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Computational models of brain current flow during transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), are increasingly used to understand and optimize clinical trials. We propose that broad dissemination requires a simple graphical user interface (GUI) software that allows users to explore and design montages in real-time, based on their own clinical/experimental experience and objectives. We introduce two complimentary open-source platforms for this purpose: BONSAI and SPHERES. BONSAI is a web (cloud) based application (available at neuralengr.com/bonsai) that can be accessed through any flash-supported browser interface. SPHERES (available at neuralengr.com/spheres) is a stand-alone GUI application that allow consideration of arbitrary montages on a concentric sphere model by leveraging an analytical solution. These open-source tES modeling platforms are designed go be upgraded and enhanced. Trade-offs between open-access approaches that balance ease of access, speed, and flexibility are discussed. [ABSTRACT FROM AUTHOR]

Background The importance of slow-wave sleep (SWS), hallmarked by the occurrence of sleep slow oscillations (SO), for the consolidation of hippocampus-dependent memories has been shown in numerous studies. Previously, the application of transcranial direct current stimulation, oscillating at the frequency of endogenous slow oscillations, during SWS enhanced memory consolidation for a hippocampus dependent task in humans suggesting a causal role of slowly oscillating electric fields for sleep dependent memory consolidation. Objective Here, we aimed to replicate and extend these findings to a rodent model. Methods Slow oscillatory direct transcranial current stimulation (SO-tDCS) was applied over the frontal cortex of rats during non-rapid eye movement (NREM) sleep and its effects on memory consolidation in the one-trial object-place recognition task were examined. A retention interval of 24 h was used to investigate the effects of SO-tDCS on long-term memory. Results Animals' preference for the displaced object was significantly greater than chance only when animals received SO-tDCS. EEG spectral power indicated a trend toward a transient enhancement of endogenous SO activity in the SO-tDCS condition. Conclusions These results support the hypothesis that slowly oscillating electric fields causal affect sleep dependent memory consolidation, and demonstrate that oscillatory tDCS can be a valuable tool to investigate the function of endogenous cortical network activity. [ABSTRACT FROM AUTHOR]

Key points The diversity of cellular targets of direct current stimulation (DCS), including somas, dendrites and axon terminals, determine the modulation of synaptic efficacy., Axon terminals of cortical pyramidal neurons are two-three times more susceptible to polarization than somas., DCS in humans results in current flow dominantly parallel to the cortical surface, which in animal models of cortical stimulation results in synaptic pathway-specific modulation of neuronal excitability., These results suggest that somatic polarization together with axon terminal polarization may be important for synaptic pathway-specific modulation of DCS, which underlies modulation of neuronal excitability during transcranial DCS., Abstract Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique to modulate cortical excitability. Although increased/decreased excitability under the anode/cathode electrode is nominally associated with membrane depolarization/hyperpolarization, which cellular compartments (somas, dendrites, axons and their terminals) mediate changes in cortical excitability remains unaddressed. Here we consider the acute effects of DCS on excitatory synaptic efficacy. Using multi-scale computational models and rat cortical brain slices, we show the following. (1) Typical tDCS montages produce predominantly tangential (relative to the cortical surface) direction currents (4-12 times radial direction currents), even directly under electrodes. (2) Radial current flow (parallel to the somatodendritic axis) modulates synaptic efficacy consistent with somatic polarization, with depolarization facilitating synaptic efficacy. (3) Tangential current flow (perpendicular to the somatodendritic axis) modulates synaptic efficacy acutely (during stimulation) in an afferent pathway-specific manner that is consistent with terminal polarization, with hyperpolarization facilitating synaptic efficacy. (4) Maximal polarization during uniform DCS is expected at distal (the branch length is more than three times the membrane length constant) synaptic terminals, independent of and two-three times more susceptible than pyramidal neuron somas. We conclude that during acute DCS the cellular targets responsible for modulation of synaptic efficacy are concurrently somata and axon terminals, with the direction of cortical current flow determining the relative influence. [ABSTRACT FROM AUTHOR]

Exploratory variability is essential for sensorimotor learning, but it is not known how and at what timescales it is regulated. We manipulated song learning in zebra finches to experimentally control the requirements for vocal exploration in different parts of their song.We first trained birds to perform a one-syllable song, and once they mastered it,weadded anewsyllable to the song model. Remarkably, when practicing the modified song, birds rapidly alternated between high and low acoustic variability to confine vocal exploration to the newly added syllable. Furthermore, even within syllables, acoustic variability changed independently across song elements that were only milliseconds apart. Analysis of the entire vocal output during learning revealed that the variability of each song element decreased as it approached the target, correlating with momentary local distance from the target and less so with the overall distance within a syllable. We conclude that vocal error is computed locally in subsyllabic timescales and that song elements can be learned and crystallized independently. Songbirds have dedicated brain circuitry for vocal babbling in the anterior forebrain pathway (AFP), which generates exploratory song patterns that drive premotor neurons at the song nucleus RA. We hypothesize that either AFP adjusts the gain of vocal exploration in fine timescales or that the sensitivity of RA premotor neurons to AFP/HVC inputs varies across song elements. [ABSTRACT FROM AUTHOR]

