bioRxiv Subject Collection: Neuroscience's Journal
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Thursday, July 10th, 2025
| Time |
Event |
| 7:47a |
Nonsense-mediated decay masks cryptic splicing events caused by TDP-43 loss
In frontotemporal dementia and amyotrophic lateral sclerosis, the RNA-binding protein TDP-43 is lost from the nucleus, leading to cryptic exon inclusion events in dozens of neuronal genes. Here, we show that many cryptic splicing events have been missed by standard RNA-sequencing analyses because they are substrates for nonsense-mediated decay. By inhibiting nonsense-mediated decay in neurons we unmask hundreds of novel cryptic splicing events caused by TDP-43 depletion, providing a new picture to TDP-43 loss of function in neurons. | | 1:31p |
RS-fMRI Evidence for Differential Within- and Between-Module Interactions Across Age
Background: Previous resting-state functional MRI (rsfMRI) studies have identified a robust inverse relationship between nodal strength and amplitude of low-frequency fluctuations (ALFF) across cortical modules. This study examined whether this negative relationship also within modules, to evaluate the validity of functional parcellation (FP) as a preprocessing step for neural network construction, and explored age-related effects on these associations. Methods: Using MOSI (modularity analysis and similarity measurements), rsfMRI data from three public datasets spanning different age cohorts were analyzed. Correlations between nodal strength (or voxel concordance within modules) and ALFF were calculated. Geometric mean p-values assessed robustness at the individual level. Results: Confirming prior findings, a significant inverse correlation between nodal strength and ALFF was observed at the between-module level (geometric p-values 10-4 to 10-5). Within-module negative associations were nonsignificant in younger cohorts (mean ages 10 and 21) but became significant in the older cohort (mean age 33). The magnitude of this negative association increased with age, consistent with maturation of local inhibitory network mechanisms. Conclusions: The findings support FP as a valid preprocessing method, with within-module inhibitory effects strengthening in adulthood. The age-dependent modulation suggests network maturation impacts local neural activity regulation, with implications for neurodevelopmental and neuropsychiatric conditions. | | 5:47p |
Avoidance engages dopaminergic punishment in Drosophila
It was classically suggested that behaviour can cause emotions (Darwin 1872). For example, smiling can make us feel happier, and in rodents the induced patterns of cardiac activity and breathing that are indicative of fear can in turn evoke it (Coles et al. 2022, Hsueh et al. 2023, Jhang et al. 2024). However, the adaptive significance of such feedback is unclear. We show that inducing backward movement, an element of avoidance behaviour in Drosophila, engages negative valence signals in these animals, and reveal the neuronal mechanisms and adaptive significance of this effect. We develop a paradigm with odours as conditioned stimuli paired with optogenetically induced backward movement instead of a punishing unconditioned stimulus, and combined these experiments with pharmacology, high-resolution video tracking, functional imaging, connectome analyses, and modelling. Our results show that backward movement engages dopaminergic punishment neurons and supports aversive memories. Such avoidance-to-punishment feedback counterbalances extinction learning and maintains learned avoidance, reducing the risk of further punishment. This can explain the long-standing 'avoidance paradox', the observation that avoidance adaptively persists even when it is successful and no punishment is received (Bolles 1972). Our results provide a neurobiologically grounded argument for an integrated view of behaviour organization and valence processing. | | 8:30p |
Canonical recurrent neural circuits: A unified sampling machine for static and dynamic inference
The brain lives in an ever-changing world and needs to infer the dynamic evolution of latent states from noisy sensory inputs. Exploring how canonical recurrent neural circuits in the brain realize dynamic inference is a fundamental question in neuroscience. Nearly all existing studies on dynamic inference focus on deterministic algorithms, whereas cortical circuits are intrinsically stochastic, with accumulating evidence suggesting that they employ stochastic Bayesian sampling algorithms. Nevertheless, nearly all circuit sampling studies focused on static inference with fixed posterior over time instead of dynamic inference, leaving a gap between circuit sampling and dynamic inference. To bridge this gap, we study the sampling-based dynamic inference in a canonical recurrent circuit model with excitatory (E) neurons and two types of inhibitory interneurons: parvalbumin (PV) and somatostatin (SOM) neurons. We find that the canonical circuit unifies Langevin and Hamiltonian sampling to infer either static or dynamic latent states with various moving speeds. Remarkably, switching sampling algorithms and adjusting model's internal latent moving speed can be realized by modulating the gain of SOM neurons without changing synaptic weights. Moreover, when the circuit employs Hamiltonian sampling, its sampling trajectories oscillate around the true latent moving state, resembling the decoded spatial trajectories from hippocampal theta sequences. Our work provides overarching connections between the canonical circuit with diverse interneurons and sampling-based dynamic inference, deepening our understanding of the circuit implementation of Bayesian sampling. | | 10:31p |
