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The study of granular avalanches in rotating drums is not only essential to understanding various complex behaviors of interest in granular media from a scientific perspective; it also has valuable applications in regard to industrial processes and geological catastrophes. Despite decades of research studies on avalanches, a proper understanding of their dynamic properties still remains a great challenge to scientists due to a lack of state-of-the-art techniques. In this study, we accurately predict the avalanche dynamic features of three-dimensional granular materials in rotating drums, by using graph neural networks on the basis of their initial static microstructures alone. We find that our method is robust to changes in various model parameters, such as the interaction potential, size polydispersity, and noise in particle coordinates. In addition, with the grain-scale velocities obtained either from our network or from numerical simulations, we find an approximately equal and strong correlation between the global velocity and global velocity fluctuation in our 3D granular avalanche systems, which further demonstrates the predictive power of our trained graph neural networks to uncover the fundamental physics of granular avalanches. We expect our method to provide more insight into the avalanche dynamics of granular materials and other amorphous systems in the future. [ABSTRACT FROM AUTHOR]

Quantum diamond magnetometers using lock-in detection have successfully detected weak bio-magnetic fields from neurons, a live mammalian muscle, and a live mouse heart. This opens up the possibility of quantum diamond magnetometers visualizing microscopic distributions of the bio-magnetic fields. Here, we demonstrate a lock-in-based widefield quantum diamond microscopy, achieving a mean volume-normalized per-pixel sensitivity of 43.9 nT μ m 1.5 / H z 0.5 . We optimize the sensitivity by implementing a double resonance with hyperfine driving and magnetic field alignment along the ⟨ 001 ⟩ orientation of the diamond. Additionally, we show that sub-ms temporal resolution (∼ 0.4 ms) can be achieved while keeping the per-pixel sensitivity at a few tens of nanotesla per second using quantum diamond microscopy. This lock-in-based diamond quantum microscopy could be a step forward in mapping functional activity in neuronal networks in micrometer spatial resolution. [ABSTRACT FROM AUTHOR]

Aging is associated with the slowdown of neuronal processing and cognitive performance in the brain; however, the exact cellular mechanisms behind this deterioration in humans are poorly elucidated. Recordings in human acute brain slices prepared from tissue resected during brain surgery enable the investigation of neuronal changes with age. Although neocortical fast-spiking cells are widely implicated in neuronal network activities underlying cognitive processes, they are vulnerable to neurodegeneration. Herein, we analyzed the electrical properties of 147 fast-spiking interneurons in neocortex samples resected in brain surgery from 106 patients aged 11–84 years. By studying the electrophysiological features of action potentials and passive membrane properties, we report that action potential overshoot significantly decreases and spike half-width increases with age. Moreover, the action potential maximum-rise speed (but not the repolarization speed or the afterhyperpolarization amplitude) significantly changed with age, suggesting a particular weakening of the sodium channel current generated in the soma. Cell passive membrane properties measured as the input resistance, membrane time constant, and cell capacitance remained unaffected by senescence. Thus, we conclude that the action potential in fast-spiking interneurons shows a significant weakening in the human neocortex with age. This may contribute to the deterioration of cortical functions by aging. • Recordings in human acute brain slices prepared from tissue resected during brain surgery enable investigating neuronal changes with age. • Action potential in fast-spiking interneurons shows a significant weakening in the human neocortex with age. • Neuron intrinsic excitability, measured as the input resistance, membrane time constant, and cell capacitance remained unaffected by senescence. • These changes in neurons' electrical activity may contribute to the deterioration of cortical functions with age. [ABSTRACT FROM AUTHOR]

In this paper, based on linear matrix inequality technique, a simple controller and a compensatory controller are designed. It can track arbitrary fixed points and any periodic orbits. In addition, a synchronization control method via Chebyshev neural network with external disturbances is proposed. An adaptive controller is given. The Chebyshev neural network is used to approximate the uncertain nonlinear function and the adaptive law is used to adjust the corresponding parameters in the system. Taking a 4D memristive chaotic system as examples, the results are in consistent with the simulations. From a framework and control theoretical point of view, the proposed synchronization approach via compensation controller and Chebyshev neural network is firstly presented. From an application point of view, the proposed scheme can simplify the complexity of controller design. It is promising in many applications for mem-systems as secure communications and neural networks. [ABSTRACT FROM AUTHOR]

Dementia, and in particular Alzheimer's disease (AD), can be characterized by disrupted functional connectivity in the brain caused by beta-amyloid deposition in neural links. Non-pharmaceutical treatments for dementia have recently explored interventions involving the stimulation of neuronal populations in the gamma band. These interventions aim to restore brain network functionality by synchronizing rhythmic energy through various stimulation modalities. Entrainment, a newly proposed non-invasive sensory stimulation method, has shown promise in improving cognitive functions in dementia patients. This study investigates the effectiveness of entrainment in terms of promoting neural synchrony and spatial connectivity across the cortex. EEG signals were recorded during a 40 Hz auditory entrainment session conducted with a group of elderly participants with dementia. Phase locking value (PLV) between different intraregional and interregional sites was examined as an attribute of network synchronization, and connectivity of local and distant links were compared during the stimulation and rest trials. Our findings demonstrate enhanced neural synchrony between the frontal and parietal regions, which are key components of the brain's default mode network (DMN). The DMN operation is known to be impacted by dementia's progression, leading to reduced functional connectivity across the parieto-frontal pathways. Notably, entrainment alone significantly improves synchrony between these DMN components, suggesting its potential for restoring functional connectivity. [ABSTRACT FROM AUTHOR]

