Oscillatory phenomena in electrophysiological networks: The coupling between cell bioelectricity and transcription.

Morphogenetic regulation during embryogenesis and regeneration rely on information transfer and coordination between different regions. Here, we explore theoretically the coupling between bioelectrical and transcriptional oscillations at the individual cell and multicellular levels. The simulations, based on a set of ion channels and intercellular gap junctions, show that bioelectrical and transcriptional waves can electrophysiologically couple distant regions of a model network in phase and antiphase oscillatory states that include synchronization phenomena. In this way, different multicellular regionalizations can be encoded by cell potentials that oscillate between depolarized and polarized states, thus allowing a spatio-temporal coding. Because the electric potential patterns characteristic of development and regeneration are correlated with the spatial distributions of signaling ions and molecules, bioelectricity can act as a template for slow biochemical signals following a hierarchy of experimental times. In particular, bioelectrical gradients that couple cell potentials to transcription rates give to each single cell a rough idea of its location in the multicellular ensemble, thus controlling local differentiation processes that switch on and off crucial parts of the genome.

Assessing Cross-Contamination in Spike-Sorted Electrophysiology Data.

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting-the rate at which spikes are assigned to the wrong cluster-has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISIv). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISIv and FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.

A comprehensive protocol for efficient differentiation of human NPCs into electrically competent neurons.

BACKGROUND: The study of neurons is fundamental to unraveling the complexities of the nervous system. Primary neuronal cultures from rodents have long been a cornerstone of experimental studies, yet limitations related to their non-human nature and ethical concerns have prompted the development of alternatives. In recent years, the derivation of neurons from human-induced pluripotent stem cells (hiPSCs) has emerged as a powerful option, offering a scalable source of cells for diverse applications. Neural progenitor cells (NPCs) derived from hiPSCs can be efficiently differentiated into functional neurons, providing a platform to study human neural physiology and pathology in vitro. However, challenges persist in achieving consistent and reproducible outcomes across experimental settings. COMPARISON WITH EXISTING METHODS: Our aim is to provide a step-by-step methodological protocol, augmenting existing procedures with additional instructions and parameters, to guide researchers in achieving reproducible results. METHODS AND RESULTS: We outline procedures for the differentiation of hiPSC-derived NPCs into electrically competent neurons, encompassing initial cell density, morphology, maintenance, and differentiation. We also describe the analysis of specific markers for assessing neuronal phenotype, along with electrophysiological analysis to evaluate biophysical properties of neuronal excitability. Additionally, we conduct a comparative analysis of three different chemical methods-KCl, N-methyl-D-aspartate (NMDA), and bicuculline-to induce neuronal depolarization and assess their effects on the induction of both fast and slow post-translational, transcriptional, and post-transcriptional responses. CONCLUSION: Our protocol provides clear instructions for generating reliable human neuronal cultures with defined electrophysiological properties to investigate neuronal differentiation and model diseases in vitro.

SHIELD: Skull-shaped hemispheric implants enabling large-scale electrophysiology datasets in the mouse brain.

To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. Although new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe skull-shaped hemispheric implants enabling large-scale electrophysiology datasets (SHIELD). These 3D-printed skull-replacement implants feature customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedure's high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase SHIELD's scientific utility, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful, large-scale electrophysiological experiments for the study of distributed neural computation.

ElecFeX is a user-friendly toolbox for efficient feature extraction from single-cell electrophysiological recordings.

Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

Facilitating the Sharing of Electrophysiology Data Analysis Results Through In-Depth Provenance Capture.

Scientific research demands reproducibility and transparency, particularly in data-intensive fields like electrophysiology. Electrophysiology data are typically analyzed using scripts that generate output files, including figures. Handling these results poses several challenges due to the complexity and iterative nature of the analysis process. These stem from the difficulty to discern the analysis steps, parameters, and data flow from the results, making knowledge transfer and findability challenging in collaborative settings. Provenance information tracks data lineage and processes applied to it, and provenance capture during the execution of an analysis script can address those challenges. We present Alpaca (Automated Lightweight Provenance Capture), a tool that captures fine-grained provenance information with minimal user intervention when running data analysis pipelines implemented in Python scripts. Alpaca records inputs, outputs, and function parameters and structures information according to the W3C PROV standard. We demonstrate the tool using a realistic use case involving multichannel local field potential recordings of a neurophysiological experiment, highlighting how the tool makes result details known in a standardized manner in order to address the challenges of the analysis process. Ultimately, using Alpaca will help to represent results according to the FAIR principles, which will improve research reproducibility and facilitate sharing the results of data analyses.

