Neural Circuits Research Articles

The role of the microbiota–gut–brain axis in methamphetamine-induced neurotoxicity: Disruption of microbial composition and short-chain fatty acid metabolism

Methamphetamine (METH) abuse is associated with significant neurotoxicity, high addiction potential, and behavioral abnormalities. Recent studies have identified a connection between the gut microbiota and METH-induced neurotoxicity and behavioral disorders. However, the underlying causal mechanisms linking the gut microbiota to METH pathophysiology remain largely unexplored. In this study, we employed fecal microbiota transplantation (FMT) and antibiotic (Abx) intervention to manipulate the gut microbiota in mice administered METH. Furthermore, we supplemented METH-treated mice with short-chain fatty acids (SCFAs) and pioglitazone (Pio) to determine the protective effects on gut microbiota metabolism. Finally, we assessed the underlying mechanisms of the gut–brain neural circuit in vagotomized mice. Our data provide compelling evidence that modulation of the gut microbiome through FMT or microbiome knockdown by Abx plays a crucial role in METH-induced neurotoxicity, behavioral disorders, gut microbiota disturbances, and intestinal barrier impairment. Furthermore, our findings highlight a novel prevention strategy for mitigating the risks to both the nervous and intestinal systems caused by METH, which involves supplementation with SCFAs or Pio.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain’s ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks’ dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

The Role of the Cerebellum in Advanced Cognitive Processes in Children.

Over the last several years, a growing body of evidence from anatomical, physiological, and functional neuroimaging studies has increasingly indicated that the cerebellum is actively involved in managing higher order cognitive functions and regulating emotional responses. It has become clear that when children experience congenital or acquired cerebellar lesions, these injuries can lead to a variety of cognitive and emotional disorders, manifesting in different combinations. This underscores the cerebellum's essential role not only throughout developmental stages but particularly in facilitating learning processes, highlighting its critical importance beyond its traditional association with motor control. Furthermore, the intricate neural circuits within the cerebellum are believed to contribute to the fine-tuning of motor actions and coordination but are also increasingly recognized for their involvement in cognitive processes such as attention, language, and problem solving. Recent research has highlighted the importance of cerebellar health and integrity for optimal functioning across various domains of the human experience.

Maternal adverse childhood experiences and infant visual-limbic white matter development

BackgroundMaternal adverse childhood experiences (ACEs) are robust predictors of mental health for both the exposed individual and the next generation; however, the pathway through which such intergenerational risk is conferred remains unknown. The current study evaluated the association between maternal ACEs and infant brain development, including an a priori focus on circuits implicated in emotional and sensory processing. MethodsThe sample included 101 mother-infant dyads from a longitudinal study. Maternal ACEs were assessed with the Adverse Childhood Questionnaire dichotomized into low (0 or 1) and high (≥2) groups. White matter microstructure, as indexed by fractional anisotropy (FA), was assessed using structural magnetic resonance imaging in infants (41.6–46.0 weeks' postconceptional age) within a priori tracts (the cingulum, fornix, uncinate, inferior frontal occipital fasciculus, and inferior longitudinal fasciculus). Exploratory analyses were also conducted across the whole brain. ResultsHigh maternal ACEs (≥2) were associated with decreased infant left inferior longitudinal fasciculus (ILF) FA (F(1,94) = 7.78, p < .006) relative to infants of low ACE mothers. No group difference was observed within the right ILF following correction for multiple comparisons (F(1,95) = 4.29, p < .041). Follow-up analyses within the left ILF demonstrated associations between high maternal ACEs and increased left radial diffusivity (F(1,95) = 5.10, p < .006). Exploratory analyses demonstrated preliminary support for differences in visual processing networks (e.g. optic tract) as well as additional circuits less frequently examined in the context of early life adversity exposure, (e.g. corticothalamic tract). ConclusionsMaternal ACEs predict neural circuit development of the inferior longitudinal fasciculus. Findings suggest that early developing sensory circuits within the infant brain are susceptible to maternal adverse childhood experiences and may have implications for the maturation of higher order emotional and cognitive circuits.

