Analysis Of Electrophysiological Data Research Articles

Evaluation of novel open-source software for cardiac optical mapping

KairoSight-3.0 is a recently released Python-based, open-source software for cardiac optical mapping analysis. Addressing challenges in high-resolution electrophysiological data analysis, KairoSight-3.0 facilitates comprehensive studies of cardiac conduction and excitation-contraction coupling. We compared its performance with ElectroMap, focusing on action potential duration and conduction velocity measurements in mouse heart models subjected to ischaemia and flecainide treatment. Our findings reveal that while both software are effective, inherent methodological differences impact measurement outcomes. KairoSight-3.0's robust analysis capabilities make it a valuable tool in cardiac research. Additionally, future directions for KairoSight-3.0 and other mapping analysis tools are explored. Statement of importanceOpen-source methods for analysis of cardiac optical mapping are vital tools in electrophysiological research. Our work directly evaluates the latest version of KarioSight, recently published in JMCC plus, with ElectroMap, an established and widely used tool. Our results show both software are effective in analysis of changes in both conduction and repolarisation. Considering the new features of KairoSight-3.0 and python implementation, our study importantly demonstrates the effectiveness of the software, highlights potential discrepancies between it and ElectroMap, and provides a perspective on future directions for KairoSight-3.0 and other software.

Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Simultaneous Multisite Extracellular Electrophysiological Recordings During Electrical Stimulation in Anesthetized Rats.

Analyzing neuronal activities is essential to deciphering the function of neural circuits. In anesthetized rodents, simultaneous multisite recording of extracellular electrophysiological activity with defined electrical stimulation is a powerful tool to dissect reciprocal relationships between brain structures. Here, we present a protocol to simultaneously record from the subthalamic nucleus and substantia nigra pars reticulata while stimulating the pedunculopontine tegmental nucleus in anesthetized rats. This protocol describes the preparation of recording and stimulating electrodes, surgery setup, and detailed recording techniques. Basic post-recording data analysis methods are included as well. This protocol can be adapted to other brain areas of interest following the outlined procedures. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assembly of recording and stimulating electrodes Basic Protocol 2: Implantation of recording and stimulating electrodes in an anesthetized rat Basic Protocol 3: Simultaneous recording from the STN and SNr with PPTg stimulation Basic Protocol 4: Histological verification of recording sites Basic Protocol 5: Analysis of electrophysiological data.

Electrophysiological Correlates of the Interaction of Physical and Numerical Size in Symbolic Number Processing: Insights from a Novel Go/Nogo Numerical Stroop Task.

The interaction of physical and numerical size has been investigated and repeatedly demonstrated in the numerical Stroop task, in which participants compare digits of different physical sizes. It is, however, not entirely clear yet what psychological processes contribute to this interaction. The aim of the present study is to investigate the role of inhibition in the interaction of physical and numerical size, by introducing a novel paradigm that is suitable to elicit inhibition-related event-related potential components. To this end, we combined the go/nogo paradigm with the numerical Stroop task while measuring EEG and reaction times. Participants were presented with Arabic number pairs and had to press a button if the number on one side was numerically larger and they had to refrain from responding if the number on the other side was numerically larger. The physical size of the number pairs was also manipulated, in order to create congruent, neutral, and incongruent trials. Behavioural results confirmed the well-established numerical distance and numerical Stroop effects. Analysis of electrophysiological data revealed the classical go/nogo electrophysiological effects with numerical stimuli, and showed that peak amplitudes were larger for nogo than for go trials on the N2, as well as on the P3 component, on frontal and midline electrodes. When analysing the congruency effects, the peak amplitude of N2 was larger in incongruent trials than in neutral and congruent trials, while there was no evidence of a congruency effect on the P3 component peaks. Further analysis of the electrophysiological data revealed an additional facilitatory effect in the go trials, as well as an additional interference effect in the nogo trials. Taken together, it seems that interference effects are probably resolved by inhibitory processes and that facilitatory effects are affected by different cognitive control processes required by go versus nogo trials.

