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Interior contour, an essential component of the built environment, has drawn wide attention from home and abroad. From the perspective of neuroaesthetics, preference for architectural interior contour is closely tied to brain activity. Therefore, revealing the underlying neural basis of aesthetic preferences for architectural interior contour in terms of neuroimaging is of great importance. We recruited 30 healthy participants for behavioural assessment and functional magnetic resonance imaging (fMRI) scans. Using percent amplitude of fluctuation (PerAF), the current study measured participants' neuroaesthetic responses to curvilinear and rectilinear architectural interior contours in the three frequency bands. When participants viewed architectural images with curvilinear contours, the PerAF was significantly reduced in multiple brain regions, in comparison to rectilinear architectural interior contours. Moreover, significant negative correlations were observed between ratings of aesthetic judgments of curvilinear space and increased PerAF values in specific regions including the left Rolandic operculum. Aesthetic judgments were significantly correlated with PerAF values in specific brain regions, including the left middle frontal gyrus. Our study reveals that participants have a higher preference for curvilinear contours over rectilinear contours. Neural response varied across different architectural contours, highlighting the complex interplay of neural mechanisms underlying aesthetic perception. [ABSTRACT FROM AUTHOR]

Tree canopy landscapes are an important component of urban forests and have the potential to influence human emotions. However, their influence on emotional responses remains unclear. The aims of this study were: (1) to determine whether the canopy landscape affects human emotions; (2) to clarify the influence of canopy landscape on individual emotional indicators; and (3) to identify the ratio of canopy landscape elements with the most beneficial effects on human emotions. Different canopy landscape images were generated, and the self-reported emotions and neural activity of the subjects assessed before and after they viewed the images. The results of the statistical analysis were intuitively displayed by a ternary phase diagram. We found that the canopy landscape affected human emotions and different proportions of canopy landscape elements led to significant differences in excitement, depression and confusion. Higher proportions of blue elements and lower proportions of green and other elements characterized the canopy landscape with the most beneficial effect on human emotions. These findings will promote further research on canopy landscapes, inform the planning and design of urban forests, and contribute to the field of landscape architecture. [ABSTRACT FROM AUTHOR]

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain. [ABSTRACT FROM AUTHOR]

[Display omitted] • Social behavior is altered by nanoplastic (NPx) administration. • NPx-induced alteration of social behavior is partly dependent on neurodevelopment. • NPx administration affects the neural circuit where the dopamine system innervates. • NPx-induced alteration of neural activity depends on neurodevelopment. • Therefore, NPx induces different effects at different developmental stages. Exposure to environmental microplastics has been demonstrated to impact health. However, its effect on development remains unclear. This study investigated whether consumption of nanoplastics (NPx) during development affects social and cognitive functions in rodents. In this study, we utilized male Institute of Cancer Research mice; they were divided into five subgroups based on the duration of NPx administration. NPx (100 nm) was orally administered via gavage for 6 days from gestational day (GTD) 7, representing the mid-gestation period, and for 5–6 days from GTD13 to birth, representing the late-gestation period; the male offspring were used for experiments. NPx was orally administered for 15 days starting at postnatal day (PND) 21 as the juvenile, PND38 as the adolescent, and PND56 as adulthood. On PND77, offspring were assessed for locomotion, social behavior, and nest-building tests. We observed that NPx administration altered dopamine system responses in GTD13 and PND56 groups. Social behavior was similarly affected by NPx treatment, with GTD13 and PND56 groups displaying decreased familiarity. Additionally, NPx treatment enhanced local field potentials in the prefrontal cortex, nucleus accumbens, and amygdala of GTD7 group and in the striatum of GTD13 group, while amphetamine treatment induced changes of local field potentials compared to saline treatment in the prefrontal cortex and the ventral tegmental area of CTR, GTD7, PND21, and PND56 groups. Taken together, these results showed that NPx treatment induced changes in social behavior partly depending on developmental stage, and these changes are associated with neural circuits innervated by the dopamine system. [ABSTRACT FROM AUTHOR]

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications. [ABSTRACT FROM AUTHOR]

The aging population suffers from memory impairments. Slow-wave activity (SWA) is composed of slow (0.5–1 Hz) and delta (1–4 Hz) oscillations, which play important roles in long-term memory and working memory function respectively. SWA disruptions might lead to memory disturbances often experienced by older adults. We conducted behavioral tests in young and older C57BL/6 J mice. SWA was monitored using wide-field imaging with voltage sensors. Cell-specific calcium imaging was used to monitor the activity of excitatory and inhibitory neurons in these mice. Older mice exhibited impairments in working memory but not memory consolidation. Voltage-sensor imaging revealed aberrant synchronization of neuronal activity in older mice. Notably, we found older mice exhibited no significant alterations in slow oscillations, whereas there was a significant increase in delta power compared to young mice. Calcium imaging revealed hypoactivity in inhibitory neurons of older mice. Combined, these results suggest that neural activity disruptions might correlate with aberrant memory performance in older mice. • Slow wave activity disruptions lead to memory disturbances in older adults. • Older mice show impairments in working memory, but not memory consolidation. • Older mice showed abnormal synchronization of neuronal activity across the cortex. • Older mice exhibited aberrant activity of inhibitory neurons within the circuit. • The neural activity disruptions may be related to memory impairments in Older mice. [ABSTRACT FROM AUTHOR]