Functional and structural maturation of auditory cortex from 2 months to 2 years old

BackgroundIn school-age children, the myelination of the auditory radiation thalamocortical pathway is associated with the latency of auditory evoked responses, with the myelination of thalamocortical axons facilitating the rapid propagation of acoustic information. Little is known regarding this auditory system function-structure association in infants and toddlers. Methods and ParticipantsThe present study tested the hypothesis that maturation of auditory radiation white-matter microstructure (e.g., fractional anisotropy (FA); measured using diffusion-weighted MRI) is associated with the latency of the infant auditory response (the P2m response, measured using magnetoencephalography, MEG) in a cross-sectional (N = 47, 2 to 24 months, 19 females) as well as longitudinal cohort (N = 18, 2 to 29 months, 8 females) of typically developing infants and toddlers. Of 18 longitudinal infants, 2 infants had data from 3 timepoints and 16 infants had data from 2 timepoints. ResultsIn the cross-sectional sample, non-linear maturation of P2m latency and auditory radiation diffusion measures were observed. Auditory radiation diffusion accounted for significant variance in P2m latency, even after removing the variance associated with age in both P2m latency and auditory radiation diffusion measures. In the longitudinal sample, latency and FA associations could be observed at the level of a single child. ConclusionsFindings provide strong support for the hypothesis that an increase in thalamocortical neural conduction velocity, due to increased axon diameter and/or myelin maturation, contributes to a decrease in the infant P2m auditory evoked response latency. SignificanceInfant multimodal brain imaging identifies brain mechanisms contributing to the rapid changes in neural circuit activity during the first two years of life.

A developmental neurotoxicity adverse outcome pathway (DNT-AOP) with voltage gate sodium channel (VGSC) inhibition as a molecular initiating event (MiE).

The adverse outcome pathway (AOP) framework serves as a practical tool for organising scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. However, a major challenge in the broader application of the AOP concept within regulatory toxicology is the development of a robust AOPs that can withstand peer review and acceptance. This is mainly due to the considerable amount of work required to substantiate the modular units of a complete AOP, which can take years from inception to completion. The methodology used here consisted of an initial assessment of a single chemical hazard using the Integrated Approach to Testing and Assessment (IATA) framework. An evidence-based approach was then used to gather empirical evidence combining systematic literature review methods with expert knowledge to ensure the effectiveness of the AOP development methodology. The structured framework used assured transparency, objectivity and comprehensiveness, and included expert knowledge elicitation for the evaluation of key event relationships (KERs). This stepwise approach led to the development of an AOP that begins with binding of chemicals to Voltage Gate Sodium Channels (VGSC/Nav) during mammalian development leading to adverse consequences in neurodevelopment evidenced as deficits in cognitive functions. Disruption of the formation of precise neural circuits by alterations in VGSC kinetics during the perinatal stages of brain development may also underlie neurodevelopmental disorders. Gaps in our understanding include the specific critical developmental windows and the quantitative relationship of binding to VGSC and subsequent disruption and cognitive function. Despite the limited quantitative information at all KER levels, regulatory applications of this AOP for DNT assessment have been identified.

Neurobiology and Treatment of Posttraumatic Stress Disorder.

The recent worldwide surge of warfare and hostilities exposes increasingly large numbers of individuals to traumatic events, placing them at risk of developing posttraumatic stress disorder (PTSD) and challenging both clinicians and service delivery systems. This overview summarizes and updates the core knowledge of the genetic, molecular, and neural circuit features of the neurobiology of PTSD and advances in evidence-based psychotherapy, pharmacotherapy, neuromodulation, and digital treatments. While the complexity of the neurobiology and the biological and clinical heterogeneity of PTSD have challenged clinicians and researchers, there is an emerging consensus concerning the underlying mechanisms and approaches to diagnosis, treatment, and prevention of PTSD. This update addresses PTSD diagnosis, prevalence, course, risk factors, neurobiological mechanisms, current standard of care, and innovations in next-generation treatment and prevention strategies. It provides a comprehensive summary and concludes with areas of research for integrating advances in the neurobiology of the disorder with novel treatment and prevention targets.

