Complex Patterns Of Activity Research Articles

Similarity of hand muscle synergies elicited by transcranial magnetic stimulation and those found during voluntary movement.

Converging evidence in human and animal models suggests that exogenous stimulation of the motor cortex (M1) elicits responses in the hand with similar modular structure to that found during voluntary grasping movements. The aim of this study was to establish the extent to which modularity in muscle responses to transcranial magnetic stimulation (TMS) to M1 resembles modularity in muscle activation during voluntary hand movements involving finger fractionation. Electromyography (EMG) was recorded from eight hand-forearm muscles in eight healthy individuals. Modularity was defined using non-negative matrix factorization to identify low-rank approximations (spatial muscle synergies) of the complex activation patterns of EMG data recorded during high-density TMS mapping of M1 and voluntary formation of gestures in the American Sign Language alphabet. Analysis of synergies revealed greater than chance similarity between those derived from TMS and those derived from voluntary movement. Both data sets included synergies dominated by single intrinsic hand muscles presumably to meet the demand for highly fractionated finger movement. These results suggest that corticospinal connectivity to individual intrinsic hand muscles may be combined with modular multimuscle activation via synergies in the formation of hand postures.NEW & NOTEWORTHY This is the first work to examine the similarity of modularity in hand muscle responses to transcranial magnetic stimulation (TMS) of the motor cortex and that derived from voluntary hand movement. We show that TMS-elicited muscle synergies of the hand, measured at rest, reflect those found in voluntary behavior involving finger fractionation. This work provides a basis for future work using TMS to investigate muscle activation modularity in the human motor system.

Motor Cortex Response to Pleasant Odor Perception and Imagery: The Differential Role of Personality Dimensions and Imagery Ability.

BackgroundNeuroimaging studies have shown a complex pattern of brain activation during perception of a pleasant odor and during its olfactory imagery. To date, little is known regarding changes in motor cortex excitability during these tasks. Bergamot essential oil (BEO) is extensively used in perfumes and cosmetics for its pleasantness. Therefore, to further our understanding of the human sense of smell, this study aimed to investigate the effect of perception and imagery of a pleasant odor (BEO) on motor cortex using Transcranial magnetic stimulation (TMS).Materials and MethodsWe examined the primary motor cortex (M1) excitability during perception of a pleasant odor (BEO) or perception of odorless saline (experiment 1). Furthermore, we tested the effect of olfactory imagery (OI) of BEO on corticospinal excitability (experiment 2). The increase in motor evoked potential (MEP) amplitude was correlated with personality dimensions scores, pleasantness, vividness, and general imagery ability.ResultsThe results indicate that the corticospinal excitability changed after both perception and imagery of a pleasant odor (BEO). The correlation analysis shows an association with neuroticism personality trait (experiment 1) and with general olfactory imagery ability (experiment 2).ConclusionBoth perception of a pleasant odor and its olfactory imagery modulate motor cortex excitability. The enhanced brain activation is affected by specific individual characteristics. Overall, our findings provide physiological evidence for a complex interaction between the olfactory and motor systems.

Selective neural coding of object, feature, and geometry spatial cues in humans.

Orienting in space requires the processing of visual spatial cues. The dominant hypothesis about the brain structures mediating the coding of spatial cues stipulates the existence of a hippocampal-dependent system for the representation of geometry and a striatal-dependent system for the representation of landmarks. However, this dual-system hypothesis is based on paradigms that presented spatial cues conveying either conflicting or ambiguous spatial information and that used the term landmark to refer to both discrete three-dimensional objects and wall features. Here, we test the hypothesis of complex activation patterns in the hippocampus and the striatum during visual coding. We also postulate that object-based and feature-based navigation are not equivalent instances of landmark-based navigation. We examined how the neural networks associated with geometry-, object-, and feature-based spatial navigation compared with a control condition in a two-choice behavioral paradigm using fMRI. We showed that the hippocampus was involved in all three types of cue-based navigation, whereas the striatum was more strongly recruited in the presence of geometric cues than object or feature cues. We also found that unique, specific neural signatures were associated with each spatial cue. Object-based navigation elicited a widespread pattern of activity in temporal and occipital regions relative to feature-based navigation. These findings extend the current view of a dual, juxtaposed hippocampal-striatal system for visual spatial coding in humans. They also provide novel insights into the neural networks mediating object versus feature spatial coding, suggesting a need to distinguish these two types of landmarks in the context of human navigation.