According to the predictive processing framework, perception emerges fromthe reciprocal exchange of predictions and prediction errors (PEs) between hierarchically organized neural circuits. The nonlemniscal division of the inferior colliculus (IC) is the earliest source of auditory PE signals, but their neuronal generators, properties, and functional relevance have remained mostly undefined. We recorded single-unitmismatch responses to auditory oddball stimulation at different intensities, together with activity evoked by two sequences of alternating tones to control frequency-specific effects. Our results reveal a differential treatment of the unpredictable "many-standards" control and the predictable "cascade" control by lemniscal and nonlemniscal IC neurons that is not present in the auditory thalamus or cortex. Furthermore, we found that frequency response areas of nonlemniscal IC neurons reflect their role in subcortical predictive processing, distinguishing three hierarchical levels: (1) nonlemniscal neurons with sharply tuned receptive fields exhibitmild repetition suppression without signaling PEs, thereby constituting the input level of the local predictive processing circuitry. (2) Neurons with broadly tuned receptive fields formthemain, "spectral" PE signaling system, which provides dynamic gain compensation to near-threshold unexpected sounds. This early enhancement of saliency reliant on spectral features was not observed in the auditory thalamus or cortex. (3) Untuned neurons form an accessory, "nonspectral" PE signaling system, which reports all surprising auditory deviances in a robust and consistentmanner, resembling nonlemniscal neurons in the auditory cortex. These nonlemniscal IC neurons show unstructured and unstable receptive fields that could result from inhibitory input controlled by corticofugal projections conveying top-down predictions. [ABSTRACT FROM AUTHOR]

Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics. Author summary: Brain activity during the resting period, commonly referred to as resting-state activity, is known to display coherent spontaneous fluctuations, which form various functional networks. Yet, the origin of these low-frequency (less than 0.05 Hz) fluctuations is poorly understood. In this work, we ask why is, in specific brain regions, such slow activity higher during rest periods compared to active periods. We use computational modeling and experimental data to demonstrate that the reduction of cholinergic release results in an increase in resting-state activity and its functional connectivity in a prominent resting state network—the DMN (Default-mode network). Our work supports the hypothesis that cellular intrinsic and synaptic changes mediated by (chemical) neuromodulatory mechanisms contribute to the increases of low-frequency fluctuations during the resting state. [ABSTRACT FROM AUTHOR]

Threat-response neural circuits are conserved across species and play roles in normal behavior and psychiatric diseases. Maladaptive changes in these neural circuits contribute to stress, mood, and anxiety disorders. Active coping in response to stressors is a psychosocial factor associated with resilience against stress-induced mood and anxiety disorders. The neural circuitry underlying active coping is poorly understood, but the functioning of these circuits could be key for overcoming anxiety and related disorders. The supramammillary nucleus (SuM) has been suggested to be engaged by threat. SuM has many projections and a poorly understood diversity of neural populations. In studies using mice, we identified a unique population of glutamatergic SuM neurons (SuMVGLUT2+::POA) based on projection to the preoptic area of the hypothalamus (POA) and found SuMVGLUT2+::POA neurons have extensive arborizations. SuMVGLUT2+::POA neurons project to brain areas that mediate features of the stress and threat responses including the paraventricular nucleus thalamus (PVT), periaqueductal gray (PAG), and habenula (Hb). Thus, SuMVGLUT2+::POA neurons are positioned as a hub, connecting to areas implicated in regulating stress responses. Here we report SuMVGLUT2+::POA neurons are recruited by diverse threatening stressors, and recruitment correlated with active coping behaviors. We found that selective photoactivation of the SuMVGLUT2+::POA population drove aversion but not anxiety like behaviors. Activation of SuMVGLUT2+::POA neurons in the absence of acute stressors evoked active coping like behaviors and drove instrumental behavior. Also, activation of SuMVGLUT2+::POA neurons was sufficient to convert passive coping strategies to active behaviors during acute stress. In contrast, we found activation of GABAergic (VGAT+) SuM neurons (SuMVGAT+) neurons did not alter drive aversion or active coping, but termination of photostimulation was followed by increased mobility in the forced swim test. These findings establish a new node in stress response circuitry that has projections to many brain areas and evokes flexible active coping behaviors. [ABSTRACT FROM AUTHOR]

Concrete symbols (e.g., sun, run) can be learned in the context of objects and actions, thereby grounding their meaning in the world. However, it is controversial whether a comparable avenue to semantic learning exists for abstract symbols (e.g., democracy). When we simulated the putative brain mechanisms of conceptual/semantic grounding using brain‐constrained deep neural networks, the learning of instances of concrete concepts outside of language contexts led to robust neural circuits generating substantial and prolonged activations. In contrast, the learning of instances of abstract concepts yielded much reduced and only short‐lived activity. Crucially, when conceptual instances were learned in the context of wordforms, circuit activations became robust and long‐lasting for both concrete and abstract meanings. These results indicate that, although the neural correlates of concrete conceptual representations can be built from grounding experiences alone, abstract concept formation at the neurobiological level is enabled by and requires the correlated presence of linguistic forms. A one‐page Accessible Summary of this article in nontechnical language is freely available in the Supporting Information online and at https://oasis‐database.org. [ABSTRACT FROM AUTHOR]

Durations in the several seconds' range are cognitively accessible during active timing. Functional neuroimaging studies suggest the engagement of the basal ganglia (BG) and supplementary motor area (SMA). However, their functional relevance and arrangement remain unclear because non‐timing cognitive processes temporally coincide with the active timing. To examine the potential contamination by parallel processes, we introduced a sensory control and a motor control to the duration‐reproduction task. By comparing their hemodynamic functions, we decomposed the neural activities in multiple brain loci linked to different cognitive processes. Our results show a dissociation of two cortical neural circuits: the SMA for both active timing and motor preparation, followed by a prefrontal–parietal circuit related to duration working memory. We argue that these cortical processes represent duration as the content but at different levels of abstraction, while the subcortical structures, including the BG and thalamus, provide the logistic basis of timing by coordinating the temporal framework across brain structures. [ABSTRACT FROM AUTHOR]

Orienting attention by social gaze cues shares some characteristics with orienting attention by non-social arrow cues, but it is unclear whether they rely on similar neural mechanisms. The present ALE-meta-analysis assessed the pattern of brain activation reported in 40 single experiments (18 with arrows, 22 with gaze), with a total number of 806 participants. Our findings show that the network for orienting attention by social gaze and by non-social arrow cues is in part functionally segregated. Orienting by both types of cues relies on the activity of brain regions involved in endogenous attention (the superior frontal gyrus). Importantly, only orienting by gaze cues was also associated with the activity of brain regions involved in exogenous attention (medial frontal gyrus), processing gaze, and mental state attribution (superior temporal sulcus, temporoparietal junction). [ABSTRACT FROM AUTHOR]

