Multi-electrode Array Data Research Articles

Visualization of incrementally learned projection trajectories for longitudinal data

Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal multi-electrode array data from brain cortical organoids when exposed to different levels of quinolinic acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimer’s disease, and its blocking antibody. We uncover valuable insights into the organoids’ electrophysiological maturation and response patterns over time under these conditions.

Impacts of DCM-linked gating pore currents on the electrophysiological characteristics of hiPSC-CM monolayers

BackgroundVariants of the SCN5A gene, which encodes the NaV1.5 cardiac sodium channel, have been linked to arrhythmic disorders associated with dilated cardiomyopathy (DCM). However, the precise pathological mechanisms remain elusive. The present study aimed to elucidate the pathophysiological consequences of the DCM-linked Nav1.5/R219H variant, which is known to generate a gating pore current, using patient-specific human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) cultured in monolayers. MethodsVentricular- and atrial-like hiPSC-CM monolayers were generated from DCM patients carrying the R219H SCN5A variant as well as from healthy control individuals. CRISPR-corrected hiPSC-CMs served as isogenic controls. Simultaneous optical mapping of action potentials (APs) and calcium transients (CaTs) was employed to measure conduction velocities (CVs) and AP durations (APDs) and served as markers of electrical excitability. Calcium handling was evaluated by assessing CaT uptake (half-time to peak), recapture (tau of decay), and durations (TD50 and TD80). A multi-electrode array (MEA) analysis was conducted on hiPSC-CM monolayers to measure field potential (FP) parameters, including corrected Fridericia FP durations (FPDc). ResultsOur results revealed that CVs were significantly reduced by more than 50 % in both ventricular- and atrial-like hiPSC-CM monolayers carrying the R219H variant compared to the control group. APDs were also prolonged in the R219H group compared to the control and CRISPR-corrected groups. CaT uptake, reuptake, and duration were also markedly delayed in the R219H group compared to the control and CRISPR-corrected groups in both the ventricular- and the atrial-like hiPSC-CM monolayers. Lastly, the MEA data revealed a notably prolonged FPDc in the ventricular- and atrial-like hiPSC-CMs carrying the R219H variant compared to the control and isogenic control groups. ConclusionsThese findings highlight the impact of the gating pore current on AP propagation and calcium homeostasis within a functional syncytium environment and offer valuable insights into the potential mechanisms underlying DCM pathophysiology.

Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror

Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effective connectivity networks from multi-electrode array data. In this paper, we apply a novel statistical method called spectral mirror estimation to the time series of inferred effective connectivity organoid networks. This method produces a one-dimensional iso-mirror representation of the dynamics of the time series of the networks which exhibits a piecewise linear structure. A classical change point algorithm is then applied to this representation, which successfully detects a change point coinciding with the neuroscientifically significant time inhibitory neurons start appearing and the percentage of astrocytes increases dramatically. This finding demonstrates the potential utility of applying the iso-mirror dynamic structure discovery method to inferred effective connectivity time series of organoid networks.

MEA-ToolBox: an Open Source Toolbox for Standardized Analysis of Multi-Electrode Array Data

Functional assessment of in vitro neuronal networks—of relevance for disease modelling and drug testing—can be performed using multi-electrode array (MEA) technology. However, the handling and processing of the large amount of data typically generated in MEA experiments remains a huge hurdle for researchers. Various software packages have been developed to tackle this issue, but to date, most are either not accessible through the links provided by the authors or only tackle parts of the analysis. Here, we present ‘‘MEA-ToolBox’’, a free open-source general MEA analytical toolbox that uses a variety of literature-based algorithms to process the data, detect spikes from raw recordings, and extract information at both the single-channel and array-wide network level. MEA-ToolBox extracts information about spike trains, burst-related analysis and connectivity metrics without the need of manual intervention. MEA-ToolBox is tailored for comparing different sets of measurements and will analyze data from multiple recorded files placed in the same folder sequentially, thus considerably streamlining the analysis pipeline. MEA-ToolBox is available with a graphic user interface (GUI) thus eliminating the need for any coding expertise while offering functionality to inspect, explore and post-process the data. As proof-of-concept, MEA-ToolBox was tested on earlier-published MEA recordings from neuronal networks derived from human induced pluripotent stem cells (hiPSCs) obtained from healthy subjects and patients with neurodevelopmental disorders. Neuronal networks derived from patient’s hiPSCs showed a clear phenotype compared to those from healthy subjects, demonstrating that the toolbox could extract useful parameters and assess differences between normal and diseased profiles.