Classification of Functional Movement Disorders with Resting-State Functional Magnetic Resonance Imaging.

Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.

Patterns of activity correlate with symptom severity in major depressive disorder patients

Objective measures, such as activity monitoring, can potentially complement clinical assessment for psychiatric patients. Alterations in rest–activity patterns are commonly encountered in patients with major depressive disorder. The aim of this study was to investigate whether features of activity patterns correlate with severity of depression symptoms (evaluated by Montgomery–Åsberg Rating Scale (MADRS) for depression). We used actigraphy recordings collected during ongoing major depressive episodes from patients not undergoing any antidepressant treatment. The recordings were acquired from two independent studies using different actigraphy systems. Data was quality-controlled and pre-processed for feature extraction following uniform procedures. We trained multiple regression models to predict MADRS score from features of activity patterns using brute-force and semi-supervised machine learning algorithms. The models were filtered based on the precision and the accuracy of fitting on training dataset before undergoing external validation on an independent dataset. The features enriched in the models surviving external validation point to high depressive symptom severity being associated with less complex activity patterns and stronger coupling to external circadian entrainers. Our results bring proof-of-concept evidence that activity patterns correlate with severity of depressive symptoms and suggest that actigraphy recordings may be a useful tool for individual evaluation of patients with major depressive disorder.

Single-cell analysis implicates TH17-to-TH2 cell plasticity in the pathogenesis of palmoplantar pustulosis

Palmoplantar pustulosis (PPP) is a severe inflammatory skin disorder characterized by eruptions of painful, neutrophil-filled pustules on the palms and soles. Although PPP has a profound effect on quality of life, itremainspoorly understood and notoriously difficult to treat. We sought to investigate the immune pathways that underlie the pathogenesis of PPP. We applied bulk and single-cell RNA sequencing (RNA-Seq) methods to the analysis of skin biopsy samples and peripheral blood mononuclear cells. We validated our results by flow cytometry and immune fluorescence microscopy RESULTS: Bulk RNA-Seq of patient skin detected an unexpected signature of T-cell activation, with a significant overexpression of several TH2 genes typically upregulated in atopic dermatitis. To further explore these findings, we carried out single-cell RNA-Seq in peripheral blood mononuclear cells of healthy and affected individuals. Memory CD4+ T cells of PPP patients were skewed toward a TH17 phenotype, a phenomenon that was particularly significant among cutaneous lymphocyte-associated antigen-positive skin-homing cells. We also identified a subset of memory CD4+ T cells that expressed both TH17 (KLRB1/CD161) and TH2 (GATA3) markers, with pseudotime analysis suggesting that the population was the result of TH17 to TH2 plasticity. Interestingly, the GATA3+/CD161+ cells were overrepresented among the peripheral blood mononuclear cells of affected individuals, both in the single-cell RNA-Seq data set and in independent flow cytometry experiments. Dual-positive cells were also detected in patient skin by immune fluorescence microscopy. PPP is associated with complex T-cell activation patterns and may explain why biologic drugs that target individual T helper cell populations have shown limited therapeutic efficacy.