Molecular and cellular pathophysiological mechanisms of hypothalamus-related disorders, such as metabolic, circadian, and sleep disorders, can be studied using in vitro models of the hypothalamus. New methods to generate hypothalamic organoids are relevant for drug screening in the context of hypothalamus-related disorders. However, advances in human hypothalamic organoids are still lacking. Patient-derived induced pluripotent stem cells (iPSCs) and neurons derived from them are becoming increasingly important as experimental models for circadian rhythm disorders. Integrating data generated in vitro , in vivo , and in silico could increase the experimental data and promote new avenues of research. Multidisciplinary approaches will be necessary to achieve relevant and translational experimental models that advance our understanding of hypothalamus-related disorders and therapeutic strategies. The hypothalamus, a small and intricate brain structure, orchestrates numerous neuroendocrine functions through specialized neurons and nuclei. Disruption of this complex circuitry can result in various diseases, including metabolic, circadian, and sleep disorders. Advances in in vitro models and their integration with new technologies have significantly benefited research on hypothalamic function and pathophysiology. We explore existing in vitro hypothalamic models and address their challenges and limitations as well as translational findings. We also highlight how collaborative efforts among multidisciplinary teams are essential to develop relevant and translational experimental models capable of replicating intricate neural circuits and neuroendocrine pathways, thereby advancing our understanding of therapeutic targets and drug discovery in hypothalamus-related disorders. [ABSTRACT FROM AUTHOR]

Aim: The Repressor Element‐1 Silencing Transcription Factor (REST) is an epigenetic master regulator playing a crucial role in the nervous system. In early developmental stages, REST downregulation promotes neuronal differentiation and the acquisition of the neuronal phenotype. In addition, postnatal fluctuations in REST expression contribute to shaping neuronal networks and maintaining network homeostasis. Here we investigate the role of the early postnatal deletion of neuronal REST in the assembly and strength of excitatory and inhibitory synaptic connections. Methods: We investigated excitatory and inhibitory synaptic transmission by patch‐clamp recordings in acute neocortical slices in a conditional knockout mouse model (RestGTi) in which Rest was deleted by delivering PHP.eB adeno‐associated viruses encoding CRE recombinase under the control of the human synapsin I promoter in the lateral ventricles of P0‐P1 pups. Results: We show that, under physiological conditions, Rest deletion increased the intrinsic excitability of principal cortical neurons in the primary visual cortex and the density and strength of excitatory synaptic connections impinging on them, without affecting inhibitory transmission. Conversely, in the presence of a pathological excitation/inhibition imbalance induced by pentylenetetrazol, Rest deletion prevented the increase in synaptic excitation and decreased seizure severity. Conclusion: The data indicate that REST exerts distinct effects on the excitability of cortical circuits depending on whether it acts under physiological conditions or in the presence of pathologic network hyperexcitability. In the former case, REST preserves a correct excitatory/inhibitory balance in cortical circuits, while in the latter REST loses its homeostatic activity and may become pro‐epileptogenic. [ABSTRACT FROM AUTHOR]

Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity. [ABSTRACT FROM AUTHOR]

The use of designer receptors exclusively activated by designer drugs (DREADDs) has led to significant advances in our understanding of the neural circuits that govern behavior. By allowing selective control over cellular activity and signaling, DREADDs have become an integral tool for defining the pathways and cellular phenotypes that regulate sleep, pain, motor activity, goal-directed behaviors, and a variety of other processes. In this review, we provide a brief overview of DREADDs and discuss notable discoveries in the neurosciences with an emphasis on circuit mechanisms. We then highlight methodological approaches to achieve pathway specific activation of DREADDs. Finally, we discuss spatial and temporal constraints of DREADDs signaling and how these features can be incorporated into experimental designs to precisely dissect circuits of interest. [ABSTRACT FROM AUTHOR]

Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results. [ABSTRACT FROM AUTHOR]

This study aims to investigate the underlying neurobiological mechanisms that regulate natural reward seeking behaviors, specifically in the context of sexual behavior and sucrose self-administration. The role of CaMKIIa+ neurons in the bed nucleus of the stria terminalis (BNST) was explored using chemogenetic silencing and -stimulation. Additionally, the study examined how these effects interacted with the internal state of the animals. Through detailed behavioral analysis, it was demonstrated that CaMKIIa+ neurons in the BNST play a significant role in the regulation of both sexual behavior and sucrose self-administration. Although the behavioral outcome measures differed between the two behaviors, the regulatory role of the CaMKIIa+ neurons in the BNST was found to converge on the modulation of the pacing of engagement in these behaviors in male rats. Moreover, our study confirmed that the internal physiological state of the animal affects how the BNST modulates these behaviors. These findings suggest that different types of natural rewards may recruit a similar brain circuitry to regulate the display of motivated behaviors. Overall, this research provides valuable insights into the neural mechanisms underlying natural reward seeking and sheds light on the interconnected nature of reward-related behaviors in male rats. [ABSTRACT FROM AUTHOR]

Background and Purpose: P2X4 receptors (P2X4R) are ligand gated cation channels that are activated by extracellular ATP released by neurons and glia. The receptors are widely expressed in the brain and have fractional calcium currents comparable with NMDA receptors. Although P2X4Rs have been reported to modulate synaptic transmission and plasticity, their involvement in shaping neuronal network activity remains to be elucidated. Experimental Approach: We investigated the effects of P2X receptors at network and synaptic level using local field potential electrophysiology, whole cell patch clamp recordings and calcium imaging in fast spiking parvalbumin positive interneurons (PVINs) in rat and mouse hippocampal slices. The stable ATP analogue ATPγS, selective antagonists and P2X4R knockout mice were used. Key Results: The P2XR agonist ATPγS reversibly decreased the power of gamma oscillations. This inhibition could be antagonized by the selective P2X4R antagonist PSB‐12062 and was not observed in P2X4−/− mice. The phasic excitatory inputs of CA3 PVINs were one of the main regulators of the gamma power. Associational fibre compound excitatory postsynaptic currents (cEPSCs) in CA3 PVINs were inhibited by P2X4R activation. This effect was reversible, dependent on intracellular calcium and dynamin‐dependent internalization of AMPA receptors. Conclusions and Implications: The results indicate that P2X4Rs are an important source of dendritic calcium in CA3 PVINs, thereby regulating excitatory synaptic inputs onto the cells and presumably the state of gamma oscillations in the hippocampus. P2X4Rs represent an effective target to modulate hippocampal network activity in pathophysiological conditions such as Alzheimer's disease and schizophrenia. [ABSTRACT FROM AUTHOR]

