Neural Circuits Research Articles

Cerebellar involvement in Parkinson's disease: Pathophysiology and neuroimaging.

Parkinson's disease (PD) is a neurodegenerative disease characterized by various motor and non-motor symptoms. The complexity of its symptoms suggests that PD is a heterogeneous neurological disorder. Its pathological changes are not limited to the substantia nigra-striatal system, but gradually extending to other regions including the cerebellum. The cerebellum is connected to a wide range of central nervous system regions that form essential neural circuits affected by PD. In addition, altered dopaminergic activity and α-synuclein pathology are found in the cerebellum, further suggesting its role in the PD progression. Furthermore, an increasing evidence obtained from imaging studies has demonstrated that cerebellar structure, functional connectivity, and neural metabolism are altered in PD when compared to healthy controls, as well as among different PD subtypes. This review provides a comprehensive summary of the cerebellar pathophysiology and results from neuroimaging studies related to both motor and non-motor symptoms of PD, highlighting the potential significance of cerebellar assessment in PD diagnosis, differential diagnosis, and disease monitoring.

Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation

While the significance of obtaining restful sleep at night and maintaining daytime alertness is well recognized for human performance and overall well-being, substantial variations exist in the development of sleepiness during diurnal waking periods. Despite the established roles of the hypothalamus and striatum in sleep-wake regulation, the specific contributions of this neural circuit in regulating individual sleep homeostasis remain elusive. This study utilized resting-state functional magnetic resonance imaging (fMRI) and mathematical modeling to investigate the role of hypothalamus-striatum connectivity in subjective sleepiness variation in a cohort of 71 healthy adults under strictly controlled in-laboratory conditions. Mathematical modeling results revealed remarkable individual differences in subjective sleepiness accumulation patterns measured by the Karolinska Sleepiness Scale (KSS). Brain imaging data demonstrated that morning hypothalamic connectivity to the dorsal striatum significantly predicts the individual accumulation of subjective sleepiness from morning to evening, while no such correlation was observed for the hypothalamus-ventral striatum connectivity. These findings underscore the distinct roles of hypothalamic connectivity to the dorsal and ventral striatum in individual sleep homeostasis, suggesting that hypothalamus-dorsal striatum circuit may be a promising target for interventions mitigating excessive sleepiness and promoting alertness.

Analysis of laser beam welding with superimposed 445 and 1070 nm wavelength lasers on copper by in situ synchrotron diagnostics

In laser welding, precision and reproducibility are fundamentally dependent on temporal and spatial processes of energy input. Induced by the dynamics of the melt pool, pressure equilibria in the vapor capillary, and solidification behavior, different weld seam qualities are achieved. To obtain the lowest possible defect frequency, new tailored joining strategies need to be investigated using multibeam and multiwavelength approaches. To improve the quality by influencing the process dynamics, a dual-beam approach is investigated that superimposes a stationary laser beam with a wavelength of 445 nm with a spatially modulated laser beam with a wavelength of 1070 nm. The aim is to utilize ∼10 times higher absorption of a 445 nm diode laser on copper with the high focusability of a 1070 nm fiber laser. In this context, the influence of the relative positions of the two beams to each other on the weld seam quality is investigated, while one of the beams moves either in front, behind, or coaxial to the other beam following the path of a line weld. The main objective is to observe how the laser beams influence each other and how the capillary depth and porosity vary for different parameters. To visualize the process dynamics, the welding experiments on copper are performed at the Deutsches Elektronen-Synchrotron DESY by means of in situ phase contrast videography. Quality-determining weld properties like the distribution of pores or process fluctuations are then extracted automatically from the image sequences by means of a trained neuronal network.

Modeling seizure networks in neuron-glia cultures using microelectrode arrays.

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks in vitro. As such, we sought to develop a novel in vitro MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

Cerebellar activity in PINK1 knockout rats during volitional gait.