Two modes of midfrontal theta suggest a role in conflict and error processing

Midfrontal theta increases during scenarios when conflicts are successfully resolved. Often considered a generic signal of cognitive control, its temporal nature has hardly been investigated. Using advanced spatiotemporal techniques, we uncover that midfrontal theta occurs as a transient oscillation or “event” at single trials with their timing reflecting computationally distinct modes. Single-trial analyses of electrophysiological data from participants performing the Flanker (N = 24) and Simon task (N = 15) were used to probe the relationship between theta and metrics of stimulus-response conflict. We specifically investigated “partial errors”, in which a small burst of muscle activity in the incorrect response effector occurred, quickly followed by a correction. We found that transient theta events in single trials could be categorized into two distinct theta modes based on their relative timing to different task events. Theta events from the first mode occurred briefly after the task stimulus and might reflect conflict-related processing of the stimulus. In contrast, theta events from the second mode were more likely to occur around the time partial errors were committed, suggesting they were elicited by a potential upcoming error. Importantly, in trials in which a full error was committed, this “error-related theta” occurred too late with respect to the onset of the erroneous muscle response, supporting the role of theta also in error correction. We conclude that different modes of transient midfrontal theta can be adopted in single trials not only to process stimulus-response conflict, but also to correct erroneous responses.

High-Throughput Cardiotoxicity Screening Using Mature Human Induced Pluripotent Stem Cell-Derived Cardiomyocyte Monolayers.

Human induced stem cell-derived cardiomyocytes (hiPSC-CMs) are used to replace and reduce the dependence on animals and animal cells for preclinical cardiotoxicity testing. In two-dimensional monolayer formats, hiPSC-CMs recapitulate the structure and function of the adult human heart muscle cells when cultured on an optimal extracellular matrix (ECM). A human perinatal stem cell-derived ECM (maturation-inducing extracellular matrix-MECM) matures the hiPSC-CM structure, function, and metabolic state in 7 days after plating. Mature hiPSC-CM monolayers also respond as expected to clinically relevant medications, with a known risk of causing arrhythmias and cardiotoxicity. The maturation of hiPSC-CM monolayers was an obstacle to the widespread adoption of these valuable cells for regulatory science and safety screening, until now. This article presents validated methods for the plating, maturation, and high-throughput, functional phenotyping of hiPSC-CM electrophysiological and contractile function. These methods apply to commercially available purified cardiomyocytes, as well as stem cell-derived cardiomyocytes generated in-house using highly efficient, chamber-specific differentiation protocols. High-throughput electrophysiological function is measured using either voltage-sensitive dyes (VSDs; emission: 488 nm), calcium-sensitive fluorophores (CSFs), or genetically encoded calcium sensors (GCaMP6). A high-throughput optical mapping device is used for optical recordings of each functional parameter, and custom dedicated software is used for electrophysiological data analysis. MECM protocols are applied for medication screening using a positive inotrope (isoprenaline) and human Ether-a-go-go-related gene (hERG) channel-specific blockers. These resources will enable other investigators to successfully utilize mature hiPSC-CMs for high-throughput, preclinical cardiotoxicity screening, cardiac medication efficacy testing, and cardiovascular research.

Activation of the α1β2γ2L GABAA Receptor by Physiological Agonists

The Cl− permeable GABAA receptor is a major contributor to cellular inhibition in the brain. The receptor is normally activated by synaptically-released or ambient GABA but is sensitive to a number of physiological compounds such as β-alanine, taurine, and neurosteroids that, to various degrees, activate the receptor and modulate responses either to the transmitter or to each other. Here, we describe α1β2γ2L GABAA receptor activation and modulation by combinations of orthosteric and allosteric activators. The overall goal was to gain insight into how changes in the levels of endogenous agonists modulate receptor activity and influence cellular inhibition. Experimental observations and simulations are described in the framework of a cyclic concerted transition model. We also provide general analytical solutions for the analysis of electrophysiological data collected in the presence of combinations of active compounds.