Multielectrode Arrays for Functional Phenotyping of Neurons from Induced Pluripotent Stem Cell Models of Neurodevelopmental Disorders.

Simple SummaryMultielectrode array technology allows researchers to record the spontaneous firing activity of cultured neurons over a period of multiple weeks or months. These data can be valuable for understanding how the functional relationships between neurons evolve as they begin to form connections and wire into a functional network. This technology has been adopted by researchers using stem cells to produce human neurons in culture to study neurodevelopmental disorders. However, the dizzying complexity and scale of the data generated have posed some challenges with the analysis and interpretation of experimental results. Here, we first provide historical context as to why multielectrode array platforms were originally developed, and use this perspective to explore some of the challenges currently facing the field. We then highlight new analysis methods, provide some guidance for improving the analysis of multielectrode array data, and discuss standardizing how these findings are communicated in scientific publications.In vitro multielectrode array (MEA) systems are increasingly used as higher-throughput platforms for functional phenotyping studies of neurons in induced pluripotent stem cell (iPSC) disease models. While MEA systems generate large amounts of spatiotemporal activity data from networks of iPSC-derived neurons, the downstream analysis and interpretation of such high-dimensional data often pose a significant challenge to researchers. In this review, we examine how MEA technology is currently deployed in iPSC modeling studies of neurodevelopmental disorders. We first highlight the strengths of in vitro MEA technology by reviewing the history of its development and the original scientific questions MEAs were intended to answer. Methods of generating patient iPSC-derived neurons and astrocytes for MEA co-cultures are summarized. We then discuss challenges associated with MEA data analysis in a disease modeling context, and present novel computational methods used to better interpret network phenotyping data. We end by suggesting best practices for presenting MEA data in research publications, and propose that the creation of a public MEA data repository to enable collaborative data sharing would be of great benefit to the iPSC disease modeling community.

Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks.

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective—direct and causal—connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.

Investigation Into the Role of ERK in Tyrosine Kinase Inhibitor-Induced Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating adverse event that can alter patient treatment options and halt candidate drug development. A case study is presented here describing the preclinical and clinical development of CC-90003, a small molecule extracellular signal-regulated kinase (ERK)1/2 inhibitor investigated as an oncology therapy. In a Phase Ia clinical trial, CC-90003 elicited adverse drug-related neuropathy and neurotoxicity that contributed to discontinued development of CC-90003 for oncology therapy. Preclinical evaluation of CC-90003 in dogs revealed clinical signs and electrophysiological changes consistent with peripheral neuropathy that were reversible. Mice did not exhibit signs of neuropathy upon daily dosing with CC-90003, supporting that rodents generally poorly predict CIPN. We sought to investigate the mechanism of CC-90003-induced peripheral neuropathy using a phenotypic in vitro assay. Translating preclinical neuropathy findings to humans proves challenging as no robust in vitro models of CIPN exist. An approach was taken to examine the influence of CIPN-associated drugs on human induced pluripotent stem cell-derived peripheral neuron (hiPSC-PN) electrophysiology on multielectrode arrays (MEA). The MEA assay with hiPSC-PNs was sensitive to CIPN-associated drugs cisplatin, sunitinib, colchicine, and importantly, to CC-90003 in concordance with clinical neuropathy incidence. Biochemical data together with in vitro MEA data for CC-90003 and 12 of its structural analogs, all having similar ERK inhibitory activity, revealed that CC-90003 disrupted in vitro neuronal electrophysiology likely via on-target ERK inhibition combined with off-target kinase inhibition and translocator protein inhibition. This approach could prove useful for assessing CIPN risk and interrogating mechanisms of drug-induced neuropathy.

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints.