Preclinical models of Parkinson's disease are imperative to gain insight into the neural circuits that contribute to gait dysfunction in advanced stages of the disease. A PTEN-induced putative kinase 1 knockout early-onset model of Parkinson's disease may be a useful rodent model to study the effects of neurotransmitter degeneration caused by a loss of PTEN-induced putative kinase 1 function on brain activity during volitional gait. The goal of this study was to measure changes in neural activity at the cerebellar vermis at 8 months of age. It was found that gait deficits, except run speed, were not significantly different from age-matched wild-type controls, as previously reported. PTEN-induced putative kinase 1 knockout (n = 4) and wild-type (n = 4) rats were implanted with a micro-electrocorticographic array placed over cerebellar vermis Lobules VI (a-c) and VII. Local field potential recordings were obtained during volitional gait across a runway. Power spectral analysis and coherence analysis were used to quantify network oscillatory activity in frequency bands of interest. Cerebellar vermis power was hypoactive in the beta (VIb, VIc and VII) and alpha (VII) bands at cerebellar vermis Lobules VIb, VIc and VII in PTEN-induced putative kinase 1 knockout rats compared with wild-type controls during gait (P < 0.05). These results suggest that gait improvement in PTEN-induced putative kinase 1 knockout rats at 8 months may be a compensatory mechanism attributed to movement corrections caused by a decreased inhibition of the alpha band of cerebellar vermis Lobule VII and beta band of Lobules VIb, VIc and VII. The PTEN-induced putative kinase 1 knockout model may be a valuable tool for understanding the circuit mechanisms underlying gait dysfunction in patients with early-onset Parkinson's disease with a functional loss of PTEN-induced putative kinase 1. Future studies investigating the cerebellar vermis as a potential biomarker and therapeutic target for the treatment of gait dysfunction in Parkinson's disease are warranted.

Control electromechanical arms by using a neural circuit

Control electromechanical arms by using a neural circuit

Synchronization evaluation of memristive photosensitive neurons in multi-neuronal systems

At the forefront of brain-computer interface (BCI) technology exploration, the synchronization phenomenon in neural networks demonstrates significant potential for application. This paper focuses on the memory characteristics and nonlinear dynamic behavior of memristors, an emerging electronic component. By innovatively integrating memristors, phototubes, and FitzHugh-Nagumo (FHN) circuits, we construct a memristive photosensitive neuron (MPN) model, aiming to explore its unique role in neural network synchronization. Utilizing the memristors' inherent memory capabilities and nonlinear properties, the MPN model mimics the dynamic behavior of biological neurons. Experiments show that synchronization is highly sensitive to the memristor's initial values, revealing intricate synchronization regulation mechanisms. Furthermore, we find that chaotic electrical currents, as environmental disturbances, have dual effects—either promoting or inhibiting MPN network synchronization—depending on specific parametric conditions. To simulate a realistic biological neural network and achieve efficient coupling among multiple MPN units, this paper adopts a Hopfield network structure. The results indicate that this structure significantly enhances the synchronization stability of the system, reduces sensitivity to initial conditions, and mitigates the adverse effects of chaotic currents. This research not only enhances our understanding of neural network synchronization but also provides novel theoretical support and technical pathways for the development of BCI technology, indicating broad application prospects in neuroscience, rehabilitative medicine, and other fields.

Neural response to monetary incentives in acquired adolescent depression after mild traumatic brain injury: Stage 2 Registered Report.

Depression is a common consequence of traumatic brain injury. Separately, spontaneous depression-arising without brain injury-has been linked to abnormal responses in motivational neural circuitry to the anticipation or receipt of rewards. It is unknown if post-injury and spontaneously occurring depression share similar phenotypic profiles. This issue is compounded by the fact that nearly all examinations of these psychiatric sequelae are post hoc: there are rarely any prospective assessments of mood and neural functioning before and after a brain injury. In this Stage 2 Registered Report, we used the Adolescent Brain Cognitive Development Consortium dataset to examine if a disruption in functional neural responses to rewards is present in patients with depression after a mild traumatic brain injury. Notably, this study provides an unparalleled opportunity to examine the trajectory of neuropsychiatric symptoms longitudinally within-subjects. This allowed us to isolate mild traumatic brain injury-specific variance independent from pre-existing functioning. Here, we focus on a case-control comparison between 43 youth who experienced a mild traumatic brain injury between MRI visits, and 43 well-matched controls. Contrary to pre-registered predictions (https://osf.io/h5uba/), there was no statistically credible increase in depression in mild traumatic brain injury cases relative to controls. Mild traumatic brain injury was associated with subtle changes in motivational neural circuit recruitment during the anticipation of incentives on the Monetary Incentive Delay paradigm. Specifically, changes in neural recruitment appeared to reflect a failure to deactivate 'task-negative' brain regions (ventromedial prefrontal cortex), alongside blunted recruitment of 'task-positive' regions (anterior cingulate, anterior insula and caudate), during the anticipation of reward and loss in adolescents following mild brain injuries. Critically, these changes in brain activity were not correlated with depressive symptoms at either visit or depression change scores before and after the brain injury. Increased time since injury was associated with a recovery of cognitive functioning-driven primarily by processing speed differences-but depression did not scale with time since injury. These cognitive changes were also uncorrelated with neural changes after mild traumatic brain injury. This report provides evidence that acquired depression may not be observed as commonly after a mild traumatic brain injury in late childhood and early adolescence, relative to findings in adult cases. Several reasons for these differing findings are considered, including sampling enrichment in retrospective cohort studies, under-reporting of depressive symptoms in parent-report data, and neuroprotective factors in childhood and adolescence.

