Ser9 in the cortex and hippocampus of streptozotocin-injected mice.
ConclusionIn conclusion, our findings suggest that isoliquiritigenin ameliorates streptozotocin-induced cognitive impairment, hyperphosphorylated tau, oxidative stress, mitochondrial dysfunction and synaptic impairment by decreasing mTOR and ERK activity and increasing GSK-3{beta} activity. | | 12:31a |
st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks
ObjectiveModeling dynamic neuronal activity within brain networks enables the precise tracking of rapid temporal fluctuations across different brain regions. However, current approaches in computational neuroscience fall short of capturing and representing the spatiotemporal dynamics within each brain network. We developed a novel weakly supervised spatiotemporal dense prediction model capable of generating personalized 4D dynamic brain networks from fMRI data, providing a more granular representation of brain activity over time.
MethodsWe developed a model that leverages the vision transformer (ViT) as its backbone, jointly encoding spatial and temporal information from fMRI inputs using two different configurations: space-time and sequential encoders. The model generates 4D brain network maps that evolve over time, capturing dynamic changes in both spatial and temporal dimensions. In the absence of ground-truth data, we used spatially constrained windowed independent component analysis (ICA) components derived from fMRI data as weak supervision to guide the training process. The model was evaluated using large-scale resting-state fMRI datasets, and statistical analyses were conducted to assess the effectiveness of the generated dynamic maps using various metrics.
ResultsOur model effectively produced 4D brain maps that captured both inter-subject and temporal variations, offering a dynamic representation of evolving brain networks. Notably, the model demonstrated the ability to produce smooth maps from noisy priors, effectively denoising the resulting brain dynamics. Additionally, statistically significant differences were observed in the temporally averaged brain maps, as well as in the summation of absolute temporal gradient maps, between patients with schizophrenia and healthy controls. For example, within the Default Mode Network (DMN), significant differences emerged in the temporally averaged space-time configurations, particularly in the thalamus, where healthy controls exhibited higher activity levels compared to subjects with schizophrenia. These findings highlight the models potential for differentiating between clinical populations.
ConclusionThe proposed spatiotemporal dense prediction model offers an effective approach for generating dynamic brain maps by capturing significant spatiotemporal variations in brain activity. Leveraging weak supervision through ICA components enables the model to learn dynamic patterns without direct ground-truth data, making it a robust and efficient tool for brain mapping.
SignificanceThis work presents an important new approach for dynamic brain mapping, potentially opening up new opportunities for studying brain dynamics within specific networks. By framing the problem as a spatiotemporal dense prediction task in computer vision, we leverage the spatiotemporal ViT architecture combined with weakly supervised learning techniques to efficiently and effectively estimate these maps. | | 12:31a |
Resting-state functional connectivity and fast spindle temporal organization contribute to episodic memory consolidation in healthy aging
Episodic memory consolidation relies on the functional specialization of brain networks and sleep quality, both of which are affected by aging. Functional connectivity during wakefulness is crucial to support the integration of newly acquired information into memory networks. Additionally, the temporal dynamics of sleep spindles facilitates overnight memory consolidation by promoting hippocampal replay and integration of memories within neocortical structures. This study aimed at exploring how resting-state functional connectivity during wakefulness contributes to sleep-dependent memory consolidation in aging, and whether spindles clustered in trains modulates this relationship.
Forty-two healthy older adults (68.82 {+/-} 3.03 years), enrolled in the Age-Well clinical trial, were included. Sleep-dependent memory consolidation was assessed using a visuo-spatial memory task performed before and after a polysomnography night. Resting-state functional connectivity data were analyzed using graph theory applied to the whole brain, specific brain networks and the hippocampus.
Lower limbic network integration and higher centrality of the anterior hippocampus were associated with better memory consolidation. Spindle trains modulated these effects, such that older participants with longer spindle trains exhibited a stronger negative association between limbic network integration and memory consolidation.
These results indicate that lower functional specialization at rest is associated with weaker memory consolidation during sleep. This aligns with the dedifferentiation hypothesis, which posits that aging is associated with reduced brain specificity, leading to less efficient cognitive functioning. These findings reveal a novel mechanism linking daytime brain network organization and sleep-dependent memory consolidation, and suggest that targeting spindle dynamics could help preserve cognitive functioning in aging. |
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