Effect of menstrual cycle and menopause on human gastric electrophysiology.

Chronic gastroduodenal symptoms disproportionately affect females of childbearing age; however, the effect of menstrual cycling on gastric electrophysiology is poorly defined. To establish the effect of the menstrual cycle on gastric electrophysiology, healthy subjects underwent noninvasive Body Surface Gastric Mapping (BSGM; 8x8 array) with the validated symptom logging App (Gastric Alimetry, New Zealand). Participants included were premenopausal females in follicular (n = 26) and luteal phases (n = 18) and postmenopausal females (n = 30) and males (n = 51) were controls. Principal gastric frequency (PGF), body mass index (BMI) adjusted amplitude, Gastric Alimetry Rhythm Index (GA-RI), Fed:Fasted Amplitude Ratio (ff-AR), meal response curves, and symptom burden were analyzed. Menstrual cycle-related electrophysiological changes were then transferred to an established anatomically accurate computational gastric fluid dynamics model (meal viscosity 0.1 Pas) to predict the impact on gastric mixing and emptying. PGF was significantly higher in the luteal versus follicular phase [mean 3.21 cpm, SD (0.17) vs. 2.94 cpm, SD (0.17), P < 0.001] and versus males [3.01 cpm, SD (0.2), P < 0.001]. In the computational model, this translated to 8.1% higher gastric mixing strength and 5.3% faster gastric emptying for luteal versus follicular phases. Postmenopausal females also exhibited higher PGF than females in the follicular phase [3.10 cpm, SD (0.24) vs. 2.94 cpm, SD (0.17), P = 0.01], and higher BMI-adjusted amplitude [40.7 µV (33.02-52.58) vs. 29.6 µV (26.15-39.65), P < 0.001], GA-RI [0.60 (0.48-0.73) vs. 0.43 (0.30-0.60), P = 0.005], and ff-AR [2.51 (1.79-3.47) vs. 1.48 (1.21-2.17), P = 0.001] than males. There were no differences in symptoms. These results define variations in gastric electrophysiology with regard to human menstrual cycling and menopause.NEW & NOTEWORTHY This study evaluates gastric electrophysiology in relation to the menstrual cycle using a novel noninvasive high-resolution methodology, revealing substantial variations in gastric activity with menstrual cycling and menopause. Gastric slow-wave frequency is significantly higher in the luteal versus follicular menstrual phase. Computational modeling predicts that this difference translates to higher rates of gastric mixing and liquid emptying in the luteal phase, which is consistent with previous experimental data evaluating menstrual cycling effects on gastric emptying.

Aging as a loss of morphostatic information: A developmental bioelectricity perspective.

Maintaining order at the tissue level is crucial throughout the lifespan, as failure can lead to cancer and an accumulation of molecular and cellular disorders. Perhaps, the most consistent and pervasive result of these failures is aging, which is characterized by the progressive loss of function and decline in the ability to maintain anatomical homeostasis and reproduce. This leads to organ malfunction, diseases, and ultimately death. The traditional understanding of aging is that it is caused by the accumulation of molecular and cellular damage. In this article, we propose a complementary view of aging from the perspective of endogenous bioelectricity which has not yet been integrated into aging research. We propose a view of aging as a morphostasis defect, a loss of biophysical prepattern information, encoding anatomical setpoints used for dynamic tissue and organ homeostasis. We hypothesize that this is specifically driven by abrogation of the endogenous bioelectric signaling that normally harnesses individual cell behaviors toward the creation and upkeep of complex multicellular structures in vivo. Herein, we first describe bioelectricity as the physiological software of life, and then identify and discuss the links between bioelectricity and life extension strategies and age-related diseases. We develop a bridge between aging and regeneration via bioelectric signaling that suggests a research program for healthful longevity via morphoceuticals. Finally, we discuss the broader implications of the homologies between development, aging, cancer and regeneration and how morphoceuticals can be developed for aging.

Electrophysiological Properties of the Medial Mammillary Bodies across the Sleep-Wake Cycle.