Deconstructing gastrulation at single-cell resolution

Gastrulation movements in all animal embryos start with regulated deformations of patterned epithelial sheets, which are driven by cell divisions, cell shape changes, and cell intercalations. Each of these behaviors has been associated with distinct aspects of gastrulation1-4 and has been a subject of intense research using genetic, cell biological, and more recently, biophysical approaches.5-14 Most of these studies, however, focus either on cellular processes driving gastrulation or on large-scale tissue deformations.15-23 Recent advances in microscopy and image processing create a unique opportunity for integrating these complementary viewpoints.24-28 Here, we take a step toward bridging these complementary strategies and deconstruct the early stages of gastrulation in the entire Drosophila embryo. Our approach relies on an integrated computational framework for cell segmentation and tracking and on efficient algorithms for event detection. The detected events are then mapped back onto the blastoderm shell, providing an intuitive visual means to examine complex cellular activity patterns within the context of their initial anatomic domains. By analyzing these maps, we identified that the loss of nearly half of surface cells to invaginations is compensated primarily by transient mitotic rounding. In addition, by analyzing mapped cell intercalation events, we derived direct quantitative relations between intercalation frequency and the rate of axis elongation. This work is setting the stage for systems-level dissection of a pivotal step in animal development.

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG.

Scientific and Methodological Support for Teachers in the Context of Gamification in Mathematics Study in the Russian System of Additional Education

The relevance of this study is due to the practical significance of the issues of using game mechanisms in the process of teaching mathematics, the lack of research into the issues of symbiosis of game and didactic technologies in mastering complex mathematical knowledge, the need to search for effective technologies to establish how they affect the development of students and the formation of a modern style of thinking. The analysis of global trends and practices of introducing elements of gamification into the education system has shown that the mastering of complex knowledge and patterns of mathematical activity, the solution of problem areas of mathematics at different levels of learning and complexity is carried out by a complex of mathematical and information methods without the integration of game elements. It has been substantiated that gaming technologies in combination with the informatization of mathematical activity give a powerful motivational charge to the study of mathematics, actualize the processes of self-organization of cognitive activity, and contribute to the development of a probabilistic style of thinking. The purpose of the study is to provide a theoretical substantiation and effective methodological support for the process of professional development of mathematics teachers in the context of gamification in mathematics study with the effect of developing a probabilistic style of thinking. The authors have developed an arrangement mechanism and determined the content of scientific and methodological support for teachers working in the constituent entities of the Russian Federation to provide additional educational services in the context of gamification in education. The developed materials, in particular, include evaluative case tests for identifying competence deficiencies in teachers, which ensure the formation of a probabilistic style of thinking based on gamification processes by managing the information saturation of the motivational field of learning. In addition, the authors proposed an additional professional education program for teachers, Mathematics in Gaming Activities (Business, Didactic and Intellectual Games) , and developed new forms of certification and assessment materials (gamification-based didactic solutions and effective practices). Using the concept of gamification, it was possible to demonstrate the activation of cognitive and motivational structures, in particular, the effective development of a probabilistic style of thinking in the process of mastering complex mathematical knowledge through its adaptation to school mathematics. The results of the study allow making it possible to bring the process of teaching students and developing their personalities to a modern qualitative level using current achievements in the field of digitalized education.

Parallel Processing of Olfactory and Mechanosensory Information in the Honey Bee Antennal Lobe.

In insects, neuronal responses to clean air have so far been reported only episodically in moths. Here we present results obtained by fast two-photon calcium imaging in the honey bee Apis mellifera, indicating a substantial involvement of the antennal lobe, the first olfactory neuropil, in the processing of mechanical stimuli. Clean air pulses generate a complex pattern of glomerular activation that provides a code for stimulus intensity and dynamics with a similar level of stereotypy as observed for the olfactory code. Overlapping the air pulses with odor stimuli reveals a superposition of mechanosensory and odor response codes with high contrast. On the mechanosensitive signal, modulations were observed in the same frequency regime as the oscillatory motion of the antennae, suggesting a possible way to detect odorless airflow directions. The transduction of mechanosensory information via the insect antennae has so far been attributed primarily to Johnston’s organ in the pedicel of the antenna. The possibility that the antennal lobe activation by clean air originates from Johnston’s organ could be ruled out, as the signal is suppressed by covering the surfaces of the otherwise freely moving and bending antennae, which should leave Johnston’s organ unaffected. The tuning curves of individual glomeruli indicate increased sensitivity at low-frequency mechanical oscillations as produced by the abdominal motion in waggle dance communication, suggesting a further potential function of this mechanosensory code. The discovery that the olfactory system can sense both odors and mechanical stimuli has recently been made also in mammals. The results presented here give hope that studies on insects can make a fundamental contribution to the cross-taxa understanding of this dual function, as only a few thousand neurons are involved in their brains, all of which are accessible by in vivo optical imaging.