Aims: The mechanism underlying the reversible unconsciousness induced by general anesthetics (GA) remains unclear. Recent studies revealed the critical roles of myelin and oligodendrocytes (OLs) in higher functions of the brain. However, it is unknown whether myelin actively participates in the regulation of GA. The aim of this study is to investigate the roles and possible mechanisms of myelin in the regulation of consciousness alterations induced by isoflurane anesthesia. Methods: First, demyelination models for the entire brain and specific neural nuclei were established to investigate the potential role of myelination in the regulation of GA, as well as its possible regional specificity. c‐Fos staining was then performed on the demyelinated nuclei to verify the impact of myelin loss on neuronal activity. Finally, the activity of neurons during isoflurane anesthesia in demyelinated mice was recorded by optical fiber photometric calcium signal. The related behavioral indicators and EEG were recorded and analyzed. Results: A prolonged emergence time was observed from isoflurane anesthesia in demyelinated mice, which suggested the involvement of myelin in regulating GA. The demyelination in distinct nuclei by LPC further clarified the region‐specific roles of isoflurane anesthesia regulation by myelin. The effect of demyelination on isoflurane anesthesia in the certain nucleus was consistent with that in neurons towards isoflurane anesthesia. Finally, we found that the mechanism of myelin in the modulation of isoflurane anesthesia is possibly through the regulation of neuronal activity. Conclusions: In brief, myelin in the distinct neural nucleus plays an essential role in regulating the process of isoflurane anesthesia. The possible mechanism of myelin in the regulation of isoflurane anesthesia is neuronal activity modification by myelin integrity during GA. Our findings enhanced the comprehension of myelin function, and offered a fresh perspective for investigating the neural mechanisms of GA. [ABSTRACT FROM AUTHOR]

Advances in neuroimaging technology, like functional near-infrared spectroscopy (fNIRS), support the evaluation of task-dependent brain activity during functional tasks, like balance, in healthy and clinical populations. To date, there have been no studies examining how interventions, like yoga, impact task-dependent brain activity in adults with chronic acquired brain injury (ABI). This pilot study compared eight weeks of group yoga (active) to group exercise (control) on balance and task-dependent neural activity outcomes. Twenty-three participants were randomized to yoga (n = 13) or exercise groups (n = 10). Neuroimaging and balance performance data were collected simultaneously using a force plate and mobile fNIRS device before and after interventions. Linear mixed-effects models were used to evaluate the effect of time, time x group interactions, and simple (i.e., within-group) effects. Regardless of group, all participants had significant balance improvements after the interventions. Additionally, regardless of group, there were significant changes in task-dependent neural activity, as well as distinct changes in neural activity within each group. In summary, using advances in sensor technology, we were able to demonstrate preliminary evidence of intervention-induced changes in balance and neural activity in adults with ABI. These preliminary results may provide an important foundation for future neurorehabilitation studies that leverage neuroimaging methods, like fNIRS. [ABSTRACT FROM AUTHOR]

Although aversive responses to sensory stimuli are common in autism spectrum disorder (ASD), it remains unknown whether the social relevance of aversive sensory inputs affects their processing. We used functional magnetic resonance imaging (fMRI) to investigate neural responses to mildly aversive nonsocial and social sensory stimuli as well as how sensory over‐responsivity (SOR) severity relates to these responses. Participants included 21 ASD and 25 typically‐developing (TD) youth, aged 8.6–18.0 years. Results showed that TD youth exhibited significant neural discrimination of socially relevant versus irrelevant aversive sensory stimuli, particularly in the amygdala and orbitofrontal cortex (OFC), regions that are crucial for sensory and social processing. In contrast, ASD youth showed reduced neural discrimination of social versus nonsocial stimuli in the amygdala and OFC, as well as overall greater neural responses to nonsocial compared with social stimuli. Moreover, higher SOR in ASD was associated with heightened responses in sensory‐motor regions to socially‐relevant stimuli. These findings further our understanding of the relationship between sensory and social processing in ASD, suggesting limited attention to the social relevance compared with aversiveness level of sensory input in ASD versus TD youth, particularly in ASD youth with higher SOR. Lay Summary: In this study, we used functional magnetic resonance imaging (fMRI) to examine brain responses to aversive sensory stimuli with and without social relevance in youth with autism spectrum disorder (ASD) and typically‐developing (TD) youth. TD youth showed greater neural responses to social compared with nonsocial sensory inputs, whereas the opposite was true in ASD youth. Results suggest that compared with TD youth, ASD youth (particularly those with more severe sensory over‐responsivity symptoms) attend more to the level of aversiveness rather than to the social relevance of sensory input. [ABSTRACT FROM AUTHOR]

We present a dataset for the analysis of human affective states using functional near-infrared spectroscopy (fNIRS). Data were recorded from thirty-one participants who engaged in two tasks. In the emotional perception task the participants passively viewed images sampled from the standard international affective picture system database, which provided ground-truth valence and arousal annotation for the stimuli. In the affective imagery task the participants actively imagined emotional scenarios followed by rating these for subjective valence and arousal. Correlates between the fNIRS signal and the valence-arousal ratings were investigated to estimate the validity of the dataset. Source-code and summaries are provided for a processing pipeline, brain activity group analysis, and estimating baseline classification performance. For classification, prediction experiments are conducted for single-trial 4-class classification of arousal and valence as well as cross-participant classifications, and comparisons between high and low arousal variants of the valence prediction tasks. Finally, classification results are presented for subject-specific and cross-participant models. The dataset is made publicly available to encourage research on affective decoding and downstream applications using fNIRS data. [ABSTRACT FROM AUTHOR]

The brain constructs spatially organized sensory maps to represent sensory information. The formation of sensory maps has traditionally been thought to depend on synchronous neuronal activity. However, recent evidence from the olfactory system suggests that cell type-specific temporal patterns of spontaneous activity play an instructive role in shaping the olfactory glomerular map. These findings challenge traditional views and highlight the importance of investigating the spatiotemporal dynamics of neural activity to understand the development of complex neural circuits. This review discusses the implications of new findings in the olfactory system and outlines future research directions. [ABSTRACT FROM AUTHOR]