The medial mammillary bodies (MBs) play an important role in the formation of spatial memories; their dense inputs from hippocampal and brainstem regions makes them well placed to integrate movement-related and spatial information, which is then extended to the anterior thalamic nuclei and beyond to the cortex. While the anatomical connectivity of the medial MBs has been well studied, much less is known about their physiological properties, particularly in freely moving animals. We therefore carried out a comprehensive characterization of medial MB electrophysiology across arousal states by concurrently recording from the medial MB and the CA1 field of the hippocampus in male rats. In agreement with previous studies, we found medial MB neurons to have firing rates modulated by running speed and angular head velocity, as well as theta-entrained firing. We extended the characterization of MB neuron electrophysiology in three key ways: (1) we identified a subset of neurons (25%) that exhibit dominant bursting activity; (2) we showed that ∼30% of theta-entrained neurons exhibit robust theta cycle skipping, a firing characteristic that implicates them in a network for prospective coding of position; and (3) a considerable proportion of medial MB units showed sharp-wave ripple (SWR) responsive firing (∼37%). The functional heterogeneity of MB electrophysiology reinforces their role as an integrative node for mnemonic processing and identifies potential roles for the MBs in memory consolidation through propagation of SWR-responsive activity to the anterior thalamus and prospective coding in the form of theta cycle skipping.

Electrophysiological properties of dorsal root ganglion neurons cultured on 3D silicon micro-pillar substrates.

BACKGROUND: Silicon-based micro-pillar substrates (MPS), as three-dimensional cell culture platforms with vertically aligned micro-patterned scaffolding structures, are known to facilitate high-quality growth and morphology of dorsal root ganglion (DRG) sensory neurons, promote neurite outgrowth and enhance neurite alignment. However, the electrophysiological aspects of DRG neurons cultured on silicon MPSs have not been thoroughly investigated, which is of greatest importance to ensure that such substrates do not disrupt neuronal homeostasis and function before their widespread adoption in diverse biomedical applications. NEW METHOD: We conducted whole-cell patch-clamp recordings to explore the electrophysiological properties of DRG neurons cultured on MPS arrays, utilizing a custom-made upright patch-clamp setup. RESULTS: Our findings revealed that DRG neurons exhibited similar electrophysiological responses on patterned MPS samples when compared to the control planar glass surfaces. Notably, there were no significant differences observed in the action potential parameters or firing patterns of action potentials between neurons grown on either substrate. COMPARISON WITH EXISTING METHODS: In the current study we for the first time confirmed that successful electrophysiological recordings can be obtained from the cells grown on MPS. CONCLUSION: Our results imply that, despite the potential alterations caused by the cumulative trauma of tissue harvest and cell dissociation, essential functional cell properties of DRG neurons appear to be relatively maintained on MPS surfaces. Therefore, vertically aligned silicon MPSs could be considered as a potentially effective three-dimensional system for supporting a controlled cellular environment in culture.

An NIR-IIb emissive transmembrane voltage nano-indicator for the optical monitoring of electrophysiological activities in vivo.

In vivo transmembrane-voltage detection reflected the electrophysiological activities of the biological system, which is crucial for the diagnosis of neuronal disease. Traditional implanted electrodes can only monitor limited regions and induce relatively large tissue damage. Despite emerging monitoring methods based on optical imaging have access to signal recording in a larger area, the recording wavelength of less than 1000 nm seriously weakens the detection depth and resolution in vivo. Herein, a Förster resonance energy transfer (FRET)-based nano-indicator, NaYbF4:Er@NaYF4@Cy7.5@DPPC (Cy7.5-ErNP) with emission in the near-infrared IIb biological window (NIR-IIb, 1500-1700 nm) is developed for transmembrane-voltage detection. Cy7.5 dye is found to be voltage-sensitive and is employed as the energy donor for the energy transfer to the lanthanide nanoparticle, NaYbF4:Er@NaYF4 (ErNP), which works as the acceptor to achieve electrophysiological signal responsive NIR-IIb luminescence. Benefiting from the high penetration and low scattering of NIR-IIb luminescence, the Cy7.5-ErNP enables both the visualization of action potential in vitro and monitoring of Mesial Temporal lobe epilepsy (mTLE) disease in vivo. This work presents a concept for leveraging the lanthanide luminescent nanoprobes to visualize electrophysiological activity in vivo, which facilitates the development of an optical nano-indicator for the diagnosis of neurological disorders.

Skin-adhesive and self-healing diagnostic wound dressings for diabetic wound healing recording and electrophysiological signal monitoring.