A meta-analysis of periodic and aperiodic M/EEG components in Parkinson's disease
Parkinsons disease is characterised by a range of motor and non-motor changes that can negatively impact quality of life. Many studies have identified potential clinical electrophysiological biomarkers of Parkinsons disease with an aim of developing new methods of identifying at-risk patients, and to form the basis of therapeutic interventions. However, these studies do not present consistent results, and a formal meta-analysis is warranted to identify reliable M/EEG characteristics across datasets. In this meta-(re-)analysis of open-access M/EEG datasets (n = 6; 4 EEG and 2 MEG), we compared periodic and aperiodic characteristics of resting-state recordings in 368 patients with Parkinsons disease and 570 age-matched healthy controls. Specifically, we compared the power and peak frequency of the aperiodic-adjusted alpha and beta oscillations, and the aperiodic exponent and offset across the two groups. Using spectral parametrisation, individuals with Parkinsons disease had higher alpha-band power and a slower alpha peak frequency compared to controls, however no group differences in beta-band power and peak frequency were identified in this resting state data. Parkinsons patients were furthermore found to have consistently higher aperiodic offset and exponent, possibly indicative of increased cortical inhibition. In conclusion, this large cohort meta-analysis points to a broadly consistent pattern of both periodic and aperiodic changes in Parkinsons patients in M/EEG signal that may be used to develop diagnostics and targeted interventions in the future. | | 10:31p |
Transcutaneous auricular vagus nerve stimulation during movement modulates motor neural circuitry without widespread cortical or autonomic activation
Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising neuromodulatory approach for treating neurological disorders, with growing interest in its potential to support motor rehabilitation. Yet, its mechanisms of action, potentially influenced by behavioral context, remain elusive. This sham-controlled study investigated transient taVNS interactions with movement in healthy adults, focusing on autonomic, neuromodulatory, and motor circuits. During a finger-tapping paradigm, heart rate (HR), galvanic skin response (GSR), pupil diameter, and electroencephalography (EEG) were recorded to probe movement-dependent stimulation effects. This study first identified a novel physiological dissociation: all measures responded to movement, but taVNS did not significantly alter HR, GSR, or general EEG spectral slope; taVNS increased pupil diameter in both conditions, but enhanced sensorimotor EEG spectral slope solely during movement. This context-specific effect on motor systems was further supported by a transcranial magnetic stimulation (TMS) experiment demonstrating increased corticospinal excitability during taVNS. These findings provide mechanistic insights into how taVNS may selectively enhance motor system responsiveness during active states, supporting future exploration of behaviorally paired stimulation protocols for neurorehabilitation. | | 10:31p |
Robust maintenance of both stimulus location and amplitude in a working memory model based on dendritic bistability
Working memory is a core feature of cognition that enables items to be maintained and manipulated over short durations of time. Stored information can be binary, such as the presence or absence of an object, or multivalued, such as the graded intensity or location of a feature. Remarkably, there are currently no computational models of working memory that can robustly maintain both the graded intensity and spatial location of a stored item. Here, we show how this limitation can be overcome if neurons contain multiple bistable dendritic compartments. We first illustrate the core mechanism in a simple network architecture for both a spiking model with conductance-based dendrites and a rate-based model that permits analytic understanding. We then implement this mechanism within a ring model architecture for spatial working memory, in which neurons are arranged in a one-dimensional line or ring such that the spatial location of an item is encoded by the set of neurons that are active. In contrast to classic ring models, which encode the binary presence and continuous location of an item, we show that the multi-dendrite-neuron model can robustly (without fine tuning and with minimal drift in response to noise) encode both the amplitude and the location of an item in working memory. This work provides a solution to the problem of encoding graded information in spatial working memory and demonstrates how dendritic computation can increase the capacity and robustness of working memory. | | 10:31p |
Effective connectivity reveals dual-route mechanism of visual prediction precision via insula and pulvinar