Compound heterozygous mutations of NTNG2 cause intellectual disability via inhibition of the CaMKII signaling

Netrin-G2 is a membrane-anchored protein and is known to play critical roles in neuronal circuit development and synaptic organization. In this study, we identify compound heterozygous mutations of c.547delC, p.(Arg183Alafs∗186) and c.605G > A, p.(Trp202∗) in NTNG2 causing a syndrome exhibiting developmental delay, intellectual disability, hypotonia, and facial dysmorphism. To elucidate the underlying cellular and molecular mechanisms, CRISPR-Cas9 technology is employed to generate a knock-in mouse model expressing the R183Afs and W202X mutations. We report that the Ntng2R183Afs/W202X mice exhibit hypotonia and impaired learning and memory. We find that the levels of CaMKII and p-GluA1Ser831 are decreased and excitatory postsynaptic transmission and long-term potentiation are impaired. To increase the activity of CaMKII, the mutant mice have received intraperitoneal injections of DCP-LA, a CaMKII agonist, and show improved cognitive function. Together, our findings reveal molecular mechanisms of how NTNG2 deficiency leads to impairments of cognitive ability and synaptic plasticity.

Prediction of vulnerability severity using vulnerability description with natural language processing and deep learning

One of the most critical aspects of a software piece is its vulnerabilities. Regardless of the years of experience, type of project, or the size of the team, it is impossible to avoid introducing vulnerabilities while developing or maintaining software. This aspect becomes crucial when the software is deployed in production or released to the final users. At that point finding vulnerabilities becomes a race between the developers and malicious intruders, whoever finds it first can either exploit it or fix it. Acknowledging this situation and using the tools and standards that we have available in the field, such as common vulnerability exposures and common vulnerability scoring systems, and based on modern researches, in this study, we propose to have an approach different from the common practices of manual classification, using a 2-layer convolutional neuronal network (CNN) to automatize the classification of vulnerabilities, speeding up this process and enabling developers to have a faster response towards vulnerabilities, producing safer software. The experimental results obtained in this study suggest that pre-trained word embeddings contributed to an increase in accuracy of approximately 2% and the overall accuracy become 0.816%.

Effect of wedge duration and electromagnetic noise on spiral wave dynamics

This paper focuses particularly on the influence of wedge duration on spiral wave formation and the regulation of electromagnetic noise. The motion stability or periodicity of a constructed regular neuronal network system is revealed by applying the master stability function method. The effect of wedge duration and electromagnetic noise on spiral wave dynamics is quantified using defined metrics, and explained by bifurcation of neuronal activity and differentiation of neuronal populations. Research results are as follows: (1) The appearing wave head rotates and evolves into a spiral pattern due to the potential difference between neurons, which is determined by wedge duration. (2) Whether it is homogeneous or heterogeneous, electromagnetic noise can effectively regulate the evolution of spiral waves. (3) Noise excitation significantly suppresses the network firing activity and alters the electric field distribution, leading to the narrowing of spiral arm and the drift of wave head. This study not only demonstrates the importance of wedge duration for spiral wave formation, but also provides guidance for stochastically regulating the spiral wave evolution.

NeuroQuantify – An Image Analysis Software for Detection and Quantification of Neuron Cells and Neurite Lengths using Deep Learning

BackgroundThe segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. New methodWe have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. ResultsNeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. Comparison with existing methodsNeuroQuantify overcomes some of the limitations of existing methods in the automatic and accurate analysis of neuronal structures. It has been developed for phase contrast images rather than fluorescence images. In addition to typical functionality of cell counting, NeuroQuantify also detects and counts neurites, measures the neurite lengths, and produces the neurite orientation distribution. ConclusionsWe offer a valuable tool to assess network development rapidly and effectively. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub at https://github.com/StanleyZ0528/neural-image-segmentation.

Population bursts in a modular neural network as a mechanism for synchronized activity in KNDy neurons.

The pulsatile activity of gonadotropin-releasing hormone neurons (GnRH neurons) is a key factor in the regulation of reproductive hormones. This pulsatility is orchestrated by a network of neurons that release the neurotransmitters kisspeptin, neurokinin B, and dynorphin (KNDy neurons), and produce episodic bursts of activity driving the GnRH neurons. We show in this computational study that the features of coordinated KNDy neuron activity can be explained by a neural network in which connectivity among neurons is modular. That is, a network structure consisting of clusters of highly-connected neurons with sparse coupling among the clusters. This modular structure, with distinct parameters for intracluster and intercluster coupling, also yields predictions for the differential effects on synchronization of changes in the coupling strength within clusters versus between clusters.

A Detailed Re-Examination of the Period Gene Rescue Experiments Shows That Four to Six Cryptochrome-Positive Posterior Dorsal Clock Neurons (DN1p) of Drosophila melanogaster Can Control Morning and Evening Activity.