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. Although previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the generalized linear model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from the mouse somatosensory cortex. Here, our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.NEW & NOTEWORTHY Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

Spikes and Nets (S&N): A New Fast, Parallel Computing, Point Process Software for Multineuronal Discharge and Connectivity Analysis

SN however, the aim of this work was to develop an easy to use, fast, short learning curve, multi-platform and parallel computing software able to manage a large number of neuronal spike train files to detect discharge patterns, connectivity, and time-dependent changes. A set of the most used spike train methods to perform single and multi-neuronal discharge pattern recognition and functional connectivity analysis were implemented in an easy-to-use, standalone, Matlab-based software toolbox: spikes and nets (S&N). The methods included for single and multi-neuronal discharge pattern analysis are raster plot, interspike intervals distribution, multiparametric burst, auto-correlation, auto-spectral, fractal, poincare, and phases. For functional connectivity analysis, cross-correlation and joint interval scatter diagram were implemented. Additionally, time segmentation analysis is available to detect temporal changes for all methods. S&N efficiently handles large numbers of neuronal discharge files at once with fast and automatic archiving of both analytical and graphical results which makes it suitable for multi-electrode array data. S&N applies up to 11 different analytical methods, including automatic file segmentation for time-dependent changes detection, and generates publication quality graphs. The developed toolbox is multi-platform and reads universal spike train files with any temporal resolution, able to process also ECG, EEG or similar data files.

ZyON: Enabling Spike Sorting on APSoC-Based Signal Processors for High-Density Microelectrode Arrays

Multi-Electrode Arrays and High-Density Multi-Electrode Arrays of sensors are a key instrument in neuroscience research. Such devices are evolving to provide ever-increasing temporal and spatial resolution, paving the way to unprecedented results when it comes to understanding the behaviour of neuronal networks and interacting with them. However, in some experimental cases, in-place low-latency processing of the sensor data acquired by the arrays is required. This poses the need for high-performance embedded computing platforms capable of processing in real-time the stream of samples produced by the acquisition front-end to extract higher-level information. Previous work has demonstrated that Field-Programmable Gate Array and All-Programmable System-On-Chip devices are suitable target technology for the implementation of real-time processors of High-Density Multi-Electrode Arrays data. However, approaches available in literature can process a limited number of channels or are designed to execute only the first steps of the neural signal processing chain. In this work, we propose an All-Programmable System-On-Chip based implementation capable of sorting neural spikes acquired by the sensors, to associate the shape of each spike to a specific firing neuron. Our system, implemented on a Xilinx Z7020 All-Programmable System-On-Chip is capable of executing on-line spike sorting up to 5500 acquisition channels, 43x more than state-of-the-art alternatives, supporting 18KHz acquisition frequency. We present an experimental study on a commonly used reference dataset, using on-line refinement of the sorting clusters to improve accuracy up to 82%, with only 4% degradation with respect to off-line analysis.

Design and fabrication of MEMS based intracranial pressure sensor for neurons study

Neural sensing has been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To date, most of the neural stimulation systems have relied on sharp thin film electrodes with poor electrical properties that cause extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. So, here in view of a safer course of action and better results, a flexible microelectrode array is proposed, which is capable of sensing neural stimulation from the neuron surface, without penetrating the tissue. A multi electrode array (MEA) is a micro-fabricated cell culture dish with embedded microelectrodes at the bottom of the dish. The size of the sensor is decided by the size of the sensor chip and package. The sensor chip is based on silicon wafer ,which is piezo resistive effect and the size is 100 × 200 μm2. It is much smaller than the reported polymer intracranial pressure sensors such as liquid crystal polymer sensors. Recently MEAs with different cell-adhesive patterns are actively used to analyse the behaviours under study in vitro neural systems. The MEAs also integrate with pressure sensor and temperature sensor. So the MEAs confirm the synaptic connection of the in vitro neuron stimulation, and a passive piezo resistive resonant sensor that accurately monitors intracranial pressures based on the Wheatstone bridge. Also it shows the present temperature condition of culture dish. As the device is fabricated using MEMS technology with biocompatible material, it has been shown to be non-irritating to cells even if it works for long term measurement. The sensitivity of the sensor is 0.84 × 10−2 mV/kPa and the minimum level of pressure is acquired in the range of 112 Pa in average. From the MEA, temperature and pressure data, the neurological disorder is identified.

