Neural Circuits Research Articles

The Impact of Different Exercise Modes in Fitness and Cognitive Indicators: Hybrid versus Tele-Exercise in Patients with Long Post-COVID-19 Syndrome.

The purpose of our study was to obtain evidence that an unsupervised tele-exercise program (TEgroup) via an online platform is a feasible alternative to a hybrid mode of supervised and unsupervised exercise (HEgroup) sessions for improving fitness indexes, respiratory and cognitive functions, and biomarkers of oxidative stress in patients recovering from COVID-19. Forty-nine patients with long post-COVID-19 were randomly divided into two groups (HEgroup: n = 24, age 60.0 ± 9.5 years versus TEgroup: n = 25, age 58.7 ± 9.5 years). For each patient, we collected data from body composition, oxidative stress, pulmonary function, physical fitness, and cognitive function before and after the 12-week exercise rehabilitation program (ERP). Our data showed differences in both groups before and after 12-week ERP on fitness indicators, body composition, and pulmonary function indicators. Our findings demonstrated differences between groups after 12-week ERP on adjustment in the domains of cognitive function (HEgroup increased the "visuospatial" domain: 3.2 ± 1.1 versus 3.5 ± 0.8 score, p = 0.008 and TEgroup increased the "memory" domain: 3.3 ± 1.0 versus 3.8 ± 0.5 score, p = 0.003; after 12-week ERP showed differences between groups in domain "attention" TEgroup: 4.8 ± 1.5 versus HEgroup: 3.6 ± 1.8 score, p = 0.014) and the diffusing capacity for carbon monoxide (HEgroup increased the percent of predicted values at 0.5 ± 32.3% and TEgroup at 26.0 ± 33.1%, p < 0.001). These findings may be attributed to the different ways of learning exercise programs, resulting in the recruitment of different neural circuits.

Desire to eat and the biology of obesity

Human nutrition constitutes a complex neurophysiological process determined by environmental, genetic, and hormonal factors. Human biology has neuroendocrine signals that trigger hunger, satiety, and determines the extent to which nutrients and certain foods are ingested. The homeostatic system that regulates appetite responds to the energy reserves in the body and to the functional mass of the same. In this participant various tissues, organs, hormones, and neural circuits throughout the body in a feedback loop between the brain and peripheral tissues. In addition, higher mental functions such as memory, attention and emotions are also involved in human nutrition, essential when determining the eating behavior of individuals. The understanding of these neurological mechanisms through chemical neurotransmitters that will be used for emergency cases in diseases related to the desire to eat such as obesity and its comorbidities

Amniotic Membrane-Derived Multichannel Hydrogels for Neural Tissue Repair.

In the pursuit of advancing neural tissue regeneration, biomaterial scaffolds have emerged as promising candidates, offering potential solutions for nerve disruptions. Among these scaffolds, multichannel hydrogels, characterized by meticulously designed micrometer-scale channels, stand out as instrumental tools for guiding axonal growth and facilitating cellular interactions. This study explores the innovative application of human amniotic membranes modified with methacryloyl domains (AMMA) in neural stem cell (NSC) culture. AMMA hydrogels, possessing a tailored softness resembling the physiological environment, are prepared in the format of multichannel scaffolds to simulate native-like microarchitecture of nerve tracts. Preliminary experiments on AMMA hydrogel films showcase their potential for neural applications, demonstrating robust adhesion, proliferation, and differentiation of NSCs without the need for additional coatings. Transitioning into the 3D realm, the multichannel architecture fosters intricate neuronal networks guiding neurite extension longitudinally. Furthermore, the presence of synaptic vesicles within the cellular arrays suggests the establishment of functional synaptic connections, underscoring the physiological relevance of the developed neuronal networks. This work contributes to the ongoing efforts to find ethical, clinically translatable, and functionally relevant approaches for regenerative neuroscience.

Computational Investigation of the Potential and Limitations of Machine Learning with Neural Network Circuits Based on Synaptic Transistors.

Synaptic transistors have been proposed to implement neuron activation functions of neural networks (NNs). While promising to enable compact, fast, inexpensive, and energy-efficient dedicated NN circuits, they also have limitations compared to digital NNs (realized as codes for digital processors), including shape choices of the activation function using particular types of transistor implementation, and instabilities due to noise and other factors present in analog circuits. We present a computational study of the effects of these factors on NN performance and find that, while accuracy competitive with traditional NNs can be realized for many applications, there is high sensitivity to the instability in the shape of the activation function, suggesting that, when highly accurate NNs are required, high-precision circuitry should be developed beyond what has been reported for synaptic transistors to date.