Animal circadian clocks play a crucial role in regulating behavioral adaptations to daily environmental changes. The fruit fly Drosophila melanogaster exhibits 2 prominent peaks of activity in the morning and evening, known as morning (M) and evening (E) peaks. These peaks are controlled by 2 distinct circadian oscillators located in separate groups of clock neurons in the brain. To investigate the clock neurons responsible for the M and E peaks, a cell-specific gene expression system, the GAL4-UAS system, has been commonly employed. In this study, we re-examined the two-oscillator model for the M and E peaks of Drosophila by utilizing more than 50 Gal4 lines in conjunction with the UAS-period16 line, which enables the restoration of the clock function in specific cells in the period (per) null mutant background. Previous studies have indicated that the group of small ventrolateral neurons (s-LNv) is responsible for controlling the M peak, while the other group, consisting of the 5th ventrolateral neuron (5th LNv) and the three cryptochrome (CRY)-positive dorsolateral neurons (LNd), is responsible for the E peak. Furthermore, the group of posterior dorsal neurons 1 (DN1p) is thought to also contain M and E oscillators. In this study, we found that Gal4 lines directed at the same clock neuron groups can lead to different results, underscoring the fact that activity patterns are influenced by many factors. Nevertheless, we were able to confirm previous findings that the entire network of circadian clock neurons controls M and E peaks, with the lateral neurons playing a dominant role. In addition, we demonstrate that 4 to 6 CRY-positive DN1p cells are sufficient to generate M and E peaks in light-dark cycles and complex free-running rhythms in constant darkness. Ultimately, our detailed screening could serve as a catalog to choose the best Gal4 lines that can be used to rescue per in specific clock neurons.

Multitask learning of a biophysically-detailed neuron model.

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behavior of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. While LFP and EEG are an important downstream application, the main focus of this paper lies in predicting dendritic voltages within each compartment to capture the entire electrophysiology of a biophysically-detailed neuron model. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials.

Motor cortical inactivation impairs corrective submovements in mice performing a hold-still center-out reach task.

Holding still and aiming reaches to spatial targets may depend on distinct neural circuits. Using automated homecage training and a sensitive joystick, we trained freely-moving mice to contact a joystick, hold their forelimb still, and then reach to rewarded target locations. Mice learned the task by initiating forelimb sequences with clearly resolved submillimeter-scale micromovements followed by millimeter-scale reaches to learned spatial targets. Hundreds of thousands of trajectories were decomposed into millions of kinematic submovements, while photoinhibition was used to test roles of motor cortical areas. Inactivation of both caudal and rostral forelimb areas preserved the ability to produce aimed reaches, but reduced reach speed. Inactivation specifically of contralateral caudal forelimb area (CFA) additionally impaired the ability to aim corrective submovements to remembered locations following target undershoots. Our findings show that motor cortical inactivations reduce the gain of forelimb movements but that inactivation specifically of contralateral CFA impairs corrective movements important for reaching a target location.

ECR Spotlight – Ruth Gutjahr

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Ruth Gutjahr is an author on ‘ Gekko gecko as a model organism for understanding aspects of laryngeal vocal evolution’, published in JEB. Ruth is a PhD student in the lab of Prof. Boris Philippe Chagnaud at the University of Graz, Austria, investigating the neuronal networks underlying animal behavior.

Recall tempo of Hebbian sequences depends on the interplay of Hebbian kernel with tutor signal timing

Understanding how neural circuits generate sequential activity is a longstanding challenge. While foundational theoretical models have shown how sequences can be stored as memories in neural networks with Hebbian plasticity rules, these models considered only a narrow range of Hebbian rules. Here, we introduce a model for arbitrary Hebbian plasticity rules, capturing the diversity of spike-timing-dependent synaptic plasticity seen in experiments, and show how the choice of these rules and of neural activity patterns influences sequence memory formation and retrieval. In particular, we derive a general theory that predicts the tempo of sequence replay. This theory lays a foundation for explaining how cortical tutor signals might give rise to motor actions that eventually become "automatic." Our theory also captures the impact of changing the tempo of the tutor signal. Beyond shedding light on biological circuits, this theory has relevance in artificial intelligence by laying a foundation for frameworks whereby slow and computationally expensive deliberation can be stored as memories and eventually replaced by inexpensive recall.

Real-Time Analysis of Neuronal Cell Cultures for CNS Drug Discovery.