The brains ability to weight predictions by their precision is a central mechanism in predictive processing, enabling optimal integration of prior expectations with incoming sensory input. Despite its theoretical significance, the neural circuitry that implements precision-weighted prediction remains unclear. Using 7-Tesla fMRI and dynamic causal modelling (DCM), this study investigated how the brain encodes the precision of predictions during a visual cueing task with high- and low-precision conditions. We focused on the key regions implicated in predictive processing: the insular cortex, the pulvinar nucleus of the thalamus, and primary visual cortex (V1). Behaviourally, participants showed significantly greater accuracy in the high-precision condition (p < .001), confirming effective task manipulation. DCM analyses revealed that high-precision predictions elicited excitatory modulation of connectivity from the insula to V1 (Pp = .95), alongside inhibitory influences from the insula to the pulvinar (Pp = .99) and from the pulvinar to V1 (Pp = .89). Furthermore, leave-one-out cross validation revealed that individual differences in behavioural sensitivity to precision were positively predicted by pulvinar-to-insula connectivity (r = .36, p = .026) and negatively predicted by the connectivity between pulvinar and V1 (pulvinar to V1: r = .35, p = .033; V1 to pulvinar: r = .37, p = .026), highlighting the behavioural relevance of these pathways. Together, these findings suggest a dual-route mechanism whereby the insula directly enhances top-down predictions in V1 while indirectly dampening bottom-up sensory input via the pulvinar. This mechanism may facilitate Bayesian integration under uncertainty and offers new hypotheses into how precision weighting may be disrupted in neuropsychiatric conditions. | | 10:31p |
Temporal presence does not affect behavioral and neurophysiological indices of pain empathy
The impact of digitally mediated social interaction on understanding others and sharing their emotions has not been thoroughly investigated. We examined how live, video-mediated interaction as opposed to watching a prerecorded video, affects behavioral, neural, and physiological aspects of empathy for pain. Thirty-five observers watched targets undergoing painful electric stimulation in an electroencephalogram study. We hypothesized that reduced temporal presence would result in diminished behavioral and electrophysiological empathic responses. However, observer's behavioral empathic responses were not diminished with reduced temporal presence. On a neural level, mid-frontal theta was sensitive to the other's pain intensity, and we observed significant physiological coupling between participants. Mu suppression, on the other hand, was not modulated by pain intensity. Importantly, neural and physiological indices of empathy were independent of temporal presence. However, exploratory analyses indicated a latency effect of temporal presence on pain-related theta activity with an earlier theta increase in interactions with high temporal presence. The results suggest that the temporal presence of individuals may not be necessary for empathy towards another's pain. Future studies may investigate more naturalistic social interactions and include motivational aspects of empathy. We discuss implications of these findings for debates on social presence and on second-person neuroscience. | | 11:47p |
Place cell activity and behaviour during task acquisition and extinction learning predict renewal outcome
Extinction learning (EL) and the subsequent renewal of learned behaviours are crucial for adaptive responding, yet the underlying neural mechanisms that differentiate successful renewal from its absence remain unclear. Here, we explored the behavioral and neurophysiological basis of spatial appetitive EL, as well as renewal failure and renewal success. We recorded place cell activity from hippocampal area CA1 in male rats that performed a context-dependent spatial appetitive learning task in a T-maze (rewarded context A), followed by EL (unrewarded context B), and subsequent renewal testing (unrewarded context A). Half of the animals exhibited significant renewal (renewers) and the other half failed to renew (non-renewers). Our findings reveal fundamental differences in learning strategies between groups revealed by differences in both spatial behavior and in place cell activity during both initial acquisition and subsequent EL. Specifically, renewers exhibited a context-based EL strategy, whereas non-renewers followed a goal-directed strategy. The spatial distribution of hippocampal place cell activity differed significantly between groups, indicating the hippocampus' role in the learning processes. Furthermore, renewers exhibited a greater extent of global remapping of place cells compared to non-renewers, consistent with predictions by our computational modeling. This suggests that global remapping serves as a key neural mechanism underlying effective renewal, allowing for the segregation of distinct memories and preventing the generalization of extinction. Our results highlight how hippocampal place cell dynamics during acquisition and EL predict later behavioral renewal outcomes, providing critical insights into the neural basis of memory updating and contextual control over learned behaviors. |
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