Action understanding involves two distinct processing levels that engage separate neural mechanisms: perception of concrete kinematic information and recognition of abstract action intentions. The mirror neuron system and the mentalizing system have both been linked to concrete action and abstract information processing, but their specific roles remain debatable. Here, we conducted a functional magnetic resonance imaging study with 26 participants who passively observed expected and unexpected actions. We performed whole-brain activation, region of interest, and effective connectivity analyses to investigate the neural correlates of these actions. Whole-brain activation analyses revealed that expected actions were associated with increased activation in the left medial superior frontal gyrus, while unexpected actions were linked to heightened activity in the left supramarginal gyrus, left superior parietal lobule, right inferior temporal gyrus, and left middle frontal gyrus. Region of interest analyses demonstrated that the left ventral premotor cortex exhibited greater activation during the observation of expected actions compared to unexpected actions, while the left inferior frontal gyrus, left superior parietal lobule, and left precuneus showed stronger activation during the observation of unexpected actions. Effective connectivity was observed between the left ventral premotor cortex and the left angular gyrus, left intraparietal sulcus, left dorsal premotor cortex, and left ventromedial prefrontal cortex with the middle frontal gyrus when observing unexpected, but not expected, actions. These findings suggest that expected actions are primarily processed by the mirror neuron system, whereas unexpected actions engage both the mirror neuron system and the mentalizing system, with these systems playing complementary roles in the understanding of unexpected actions.

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson’s, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren’t we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input–output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

The wiring of visual circuits requires that retinal neurons functionally connect to specific brain targets, a process that involves activity-dependent signaling between retinal axons and their postsynaptic targets. Vision loss in various ophthalmological and neurological diseases is caused by damage to the connections from the eye to the brain. How postsynaptic brain targets influence retinal ganglion cell (RGC) axon regeneration and functional reconnection with the brain targets remains poorly understood. Here, we established a paradigm in which the enhancement of neural activity in the distal optic pathway, where the postsynaptic visual target neurons reside, promotes RGC axon regeneration and target reinnervation and leads to the rescue of optomotor function. Furthermore, selective activation of retinorecipient neuron subsets is sufficient to promote RGC axon regeneration. Our findings reveal a key role for postsynaptic neuronal activity in the repair of neural circuits and highlight the potential to restore damaged sensory inputs via proper brain stimulation.

Sweeteners cannot completely replace the satisfaction provided by sugars, even though they offer a similar flavor perception and much higher sweetness intensity than sugars. Clarifying the biological mechanism of this phenomenon is important to improve the functional evaluation system for sweeteners and promote sweetener innovations. Herein, we review the research progress on the difference in the behavioral preferences of animals, the activity of brain regions and the activation patterns of the gut-brain axis induced by sugars and sweeteners, and we uncover the underlying reason why the brain distinguishes sugars from sweeteners, causing differences in individual behavioral preferences. Moreover, we propose that animal behavior, neural activity in brain regions, and the capacity to activate key receptors can be used to evaluate the gut-brain axis effects of sweeteners, which will provide a reference for innovative developments in the field of sweeteners.

BackgroundSensory over-responsivity (SOR) is an impairing sensory processing challenge in autism spectrum disorder (ASD) which shows heterogenous developmental trajectories and appears to improve into adulthood in some but not all autistic individuals. However, the neural mechanisms underlying interindividual differences in these trajectories are currently unknown.MethodsHere, we used functional magnetic resonance imaging (fMRI) to investigate the association between age and neural activity linearly and nonlinearly in response to mildly aversive sensory stimulation as well as how SOR severity moderates this association. Participants included 52 ASD (14F) and 41 (13F) typically developing (TD) youth, aged 8.6-18.0 years.ResultsWe found that in pre-teens, ASD children showed widespread activation differences in sensorimotor, frontal and cerebellar regions compared to TD children, while there were fewer differences between ASD and TD teens. In TD youth, older age was associated with less activation in the prefrontal cortex. In contrast, in ASD youth, older age was associated with more engagement of sensory integration and emotion regulation regions. In particular, orbitofrontal and medial prefrontal cortices showed a nonlinear relationship with age in ASD, with an especially steep increase in sensory-evoked neural activity during the mid-to-late teen years. There was also an interaction between age and SOR severity in ASD youth such that these age-related trends were more apparent in youth with higher SOR.LimitationsThe cross-sectional design limits causal interpretations of the data. Future longitudinal studies will be instrumental in determining how prefrontal engagement and SOR co-develop across adolescence.ConclusionsOur results suggest that enhanced recruitment of prefrontal regions may underlie age-related decreases in SOR for a subgroup of ASD youth.

Aims: The hippocampus has been reported to be morphologically and neurochemically altered in schizophrenia (SZ). Hyperlocomotion is a characteristic SZ‐associated behavioral phenotype, which is associated with dysregulated dopamine system function induced by hippocampal hyperactivity. However, the neural mechanism of hippocampus underlying hyperlocomotion remains largely unclear. Methods: Mouse pups were injected with N‐methyl‐D‐aspartate receptor antagonist (MK‐801) or vehicle twice daily on postnatal days (PND) 7–11. In the adulthood phase, one cohort of mice underwent electrode implantation in field CA1 of the hippocampus for the recording local field potentials and spike activity. A separate cohort of mice underwent surgery to allow for calcium imaging of the hippocampus while monitoring the locomotion. Lastly, the effects of atypical antipsychotic (aripiprazole, ARI) were evaluated on hippocampal neural activity. Results: We found that the hippocampal theta oscillations were enhanced in MK‐801‐treated mice, but the correlation coefficient between the hippocampal spiking activity and theta oscillation was reduced. Consistently, although the rate and amplitude of calcium transients of hippocampal neurons were increased, their synchrony and correlation to locomotion speed were disrupted. ARI ameliorated perturbations produced by the postnatal MK‐801 treatment. Conclusions: These results suggest that the disruption of neural coordination may underly the neuropathological mechanism for hyperlocomotion of SZ. [ABSTRACT FROM AUTHOR]