The ability to screen for agents that can promote the development and/or maintenance of neuronal networks creates opportunities for the discovery of novel agents for the treatment of central nervous system (CNS) disorders. Over the past 10 years, advances in robotics, artificial intelligence, and machine learning have paved the way for the improved implementation of live-cell imaging systems for drug discovery. These instruments have revolutionized our ability to quickly and accurately acquire large standardized datasets when studying complex cellular phenomena in real-time. This is particularly useful in the field of neuroscience because real-time analysis can allow efficient monitoring of the development, maturation, and conservation of neuronal networks by measuring neurite length. Unfortunately, due to the relative infancy of this type of analysis, standard practices for data acquisition and processing are lacking, and there is no standardized format for reporting the vast quantities of data generated by live-cell imaging systems. This paper reviews the current state of live-cell imaging instruments, with a focus on the most commonly used equipment (IncuCyte systems). We provide an in-depth analysis of the experimental conditions reported in publications utilizing these systems, particularly with regard to studying neurite outgrowth. This analysis sheds light on trends and patterns that will enhance the use of live-cell imaging instruments in CNS drug discovery.

Host circuit engagement of human cortical organoids transplanted in rodents.

Human neural organoids represent promising models for studying neural function; however, organoids grown in vitro lack certain microenvironments and sensory inputs that are thought to be essential for maturation. The transplantation of patient-derived neural organoids into animal hosts helps overcome some of these limitations and offers an approach for neural organoid maturation and circuit integration. Here, we describe a method for transplanting human stem cell-derived cortical organoids (hCOs) into the somatosensory cortex of newborn rats. The differentiation of human induced pluripotent stem cells into hCOs occurs over 30-60 days, and the transplantation procedure itself requires ~0.5-1 hours per animal. The use of neonatal hosts provides a developmentally appropriate stage for circuit integration and allows the generation and experimental manipulation of a unit of human neural tissue within the cortex of a living animal host. After transplantation, animals can be maintained for hundreds of days, and transplanted hCO growth can be monitored by using brain magnetic resonance imaging. We describe the assessment of human neural circuit function in vivo by monitoring genetically encoded calcium responses and extracellular activity. To demonstrate human neuron-host functional integration, we also describe a procedure for engaging host neural circuits and for modulating animal behavior by using an optogenetic behavioral training paradigm. The transplanted human neurons can then undergo ex vivo characterization across modalities including dendritic morphology reconstruction, single-nucleus transcriptomics, optogenetic manipulation and electrophysiology. This approach may enable the discovery of cellular phenotypes from patient-derived cells and uncover mechanisms that contribute to human brain evolution from previously inaccessible developmental stages.

Physiological Biomarkers of Upper Motor Neuron Dysfunction in ALS.

Upper motor neuron (UMN) dysfunction is an important feature of amyotrophic lateral sclerosis (ALS) for the diagnosis and understanding of pathogenesis. The identification of UMN signs forms the basis of ALS diagnosis, although may be difficult to discern, especially in the setting of severe muscle weakness. Transcranial magnetic stimulation (TMS) techniques have yielded objective physiological biomarkers of UMN dysfunction in ALS, enabling the interrogation of cortical and subcortical neuronal networks with diagnostic, pathophysiological, and prognostic implications. Transcranial magnetic stimulation techniques have provided pertinent pathogenic insights and yielded novel diagnostic and prognostic biomarkers. Cortical hyperexcitability, as heralded by a reduction in short interval intracortical inhibition (SICI) and an increase in short interval intracortical facilitation (SICF), has been associated with lower motor neuron degeneration, patterns of disease evolution, as well as the development of specific ALS clinical features including the split hand phenomenon. Reduction in SICI has also emerged as a potential diagnostic aid in ALS. More recently, physiological distinct inhibitory and facilitatory cortical interneuronal circuits have been identified, which have been shown to contribute to ALS pathogenesis. The triple stimulation technique (TST) was shown to enhance the diagnostic utility of conventional TMS measures in detecting UMN dysfunction. Resting-state EEG is a novel neurophysiological technique developed for directly interrogating cortical neuronal networks in ALS, that have yielded potentially useful physiological biomarkers of UMN dysfunction. The present review discusses physiological biomarkers of UMN dysfunction in ALS, encompassing conventional and novel TMS techniques developed to interrogate the functional integrity of the corticomotoneuronal system, focusing on pathogenic, diagnostic, and prognostic utility.