In the animal kingdom, threat information is perceived mainly through vision. The subcortical visual pathway plays a critical role in the rapid processing of visual information-induced fear, and triggers a response. Looming-evoked behavior in rodents, mimicking response to aerial predators, allowed identify the neural circuitry underlying instinctive defensive behaviors; however, the influence of disk/background contrast on the looming-induced behavioral response has not been examined, either in rats or mice. We studied the influence of the dark disk/gray background contrast in the type of rat and mouse defensive behavior in the looming arena, and we showed that rat and mouse response as a function of disk/background contrast adjusted to a sigmoid-like relationship. Both sex and age biased the contrast-dependent response, which was dampened in rats submitted to retinal unilateral or bilateral ischemia. Moreover, using genetically manipulated mice, we showed that the three type of photoresponsive retinal cells (i.e., cones, rods, and intrinsically photoresponsive retinal ganglion cells (ipRGCs)), participate in the contrast-dependent response, following this hierarchy: cones > > rods > > > ipRGCs. The cone and rod involvement was confirmed using a mouse model of unilateral non-exudative age-related macular degeneration, which only damages canonical photoreceptors and significantly decreased the contrast sensitivity in the looming arena. [ABSTRACT FROM AUTHOR]

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics. Author summary: Astrocytes, a non-neuronal cell type, constitute a significant portion of all cells in the brain, yet our understanding of their involvement in neural computation remains limited. In this paper, we use computational modeling to examine how astrocytes may interact with neurons to expand the types of activity and, ultimately, computations that neurons can produce. Two features of astrocytes make them interesting in this context. First, a single astrocyte is able to modulate dozens of neurons and synaptic connections between them. Furthermore, this modulation occurs at slower temporal scales than the neural activity itself, thus meaning that astrocytes could have long-lasting effects on neural activity and connectivity. Our computational models capture both of these features. Using these models, we show that networks with astrocytes can more readily adapt to slowly varying task parameters versus those with neurons alone. We further analyze the models to understand, in mathematical terms, why such effects may arise. Our results shed light on the potential computational role of this common, but enigmatic type of brain cell. [ABSTRACT FROM AUTHOR]

Unit activity in particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses to static images along the primate ventral visual cortex. Linear combinations of DNN unit activities are widely used to build predictive models of neuronal activity in the visual cortex. Nevertheless, prediction performance in these models is often investigated on stimulus sets consisting of everyday objects under naturalistic settings. Recent work has revealed a generalization gap in how predicting neuronal responses to synthetically generated out-of-distribution (OOD) stimuli. Here, we investigated how the recent progress in improving DNNs' object recognition generalization, as well as various DNN design choices such as architecture, learning algorithm, and datasets have impacted the generalization gap in neural predictivity. We came to a surprising conclusion that the performance on none of the common computer vision OOD object recognition benchmarks is predictive of OOD neural predictivity performance. Furthermore, we found that adversarially robust models often yield substantially higher generalization in neural predictivity, although the degree of robustness itself was not predictive of neural predictivity score. These results suggest that improving object recognition behavior on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex. Author summary: Inspired by the neural circuits of the brain, deep neural networks (DNN) have been steadily improving in their ability to perform foundational visual tasks such as object recognition. Whereas, early models struggled with generalization to abstract visual domains such as line drawings and cartoons, recent advancement have approached near-human recognition capabilities. Moreover, the unit activity in these networks exhibit strong similarities with the activity of single-unit recordings along the primate ventral visual cortex. This capability of DNNs has provided visual neuroscientists with precise models for exploring the neural underpinnings of object recognition. Our research probes whether enhancements in neural networks' recognition of out-of-distribution objects correlate with improved predictability of brain activity in the visual cortex of monkeys to synthetic stimuli. We found that the out of distribution object recognition performance on natural image datasets is not a reliable measure of neural predictivity. However, DNN models that were trained to be more resilient to adversarially generated noise patterns as well as DNN ensembles, consistently yielded better generalization in neural predictivity. Altogether, our results suggest that improving object recognition behaviour on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex. [ABSTRACT FROM AUTHOR]

Fluctuations in the activity of sensory neurons often predict perceptual decisions. This connection can be quantified with a metric called choice probability (CP), and there is a longstanding debate about whether CP reflects a causal influence on decisions or an echo of decision-making activity elsewhere in the brain. Here, we show that CP can reflect a third variable, namely, the movement used to indicate the decision. In a standard visual motion discrimination task, neurons in the middle temporal (MT) area of primate cortex responded more strongly during trials that involved a saccade toward their receptive fields. This variability accounted for much of the CP observed across the neuronal population, and it arose through training. Moreover, pharmacological inactivation of MT biased behavioral responses away from the corresponding visual field locations. These results demonstrate that training on a task with fixed sensorimotor contingencies introduces movement-related activity in sensory brain regions and that this plasticity can shape the neural circuitry of perceptual decision-making. [ABSTRACT FROM AUTHOR]