The Relationship Between Emotional Content and Phonological Processing in Persian Speaking Children Who Stutter: A Study by Event-related Potential

Introduction: Emotion can contribute to the severity of stuttering, although the underlying mechanism is unknown. Event-related Potential (ERP) could be very helpful for assessing emotional processing in persons with stuttering. Our study aimed at the investigation of phonological processing for emotional and neutral words in Children Who Stutter (CWS) by ERP.
 Materials and Methods: Ten CWS were given 120 emotional and neutral words to read. Phonological processing was assessed by aloud reading task, while simultaneously ERP was recorded. The results were analyzed as behavioral (reaction time and accuracy) and electrophysiological (amplitude and topography).
 Results: There were significant differences in reaction time and accuracy between positive, negative, and neutral words (P<0.05). The electrophysiological data analysis showed significant differences for a minimum of amplitude in the left frontal area, for a maximum of amplitude in the right temporal area, and peak to peak distance in the left frontal area (P<0.05). Visual inspection suggested that recorded fluctuations have a bigger amplitude range for neutral words in all brain regions, except prefrontal, frontal and right frontal.
 Conclusion: Valence would affect behavioral measures. Generally, emotion facilitates word processing by reducing activity in anterior brain areas in phonological processing time.

Automated Robust Interpretation of Intraoperative Electrophysiological Signals – A Bayesian Deep Learning Approach

Abstract Intraoperative neurophysiological monitoring (IONM) is an essential tool during numerous surgical interventions to assess and monitor the functional integrity of neural structures at risk. A reliable signal interpretation is of importance to support medical staff by reducing manual evaluation. Deep learning (DL) techniques proved to be a robust tool for the analysis of neurophysiological data. The large amount of required manually labeled data as well as the lack of interpretability of the results however often limit the use of DL in medical scenarios. A possible way to tackle these obstacles is the utilization of Bayesian deep learning (BDL) methods. The modelling of uncertainties in the network parameters and the thereby possible quantification of predictive uncertainties allows both the identification of potential erroneous predictions as well as the targeted selection of informative signals in the context of active learning. To evaluate the applicability of BDL for the analysis of electrophysiological data as well as to increase the training efficiency by active learning, we implemented a multi-task Bayesian Convolutional Neural Network (BCNN) for the simultaneous classification of action potentials and the assessment of relevant signal characteristics (latency, maximum, minimum). We compare the results for electromyographical signals (EMG), containing in total approximately twelve thousand signals from 34 patients, with both a traditional non-Bayesian single-task and multi-task CNN. For all models, including the BCNN, we could achieve similar performances with detection rates over 97% accuracy. Further, we could improve training efficiency of the BCNN using pool-based active learning and therefore significantly reduce the required amount of manual labeling. The evaluated predictive uncertainties of the BCNN prove useful both for the efficient selection of informative signals in the context of active learning as well as the interpretation of the predictive posterior distribution and therefore trustworthiness of the classifications.

Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis.

This study aims at investigating the development of premature infants' autonomic nervous system (ANS) based on a quantitative analysis of the heart-rate variability (HRV) with a variety of novel features. Additionally, the role of heart-rate drops, known as bradycardias, has been studied in relation to both clinical and novel sympathovagal indices. ECG data were measured for at least 3 h in 25 preterm infants (gestational age ≤32 weeks) for a total number of 74 recordings. The post-menstrual age (PMA) of each patient was estimated from the RR interval time-series by means of multivariate linear-mixed effects regression. The tachograms were segmented based on bradycardias in periods after, between and during bradycardias. For each of those epochs, a set of temporal, spectral and fractal indices were included in the regression model. The best performing model has R2 = 0.75 and mean absolute error MAE = 1.56 weeks. Three main novelties can be reported. First, the obtained maturation models based on HRV have comparable performance to other development models. Second, the selected features for age estimation show a predominance of power and fractal features in the very-low- and low-frequency bands in explaining the infants' sympathovagal development from 27 PMA weeks until 40 PMA weeks. Third, bradycardias might disrupt the relationship between common temporal indices of the tachogram and the age of the infant and the interpretation of sympathovagal indices. This approach might provide a novel overview of post-natal autonomic maturation and an alternative development index to other electrophysiological data analysis.