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Background. Stress, a phenomenon which has turned into an epidemic, is a major health concern because it affects the general population, having a strong negative impact on the psychoemotional, physical and social health. Aims. The aim of this research was to investigate the impact of Health&Wellness Coaching (HWC) intervention on stress levels, as evidenced by physical/mental/psychological changes, and to identify non-invasive ways of early detection of high stress levels, which may play an essential role in the prophylaxis of chronic diseases often associated with stress. Methods. The HWC intervention was carried out on 24 subjects, and consisted in 8 online sessions for each of them. The sessions were mixed training and coaching sessions, held once every two weeks, and lasted for approximately 60 min/session. In the training sessions, useful medical information related to stress was provided (info training), while in the coaching sessions subjects were guided and empowered to reduce their stress levels. Results. The results showed an optimization of physical/mental/psychological parameters, with a simultaneous decrease in perceived stress levels, and a favorable impact on the subjects’ quality of life, as evidenced by psychometric measurements (DASS_21R, Decisional Capacity and QOLI) and mental/brain measurements using fNIRS technology (Functional Near- Infrared Spectroscopy, with the Mendi device). Conclusion. The present research confirms that HWC is an effective intervention in reducing the level of perceived stress and we believe that it can be a promising alternative for the prophylaxis of chronic diseases, and that DASS-21R could be a useful tool for the detection of a high degree of psycho/emotional/mental impairment, which can negatively influence the level of stress in the body, i.e. QOLI can detect the most affected areas of satisfaction with life. [ABSTRACT FROM AUTHOR]

Microfluidic technology is unrivaled in its ability to apply soluble chemical stimuli with high spatiotemporal precision. Analogous, light-sheet microscopy is unmatched in its ability of low phototoxic but fast volumetric in vivo imaging with single cell resolution. Due to their optical translucency during the larval stages, zebrafish (Danio rerio) are an ideal model to combine both techniques; yet, thus far this required light-sheet microscopes, which were in most cases custom-built and adapted to the available softlithographic chip technology. Our aim was to use a commercial light-sheet microscope to illuminate a microfluidic chip from two opposite lateral directions and to record images with the detection objective placed orthogonally above the chip. Deep tissue penetration can be achieved by superimposing beams from opposite directions to form a single light sheet. But a microfluidic chip that allows a) targeted stimulus application in a closed microenvironment, b) interference-free incoupling of excitation light from two directions and c) outcoupling of fluorescence in the perpendicular direction through an optically perfect cover glass was not known until now. Here, we present a monolithic glass chip with the required plane-parallel sidewalls and cover slide closure at the top, constructed by advanced femtosecond laser ablation, thermal bonding and surface smoothing processes. In addition, the 3D shape of a fish fixator unit was tailored to match the body shape of a zebrafish larva to ensure stable positioning during whole-brain recording. With hydrodynamic focusing a targeted partial exposure of the larva's head to chemical stimuli and fast position switching (in less than 10 s) was possible. With the capabilities of this unique monolithic glass chip and its up-scalable wafer-level fabrication process, the new NeuroExaminer is prone to become an excellent addition to neurobiology laboratories already equipped with high-quality commercial light sheet microscopes. [ABSTRACT FROM AUTHOR]

It has been more than a century since Pío del Río‐Hortega first characterized microglia in histological stains of brain tissue. Since then, significant advances have been made in understanding the role of these resident central nervous system (CNS) macrophages. In particular, it is now known that microglia can sense neural activity and modulate neuronal circuits accordingly. We review the mechanisms by which microglia detect changes in neural activity to then modulate synapse numbers in the developing and mature CNS. This includes responses to both spontaneous and experience‐driven neural activity. We further discuss activity‐dependent mechanisms by which microglia regulate synaptic function and neural circuit excitability. Together, our discussion provides a comprehensive review of the activity‐dependent functions of microglia within neural circuits in the healthy CNS, and highlights exciting new open questions related to understanding more fully microglia as key components and regulators of neural circuits. [ABSTRACT FROM AUTHOR]

Non‐human primates (NHPs) are crucial models for studies of neuronal activity. Emerging photoacoustic imaging modalities offer excellent tools for studying NHP brains with high sensitivity and high spatial resolution. In this research, a photoacoustic microscopy (PAM) device was used to provide a label‐free quantitative characterization of cerebral hemodynamic changes due to peripheral mechanical stimulation. A 5 × 5 mm area within the somatosensory cortex region of an adult squirrel monkey was imaged. A deep, fully connected neural network was characterized and applied to the PAM images of the cortex to enhance the vessel structures after mechanical stimulation on the forelimb digits. The quality of the PAM images was improved significantly with a neural network while preserving the hemodynamic responses. The functional responses to the mechanical stimulation were characterized based on the improved PAM images. This study demonstrates capability of PAM combined with machine learning for functional imaging of the NHP brain. [ABSTRACT FROM AUTHOR]

Simple Summary: This study delves into the turning behavior of pigeons by examining the neural mechanisms of their midbrain motor nucleus. Correlating brain oscillations with turning behavior, we identified the distinct roles of oscillatory patterns in different frequency bands during active and passive turning behavior. Specifically, 80 Hz stimulation induced higher-frequency oscillation patterns. These findings unveil the intricate relationship between neural oscillations and pigeon turning, highlighting the significance of specific frequency bands. This study enhances our understanding of avian brain–behavior connections, offering valuable insights for further research on avian locomotion neural processes and serving as a reference for future studies on neuromodulation techniques in flying animal robots. Flexible turning behavior endows Homing Pigeons (Columba livia domestica) with high adaptability and intelligence in long-distance flight, foraging, hazard avoidance, and social interactions. The present study recorded the activity pattern of their local field potential (LFP) oscillations and explored the relationship between different bands of oscillations and turning behaviors in the formatio reticularis medialis mesencephali (FRM). The results showed that the C (13–60 Hz) and D (61–130 Hz) bands derived from FRM nuclei oscillated significantly in active turning, while the D and E (131–200 Hz) bands oscillated significantly in passive turning. Additionally, compared with lower-frequency stimulation (40 Hz and 60 Hz), 80 Hz stimulation can effectively activate the turning function of FRM nuclei. Electrical stimulation elicited stronger oscillations of neural activity, which strengthened the pigeons' turning locomotion willingness, showing an enhanced neural activation effect. These findings suggest that different band oscillations play different roles in the turning behavior; in particular, higher-frequency oscillations (D and E bands) enhance the turning behavior. These findings will help us decode the complex relationship between bird brains and behaviors and are expected to facilitate the development of neuromodulation techniques for animal robotics. [ABSTRACT FROM AUTHOR]