Clinical effects of transcranial electrical stimulation with weak currents are remarkable considering the low amplitude of the electric fields acting on the brain. Elucidating the processes by which small currents affect ongoing brain activity is of paramount importance for the rational design of noninvasive electrotherapeutic strategies and to determine the relevance of endogenous fields. We propose that in active neuronal networks, weak electrical fields induce small but coherent changes in the firing rate and timing of neuronal populations that can be magnified by dynamic network activity. Specifically, we show that carbachol-induced gamma oscillations (25-35 Hz) in rat hippocampal slices have an inherent rate-limiting dynamic and timing precision that govern susceptibility to low-frequency weak electric fields (<50 Hz;10 V/m). This leads to a range of nonlinear responses, including the following: (1) asymmetric power modulation by DC fields resulting from balanced excitation and inhibition; (2) symmetric power modulation by lower frequency AC fields with a net-zero change in firing rate; and (3) half-harmonic oscillations for higher frequency AC fields resulting from increased spike timing precision. These underlying mechanisms were elucidated by slice experiments and a parsimonious computational network model of singlecompartment spiking neurons responding to electric field stimulation with small incremental polarization. Intracellular recordings confirmed model predictions on neuronal timing and rate changes, as well as spike phase-entrainment resonance at 0.2 V/m. Finally, our data and mechanistic framework provide a functional role for endogenous electric fields, specifically illustrating that modulation of gamma oscillations during theta-modulated gamma activity can result from field effects alone. [ABSTRACT FROM AUTHOR]

Our society's information technology advancements have resulted in the increasingly problematic issue of information overload--i.e., we have more access to information than we can possibly process. This is nowhere more apparent than in the volume of imagery and video that we can access on a daily basis--for the general public, availability of YouTube video and Google Images, or for the image analysis professional tasked with searching security video or satellite reconnaissance. Which images to look at and how to ensure we see the images that are of most interest to us, begs the question of whether there are smart ways to triage this volume of imagery. Over the past decade, computer vision research has focused on the issue of ranking and indexing imagery. However, computer vision is limited in its ability to identify interesting imagery, particularly as "interesting" might be defined by an individual. In this paper we describe our efforts in developing brain-computer interfaces (BCIs) which synergistically integrate computer vision and human vision so as to construct a system for image triage. Our approach exploits machine learning for real-time decoding of brain signals which are recorded noninvasively via electroencephalography (EEG). The signals we decode are specific for events related to imagery attracting a user's attention. We describe two architectures we have developed for this type of cortically coupled computer vision and discuss potential applications and challenges for the future. [ABSTRACT FROM AUTHOR]

Neuronal gain adaptation has been proposed as the underlying mechanism leading to the perception of phantom sounds such as Zwicker tones and tinnitus. In this gain-adaptation theory, cochlear compression plays a significant role with weaker compression leading to stronger phantom percepts. The specific aim of this study was to find a link between the strength of neuronal gain adaptation and cochlear compression. Compression was assessed using distortion product otoacoustic emissions (DPOAEs). Gain adaptation is hypothesized to manifest itself in the sensitization observed for the detection of masked tones when preceded by notched noise. Perceptual thresholds for pure tones in notched noise were measured at multiple frequencies following various priming signals. The observed sensitization was larger than expected from the combined effect of the various maskers. However, there was no link between sensitization and compression. Instead, across subjects, stronger sensitization correlated with stronger DPOAEs evoked by low-level primaries. In addition, growth of DPOAEs correlated reliably with perceptual thresholds across frequencies within subjects. Together, the data suggest that short-term dynamic adaptation leading to perceptual sensitization is the result of an active process mediated by the outer hair cells, which are thought to modulate the gain of the cochlear amplifier via efferent feedback. [ABSTRACT FROM AUTHOR]

Watching a speaker's facial movements can dramatically enhance our ability to comprehend words, especially in noisy environments. From a general doctrine of combining information from different sensory modalities (the principle of inverse effectiveness), one would expect that the visual signals would be most effective at the highest levels of auditory noise. In contrast, we find, in accord with a recent paper, that visual information improves performance more at intermediate levels of auditory noise than at the highest levels, and we show that a novel visual stimulus containing only temporal information does the same. We present a Bayesian model of optimal cue integration that can explain these conflicts. In this model, words are regarded as points in a multidimensional space and word recognition is a probabilistic inference process. When the dimensionality of the feature space is low, the Bayesian model predicts inverse effectiveness; when the dimensionality is high, the enhancement is maximal at intermediate auditory noise levels. When the auditory and visual stimuli differ slightly in high noise, the model makes a counterintuitive prediction: as sound quality increases, the proportion of reported words corresponding to the visual stimulus should first increase and then decrease. We confirm this prediction in a behavioral experiment. We conclude that auditory-visual speech perception obeys the same notion of optimality previously observed only for simple multisensory stimuli. [ABSTRACT FROM AUTHOR]