Electroacupuncture Alleviates Memory Deficits in APP/PS1 Mice by Targeting Serotonergic Neurons in Dorsal Raphe Nucleus.

Alzheimer's disease (AD) has become a significant global concern, but effective drugs able to slow down AD progression is still lacked. Electroacupuncture (EA) has been demonstrated to ameliorate cognitive impairment in individuals with AD. However, the underlying mechanisms remains poorly understood. This study aimed at examining the neuroprotective properties of EA and its potential mechanism of action against AD. APP/PS1 transgenic mice were employed to evaluate the protective effects of EA on Shenshu (BL 23) and Baihui (GV 20). Chemogenetic manipulation was used to activate or inhibit serotonergic neurons within the dorsal raphe nucleus (DRN). Learning and memory abilities were assessed by the novel object recognition and Morris water maze tests. Golgi staining, western blot, and immunostaining were utilized to determine EA-induced neuroprotection. EA at Shenshu (BL 23) and Baihui (GV 20) effectively ameliorated learning and memory impairments in APP/PS1 mice. EA attenuated dendritic spine loss, increased the expression levels of PSD95, synaptophysin, and brain-derived neurotrophic factor in hippocampus. Activation of serotonergic neurons within the DRN can ameliorate cognitive deficits in AD by activating glutamatergic neurons mediated by 5-HT1B. Chemogenetic inhibition of serotonergic neurons in the DRN reversed the effects of EA on synaptic plasticity and memory. EA can alleviate cognitive dysfunction in APP/PS1 mice by activating serotonergic neurons in the DRN. Further study is necessary to better understand how the serotonergic neurons-related neural circuits involves in EA-induced memory improvement in AD.

Secondary Order RC Sensor Neuron Circuit for Direct Input Encoding in Spiking Neural Network

Secondary Order RC Sensor Neuron Circuit for Direct Input Encoding in Spiking Neural Network

Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain.

Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.

Single-cell and spatial omics: exploring hypothalamic heterogeneity.

Elucidating the complex dynamic cellular organization In the hypothalamus Is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.

Antiferromagnetic artificial neuron modeling of the withdrawal reflex.

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.

Dissociable hindbrain GLP1R circuits for satiety and aversion.

The most successful obesity therapeutics, glucagon-like peptide-1 receptor (GLP1R) agonists, cause aversive responses such as nausea and vomiting1,2, effects that may contribute to their efficacy. Here, we investigated the brain circuits that link satiety to aversion, and unexpectedly discovered that the neural circuits mediating these effects are functionally separable. Systematic investigation across drug-accessible GLP1R populations revealed that only hindbrain neurons are required for the efficacy of GLP1-based obesity drugs. In vivo two-photon imaging of hindbrain GLP1R neurons demonstrated that most neurons are tuned to either nutritive or aversive stimuli, but not both. Furthermore, simultaneous imaging of hindbrain subregions indicated that area postrema (AP) GLP1R neurons are broadly responsive, whereas nucleus of the solitary tract (NTS) GLP1R neurons are biased towards nutritive stimuli. Strikingly, separate manipulation of these populations demonstrated that activation of NTSGLP1R neurons triggers satiety in the absence of aversion, whereas activation of APGLP1R neurons triggers strong aversion with food intake reduction. Anatomical and behavioural analyses revealed that NTSGLP1R and APGLP1R neurons send projections to different downstream brain regions to drive satiety and aversion, respectively. Importantly, GLP1R agonists reduce food intake even when the aversion pathway is inhibited. Overall, these findings highlight NTSGLP1R neurons as a population that could be selectively targeted to promote weight loss while avoiding the adverse side effects that limit treatment adherence.

Developmental trajectories of EEG aperiodic and periodic components in children 2–44 months of age

The development of neural circuits has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, periodic EEG power features and aperiodic components were examined from longitudinal EEGs collected from 592 healthy 2–44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Developmental changes in periodic peaks include (1) the presence and then absence of a 9-10 Hz alpha peak between 2-6 months, (2) nonlinear changes in high beta peaks (20-30 Hz) between 4-18 months, and (3) the emergence of a low beta peak (12-20 Hz) in some infants after six months of age. We hypothesized that the emergence of the low beta peak may reflect maturation of thalamocortical network development. Infant anesthesia studies observe that GABA-modulating anesthetics do not induce thalamocortical mediated frontal alpha coherence until 10-12 months of age. Using a small cohort of infants (n = 23) with EEG before and during GABA-modulating anesthesia, we provide preliminary evidence that infants with a low beta peak have higher anesthesia-induced alpha coherence compared to those without a low beta peak.