Performing efficient wound management is essential for infected diabetic wounds due to the complex pathology. Flexible electronics have been recognized as one of the promising solutions for wound management. Herein, a kind of skin-adhesive and self-healing flexible bioelectronic was developed, which could be employed as a diagnostic wound dressing to record diabetic wound healing and monitor electrophysiological signals of the patients. The flexible substrate of diagnostic wound dressings showed excellent tissue adhesive (to various substrates including biological samples), self-healing (fracture strength restores by 96%), and intrinsic antibacterial properties (antibacterial ratio >96% against multidrug-resistant bacteria). The diagnostic wound dressings could record the glucose level (1-30 mM), pH values (4-7), and body temperature (18.8-40.0 °C) around the infected diabetic wounds. Besides, the dressings could help optimize treatment strategies based on electrophysiological signals of patients monitored in real-time. This study contributes to developing flexible bioelectronics for the diagnosis and management of diabetic wounds.

Correlation between a commercial electrophysiological test of sudomotor function and intraepidermal nerve fiber density in hereditary transthyretin amyloidosis.

INTRODUCTION/AIMS: In the early stage, hereditary transthyretin (ATTRv) amyloidosis predominantly affects small nerve fibers, resulting in autonomic dysfunction and impaired sensation of pain and temperature. Evaluation of small fiber neuropathy (SFN) is therefore important for early diagnosis and treatment of ATTRv amyloidosis. Herein, we aimed to investigate the accuracy of a quick and non-invasive commercial sudomotor function test (SFT) for the assessment of SFN in ATTRv amyloidosis. METHODS: We performed the SFT in 39 Japanese adults with ATTRv amyloidosis, and we analyzed the correlations between electrochemical skin conductance (ESC) values obtained via the SFT and the parameters of other neuropathy assessment methods. RESULTS: ESC in the feet demonstrated significant, moderate correlations with intraepidermal nerve fiber density (IENFD) results (Spearman's rank correlation coefficient [rs ], 0.58; p < .002) and other neuropathy assessment methods including the sensory nerve action potential amplitude in the nerve conduction studies (rs , 0.52; p < .001), the Neuropathy Impairment Score (rs , -0.45; p < .01), the heat-pain detection threshold (rs , -0.62; p < .0001), and the autonomic section of the Kumamoto ATTRv clinical score (rs , -0.53; p < .0001). DISCUSSION: In this study, we found that ESC values in the feet via the SFT demonstrated significant, moderate correlations with IENFD and other SFN assessment methods in patients with ATTRv amyloidosis, suggesting that the SFT appears to be an appropriate method for assessment of SFN in this disease.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Electrophysiological Analyses of Human Dorsal Root Ganglia and Human Induced Pluripotent Stem Cell-derived Sensory Neurons From Male and Female Donors.

Human induced pluripotent stem cell-derived sensory neurons (hiPSC-SNs) and human dorsal root ganglia neurons (hDRG-N) are popular tools in the field of pain research; however, few groups make use of both approaches. For screening and analgesic validation purposes, important characterizations can be determined of the similarities and differences between hDRG-N and hiPSC-SNs. This study focuses specifically on the electrophysiology properties of hDRG-N in comparison to hiPSC-SNs. We also compared hDRG-N and hiPSC-SNs from both male and female donors to evaluate potential sex differences. We recorded neuronal size, rheobase, resting membrane potential, input resistance, and action potential waveform properties from 83 hiPSCs-SNs (2 donors) and 108 hDRG-N neurons (8 donors). We observed several statistically significant electrophysiological differences between hDRG-N and hiPSC-SNs, such as size, rheobase, input resistance, and several action potential waveform properties. Correlation analysis also revealed many properties that were positively or negatively correlated, some of which were differentially correlated between hDRG-N and hiPSC-SNs. This study shows several differences between hDRG-N and hiPSC-SNs and allows a better understanding of the advantages and disadvantages of both for use in pain research. We hope this study will be a valuable resource for pain researchers considering the use of these human in vitro systems for mechanistic studies and/or drug development projects. PERSPECTIVE: hiPSC-SNs and hDRG-N are popular tools in the field of pain research. This study allows for a better functional understanding of the pros and cons of both tools.

Mind In Vitro Platforms: Versatile, Scalable, Robust, and Open Solutions to Interfacing with Living Neurons.

Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro-electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built-in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell-derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long-term recording. Overall, this technology, termed "mind in vitro" to underscore the computing inspiration, provides an end-to-end solution that can be widely deployed due to its affordable (>10× cost reduction) and open-source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.

Electrical signalling and plant response to herbivory: A short review.