Hippocampal storage and recall of neocortical "What"-"Where" representations.

A key question for understanding the function of the hippocampus in memory is how information is recalled from the hippocampus to the neocortex. This was investigated in a neuronal network model of the hippocampal system in which "What" and "Where" neuronal firing rate vectors were applied to separate neocortical modules, which then activated entorhinal cortex "What" and "Where" modules, then the dentate gyrus, then CA3, then CA1, then the entorhinal cortex, and then the backprojections to the neocortex. A rate model showed that the whole system could be trained to recall "Where" in the neocortex from "What" applied as a retrieval cue to the neocortex, and could in principle be trained up towards the theoretical capacity determined largely by the number of synapses onto any one neuron divided by the sparseness of the representation. The trained synaptic weights were then imported into an integrate-and-fire simulation of the same architecture, which showed that the time from presenting a retrieval cue to a neocortex module to recall the whole memory in the neocortex is approximately 100 ms. This is sufficiently fast for the backprojection synapses to be trained onto the still active neocortical neurons during storage of the episodic memory, and this is needed for recall to operate correctly to the neocortex. These simulations also showed that the long loop neocortex-hippocampus-neocortex that operates continuously in time may contribute to complete recall in the neocortex; but that this positive feedback long loop makes the whole dynamical system inherently liable to a pathological increase in neuronal activity. Important factors that contributed to stability included increased inhibition in CA3 and CA1 to keep the firing rates low; and temporal adaptation of the neuronal firing and of active synapses, which are proposed to make an important contribution to stabilizing runaway excitation in cortical circuits in the brain.

Agrypnia excitata: a human model to explore the derailment of sleep-wake cycle integrated control.

The commemoration of the 70th anniversary of rapid eye movement sleep discovery offers a unique possibility to reassess the peculiar organic condition of agrypnia excitata. Agrypnia excitata is characterized by a severe loss of sleep leading to a complete derangement of physiological sleep-wake cycle and body homeostasis. Agrypnia excitata is a definite clinico-neurophysiological condition characterized by: (1) slow-wave sleep loss with disruption of sleepwake cycle; (2) a 24-hr motor and autonomic overactivity; and (3) peculiar episodes of oneiric stupor. Agrypnia excitata may happen within different pathophysiologies, such as delirium tremens, Morvan's syndrome and fatal familial insomnia, suggesting some general reflections on the composition and function of the cerebral neuronal network generating wake and sleep behaviour and regulating body homeostasis, with a focus on rapid eye movement sleep.

Involvement of basolateral amygdala-rostral anterior cingulate cortex in mechanical allodynia and anxiety-like behaviors and potential mechanisms of electroacupuncture.

Chronic pain is highly associated with anxiety. Electroacupuncture (EA) is effective in relieving pain and anxiety. Currently, little is known about the neural mechanisms underlying the comorbidity of chronic pain and anxiety and the EA mechanism. This study investigated a potential neural circuit underlying the comorbid and EA mechanisms. Spared nerve injury (SNI) surgery established the chronic neuropathic pain mouse model. The neural circuit was activated or inhibited using the chemogenetic method to explore the relationship between the neural circuit and mechanical allodynia and anxiety-like behaviors. EA combined with the chemogenetic method was used to explore whether the effects of EA were related to this neural circuit. EA attenuated mechanical allodynia and anxiety-like behaviors in SNI mice, which may be associated with the activity of CaMKII neurons in the basolateral amygdala (BLA). Inhibition of BLACaMKII-rACC induced mechanical allodynia and anxiety-like behaviors in sham mice. Activation of the BLACaMKII-rACC alleviated neuropathic pain and anxiety-like behaviors in SNI mice. The analgesic and anxiolytic effects of 2 Hz EA were antagonized by the inhibition of the BLACaMKII-rACC. BLACaMKII-rACC mediates mechanical allodynia and anxiety-like behaviors. The analgesic and anxiolytic effects of 2 Hz EA may be associated with the BLACaMKII-rACC.