Statistical decision theory and multiscale analyses of human brain data

BackgroundIn the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only. New methodWe discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data. ResultsWe provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6. Comparison with existing methodsThis is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed. ConclusionsWe discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.

Determining the correct stoichiometry of Kv2.1/Kv6.4 heterotetramers, functional in multiple stoichiometrical configurations

The electrically silent (KvS) members of the voltage-gated potassium (Kv) subfamilies Kv5, Kv6, Kv8, and Kv9 selectively modulate Kv2 subunits by forming heterotetrameric Kv2/KvS channels. Based on the reported 3:1 stoichiometry of Kv2.1/Kv9.3 channels, we tested the hypothesis that Kv2.1/Kv6.4 channels express, in contrast to the assumed 3:1, in a 2:2 stoichiometry. We investigate the Kv2.1/Kv6.4 stoichiometry using single subunit counting and functional characterization of tetrameric concatemers. For selecting the most probable stoichiometry, we introduce a model-selection method that is applicable for any multimeric complex by investigating the stoichiometry of Kv2.1/Kv6.4 channels. Weighted likelihood calculations bring rigor to a powerful technique. Using the weighted-likelihood model-selection method and analysis of electrophysiological data, we show that Kv2.1/Kv6.4 channels express, in contrast to the assumed 3:1, in a 2:2 stoichiometry. Within this stoichiometry, the Kv6.4 subunits have to be positioned alternating with Kv2.1 to express functional channels. The variability in Kv2/KvS assembly increases the diversity of heterotetrameric configurations and extends the regulatory possibilities of KvS by allowing the presence of more than one silent subunit.

Electrophysiological models of the human pancreatic δ‐cell: From single channels to the firing of action potentials

Minimal mathematical models were developed to describe the electrophysiological properties of human δ-cells. Markov models of single channels were first developed based on the analysis of electrophysiological data. Monte Carlo simulations of voltage-clamp experiments were performed in an iteratively optimization procedure to estimate the number of channels required to reproduce the main characteristics of the macroscopic currents recorded experimentally. A membrane model of the firing of action potentials was then developed based on the kinetic schemes of single channels and the number of channels estimated. We showed that macroscopic currents of human δ-cells can be reproduced by minimal models of single channels when the appropriate number of channels is considered. In addition, our simulations suggest that human δ-cells are capable of generating action potentials through the interaction of the ionic currents involved. Finally, we determined the relative contribution of the currents underlying the firing of action potentials in human pancreatic δ-cells, which allowed us to propose a qualitative model of an action potential in terms of the underlying ionic currents.

Noise analysis of cytosolic calcium image data

Cellular Ca2+ signals are often constrained to cytosolic micro- or nano-domains where stochastic openings of Ca2+ channels cause large fluctuations in local Ca2+ concentration (Ca2+ ‘noise’). With the advent of TIRF microscopy to image the fluorescence of Ca2+-sensitive probes from attoliter volumes it has become possible to directly monitor these signals, which closely track the gating of plasmalemmal and ER Ca2+-permeable channels. Nevertheless, it is likely that many physiologically important Ca2+ signals are too small to resolve as discrete events in fluorescence recordings. By analogy with noise analysis of electrophysiological data, we explore here the use of statistical approaches to detect and analyze such Ca2+ noise in images obtained using Ca2+-sensitive indicator dyes. We describe two techniques - power spectrum analysis and spatio-temporal correlation - and demonstrate that both effectively identify discrete, spatially localized calcium release events (Ca2+ puffs). Moreover, we show they are able to detect localized noise fluctuations in a case where discrete events cannot directly be resolved.