NDDN: A Cloud-Based Neuroinformation Database for Developing Neuronal Networks.

Electrical activity of developing dissociated neuronal networks is of immense significance for understanding the general properties of neural information processing and storage. In addition, the complexity and diversity of network activity patterns make them ideal candidates for developing novel computational models and evaluating algorithms. However, there are rare databases which focus on the changing network dynamics during development. Here, we describe the design and implementation of Neuroinformation Database for Developing Networks (NDDN), a repository for electrophysiological data collected from long-term cultured hippocampal networks. The NDDN contains over 15 terabytes of multielectrode array data consisting of 25,380 items collected from 105 culture batches. Metadata including culturing and recording information and stimulation/drug application protocols are linked to each data item. A Matlab toolbox named MEAKit is also provided with the NDDN to ease the analysis of downloaded data items. We expect that NDDN may contribute to both the fields of experimental and computational neuroscience.

Detection of synchronized burst firing in cultured human induced pluripotent stem cell-derived neurons using a 4-step method

Human induced pluripotent stem cell-derived neurons are promising for use in toxicity evaluations in nonclinical studies. The multi-electrode array (MEA) assay is used in such evaluation systems because it can measure the electrophysiological function of a neural network noninvasively and with high throughput. Synchronized burst firing (SBF) is the main analytic parameter of pharmacological effects in MEA data, but an accurate method for detecting SBFs has not been established. In this study, we present a 4-step method that accurately detects a target SBF confirmed by the researcher's interpretation of a raster plot. This method calculates one set parameter per step, in the following order: the inter-spike interval (ISI), the number of spikes in an SBF, the inter-SBF interval, and the number of spikes in an SBF again. We found that the 4-step method is advantageous over the conventional method because it determines the preferable duration of an SBF, accurately distinguishes continuous SBFs, detects weak SBFs, and avoids false detection of SBFs. We found also that pharmacological evaluations involving SBF analysis may differ depending on whether the 4-step or conventional threshold method is used. This 4-step method may contribute to improving the accuracy of drug toxicity and efficacy evaluations using human induced pluripotent stem cell-derived neurons.

Lycium Barbarum Polysaccharides Protect Retina in rd1 Mice During Photoreceptor Degeneration.

PurposeAs an active component in wolfberry, lycium barbarum polysaccharides (LBP) are capable of protecting retinal neurons in several animal disease models. Here, we asked whether LBP rescues the retinal morphology and function in rd1 mouse, a photoreceptor fast-degenerating animal model of retinitis pigmentosa, and in particular focused on LBP's effects on the function of retinal ganglion cells (RGCs) during photoreceptor degeneration.MethodsAn equal volume of LBP or control vehicle was daily intraperitoneal (i.p.) injected in rd1 mice from postnatal day 4 (P4) to P14, P20, or P24 when photoreceptors completely degenerate. Immunostaining, electroretinogram (ERG), visual behavior tests and multielectrode array (MEA) recordings were assessed to determine the structure and function of the treated retina.ResultsLBP treatment greatly promoted photoreceptor survival, enhanced ERG responses, and improved visual behaviors in rd1 mice. MEA data showed that LBP treatment in general decreased the abnormally high spontaneous spiking that occurs in rd1 mice, and increased the percentage of light-responsive RGCs as well as their light-evoked response, light sensitivity, signal-to-noise ratio, and response speed. Interestingly, LBP treatment affected ON and OFF responses differently.ConclusionsLBP improves retinal morphology and function in rd1 mice, and delays the functional decay of RGCs during photoreceptor degeneration. This is the first study that has examined in detail the effects of LBP on RGC responses. Our data suggest that LBP may help extend the effective time window before more invasive RP therapeutic approaches such as retinoprosthesis are applied.