Investigation of recessed‐source/drain SOI feedback FET‐based integrate and fire neuron circuit with compact model of threshold switching devices

AbstractIn this article, the investigation of recessed‐source/drain (Re‐S/D) SOI feedback FET (FBFET)‐based integrate and fire (IF) neuron circuit parameters is presented using a threshold switching device compact model. FBFETs offer high ION and low SS with minimal power consumption, operating efficiently at lower voltages and currents than conventional MOSFETs. Utilizing ION/IOFF ratio and threshold voltage limits (Vt2/Vt1) of the device, a model is developed to mimic hysteresis characteristics, which is then used to implement an IF neuron circuit. Our findings show that altering the Re‐S/D thickness between 0 and 50 nm enhances the ION of the device under study while decreasing hysteresis width. We detected a significant increase in output spike frequency of 46.8% and 65.14% for input current pulse amplitudes of 5 and 20 nA, respectively. Furthermore, increasing the Re‐S/D thickness from 0 to 50 nm led to a significant 29.97% enhancement in spike amplitude. In addition, when using input current pulse amplitudes of 5 and 20 nA, we saw energy savings per spike of 3.36% and 12.7%, respectively. At the same time, there was an increase in power of 8.69% and 9.54%. These enhancements in performance metrics establish our proposed integrate and fire neuron circuit as a promising candidate for efficient neuromorphic system implementation.

Coactivation of the Laryngeal Muscles in Pigs Without External Neural Control Indicates Existence of an Intrinsic Neuronal Network

Electrophysiological studies of the larynx expose the mechanisms by which voice production is controlled. Previous studies have revealed certain phenomena during laryngeal oscillations that suggest a complex control mechanism.Starting from the principle of agonist-antagonist muscular pairing, the aim of this study was to gain a deeper insight into the function of the cricothyroid and thyroarytenoid muscles, both central to voice production.Electromyographic recordings were used to determine the response of the two muscles to different stimulation situations in an ex-vivo animal model of the denervated larynx of pigs (n=26).Using a set of different experiments, it was shown that when one muscle (cricothyroid or thyroarytenoid muscle) was electrically stimulated, a response was observed in the other muscle, which in the otherwise denervated larynx was caused only by the applied stimulation and exhibited the characteristics of compound action potentials. This response was reproducible in all larynxes examined and was present bidirectionally. No response was registered in the absence of stimulation.Results show the existence of coactivation of the cricothyroid and thyroarytenoid muscles in the absence of external innervation hinting at the presence of a localized neuronal network of the larynx that has not been described previously. Further morphological investigation is needed to determine the presence of this internal laryngeal neuronal network.

Searching for long timescales without fine tuning.

Animal behavior occurs on timescales much longer than the response times of individual neurons. In many cases, it is plausible that these long timescales emerge from the recurrent dynamics of electrical activity in networks of neurons. In linear models, timescales are set by the eigenvalues of a dynamical matrix whose elements measure the strengths of synaptic connections between neurons. It is not clear to what extent these matrix elements need to be tuned to generate long timescales; in some cases, one needs not just a single long timescale but a whole range. Starting from the simplest case of random symmetric connections, we combine maximum entropy and random matrix theory methods to construct ensembles of networks, exploring the constraints required for long timescales to become generic. We argue that a single long timescale can emerge generically from realistic constraints, but a full spectrum of slow modes requires more tuning. Langevin dynamics that generates patterns of synaptic connections drawn from these ensembles involves a combination of Hebbian learning and activity-dependent synaptic scaling.

Memristive Circuit Implementation of Caenorhabditis Elegans Mechanism for Neuromorphic Computing.