Alterations in the experience-dependent and autonomous elaboration of neural circuits are assumed to underlie autism spectrum disorder (ASD), though it is unclear what synaptic traits are responsible. Here, utilizing a valproic acid–induced ASD marmoset model, which shares common molecular features with idiopathic ASD, we investigate changes in the structural dynamics of tuft dendrites of upper-layer pyramidal neurons and adjacent axons in the dorsomedial prefrontal cortex through two-photon microscopy. In model marmosets, dendritic spine turnover is upregulated, and spines are generated in clusters and survived more often than in control marmosets. Presynaptic boutons in local axons, but not in commissural long-range axons, demonstrate hyperdynamic turnover in model marmosets, suggesting alterations in projection-specific plasticity. Intriguingly, nasal oxytocin administration attenuates clustered spine emergence in model marmosets. Enhanced clustered spine generation, possibly unique to certain presynaptic partners, may be associated with ASD and be a potential therapeutic target. Using in vivo two-photon microscopy, the structural dynamics of synapses in the prefrontal cortex of an autism model marmoset were studied. In the marmoset, clustered dendritic spine generation was heightened, which was attenuated by oxytocin. [ABSTRACT FROM AUTHOR]

• RMTg lesions reduce LHb induced inhibition of DA firing. • RMTg lesions reduce foot shock induced inhibition of DA firing. • Lower prevalence of neurons with rebound excitation after RMTg lesion. • RMTg lesions increase LHb induced excitation of DA firing. • Low correspondence between foot shock and LHb stimulation response. Dopamine (DA) neurons of the substantia nigra (SN) and ventral tegmental area generally respond to aversive stimuli or the absence of expected rewards with transient inhibition of firing rates, which can be recapitulated with activation of the lateral habenula (LHb) and eliminated by lesioning the intermediating rostromedial tegmental nucleus (RMTg). However, a minority of DA neurons respond to aversive stimuli, such as foot shock, with a transient increase in firing rate, an outcome that rarely occurs with LHb stimulation. The degree to which individual neurons respond to these two stimulation modalities with the same response phenotype and the role of the RMTg is not known. Here, we record responses from single SN DA neurons to alternating activation of the LHb and foot shock in male rats. Lesions of the RMTg resulted in a shift away from inhibition to no response during both foot shock and LHb stimulation. Furthermore, lesions unmasked an excitatory response during LHb stimulation. The response correspondence within the same neuron between the two activation sources was no different from chance in sham controls, suggesting that external inputs rather than intrinsic DA neuronal properties are more important to response outcome. These findings contribute to a literature that shows a complex neurocircuitry underlies the regulation of DA activity and, by extension, behaviors related to learning, anhedonia, and cognition. [ABSTRACT FROM AUTHOR]

Tissue-clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeterthick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses. [ABSTRACT FROM AUTHOR]

Information seeking, such as standing on tiptoes to look around in humans, is observed across animals and helps survival. Its rodent analog--unsupported rearing on hind legs--was a classic model in deciphering neural signals of cognition and is of intense renewed interest in preclinical modeling of neuropsychiatric states. Neural signals and circuits controlling this dedicated decision to seek information remain largely unknown. While studying subsecond timing of spontaneous behavioral acts and activity of melaninconcentrating hormone (MCH) neurons (MNs) in behaving male and female mice, we observed large MN activity spikes that aligned to unsupported rears. Complementary causal, loss and gain of function, analyses revealed specific control of rear frequency and duration by MNs and MCHR1 receptors. Activity in a key stress center of the brain--the locus ceruleus noradrenaline cells--rapidly inhibited MNs and required functional MCH receptors for its endogenous modulation of rearing. By defining a neural module that both tracks and controls rearing, these findings may facilitate further insights into biology of information seeking. [ABSTRACT FROM AUTHOR]

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body. Author summary: Learning in situations where reward is only rarely encountered is difficult. It is hard to discover the right sequence of actions when most actions, most of the time, provide no apparent progress towards a goal. Inspired by a neural circuit in the insect brain, and using direct electrical connections between neurons as well as synaptic connections, we present a new algorithm for learning. The model represents the states of the world with nodes and an electrical connection between two nodes is strengthened when the two corresponding states occur consecutively. The connections between nodes can also become associated to output actions that correlate with (hence are assumed to cause) transitions between states. When a particular goal is chosen or associated with a reward, for example, the target location in a navigation task, a flow of electrical current through the nodes will find the shortest path from the present state to the goal state and trigger the appropriate actions. [ABSTRACT FROM AUTHOR]

While depolarization of the neuronal membrane is known to evoke the neurotransmitter release from synaptic vesicles, hyperpolarization is regarded as a resting state of chemical neurotransmission. Here, we report that hyperpolarizing neurons can actively signal neural information by employing undocked hemichannels. We show that UNC-7, a member of the innexin family in Caenorhabditis elegans, functions as a hemichannel in thermosensory neurons and transmits temperature information from the thermosensory neurons to their postsynaptic interneurons. By monitoring neural activities in freely behaving animals, we find that hyperpolarizing thermosensory neurons inhibit the activity of the interneurons and that UNC-7 hemichannels regulate this process. UNC-7 is required to control thermotaxis behavior and functions independently of synaptic vesicle exocytosis. Our findings suggest that innexin hemichannels mediate neurotransmission from hyperpolarizing neurons in a manner that is distinct from the synaptic transmission, expanding the way of neural circuitry operations. [ABSTRACT FROM AUTHOR]

Several lines of evidence demonstrate that the brain histaminergic system is fundamental for cognitive processes and the expression of memories. Here, we investigated the effect of acute silencing or activation of histaminergic neurons in the hypothalamic tuberomamillary nucleus (TMNHA neurons) in vivo in both sexes in an attempt to provide direct and causal evidence of the necessary role of these neurons in recognition memory formation and retrieval. To this end, we compared the performance of mice in two non-aversive and non-rewarded memory tests, the social and object recognition memory tasks, which are known to recruit different brain circuitries. To directly establish the impact of inactivation or activation of TMNHA neurons, we examined the effect of specific chemogenetic manipulations during the formation (acquisition/consolidation) or retrieval of recognition memories. We consistently found that acute chemogenetic silencing of TMNHA neurons disrupts the formation or retrieval of both social and object recognition memory in males and females. Conversely, acute chemogenetic activation of TMNHA neurons during training or retrieval extended social memory in both sexes and object memory in a sex-specific fashion. These results suggest that the formation or retrieval of recognition memory requires the tonic activity of histaminergic neurons and strengthen the concept that boosting the brain histaminergic system can promote the retrieval of apparently lost memories. [ABSTRACT FROM AUTHOR]