Analysis and Modeling of Subthreshold Neural Multi-Electrode Array Data by Statistical Field Theory

Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous neuronal network activity. The recorded signals comprise several distinct components: Apart from artifacts without biological significance, one can distinguish between spikes (action potentials) and subthreshold fluctuations (local fields potentials). Here we aim to develop a theoretical model that allows for a compact and robust characterization of subthreshold fluctuations in terms of a Gaussian statistical field theory in two spatial and one temporal dimension. What is usually referred to as the driving noise in the context of statistical physics is here interpreted as a representation of the neural activity. Spatial and temporal correlations of this activity give valuable information about the connectivity in the neural tissue. We apply our methods on a dataset obtained from MEA-measurements in an acute hippocampal brain slice from a rat. Our main finding is that the empirical correlation functions indeed obey the logarithmic behavior that is a general feature of theoretical models of this kind. We also find a clear correlation between the activity and the occurrence of spikes. Another important insight is the importance of correctly separating out certain artifacts from the data before proceeding with the analysis.

Use of local field potentials of dissociated cultures grown on multi-electrode arrays for pharmacological assays.

In vitro Multi-Electrode Arrays (MEA) are an extracellular recording technology that enables the analysis of networks of neurons in vitro. Neurons in culture exhibit a range of behavioral dynamics, which can be measured in terms of individual action potentials, network-wide synchronized firing and a host of other features that characterize network activity. MEA data analysis was historically focused on high frequency spike data forgoing the low frequency content of the signal. In this study, we use local field potentials, which are low frequency components of MEA signals, to differentiate between two types of antiepileptic drugs (p<;0.0001) with different mechanisms of action.

Use of adaptive network burst detection methods for multielectrode array data and the generation of artificial spike patterns for method evaluation

Objective. Multielectrode arrays are an informative extracellular recording technology that enables the analysis of cultured neuronal networks and network bursts (NBs) are a dominant feature observed in these recordings. This paper focuses on the validation of NB detection methods on different network activity patterns and developing a detection method that performs robustly across a wide variety of activity patterns. Approach. A firing rate based approach was used to generate artificial spike timestamps where NBs were introduced as episodes where the probability of spiking increases. Variations in firing and bursting characteristics were also included. In addition, an improved methodology of detecting NBs is proposed, based on time-binned average firing rates and time overlaps of single channel bursts. The robustness of the proposed method was compared against three existing algorithms using simulated, publicly available and newly acquired data. Main results. A range of activity patterns were generated by changing simulation variables that correspond to NB duration (40–2200 ms), intervals (0.3–16 s), firing rates (0.1–1 spikes s−1), local burst percentage (0%–90%), number of channels in local bursts (20–40) as well as the number of tonic and frequently-bursting channels. By extracting simulation parameters directly from real data, we generated synthetic data that closely resemble activity of mouse and rat cortical cultures at native and chemically perturbed states. In 50 simulated data sets with randomly selected parameter values, the improved NB detection method performed better (ascertained by the f-measure) than three existing methods (p < 0.005). The improved method was also able to detect clustered, long-tailed and short-frequent NBs on real data. Significance. This work presents an objective method of assessing the applicability of NB detection methods for different neuronal activity patterns. Furthermore, it proposes an improved NB detection method that can be used robustly across a range of data types.

A Human Induced Pluripotent Stem Cell-Derived Cardiomyocyte (hiPSC-CM) Multielectrode Array Assay for Preclinical Cardiac Electrophysiology Safety Screening.

Cardiotoxicity is a leading cause of compound attrition during drug development. Preclinical models used to assess the risk for compound-induced effects on cardiac electrophysiology largely rely on animals that can differ in terms of sensitivity and specificity to the targeted clinical response. There is currently no in vitro human cardiomyocyte model for routine preclinical compound screening, as adult human cardiac tissue is unsuitable for such screening. The commercial availability of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) makes possible the development of assays for assessing compound-induced effects on cardiac function in a human cardiomyocyte-like model. Using multielectrode array (MEA) technology with hiPSC-CMs provides a facile screen for compound-induced effects on cardiac electrophysiology. The MEA data generated from hiPSC-CMs correlate well with the results of conventional preclinical assays and clinical findings. Described in this unit is a technique for measuring extracellular field potentials from hiPSC-CMs using MEA technology to screen for compound-induced effects on cardiac electrophysiology.

Highly scalable parallel processing of extracellular recordings of Multielectrode Arrays.