Dynamic Diversity of Glial Response Among Species in Spinal Cord Injury.

The glial scar that forms after traumatic spinal cord injury (SCI) is mostly composed of microglia, NG2 glia, and astrocytes and plays dual roles in pathophysiological processes induced by the injury. On one hand, the glial scar acts as a chemical and physical obstacle to spontaneous axonal regeneration, thus preventing functional recovery, and, on the other hand, it partly limits lesion extension. The complex activation pattern of glial cells is associated with cellular and molecular crosstalk and interactions with immune cells. Interestingly, response to SCI is diverse among species: from amphibians and fishes that display rather limited (if any) glial scarring to mammals that exhibit a well-identifiable scar. Additionally, kinetics of glial activation varies among species. In rodents, microglia become activated before astrocytes, and both glial cell populations undergo activation processes reflected amongst others by proliferation and migration toward the injury site. In primates, glial cell activation is delayed as compared to rodents. Here, we compare the spatial and temporal diversity of the glial response, following SCI amongst species. A better understanding of mechanisms underlying glial activation and scar formation is a prerequisite to develop timely glial cell-specific therapeutic strategies that aim to increase functional recovery.

Divergence in Population Coding for Space between Dorsal and Ventral CA1.

Molecular, anatomic, and behavioral studies show that the hippocampus is structurally and functionally heterogeneous, with dorsal hippocampus implicated in mnemonic processes and spatial navigation and ventral hippocampus involved in affective processes. By performing electrophysiological recordings of large neuronal populations in dorsal and ventral CA1 in head-fixed mice navigating a virtual environment, we found that this diversity resulted in different strategies for population coding of space. Populations of neurons in dorsal CA1 showed more complex patterns of activity, which resulted in a higher dimensionality of neural representations that translated to more information being encoded, as compared ensembles in vCA1. Furthermore, a pairwise maximum entropy model was better at predicting the structure of these global patterns of activity in ventral CA1 as compared with dorsal CA1. Taken together, the different coding strategies we uncovered likely emerge from anatomic and physiological differences along the longitudinal axis of hippocampus and that may, in turn, underpin the divergent ethological roles of dorsal and ventral CA1.

Delta.AR: An augmented reality-based visualization platform for 3D genome

Delta.AR: An augmented reality-based visualization platform for 3D genome

Stable behavioral state-specific large scale activity patterns in the developing cortex of neonates

Intrinsic neuronal activity is a hallmark of the developing brain. In rodents, a handful of such activities were described in different cortical areas but the unifying macroscopic perspective is still lacking. Here we combined large-scale in vivo Ca2+ imaging of the dorsal cortex in non-anesthetized neonatal mice with mathematical analyses to reveal unique behavioral state-specific maps of intrinsic activity. These maps were remarkably stable over time within and across experiments and used patches of correlated activity with little hemispheric symmetry as well as stationary and propagating waves as building blocks. Importantly, the maps recorded during motion and rest were almost inverse, with frontoparietal areas active during motion and posterior-lateral areas active at rest. The retrosplenial cortex engaged in both resting- and motion-related activities via functional long-range connections with respective cortical areas. The data obtained bind different region-specific activity patterns described so far into a single consistent picture and set the stage for future inactivation studies, probing the exact function of this complex activity pattern for cortical wiring in neonates.

Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation

Caenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyzed the distribution of the synaptic and gap connection weights. In this study, we examined whether a motor neuron and muscle network can generate oscillations for both forward and backward movement and analyzed the distribution of the trained synaptic and gap connection weights through a machine learning approach. This paper presents a connectome-based neural network model consisting of motor neurons of classes A, B, D, AS, and muscle, considering both synaptic and gap connections. A supervised learning method called backpropagation through time was adapted to train the connection parameters by feeding teacher data composed of the command neuron input and muscle cell activation. Simulation results confirmed that the motor neuron circuit could generate oscillations with different phase patterns corresponding to forward and backward movement, and could be switched at arbitrary times according to the binary inputs simulating the output of command neurons. Subsequently, we confirmed that the trained synaptic and gap connection weights followed a Boltzmann-type distribution. It should be noted that the proposed model can be trained to reproduce the activity patterns measured for an animal (HRB4 strain). Therefore, the supervised learning approach adopted in this study may allow further analysis of complex activity patterns associated with movements.

Probing the Neural Systems Underlying Flexible Dimensional Attention.

Flexibly shifting attention between stimulus dimensions (e.g., shape and color) is a central component of regulating cognition for goal-based behavior. In the present report, we examine the functional roles of different cortical regions by manipulating two demands on task switching that have been confounded in previous studies-shifting attention between visual dimensions and resolving conflict between stimulus-response representations. Dimensional shifting was manipulated by having participants shift attention between dimensions (either shape or color; dimension shift) or keeping the task-relevant dimension the same (dimension same). Conflict between stimulus-response representations was manipulated by creating conflict between response-driven associations from the previous set of trials and the stimulus-response mappings on the current set of trials (e.g., making a leftward response to a red stimulus during the previous task, but being required to make a rightward response to a red stimulus in the current task; stimulus-response conflict), or eliminating conflict by altering the features of the dimension relevant to the sorting rule (stimulus-response no-conflict). These manipulations revealed activation along a network of frontal, temporal, parietal, and occipital cortices. Specifically, dimensional shifting selectively activated frontal and parietal regions. Stimulus-response conflict, on the other hand, produced decreased activation in temporal and occipital cortices. Occipital regions demonstrated a complex pattern of activation that was sensitive to both stimulus-response conflict and dimensional attention switching. These results provide novel information regarding the distinct role that frontal cortex plays in shifting dimensional attention and posterior cortices play in resolving conflict at the stimulus level.

Hot planets around cool stars – two short-period mini-Neptunes transiting the late K-dwarf TOI-1260

ABSTRACT We present the discovery and characterization of two sub-Neptunes in close orbits, as well as a tentative outer planet of a similar size, orbiting TOI-1260 – a low metallicity K6 V dwarf star. Photometry from Transiting Exoplanet Survey Satellite(TESS) yields radii of Rb = 2.33 ± 0.10 and Rc = 2.82 ± 0.15 R⊕, and periods of 3.13 and 7.49 d for TOI-1260 b and TOI-1260 c, respectively. We combined the TESS data with a series of ground-based follow-up observations to characterize the planetary system. From HARPS-N high-precision radial velocities we obtain Mb = $8.6 _{ - 1.5 } ^ { + 1.4 }$ and Mc = $11.8 _{ - 3.2 } ^ { + 3.4 }$ M⊕. The star is moderately active with a complex activity pattern, which necessitated the use of Gaussian process regression for both the light-curve detrending and the radial velocity modelling, in the latter case guided by suitable activity indicators. We successfully disentangle the stellar-induced signal from the planetary signals, underlining the importance and usefulness of the Gaussian process approach. We test the system’s stability against atmospheric photoevaporation and find that the TOI-1260 planets are classic examples of the structure and composition ambiguity typical for the 2–3 R⊕ range.