Background: Mechanisms underlying cognitive dysfunction following traumatic brain injury (TBI) partially due to abnormal amyloid processor protein (APP) cleavage and neural hyperactivity. Binding of the extension domain of APP (ExD17) to the GABAbR1 receptor results in reduced neural activity, which might play a role in the mechanisms of cognitive dysfunction caused by TBI. Methods: Stretch‐induced injury was utilized to establish a cell injury model in HT22 cells. The TBI model was created by striking the exposed brain tissue with a free‐falling weight. Topical or intraperitoneal administration of ExD17 was performed. Cell viability was assessed through a cell counting kit‐8 assay, while intracellular Ca2+ was measured using Fluo‐4. Western blotting was used to investigate the expression of APP amyloidogenic cleavage proteins, GABAbR1, phospholipase C (PLC), PLCB3, and synaptic proteins. ELISA was performed to analyze the levels of Aβ42. Seizures were assessed using electroencephalography (EEG). Behaviors were evaluated through the novel object recognition test, open field test, elevated plus maze test, and nest‐building test. Results: ExD17 improved cell viability and reduced intracellular calcium in the cell injury model. The treatment also suppressed the increased expression of APP amyloidogenic cleavage proteins and Aβ42 in both cell injury and TBI models. ExD17 treatment reversed the abnormal expression of GABAbR1, GRIA2, p‐PLCG1/PLCG1 ratio, and p‐PLCB3/PLCB3 ratio. In addition, ExD17 treatment reduced neural activity, seizure events, and their duration in TBI. Intraperitoneal injection of ExD17 improved behavioral outcomes in the TBI mouse model. Conclusions: ExD17 treatment results in a reduction of amyloidogenic APP cleavage and neuroexcitotoxicity, ultimately leading to an improvement in the behavioral deficits observed in TBI mice. [ABSTRACT FROM AUTHOR]

Current trends in neurobiological research focus on analyzing complex interactions within brain structures. To conduct relevant experiments, it is often essential to employ animals with unhampered mobility and utilize electrophysiological equipment capable of wirelessly transmitting data. In prior research, we introduced an open-source wireless electrophysiology system to surmount these challenges. Nonetheless, this prototype exhibited several limitations, such as a hefty weight for the wireless module, redundant system components, a diminished sampling rate, and limited battery longevity. In this study, we unveil an enhanced version of the open-source wireless electrophysiology system, tailored for in vivo monitoring of neural activity in rodent brains. This new system has been successfully tested in real-time recordings of in vivo neural activity. Consequently, our development offers researchers a cost-effective and proficient tool for studying complex brain functions. [ABSTRACT FROM AUTHOR]

Functional Magnetic Resonance Imaging (fMRI) is the popular technique where it is possible to capture neural activity in brain regions when subjected to different stimuli. However, due to fMRI datasets' high dimensional and sparse nature, the best features' selection plays an essential role in providing the best classification accuracy in fMRI models. This paper selects the stable feature set from the fMRI dataset using hybrid Fast Fourier Transform with Particle Swarm Optimization and Genetic Algorithm (FFTPSOGA). Fast Fourier Transform (FFT) is used on the extracted features by PSO-GA to convert the magnitude of features into phase values for better performance. Next, the machine learning algorithms of GaussianNB, Support Vector Machine (SVM), and XGboost has been trained based on these extracted features of six subjects of the dataset. The experimental analysis reveals that the proposed algorithm resulted in optimum features that helped extract informative Regions of Interest (ROI) with better classification accuracy. Our implemented algorithm FFTPSOGA extracted the best voxels in six subjects of the dataset by selecting minimum ROIs with a model classification accuracy of 0.98, 0.95, 0.95, 0.95, 0.97, and 0.96 for the SVM classifier. Comparison of the proposed scheme with state-of-the-art techniques show that our algorithm resulted in best voxels and outperformed work in [1, 9, 25] by achieving higher accuracy of 98% and low computational costs with only 127 number of features. Due to its better performance, we believe that it can be used for the pattern identification of brain responses in multi-subject fMRI data. [ABSTRACT FROM AUTHOR]

Integrating multisensory inputs to generate accurate perception and guide behavior is among the most critical functions of the brain. Subcortical regions such as the amygdala are involved in sensory processing including vision and audition, yet their roles in multisensory integration remain unclear. In this study, we systematically investigated the function of neurons in the amygdala and adjacent regions in integrating audiovisual sensory inputs using a semi-chronic multi-electrode array and multiple combinations of audiovisual stimuli. From a sample of 332 neurons, we showed the diverse response patterns to audiovisual stimuli and the neural characteristics of bimodal over unimodal modulation, which could be classified into four types with differentiated regional origins. Using the hierarchical clustering method, neurons were further clustered into five groups and associated with different integrating functions and sub-regions. Finally, regions distinguishing congruent and incongruent bimodal sensory inputs were identified. Overall, visual processing dominates audiovisual integration in the amygdala and adjacent regions. Our findings shed new light on the neural mechanisms of multisensory integration in the primate brain. [ABSTRACT FROM AUTHOR]