Technological advances of Multielectrode Arrays (MEAs) used for multisite, parallel electrophysiological recordings, lead to an ever increasing amount of raw data being generated. Arrays with hundreds up to a few thousands of electrodes are slowly seeing widespread use and the expectation is that more sophisticated arrays will become available in the near future. In order to process the large data volumes resulting from MEA recordings there is a pressing need for new software tools able to process many data channels in parallel. Here we present a new tool for processing MEA data recordings that makes use of new programming paradigms and recent technology developments to unleash the power of modern highly parallel hardware, such as multi-core CPUs with vector instruction sets or GPGPUs. Our tool builds on and complements existing MEA data analysis packages. It shows high scalability and can be used to speed up some performance critical pre-processing steps such as data filtering and spike detection, helping to make the analysis of larger data sets tractable.

Editorial: Complexity and emergence in brain network analyses.

Network science has become an invaluable tool for neuroimaging analysis and has advanced our understanding of the brain's complex structural and functional topology. Network science, an interdisciplinary offshoot of graph theory, conceives of the brain as a system of nodes, representing brain regions or voxels, and edges, representing the structural or functional connection between these regions or voxels. Such a model is considered appealing in neuroscience as it describes a system with various interacting regions that produces complex behaviors. This conception of the brain has revealed that the brain exhibits “small-world” properties, namely that brain networks are systems with regional specialization and efficient global information transfer. More importantly, studies in brain networks have furthered our understanding of diseases and disorders affecting the brain. Assessing group differences in representative samples of brain networks has become increasingly important, yet appropriate methodological developments have remained relatively sparse. Additionally, there is increased interest in understanding dynamic changes, or how a network changes over time. Currently, most network analysis tools are designed to analyze a static representation, thus changes that occur over time are often overlooked or ignored. As the field continues to grow, it is becoming increasingly important to develop methods for understanding the complexity of these data. This research topic features selected contributions that explores group analysis and dynamics in brain network studies including methodological and practical applications employing various imaging modalities. This snapshot of the field represents the growing interest to expand and validate new methods for use in the field of brain network neuroscience. “Defining nodes in complex brain networks” is a review article discussing the methodological choices for defining nodes, particularly how the choice of atlas-based or voxel-based methods affects the computed network (Stanley et al., 2013). “Routing in the brain” is a commentary on network routing focusing on how current methods and models may not be adequate for brain networks. Further exploration of the packet switching network model may be more realistic and appropriate for the brain (Graham, 2014). “A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity” is a methods article introducing a Bayesian model for estimating functional networks, from functional magnetic resonance imaging (fMRI) data, leveraging complementary structural diffusion tensor imaging (DTI) data (Xue et al., 2015). “A permutation testing framework to compare groups of brain networks” is a methods article describing a permutation testing framework to statistically compare groups of functional brain networks while incorporating topological features inherent in each individual network (Simpson et al., 2013). “Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses” is a methods article describing a statistical approach for computing uncertainty in static and dynamic functional networks and aggregating network measures in task-related electrocorticography (ECoG) data (Stephen et al., 2014). “Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity” is a methods article introducing a generalization of the vector auto-regressive (VAR) model for comparing effective connectivity between experimental conditions while accounting for between-subject heterogeneity (Gorrostieta et al., 2013). “Statistical network analysis for functional MRI: summary networks and group comparisons” is a review article discussing the construction of summary networks and how to test for topological differences in groups of networks when these groups also exhibit significant differences in density (Ginestet et al., 2014). “Detecting functional connectivity change points for single-subject fMRI data” is a methods article that extends Dynamic Connectivity Regression (DCR), a data-driven technique which detects change points in functional connectivity between brain regions to determine state changes over the course of an experimental task, by introducing a novel algorithm aimed at increasing the estimation of networks for individual subject data (Cribben et al., 2013). “The Laplacian spectrum of neural networks” is an original research article examining the use of the Laplacian spectrum in anatomic networks of the macaque, cat and Caenorhabditis elegans. This method describes network structure at a systems level, assessing systemic infrastructural properties of the entire network as opposed to properties of specific nodes or connections (De Lange et al., 2014). “Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro” is an original research article on network dynamics in multi-electrode array data focusing on changes in the functional connectivity of evoked gamma oscillations in cortical circuits (Carmeli et al., 2013).