It has been widely recognized that electroacupuncture (EA) inducing the release of β-endorphin represents a crucial mechanism of EA analgesia. The arcuate nucleus (ARC) in the hypothalamus is a vital component of the endogenous opioid peptide system. Serving as an integration center, the periaqueductal gray (PAG) receives neural fiber projections from the frontal cortex, insular cortex, and ARC. However, the specific mechanisms how EA facilitates the release of β-endorphin within the ARC, eliciting analgesic effects are yet to be elucidated. In this study, we conducted in vivo and in vitro experiments by transcriptomics, microdialysis, photogenetics, chemical genetics, and calcium imaging, combined with transgenic animals. Firstly, we detected 2 Hz EA at the Zusanli (ST36) increased the level of β-endorphin and transcriptional level of proopiomelanocortin (POMC). Our transcriptomics profiling demonstrated that 2 Hz EA at the ST36 modulates the expression of c-Fos and Jun B in ARC brain nuclear cluster, and the transcriptional regulation of 2 Hz EA mainly occur in POMC neurons by Immunofluorescence staining verification. Meaning while, 2 Hz EA specifically activated the cAMP-PKA-CREB signaling pathway in ARC which mediating the c-Fos and Jun B transcription, and 2 Hz EA analgesia is dependent on the activation of cAMP-PKA-CREB signaling pathway in ARC. In order to investigate how the β-endorphin produced in ARC transfer to integration center PAG, transneuronal tracing technology was used to observe the 2 Hz EA promoted the neural projection from ARC to PAG compared to 100 Hz EA and sham mice. Inhibited PAGGABA neurons, the transfer of β-endorphin from the ARC nucleus to the PAG nucleus through the ARCPOMC-PAGGABA neural circuit. Furthermore, by manipulating the excitability of POMC neurons from ARCPOMC to PAGGABA using inhibitory chemogenetics and optogenetics, we found that this inhibition significantly reduced transfer of β-endorphin from the ARC nucleus to the PAG nucleus and the effectiveness of 2 Hz EA analgesia in neurological POMC cyclization recombination enzyme (Cre) mice and C57BL/6J mice, which indicates that the transfer of β-endorphin depends on the activation of POMC neurons prefect from ARCPOMC to PAGGABA. These findings contribute to our understanding of the neural circuitry underlying the EA pain-relieving effects and maybe provide valuable insights for optimizing EA stimulation parameters in clinical pain treatment using the in vivo dynamic visual investigating the central analgesic mechanism. [ABSTRACT FROM AUTHOR]

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation. A review of computational tools for mapping networks from in vitro neuron morphology, facilitating topological analysis of cell organization. [ABSTRACT FROM AUTHOR]

The primary neural circuit affected in Amyotrophic Lateral Sclerosis (ALS) patients is the corticospinal motor circuit, originating in upper motor neurons (UMNs) in the cerebral motor cortex which descend to synapse with the lower motor neurons (LMNs) in the spinal cord to ultimately innervate the skeletal muscle. Perturbation of these neural circuits and consequent loss of both UMNs and LMNs, leading to muscle wastage and impaired movement, is the key pathophysiology observed. Despite decades of research, we are still lacking in ALS disease-modifying treatments. In this review, we document the current research from patient studies, rodent models, and human stem cell models in understanding the mechanisms of corticomotor circuit dysfunction and its implication in ALS. We summarize the current knowledge about cortical UMN dysfunction and degeneration, altered excitability in LMNs, neuromuscular junction degeneration, and the non-cell autonomous role of glial cells in motor circuit dysfunction in relation to ALS. We further highlight the advances in human stem cell technology to model the complex neural circuitry and how these can aid in future studies to better understand the mechanisms of neural circuit dysfunction underpinning ALS. [ABSTRACT FROM AUTHOR]

Alcohol use disorder is characterized by compulsive alcohol-seeking behavior, which is associated with dysregulation of afferent projections from the medial prefrontal cortex to the basolateral amygdala (BLA). However, the contribution of the cell type–specific mechanism in this neuronal circuit to alcohol-seeking behavior remains unclear. Mice were trained with 2-bottle choice and operant alcohol self-administration procedures. Anterograde and retrograde viral methods traced the connection between dopamine type 1 receptor (D1R) neurons and BLA neurons. Electrophysiology and in vivo optogenetic techniques were used to test the function of neural circuits in alcohol-seeking behavior. Chronic alcohol consumption preferentially changed the activity of posterior BLA (pBLA) neurons but not anterior BLA (aBLA) neurons and overexcited D1R neurons in the medial prefrontal cortex. Interestingly, we found that 2 populations of D1R neurons, anterior and posterior (pD1R) neurons, separately targeted the aBLA and pBLA, respectively, and only a few D1R neurons innervated both aBLA and pBLA neurons. Furthermore, pD1R neurons exhibited more excitability than anterior D1R neurons in alcohol-drinking mice. Moreover, we observed enhanced glutamatergic transmission and an increased NMDA/AMPA receptor ratio in the medial prefrontal cortex inputs from pD1R neurons to the pBLA. Optogenetic long-term depression induction of the pD1R-pBLA circuit reduced alcohol-seeking behavior, while optogenetic long-term depression or long-term potentiation induction of the anterior D1R–aBLA circuit produced no change in alcohol intake. The pD1R-pBLA circuit mediates chronic alcohol consumption, which may suggest a cell type–specific neuronal mechanism underlying reward-seeking behavior in alcohol use disorder. [ABSTRACT FROM AUTHOR]