Although adults and children differ in self-vs.-other perception, a developmental perspective on this discriminative ability at the brain level is missing. This study examined neural activation for self-vs.-other in a sample of 39 participants spanning four different age groups, from 4-year-olds to adults. Self-related stimuli elicited higher neural activity within two brain regions related to self-referential thinking, empathy, and social cognition processes. Second, stimuli related to 'others' (i.e., unknown peer) elicited activation within nine additional brain regions. These regions are associated with multisensory processing, somatosensory skills, language, complex visual stimuli, self-awareness, empathy, theory of mind, and social recognition. Overall, activation maps were gradually increasing with age. However, patterns of activity were non-linear within the medial cingulate cortex for 'self' stimuli and within the left middle temporal gyrus for 'other' stimuli in 7–10-year-old participants. In both cases, there were no self-vs.-other differences. It suggests a critical period where the perception of self and others are similarly processed. Furthermore, 11–19-year-old participants showed no differences between others and self within the left inferior orbital gyrus, suggesting less distinction between self and others in social learning. Understanding the neural bases of self-vs.-other discrimination during development can offer valuable insights into how social contexts can influence learning processes during development, such as when to introduce peer-to-peer teaching or group learning. [ABSTRACT FROM AUTHOR]

Developmental neural activity is a common feature of neural circuit assembly. Although glia have established roles in synapse development, the contribution of neuron-glia interactions to developmental activity remains largely unexplored. Here we show that astrocytes are necessary for developmental activity during synaptogenesis in Drosophila. Using wide-field epifluorescence and two-photon imaging, we show that the glia of the central nervous system participate in developmental activity with type-specific patterns of intracellular calcium dynamics. Genetic ablation of astrocytes, but not of cortex or ensheathing glia, leads to severe attenuation of neuronal activity. Similarly, inhibition of neuronal activity results in the loss of astrocyte calcium dynamics. By altering these dynamics, we show that astrocytic calcium cycles can influence neuronal activity but are not necessary per se. Taken together, our results indicate that, in addition to their recognized role in the structural maturation of synapses, astrocytes are also necessary for the function of synapses during development.

Abstract Background Bulimia nervosa (BN) is an eating disorder characterized by recurrent binge eating and compensatory behaviors. The thalamus plays a crucial role in the neural circuitry related to eating behavior and needs to be further explored in BN. Methods In this study, 49 BN patients and 44 healthy controls (HCs) were recruited. We applied the fractional amplitude of low-frequency fluctuation to investigate regional brain activity in the thalamus and functional connectivity (FC) to examine the synchronization of activity between thalamic subregions and other brain regions in both groups. All results underwent false discovery rate (p

Abstract Background Sensory over-responsivity (SOR) is an impairing sensory processing challenge in autism spectrum disorder (ASD) which shows heterogenous developmental trajectories and appears to improve into adulthood in some but not all autistic individuals. However, the neural mechanisms underlying interindividual differences in these trajectories are currently unknown. Methods Here, we used functional magnetic resonance imaging (fMRI) to investigate the association between age and neural activity linearly and nonlinearly in response to mildly aversive sensory stimulation as well as how SOR severity moderates this association. Participants included 52 ASD (14F) and 41 (13F) typically developing (TD) youth, aged 8.6–18.0 years. Results We found that in pre-teens, ASD children showed widespread activation differences in sensorimotor, frontal and cerebellar regions compared to TD children, while there were fewer differences between ASD and TD teens. In TD youth, older age was associated with less activation in the prefrontal cortex. In contrast, in ASD youth, older age was associated with more engagement of sensory integration and emotion regulation regions. In particular, orbitofrontal and medial prefrontal cortices showed a nonlinear relationship with age in ASD, with an especially steep increase in sensory-evoked neural activity during the mid-to-late teen years. There was also an interaction between age and SOR severity in ASD youth such that these age-related trends were more apparent in youth with higher SOR. Limitations The cross-sectional design limits causal interpretations of the data. Future longitudinal studies will be instrumental in determining how prefrontal engagement and SOR co-develop across adolescence. Conclusions Our results suggest that enhanced recruitment of prefrontal regions may underlie age-related decreases in SOR for a subgroup of ASD youth.

BackgroundWith the increasing popularity of calcium imaging in neuroscience research, choosing the right methods to analyze calcium imaging data is critical to address various scientific questions. Unlike spike trains measured using electrodes, fluorescence intensity traces provide an indirect and noisy measurement of the underlying neuronal activities. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step.MethodsIn this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding.ResultsWe found that (1) the two approaches lead to diverging results; (2) estimated spike trains, when smoothed or binned appropriately, usually lead to satisfactory performances, such as more accurate estimation of cluster membership; (3) although estimate spike train produce results more similar to true spike data than trace data, we found that the PCA results from trace data might better reflect the underlying neuronal ensembles (clusters); and (4) for both approaches, decobability can be improved by using denoising or smoothing methods.Comparison with existing methodsOur simulations and applications to real data suggest that estimated spike data outperform trace data in cluster analysis and give comparable results for population decoding. In addition, the decobability of estimated spike data can be slightly better than that of calcium trace data with appropriate filtering / smoothing methods.ConclusionWe conclude that spike detection might be a useful pre-processing step for certain problems such as clustering; however, the continuous nature of calcium imaging data provides a natural smoothness that might be helpful for problems such as dimensional reduction.