Divergence in Population Coding for Space between Dorsal and Ventral CA1

Molecular, anatomic, and behavioral studies show that the hippocampus is structurally and functionally heterogeneous, with dorsal hippocampus implicated in mnemonic processes and spatial navigation and ventral hippocampus involved in affective processes. By performing electrophysiological recordings of large neuronal populations in dorsal and ventral CA1 in head-fixed mice navigating a virtual environment, we found that this diversity resulted in different strategies for population coding of space. We found that the populations of neurons in dorsal CA1 had a higher dimensionality and showed more complex patterns of activity, translating to more information being encoded, as compared ensembles in vCA1. Furthermore, a pairwise maximum entropy model was better at predicting the structure of global patterns of activity in ventral CA1 as compared to dorsal CA1. Taken together, we uncovered different coding strategies that likely emerge from anatomical and physiological differences along the longitudinal axis of hippocampus and that may, in turn, underpin the divergent ethological roles of dorsal and ventral CA1.

Cycle length statistics during human atrial fibrillation reveal refractory properties of the underlying substrate: a combined in silico and clinical test of concept study.

The treatment of atrial fibrillation beyond pulmonary vein isolation has remained an unsolved challenge. Targeting regions identified by different substrate mapping approaches for ablation resulted in ambiguous outcomes. With the effective refractory period being a fundamental prerequisite for the maintenance of fibrillatory conduction, this study aims at estimating the effective refractory period with clinically available measurements. A set of 240 simulations in a spherical model of the left atrium with varying model initialization, combination of cellular refractory properties, and size of a region of lowered effective refractory period was implemented to analyse the capabilities and limitations of cycle length mapping. The minimum observed cycle length and the 25% quantile were compared to the underlying effective refractory period. The density of phase singularities was used as a measure for the complexity of the excitation pattern. Finally, we employed the method in a clinical test of concept including five patients. Areas of lowered effective refractory period could be distinguished from their surroundings in simulated scenarios with successfully induced multi-wavelet re-entry. Larger areas and higher gradients in effective refractory period as well as complex activation patterns favour the method. The 25% quantile of cycle lengths in patients with persistent atrial fibrillation was found to range from 85 to 190 ms. Cycle length mapping is capable of highlighting regions of pathologic refractory properties. In combination with complementary substrate mapping approaches, the method fosters confidence to enhance the treatment of atrial fibrillation beyond pulmonary vein isolation particularly in patients with complex activation patterns.

Adult mouse dorsal root ganglia neurons form aberrant glutamatergic connections in dissociated cultures.

Cultured sensory neurons can exhibit complex activity patterns following stimulation in terms of increased excitability and interconnected responses of multiple neurons. Although these complex activity patterns suggest a network-like configuration, research so far had little interest in synaptic network formation ability of the sensory neurons. To identify interaction profiles of Dorsal Root Ganglia (DRG) neurons and explore their putative connectivity, we developed an in vitro experimental approach. A double transgenic mouse model, expressing genetically encoded calcium indicator (GECI) in their glutamatergic neurons, was produced. Dissociated DRG cultures from adult mice were prepared with a serum-free protocol and no additional growth factors or cytokines were utilized for neuronal sensitization. DRG neurons were grown on microelectrode arrays (MEA) to induce stimulus-evoked activity with a modality-free stimulation strategy. With an almost single-cell level electrical stimulation, spontaneous and evoked activity of GCaMP6s expressing neurons were detected under confocal microscope. Typical responses were analyzed, and correlated calcium events were detected across individual DRG neurons. Next, correlated responses were successfully blocked by glutamatergic receptor antagonists, which indicated functional synaptic coupling. Immunostaining confirmed the presence of synapses mainly in the axonal terminals, axon-soma junctions and axon-axon intersection sites. Concisely, the results presented here illustrate a new type of neuron-to-neuron interaction in cultured DRG neurons conducted through synapses. The developed assay can be a valuable tool to analyze individual and collective responses of the cultured sensory neurons.