Individual variability in response to rewarding stimuli is a striking but understudied phenomenon. The mesolimbic dopamine system is critical in encoding the reinforcing properties of both natural reward and alcohol; however, how innate or baseline differences in the response dynamics of this circuit define individual behavior and shape future vulnerability to alcohol remain unknown. Using naturalistic behavioral assays, a voluntary alcohol drinking paradigm, in vivo fiber photometry, in vivo electrophysiology, and chemogenetics, we investigated how differences in mesolimbic neural circuit activity contribute to the individual variability seen in reward processing and, by proxy, alcohol drinking. We first characterized heterogeneous behavioral and neural responses to natural reward and defined how these baseline responses predicted future individual alcohol-drinking phenotypes in male mice. We then determined spontaneous ventral tegmental area dopamine neuron firing profiles associated with responses to natural reward that predicted alcohol drinking. Using a dual chemogenetic approach, we mimicked specific mesolimbic dopamine neuron firing activity before or during voluntary alcohol drinking to link unique neurophysiological profiles to individual phenotype. We show that hyperdopaminergic individuals exhibit a lower neuronal response to both natural reward and alcohol that predicts lower levels of alcohol consumption in the future. These findings reveal unique, circuit-specific neural signatures that predict future individual vulnerability or resistance to alcohol and expand the current knowledge base on how some individuals are able to titrate their alcohol consumption whereas others go on to engage in unhealthy alcohol-drinking behaviors. [ABSTRACT FROM AUTHOR]

Recovery of consciousness after coma remains one of the most challenging areas for accurate diagnosis and effective therapeutic engagement in the clinical neurosciences. Recovery depends on preservation of neuronal integrity and evolving changes in network function that re-establish environmental responsiveness. It typically occurs in defined steps: it begins with eye opening and unresponsiveness in a vegetative state, then limited recovery of responsiveness characterizes the minimally conscious state, and this is followed by recovery of reliable communication. This review considers several points for novel interventions, for example, in persons with cognitive motor dissociation in whom a hidden cognitive reserve is revealed. Circuit mechanisms underlying restoration of behavioral responsiveness and communication are discussed. An emerging theme is the possibility to rescue latent capacities in partially damaged human networks across time. These opportunities should be exploited for therapeutic engagement to achieve individualized solutions for restoration of communication and environmental interaction across varying levels of recovery. Here, Nicholas Schiff reviews several entry points for innovations aimed at recovery of consciousness after coma. Opportunities for rescuing latent capacities in partially damaged human neurons are considered across a range of recoveries from coma to chronic cognitive impairment. [ABSTRACT FROM AUTHOR]

The purpose of this research is to investigate the synchronization and control for uncertain memristor-based cellular neural network and its application in secure communication. To address the issue, a novel sliding mode function is designed, on which the system states can effectively converge to the equilibrium point after reaching the sliding mode surface. Moreover, the corresponding controller is constructed by employing the proposed sliding mode function. The proposed control strategy achieves the synchronization of the uncertain memristor-based cellular neural network, and effectively addresses the integral saturation existing in traditional one. In addition, the control performance, including convergence speed, control accuracy, robustness and security, are significantly enhanced. Furthermore, the stability of the system is discussed based on Lyapunov theory. Finally, comparative tests and application examples are presented to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Understanding neurogenetic mechanisms underlying neuropsychiatric disorders such as schizophrenia and autism is complicated by their inherent clinical and genetic heterogeneity. Williams syndrome (WS), a rare neurodevelopmental condition in which both the genetic alteration (hemideletion of ~ twenty-six 7q11.23 genes) and the cognitive/behavioral profile are well-defined, offers an invaluable opportunity to delineate gene-brain-behavior relationships. People with WS are characterized by increased social drive, including particular interest in faces, together with hallmark difficulty in visuospatial processing. Prior work, primarily in adults with WS, has searched for neural correlates of these characteristics, with reports of altered fusiform gyrus function while viewing socioemotional stimuli such as faces, along with hypoactivation of the intraparietal sulcus during visuospatial processing. Here, we investigated neural function in children and adolescents with WS by using four separate fMRI paradigms, two that probe each of these two cognitive/behavioral domains. During the two visuospatial tasks, but not during the two face processing tasks, we found bilateral intraparietal sulcus hypoactivation in WS. In contrast, during both face processing tasks, but not during the visuospatial tasks, we found fusiform hyperactivation. These data not only demonstrate that previous findings in adults with WS are also present in childhood and adolescence, but also provide a clear example that genetic mechanisms can bias neural circuit function, thereby affecting behavioral traits. [ABSTRACT FROM AUTHOR]

GABAergic interneurons play a critical role in maintaining neural circuit balance, excitation–inhibition regulation, and cognitive function modulation. In tuberous sclerosis complex (TSC), GABAergic neuron dysfunction contributes to disrupted network activity and associated neurological symptoms, assumingly in a cell type-specific manner. This GABAergic centric study focuses on identifying specific interneuron subpopulations within TSC, emphasizing the unique characteristics of medial ganglionic eminence (MGE)- and caudal ganglionic eminence (CGE)-derived interneurons. Using single-nuclei RNA sequencing in TSC patient material, we identify somatostatin-expressing (SST+) interneurons as a unique and immature subpopulation in TSC. The disrupted maturation of SST+ interneurons may undergo an incomplete switch from excitatory to inhibitory GABAergic signaling during development, resulting in reduced inhibitory properties. Notably, this study reveals markers of immaturity specifically in SST+ interneurons, including an abnormal NKCC1/KCC2 ratio, indicating an imbalance in chloride homeostasis crucial for the postsynaptic consequences of GABAergic signaling as well as the downregulation of GABAA receptor subunits, GABRA1, and upregulation of GABRA2. Further exploration of SST+ interneurons revealed altered localization patterns of SST+ interneurons in TSC brain tissue, concentrated in deeper cortical layers, possibly linked to cortical dyslamination. In the epilepsy context, our research underscores the diverse cell type-specific roles of GABAergic interneurons in shaping seizures, advocating for precise therapeutic considerations. Moreover, this study illuminates the potential contribution of SST+ interneurons to TSC pathophysiology, offering insights for targeted therapeutic interventions. [ABSTRACT FROM AUTHOR]