Calcium imaging, especially two-photon imaging, has become essential in neuroscience for studying neuronal and astrocytic activity under in vivo and in vitro conditions. Current advances in the development of calcium sensors as well as imaging hardware enable high-frequency measurements of calcium signals in hundreds of cells simultaneously. The analysis of these large datasets requires special tools and usually a certain level of programming experience. Despite advancements in calcium imaging analysis software development, significant gaps remain, particularly for data acquired at a high sampling rate that would allow for the spectral analysis of calcium signals. The FluoAnalysis MATLAB toolbox addresses these gaps by offering a comprehensive solution for analyzing simultaneously measured calcium imaging and electrophysiological data. It features both GUI-based and command-line approaches, emphasizing frequency domain analysis to reveal network-level oscillatory signals linked to single-cell activity. In addition, the toolbox puts special emphasis on differentiating between astrocytes and neurons, revealing the interactions between the network activity of the two major cell types of the brain. It facilitates a streamlined workflow for data loading, ROI identification, cell classification, fluorescence intensity calculation, spectral analysis, and report generation, supporting both manual and automated high-throughput analysis. This versatile platform enables the comprehensive analysis of large imaging datasets. In conclusion, the FluoAnalysis MATLAB toolbox provides a robust and versatile platform for the integrated analysis of calcium imaging and electrophysiological data, supporting diverse neuroscience research applications.

Advances in neuroimaging technology, like functional near-infrared spectroscopy (fNIRS), support the evaluation of task-dependent brain activity during functional tasks, like balance, in healthy and clinical populations. To date, there have been no studies examining how interventions, like yoga, impact task-dependent brain activity in adults with chronic acquired brain injury (ABI). This pilot study compared eight weeks of group yoga (active) to group exercise (control) on balance and task-dependent neural activity outcomes. Twenty-three participants were randomized to yoga (n = 13) or exercise groups (n = 10). Neuroimaging and balance performance data were collected simultaneously using a force plate and mobile fNIRS device before and after interventions. Linear mixed-effects models were used to evaluate the effect of time, time x group interactions, and simple (i.e., within-group) effects. Regardless of group, all participants had significant balance improvements after the interventions. Additionally, regardless of group, there were significant changes in task-dependent neural activity, as well as distinct changes in neural activity within each group. In summary, using advances in sensor technology, we were able to demonstrate preliminary evidence of intervention-induced changes in balance and neural activity in adults with ABI. These preliminary results may provide an important foundation for future neurorehabilitation studies that leverage neuroimaging methods, like fNIRS.

Numerous brain regions have been identified as contributing to withdrawal behaviors, but it is unclear the way in which these brain regions as a whole lead to withdrawal. The search for a final common brain pathway that is involved in withdrawal remains elusive. To address this question, we implanted osmotic minipumps containing either saline, nicotine (24 mg/kg/d), cocaine (60 mg/kg/d), or methamphetamine (4 mg/kg/d) for one week in male C57BL/6J mice. After one week, the minipumps were removed and brains collected 8 h (saline, nicotine, and cocaine) or 12 h (methamphetamine) after removal. We then performed single-cell whole-brain imaging of neural activity during the withdrawal period when brains were collected. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical-driven to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway associated with withdrawal.

Abstract Background In the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including $$Caenorhabditis\ elegans$$ C a e n o r h a b d i t i s e l e g a n s . Because current neural activity data in C. elegans contain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules. Results In this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data from C. elegans. WormTensor uses a distance measure, modified shape-based distance to account for the lags and the mutual inhibition of cell–cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals. Conclusion We applied the method to 24 individual C. elegans and successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN package https://cran.r-project.org/web/packages/WormTensor .

Glucocorticoids are primary stress hormones that exert neuronal effects via both genomic and non-genomic signaling pathways. However, their rapid non-genomic effects and underlying mechanisms on neural activities remain elusive. In the present study, we investigated the rapid non-genomic effect of glucocorticoids on Kv2.2 channels in cultured HEK293 cells and acute brain slices including cortical pyramidal neurons and calyx-type synapses in the brain stem. We found that cortisol, the endogenous glucocorticoids, rapidly increased Kv2.2 currents by increasing the single-channel open probability in Kv2.2-expressing HEK293 cells through activation of the membrane-associated glucocorticoid receptor. Bovine serum albumin-conjugated dexamethasone, a membrane-impermeable agonist of the glucocorticoid receptor, could mimic the effect of cortisol on Kv2.2 channels. The cortisol-increased Kv2.2 currents were induced by activation of the extracellular signal-regulated protein kinase (ERK) 1/2 kinase, which could be inhibited by U0126, an antagonist of the ERK signaling pathway. In layer 2 cortical pyramidal neurons and the calyx of Held synapses, cortisol suppressed the action potential firing frequency during depolarization and reduced the successful rate upon high-frequency stimulation by activating Kv2.2 channels. We further examined the postsynaptic responses and found that cortisol did not affect the mEPSC and evoked EPSC, but increased the activity-dependent synaptic depression induced by a high-frequency stimulus train. In conclusion, glucocorticoids can rapidly activate Kv2.2 channels through membrane-associated glucocorticoid receptors via the ERK1/2 signaling pathway, suppress presynaptic action potential firing, and inhibit synaptic transmission and plasticity. This may be a universal mechanism of the glucocorticoid-induced non-genomic effects in the central nervous system.