Fingolimod (FTY720) improves the functional recovery and myelin preservation of the optic pathway in focal demyelination model of rat optic chiasm

It has been shown that fingolimod (FTY720) possesses beneficial effects on remyelination in the central nervous system (CNS). In this study, the effects of FTY720 and sodium valproate (VPA) as histone deacetylase inhibitor (HDAC) on the conductivity of visual signals, extent of demyelination area, glial activation, and expression levels of HDAC1and S1PR1 have been evaluated in the optic chiasm of lysolecithin (LPC)-induced demyelination model. In order to produce this demyelination model, LPC (1%, 2 μL) was injected into the rat optic chiasm. Latency of visual waves was measured by visual evoked potential (VEP) recording. The extent of demyelination area and level of glial activation were assessed using immunostaining. Gene expression analysis was performed to evaluate the expression levels of HDAC1, S1PR1, Olig2, and MBP in the optic chiasm. Analysis of electrophysiological data showed that LPC administration increased the latency of visual signals. FTY720 improved the functional recovery of the visual pathway and reduced the level of glial activation in the optic chiasm. FTY720 enhanced myelin repair and up-regulated the expression levels of Olig2 and MBP. Additionally, the expression levels of HDAC1 and S1PR1 were significantly reduced in animals treated with FTY720. In contrast to FTY720 treated animals, administration of VPA could not significantly improve the functional recovery of optic pathway following LPC injection. Cumulatively, the results of the present study demonstrate that FTY720 application improves the functional recovery of the optic pathway by reducing demyelination levels, amelioration of glial activation, and down-regulating of S1PR1 and HDAC1.

Neural correlates of speech quality dimensions analyzed using electroencephalography (EEG)

Objective. By means of subjective psychophysical methods, quality of transmitted speech has been decomposed into three perceptual dimensions named ‘discontinuity’ (F), ‘noisiness’ (N) and ‘coloration’ (C). Previous studies using electroencephalography (EEG) already reported effects of perceived intensity of single quality dimensions on electrical brain activity. However, it has not been investigated so far, whether the dimensions themselves are dissociable on a neurophysiological level of analysis. Approach. Pursuing this goal in the present study, a high-quality (HQ) recording of a spoken word was degraded on each dimension at a time, resulting in three quality-impaired stimuli (F, N, C) which were on average described as being equal in perceived degradation intensity. Participants performed a three-stimulus oddball task, involving the serial presentation of different stimulus types: (1) HQ or degraded ‘standard’ stimuli to establish sensory/perceptual quality references. (2) Degraded ‘oddball’ stimuli to cause random, infrequent deviations from those references. EEG was employed to examine the neuro-electrical correlates of speech quality perception. Main results. Emphasis was placed on modulations in temporal and morphological characteristics of the P300 component of the event-related brain potential (ERP), whose subcomponents P3a and P3b are commonly linked to attentional orienting and task relevance categorization, respectively. Electrophysiological data analysis () revealed significant modulations of P300 amplitude and latency by the perceptual dimensions underlying both quality references and oddball stimuli. Significance. The present study exemplifies the utility of physiological methods like EEG for dissociating speech degradations not only based on perceived intensity level, but also their distinctive quality dimension.

Characterization of Temperature-Dependent Gating in Archaebacterial Calcium Activated Potassium Channel

MthK is an archaebacterial K+ channel, which is gated by large cytoplasmic RCK domains that facilitate Ca2+-activation of the channel. This bacterial channel is a good model for studying the mechanisms underlying calcium dependent gating in ion channels because it tractable to both structural and functional studies. An earlier study has also suggested that the calcium-dependent gating of MthK is also regulated by temperature (Parfenova, L. V. et al. (2006). J Biol Chem 281, 21131-21138). Here, using inside-out patch-clamping recording of inactivation removed MthK from spheroplasts, I have characterized the temperature-dependence of MthK channel at varying Ca2+ concentrations. Our preliminary data shows that the activation of this inactivation-removed channel is highly temperature-dependent. We have also purified and reconstituted this inactivation removed MthK in soybean polar lipids and found similar temperature-dependence albeit at lower concentrations of calcium. This suggests that the temperature-dependence of MthK is intrinsic. Furthermore, allosteric analysis of electrophysiological data shows that temperature unexpectedly modulates coupling between Ca2+ sensor and the pore rather than acting through a completely independent allosteric domain.