Understanding the nervous system is an important but perhaps ambitious goal, particularly for students in lower secondary education. It is important because of its' direct role in both mental and physical health, and it is ambitious because instruction focuses on the human nervous system, which is extremely complex, and subject to numerous misconceptions. Despite its' complexity, the science curricula, both nationally and internationally, emphasize an understanding of the system, and not just knowledge of isolated facts. But what does it mean to understand this system, and what content knowledge is critical for understanding it? Unfortunately, the curricula are usually too general to answer these questions, therefore other sources of information are needed. Using the science literature, the present study defines the system level of the nervous system and proposes three basic aspects necessary to understand it: 1) neural circuit architecture, 2) synaptic action, and 3) nerve signal origin. With this background, the aim of the present study is to identify lower secondary school students' conceptions of these three aspects, and to determine how they impact students' understanding of the system. To reach this aim, the study used a questionary which allowed for a mixed method design, and the results show that many students have an immediate conception of the brain as the origin of nerve signals. In addition, many students hold the alternative conceptions that 1) synaptic action is exclusively excitatory, and that 2) neural circuits consists of neurons connected in a chain, one single neuron after another. These alternative conceptions prevent students from understanding the system. Implications for instruction are discussed in the context of conceptual learning theories, and teaching strategies are proposed. Since similar curricula goals and textbook content exist in several countries, the present results may be representative across nations. [ABSTRACT FROM AUTHOR]

Monosynaptic connectivity mapping is crucial for building circuit-level models of neural computation. Two-photon optogenetic stimulation, when combined with whole-cell recording, enables large-scale mapping of physiological circuit parameters. In this experimental setup, recorded postsynaptic currents are used to infer the presence and strength of connections. For many cell types, nearby connections are those we expect to be strongest. However, when the postsynaptic cell expresses opsin, optical excitation of nearby cells can induce direct photocurrents in the postsynaptic cell. These photocurrent artifacts contaminate synaptic currents, making it difficult or impossible to probe connectivity for nearby cells. To overcome this problem, we developed a computational tool, Photocurrent Removal with Constraints (PhoRC). Our method is based on a constrained matrix factorization model which leverages the fact that photocurrent kinetics are less variable than those of synaptic currents. We demonstrate on real and simulated data that PhoRC consistently removes photocurrents while preserving synaptic currents, despite variations in photocurrent kinetics across datasets. Our method allows the discovery of synaptic connections which would have been otherwise obscured by photocurrent artifacts, and may thus reveal a more complete picture of synaptic connectivity. PhoRC runs faster than real time and is available as open source software. Author summary: Mapping the presence and strength of connections between neurons is necessary for understanding how the structure of neural circuits gives rise to their function. Historically, directly measuring these connections has required difficult and time-consuming paired-patch experiments. However, recent developments in the field of two-photon optogenetics enable mapping experiments with much higher throughput. In these new experiments, a single postsynaptic neuron is recorded using an electrode, and a two-photon laser is used to optogenetically activate nearby neurons. When the laser activates a connected presynaptic neuron, the experimenter observes an electrical current on the electrode. The size of the current can then be used to determine the strength of the connection. However, this technique has a drawback: when stimulating near the postsynaptic cell, the laser can directly evoke an electrical current—called a photocurrent—even if no connection is present. This is problematic because it confounds estimates of connectivity. In this work, we developed a computational tool which removes the photocurrent artifact while preserving signals corresponding to true neuronal connections. We expect that this tool will serve as a resource for experimentalists, enabling a more complete picture of synaptic connectivity. [ABSTRACT FROM AUTHOR]

Rationale: Previous work identified an attenuating effect of the matrix metalloproteinase (MMP) inhibitor doxycycline on fear memory consolidation. This may present a new mechanistic approach for the prevention of trauma-related disorders. However, so far, this has only been unambiguously demonstrated in a cued delay fear conditioning paradigm, in which a simple geometric cue predicted a temporally overlapping aversive outcome. This form of learning is mainly amygdala dependent. Psychological trauma often involves the encoding of contextual cues, which putatively necessitates partly different neural circuits including the hippocampus. The role of MMP signalling in the underlying neural pathways in humans is unknown. Methods: Here, we investigated the effect of doxycycline on configural fear conditioning in a double-blind placebo-controlled randomised trial with 100 (50 females) healthy human participants. Results: Our results show that participants successfully learned and retained, after 1 week, the context-shock association in both groups. We find no group difference in fear memory retention in either of our pre-registered outcome measures, startle eye-blink responses and pupil dilation. Contrary to expectations, we identified elevated fear-potentiated startle in the doxycycline group early in the recall test, compared to the placebo group. Conclusion: Our results suggest that doxycycline does not substantially attenuate contextual fear memory. This might limit its potential for clinical application. [ABSTRACT FROM AUTHOR]

The escalating incidence of diabetes mellitus has amplified the global impact of diabetic retinopathy. There are known structural and functional changes in the diabetic retina that precede the fundus photography abnormalities which currently are used to diagnose clinical diabetic retinopathy. Understanding these subclinical alterations is important for effective disease management. Histology and high‐resolution clinical imaging reveal that the entire neurovascular unit, comprised of retinal vasculature, neurons and glial cells, is affected in subclinical disease. Early functional manifestations are seen in the form of blood flow and electroretinography disturbances. Structurally, there are alterations in the cellular components of vasculature, glia and the neuronal network. On clinical imaging, changes to vessel density and thickness of neuronal layers are observed. How these subclinical disturbances interact and ultimately manifest as clinical disease remains elusive. However, this knowledge reveals potential early therapeutic targets and the need for imaging modalities that can detect subclinical changes in a clinical setting. [ABSTRACT FROM AUTHOR]

Highlights: • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images. • Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion. • The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results. • The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets. BACKGROUND: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming. OBJECTIVE: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors. METHOD: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness. RESULTS: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively. CONCLUSION: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice. [ABSTRACT FROM AUTHOR]

The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behavior, it is crucial to convene as much information as possible about their topological organization. However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the transfer entropy between system elements. The topological information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition (Φ -ID), allow one to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics, and information. Here, we apply Φ -ID on in silico and in vitro data to decompose the usual transfer entropy measure into different modes of information transfer, namely, synergistic, redundant, or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology. [ABSTRACT FROM AUTHOR]