Background: Bulimia nervosa (BN) is an eating disorder characterized by recurrent binge eating and compensatory behaviors. The thalamus plays a crucial role in the neural circuitry related to eating behavior and needs to be further explored in BN. Methods: In this study, 49 BN patients and 44 healthy controls (HCs) were recruited. We applied the fractional amplitude of low-frequency fluctuation to investigate regional brain activity in the thalamus and functional connectivity (FC) to examine the synchronization of activity between thalamic subregions and other brain regions in both groups. All results underwent false discovery rate (p < 0.05, FDR correction) correction. Pearson correlation analysis was performed to assess the relationship between the patients' abnormal clinical performance and the thalamic alterations (p < 0.05, FDR correction). Results: We found no significant differences in neural activity between BN patients and HCs in the sixteen thalamic subregions. However, compared to the HCs, the individuals with BN showed decreased FC between the thalamic subregions and several regions, including the bilateral prefrontal cortex, right inferior parietal lobule, right supplementary motor area, right insula, cingulate gyrus and vermis. Additionally, BN patients showed increased FC between the thalamic subregions and visual association regions, primary sensorimotor cortex, and left cerebellum. These altered FC patterns in the thalamus were found to be correlated with clinical variables (the frequency of binge eating/purging per week and external eating behavior scale scores) in the BN group. All results have passed FDR correction. Conclusions: Our study provides evidence that there is disrupted FC between thalamic subregions and other brain regions in BN patients during resting state. These regions are primarily located within the frontoparietal network, default mode network, somatosensory, and visual network. These findings elucidate the neural activity characteristics underlying BN and suggest that thalamic subregions have potential as targets for future neuromodulation interventions. Plain English summary: The high recurrence rate of bulimia nervosa (BN) poses a clinical challenge, and thus, it is crucial to improve the characterization and identification of brain functional abnormalities as direct targets for novel therapies. To investigate the neural circuitry associated with BN, the thalamus is a critical node since it serves as a higher-order relay point in the cortico-thalamo-cortical information pathway. Our findings reveal that altered functional connectivity (FC) between thalamic nuclei and other brain regions is evident throughout the whole brain, particularly within the frontoparietal network, default mode network, somatosensory, and visual network. These changes in FC are significantly associated with disordered eating behavior and the severity of illness in BN patients. Therefore, these findings help identify the neural mechanisms underlying disordered eating behavior and BN severity and suggest potential targets for future neuromodulation interventions. [ABSTRACT FROM AUTHOR]

Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response. Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions. After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969). Relatively small sample size and not a double-blind design. The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction. • Depressed patients with early antidepressant response (ER) showed decreased regional periodic power comparing to controls. • Depressed patients with ER showed increased alpha and beta functional couplings across brain regions than those without ER. • ROCs confirmed predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy. [ABSTRACT FROM AUTHOR]

Microglial processes survey the brain parenchyma, but it is unknown whether this process is influenced by the cell activity of nearby microglia under physiological conditions. Herein, we showed that microglial process dynamics differ when facilitated by astrocytic activity and pre-synaptic activity. The results revealed distinct microglial process dynamics associated with the activity of other brain cells. [ABSTRACT FROM AUTHOR]

Introduction: Our brain is bombarded by a diverse range of visual stimuli, which are converted into corresponding neuronal responses and processed throughout the visual system. The neural activity patterns that result from these external stimuli vary depending on the object or scene being observed, but they also change as a result of internal or behavioural states. This raises the question of to what extent it is possible to predict the presented visual stimuli from neural activity across behavioural states, and how this varies in different brain regions. Methods: To address this question, we assessed the computational capacity of decoders to extract visual information in awake behaving mice, by analysing publicly available standardised datasets from the Allen Brain Institute. We evaluated how natural movie frames can be distinguished based on the activity of units recorded in distinct brain regions and under different behavioural states. This analysis revealed the spectrum of visual information present in different brain regions in response to binary and multiclass classification tasks. Results: Visual cortical areas showed highest classification accuracies, followed by thalamic and midbrain regions, with hippocampal regions showing close to chance accuracy. In addition, we found that behavioural variability led to a decrease in decoding accuracy, whereby large behavioural changes between train and test sessions reduced the classification performance of the decoders. A generalised linear model analysis suggested that this deterioration in classification might be due to an independent modulation of neural activity by stimulus and behaviour. Finally, we reconstructed the natural movie frames from optimal linear classifiers, and observed a strong similarity between reconstructed and actual movie frames. However, the similarity was significantly higher when the decoders were trained and tested on sessions with similar behavioural states. Conclusion: Our analysis provides a systematic assessment of visual coding in the mouse brain, and sheds light on the spectrum of visual information present across brain areas and behavioural states. [ABSTRACT FROM AUTHOR]

In clinical populations, the movement of cerebrospinal fluid (CSF) during sleep is a growing area of research with potential mechanistic connections in both neurodegenerative (e.g., Alzheimer's Disease) and neurodevelopmental disorders. However, we know relatively little about the processes that influence CSF movement. To inform clinical intervention targets this study assesses the coupling between (a) real-time CSF movement, (b) neuronal-driven movement, and (c) non-neuronal systemic physiology driven movement. This study included eight young, healthy volunteers, with concurrently acquired neurofluid dynamics using functional Magnetic Resonance Imaging (MRI), neural activity using Electroencephalography (EEG), and non-neuronal systemic physiology with peripheral functional Near-Infrared Spectroscopy (fNIRS). Neuronal and non-neuronal drivers were assessed temporally; wherein, EEG measured slow wave activity that preceded CSF movement was considered neuronally driven. Similarly, slow wave oscillations (assessed via fNIRS) that coupled with CSF movement were considered non-neuronal systemic physiology driven. Our results document neural contributions to CSF movement were only present during light NREM sleep but low-frequency non-neuronal oscillations were strongly coupled with CSF movement in all assessed states – awake, NREM-1, NREM-2. The clinical/research implications of these findings are two-fold. First, neuronal-driven oscillations contribute to CSF movement outside of deep sleep (NREM-3); therefore, interventions aimed at increasing CSF movement may yield meaningful increases with the promotion of NREM sleep more generally – a focus on NREM S3 may not be needed. Second, non-neuronal systemic oscillations contribute across wake and sleep stages; therefore, interventions may increase CSF movement by manipulating systemic physiology. • Coupling between cerebral hemodynamics and CSF movement in the low-frequency range is independent of the wake/sleep states in young healthy adults. • EEG slow wave activity was observed shortly before cerebral blood volume changes in both cranial and caudal directions, exclusively during NREM sleep. • Non-neuronal systemic physiology continue to contribute significantly towards low-frequency neurofluid oscillations during NREM sleep similar to resting wakefulness. • These results inform the development of therapeutics aimed at improving CSF circulation with precise targets (neuronal or systemic physiological mechanisms) based on wake/sleep state. [ABSTRACT FROM AUTHOR]