To overcome the energy efficiency bottleneck of the von Neumann architecture and scaling limit of silicon transistors, an emerging but promising solution is neuromorphic computing, a new computing paradigm inspired by how biological neural networks handle the massive amount of information in a parallel and efficient way. Recently, there is a surge of interest in the nematode worm Caenorhabditis elegans (C. elegans), an ideal model organism to probe the mechanisms of biological neural networks. In this article, we propose a neuron model for C. elegans with leaky integrate-and-fire (LIF) dynamics and adjustable integration time. We utilize these neurons to build the C. elegans neural network according to their neural physiology, which comprises: 1) sensory modules; 2) interneuron modules; and 3) motoneuron modules. Leveraging these block designs, we develop a serpentine robot system, which mimics the locomotion behavior of C. elegans upon external stimulus. Moreover, experimental results of C. elegans neurons presented in this article reveals the robustness (1% error w.r.t. 10% random noise) and flexibility of our design in term of parameter setting. The work paves the way for future intelligent systems by mimicking the C. elegans neural system.

Unveiling Current Advances in Bioelectronic Medicine: A Comprehensive Review

Background: Bioelectronic medicine is an emerging therapy that makes use of neural signals and the nervous system to help in the treatment of injuries and diseases. The nervous sys-tem consists of disciplined circuits that involve the communication of every major organ of the human body as all the organs of the human body are regulated and controlled by neural circuits. Objective: The objective of this review is to overview the current implementations of bioelectron-ic medicines within the human body. The main target is to heal the body without administering a wide array of exogenous drugs. On understanding these neural circuits at a molecular level, it could be possible to understand, manipulate, and modify the body’s functioning with the help of electrical impulses to modify neural impulses. Methods: The literature related to bioelectronic medicines, and their applications was collected through different websites, academic research portals, and databases, sorted, and presented in this review. Results: Bioelectronic medicine is emerging as a cutting-edge area in healthcare, demonstrating its potential to transform the diagnosis and management of inflammation and related conditions. With established efficacy in a variety of disorders and marketed available treatments, it highlights an enormous shift toward individualized and specialized therapeutic approaches, providing prom-ise for improved outcomes and a higher quality of life for patients worldwide. Conclusion: The study focuses on potential advances in bioelectronic medicine for alleviating in-flammation and inflammatory disorders such as rheumatoid arthritis, diabetes, and spinal cord in-jury. Bioelectronic treatments provide innovative therapeutic options with the potential for con-siderable clinical effects.

A novel neuronal circuit: Tanycytes mediate defensive metabolic responses following acute high‐temperature exposure

A novel neuronal circuit: Tanycytes mediate defensive metabolic responses following acute high‐temperature exposure

Cardiac fibroblasts: answering the call.

Cardiac fibroblasts play a pivotal role in maintaining heart homeostasis by depositing extracellular matrix (ECM) to provide structural support for the myocardium, vasculature, and neuronal network and by contributing to essential physiological processes. In response to injury such as myocardial infarction or pressure overload, fibroblasts become activated, leading to increased ECM production that can ultimately drive left ventricular remodeling and progress to heart failure. Recently, the American Journal of Physiology-Heart and Circulatory Physiology issued a call for papers on cardiac fibroblasts that yielded articles with topics spanning fibroblast physiology, technical considerations, signaling pathways, and interactions with other cell types. This mini-review summarizes those articles and places the new findings in the context of what is currently known for cardiac fibroblasts and what future directions remain.

Transcriptomic cell-type specificity of local cortical circuits.

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types.

A high‐performance asynchronous readout circuit with cascade function for a neural recording micro‐electrode array

AbstractThe data‐driven machine learning technology used for neural decoding emphasizes the requirements of in vivo and in vitro neural signal acquisition with high spatial and temporal resolution. However, micro‐electrode arrays (MEAs) that simultaneously achieve high spatial and temporal resolution neural signal acquisition are not yet available. Meanwhile, the high data bandwidth of large‐scale MEA brings challenges in power consumption, data transmission, storage, and neural signal processing. This research aims to improve the spatial and temporal resolution of MEA, reduce the cost and time of large‐scale MEA customization through cascading, and reduce the bandwidth of large‐scale MEA through spike compression. Firstly, based on in‐pixel spike detection, a row‐based neural spike readout mechanism and related array circuit to improve the neural spike readout performance and temporal resolution of neural signal acquisition is proposed. To further enhance the spatial resolution and reduce the risk of large‐scale MEA fabrication, the cascading capability of the readout circuit is explored. Lastly, spatial correlation‐based neural spike encoding is proposed to reduce the data bandwidth, achieving a 5.2× compression rate. This is a study on implementing large‐scale MEA through cascaded readout circuits and novel study to utilize the spatial correlation between detected neural spikes for further compression.