M110 - ELECTROPHYSIOLOGIC DIFFERENCES TO ALCOHOL CHALLENGE IN INDIVIDUALS POLYMORPHIC AT ADH1B LOCI

Background The ADH Class 1 is the major group for metabolizing alcohol in humans. Subunits which constitute class 1 ADH are encoded by the three genes ADH1A, ADH1B, and ADH1C, giving rise to A, B, and C subunits. Genetic polymorphisms, occur at ADH1B to give rise to ADH1B1, ADH1B2, and ADH1B3 subunits. Concerning the ADH1B gene, individuals with the ADH1B3 genotype appear to be more resistant to the stimulating effects of alcohol, possibly due to a faster-metabolizing capacity of this variant compared to the wild-type (ADH1B1). The purpose of this study was to examine the effect of ADH1B polymorphisms on electroencephalography, using the alcohol clamp technique. We hypothesize that individuals heterozygous for the ADH1B3 allele will have lower alpha power in frontoparietal electrodes, suggesting less relaxation effect during alcohol ingestion compared to those homozygous for ADH1B1. Methods A pilot sample of individuals (N=28) genotyped at the ADH1B locus received continuous EEG recording during infusion, using the alcohol clamp technique. The collected EEG data was analyzed using EEGLAB, an open source electrophysiological data analysis software application. The data was preprocessed using FASTER, an EEGLAB plugin for Automated EEG artifact Rejection. The FASTER process applies independent component analysis and statistical thresholds for artifact rejection. The accepted EEG epochs were then submitted to the pwelch FFT implementation using the inbuilt functions of EEGLAB, which computed the power of the EEG in 0.24 Hz steps over a total range of 1.5 – 40 Hz. The power spectra were averaged over epochs and averaged amplitude peaks extracted. The estimates of power within the delta (1.5–3.2 Hz), theta (3.8– 7.02 Hz), alpha (7.8–12.5 Hz), and slow (13.2–18.75 Hz) and fast (19.5–39.8 Hz) beta frequency bands were computed as the area under the PSD curve. Results Subjects with ADH1B1/1 have increased alpha power in CZ, FZ and PZ leads during baseline (i.e. pre-alcohol exposure) eye-closed resting EEG, with the greatest difference (between ADH1B1/1 and ADH1B1/3 observed in the PZ leads (P Discussion Based on rich evidence from neurophysiological studies, increased alpha power is indicative of hypoarousal; therefore, our data subjects more evidence for cortical hypoarousal in anticipation of alcohol challenge among individuals with ADH1B1/1 polymorphism.

Integrated analysis of anatomical and electrophysiological human intracranial data.

The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated, taking under an hour to complete, and readily adjusted to iEEG data sets with other characteristics.

Corticostriatal network dysfunction in Huntington's disease: Deficits in neural processing, glutamate transport, and ascorbate release.

This review summarizes evidence for dysfunctional connectivity between cortical and striatal neurons in Huntington's disease (HD), a fatal neurodegenerative condition caused by a single gene mutation. The focus is on data derived from recording of electrophysiological signals in behaving transgenic mouse models. Firing patterns of individual neurons and the frequency oscillations of local field potentials indicate a disruption in corticostriatal processing driven, in large part, by interactions between cells that contain the mutant gene rather than the mutant gene alone. Dysregulation of glutamate, an excitatory amino acid released by cortical afferents, plays a key role in the breakdown of corticostriatal communication, a process modulated by ascorbate, an antioxidant vitamin found in high concentration in striatum. Up-regulation of glutamate transport by drug administration or viral-vector delivery improves ascorbate homeostasis and neurobehavioral processing in HD mice. Further analysis of electrophysiological data, including the use of sophisticated computational strategies, is required to discern how behavioral demands modulate the flow of corticostriatal information and its disruption by HD. Long before massive cell loss occurs, HD impairs the mechanisms by which cortical and striatal neurons communicate. A key problem identified in transgenic animal models is dysregulation of the dynamic changes in extracellular glutamate and ascorbate. Improved understanding of how these neurochemical systems impact corticostriatal communication is necessary before an effective therapeutic strategy can emerge.