For a long time, electrical signaling was neglected at the expense of signaling studies in plants being concentrated with chemical and hydraulic signals. Studies conducted in recent years have revealed that plants are capable of emitting, processing, and transmitting bioelectrical signals to regulate a wide variety of physiological functions. Many important biological and physiological phenomena are accompanied by these cellular electrical manifestations, which supports the hypothesis about the importance of bioelectricity as a fundamental 'model' for response the stresses environmental and for activities regeneration of these organisms. Electrical signals have also been characterized and discriminated against in genetically modified plants under stress mediated by sucking insects and/or by the application of systemic insecticides. Such results can guide future studies that aim to elucidate the factors involved in the processes of resistance to stress and plant defense, thus aiding in the development of successful strategies in integrated pest management. Therefore, this mini review includes the results of studies aimed at electrical signaling in response to biotic stress. We also demonstrated how the generation and propagation of electrical signals takes place and included a description of how these electrical potentials are measured.

Electroanatomical mapping of the stomach with simultaneous biomagnetic measurements.

Gastric motility is coordinated by bioelectric slow waves (SWs) and dysrhythmic SW activity has been linked with motility disorders. Magnetogastrography (MGG) is the non-invasive measurement of the biomagnetic fields generated by SWs. Dysrhythmia identification using MGG is currently challenging because source models are not well developed and the impact of anatomical variation is not well understood. A novel method for the quantitative spatial co-registration of serosal SW potentials, MGG, and geometric models of anatomical structures was developed and performed on two anesthetized pigs to verify feasibility. Electrode arrays were localized using electromagnetic transmitting coils. Coil localization error for the volume where the stomach is normally located under the sensor array was assessed in a benchtop experiment, and mean error was 4.2±2.3mm and 3.6±3.3° for a coil orientation parallel to the sensor array and 6.2±5.7mm and 4.5±7.0° for a perpendicular coil orientation. Stomach geometries were reconstructed by fitting a generic stomach to up to 19 localization coils, and SW activation maps were mapped onto the reconstructed geometries using the registered positions of 128 electrodes. Normal proximal-to-distal and ectopic SW propagation patterns were recorded from the serosa and compared against the simultaneous MGG measurements. Correlations between the center-of-gravity of normalized MGG and the mean position of SW activity on the serosa were 0.36 and 0.85 for the ectopic and normal propagation patterns along the proximal-distal stomach axis, respectively. This study presents the first feasible method for the spatial co-registration of MGG, serosal SW measurements, and subject-specific anatomy. This is a significant advancement because these data enable the development and validation of novel non-invasive gastric source characterization methods.

Ultraflexible electrode arrays for months-long high-density electrophysiological mapping of thousands of neurons in rodents.

Penetrating flexible electrode arrays can simultaneously record thousands of individual neurons in the brains of live animals. However, it has been challenging to spatially map and longitudinally monitor the dynamics of large three-dimensional neural networks. Here we show that optimized ultraflexible electrode arrays distributed across multiple cortical regions in head-fixed mice and in freely moving rats allow for months-long stable electrophysiological recording of several thousand neurons at densities of about 1,000 neural units per cubic millimetre. The chronic recordings enhanced decoding accuracy during optogenetic stimulation and enabled the detection of strongly coupled neuron pairs at the million-pair and millisecond scales, and thus the inference of patterns of directional information flow. Longitudinal and volumetric measurements of neural couplings may facilitate the study of large-scale neural circuits.

Analysis of task-related MEG functional brain networks using dynamic mode decomposition.

Objective.Functional connectivity networks explain the different brain states during the diverse motor, cognitive, and sensory functions. Extracting connectivity network configurations and their temporal evolution is crucial for understanding brain function during diverse behavioral tasks.Approach.In this study, we introduce the use of dynamic mode decomposition (DMD) to extract the dynamics of brain networks. We compared DMD with principal component analysis (PCA) using real magnetoencephalography data during motor and memory tasks.Main results.The framework generates dominant connectivity brain networks and their time dynamics during simple tasks, such as button press and left-hand movement, as well as more complex tasks, such as picture naming and memory tasks. Our findings show that the proposed methodology with both the PCA-based and DMD-based approaches extracts similar dominant connectivity networks and their corresponding temporal dynamics.Significance.We believe that the proposed methodology with both the PCA and the DMD approaches has a very high potential for deciphering the spatiotemporal dynamics of electrophysiological brain network states during tasks.