Brain image data processing using collaborative data workflows on Texera.

In the realm of neuroscience, mapping the three-dimensional (3D) neural circuitry and architecture of the brain is important for advancing our understanding of neural circuit organization and function. This study presents a novel pipeline that transforms mouse brain samples into detailed 3D brain models using a collaborative data analytics platform called "Texera." The user-friendly Texera platform allows for effective interdisciplinary collaboration between team members in neuroscience, computer vision, and data processing. Our pipeline utilizes the tile images from a serial two-photon tomography/TissueCyte system, then stitches tile images into brain section images, and constructs 3D whole-brain image datasets. The resulting 3D data supports downstream analyses, including 3D whole-brain registration, atlas-based segmentation, cell counting, and high-resolution volumetric visualization. Using this platform, we implemented specialized optimization methods and obtained significant performance enhancement in workflow operations. We expect the neuroscience community can adopt our approach for large-scale image-based data processing and analysis.

Developmental trajectories of EEG aperiodic and periodic components in children 2–44 months of age

The development of neural circuits has long-lasting effects on brain function, yet our understanding of early circuit development in humans remains limited. Here, periodic EEG power features and aperiodic components were examined from longitudinal EEGs collected from 592 healthy 2–44 month-old infants, revealing age-dependent nonlinear changes suggestive of distinct milestones in early brain maturation. Developmental changes in periodic peaks include (1) the presence and then absence of a 9-10 Hz alpha peak between 2-6 months, (2) nonlinear changes in high beta peaks (20-30 Hz) between 4-18 months, and (3) the emergence of a low beta peak (12-20 Hz) in some infants after six months of age. We hypothesized that the emergence of the low beta peak may reflect maturation of thalamocortical network development. Infant anesthesia studies observe that GABA-modulating anesthetics do not induce thalamocortical mediated frontal alpha coherence until 10-12 months of age. Using a small cohort of infants (n = 23) with EEG before and during GABA-modulating anesthesia, we provide preliminary evidence that infants with a low beta peak have higher anesthesia-induced alpha coherence compared to those without a low beta peak.

Neurobiology of Obsessive-Compulsive Disorder from Genes to Circuits: Insights from Animal Models.

Obsessive-compulsive disorder (OCD) is a chronic, severe psychiatric disorder that has been ranked by the World Health Organization as one of the leading causes of illness-related disability, and first-line interventions are limited in efficacy and have side-effect issues. However, the exact pathophysiology underlying this complex, heterogeneous disorder remains unknown. This scenario is now rapidly changing due to the advancement of powerful technologies that can be used to verify the function of the specific gene and dissect the neural circuits underlying the neurobiology of OCD in rodents. Genetic and circuit-specific manipulation in rodents has provided important insights into the neurobiology of OCD by identifying the molecular, cellular, and circuit events that induce OCD-like behaviors. This review will highlight recent progress specifically toward classic genetic animal models and advanced neural circuit findings, which provide theoretical evidence for targeted intervention on specific molecular, cellular, and neural circuit events.

Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity.

Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Semaphorin 6D tunes amygdalar circuits for emotional, metabolic, and inflammatory outputs.

Regulated neural-metabolic-inflammatory responses are essential for maintaining physiological homeostasis. However, the molecular machinery that coordinates neural, metabolic, and inflammatory responses is largely unknown. Here, we show that semaphorin 6D (SEMA6D) coordinates anxiogenic, metabolic, and inflammatory outputs from the amygdala by maintaining synaptic homeostasis. Using genome-wide approaches, we identify SEMA6D as a pleiotropic gene for both psychiatric and metabolic traits in human. Sema6d deficiency increases anxiety in mice. When fed a high-fat diet, Sema6d-/- mice display attenuated obesity and enhanced myelopoiesis compared with control mice due to higher sympathetic activity via the β3-adrenergic receptor. Genetic manipulation and spatial and single-nucleus transcriptomics reveal that SEMA6D in amygdalar interneurons is responsible for regulating anxiogenic and autonomic responses. Mechanistically, SEMA6D is required for synaptic maturation and γ-aminobutyric acid transmission. These results demonstrate that SEMA6D is important for the normal functioning of the neural circuits in the amygdala, coupling emotional, metabolic, and inflammatory responses.

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques

Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here, we have male macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the area’s ability for cross-context decoding. Lastly, correlational analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between neurons in prefrontal cortex maintain a stable population code and context-invariant beliefs during naturalistic behavior.