To determine the effects of high-frequency electrical stimulation on electrographic seizure activity during and after stimulation (ON-effect and OFF-effect). The modulation and suppression of epileptiform activity during (ON-effect) and after (OFF-effect) high-frequency electrical stimulation was investigated using the high-K+ and picrotoxin hippocampal slice epilepsy models. Uniform sinusoidal fields (50 Hz) were applied with various intensity levels for 1 min across brain slices. Extracellular and intracellular activity were monitored during and after stimulation. The ON-effects of high-frequency stimulation were highly variable across individual slices and models; ON-effects included modulation of activity, pacing, partial suppression, or activity resembling spreading-depression. On average, epileptic activity, measured as power in the extracellular fields, increased significantly during stimulation. Following the termination of electrical stimulation, a robust poststimulation suppression period was observed. This OFF suppression was observed even at relatively moderate stimulation intensities. The duration of OFF suppression increased with stimulation intensity, independent of ON-effects. Antagonism of GABAA function did not directly effect OFF suppression duration. The present results suggest that “rational” seizure control protocols using intermittent high-frequency electrical stimulation should control for both ON and OFF effects. [ABSTRACT FROM AUTHOR]

We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEC sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEC in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEC analysis to efficiently detect neural signatures correlated with the recognition event. [ABSTRACT FROM AUTHOR]

Probabilistic models of image statistics underlie many approaches in image analysis and processing. An important class of such models have variables whose dependency graph is a tree. If the hidden variables take values on a finite set, most computations with the model can be performed exactly, including the likelihood calculation, training with the EM algorithm, etc. Crouse et al. developed one such model, the hidden Markov free (HMT). They took particular care to limit the complexity of their model. We argue that it is beneficial to allow more complex tree-structured models, describe the use of information theoretic penalties to choose the model complexity, and present experimental results to support these proposals. For these experiments, we use what we call the hierarchical image probability (HIP) model. The differences between the HIP and the HMT models include the use of multivariate Gaussians to model the distributions of local vectors of wavelet coefficients and the use of different numbers of hidden states at each resolution. We demonstrate the broad utility of image distributions by applying the HIP model to classification, synthesis, and compression, across a variety of image types, namely, electrooptical, synthetic aperture radar, and mammograms (digitized X-rays). In all cases, we compare with the HMT. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we use single-trial analysis of electroencephalography (EEG) to ascertain the cortical origins of response time variability in a rapid serial visual presentation (RSVP) task. We extract spatial components that maximally discriminate between target and distractor stimulus conditions over specific time windows between stimulus onset and the time of a motor response. We then compute the peak latency of this differential activity on a trial-by-trial basis, and correlate this with response time. We find, for our nine participants, that the majority of the latency is introduced by component activity which begins far-frontally 200 ms prior to the response and proceeds to become parietally distributed near the time of response. This activity is consistent with the hypothesis that cortical networks involved in generating the late positive complexes may be the origins of the observed response time variability in rapid discrimination of visual objects. [Copyright &y& Elsevier]

Abstract: In this paper, we describe a simple set of “recipes” for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources. [Copyright &y& Elsevier]

We present an algorithm for blindly recovering constituent source spectra from magnetic resonance (MR) chemical shift imaging (CSI) of the human brain. The algorithm, which we call constrained nonnegative matrix factorization (cNMF), does not enforce independence or sparsity, instead only requiring the source and mixing matrices to be nonnegative. It is based on the nonnegative matrix factorization (NMF) algorithm, extending it to include a constraint on the positivity of the amplitudes of the recovered spectra. This constraint enables recovery of physically meaningful spectra even in the presence of noise that causes a significant number of the observation amplitudes to be negative. We demonstrate and characterize the algorithm's performance using 31P volumetric brain data, comparing the results with two different blind source separation methods: Bayesian spectral decomposition (BSD) and nonnegative sparse coding (NNSC). We then in- corporate the cNMF algorithm into a hierarchical decomposition framework, showing that it can be used to recover tissue-specific spectra given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the hierarchical procedure on ¹H brain data and conclude that the computational efficiency of the algorithm makes it well-suited for use in diagnostic work-up. [ABSTRACT FROM AUTHOR]

We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subject's perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms. [ABSTRACT FROM AUTHOR]

Electrical motor cortex stimulation can produce corticospinal system plasticity and enhance motor function after injury. We investigate molecular mechanisms of structural and physiological plasticity following electrical neuromodulation, focusing on identifying molecular predictors, or biomarkers, for durable plasticity. We used two neuromodulation protocols, repetitive multipulse stimulation (rMPS) and patterned intermittent theta burst stimulation (iTBS), incorporating different stimulation durations and follow-up periods. We compared neuromodulation efficacy in promoting corticospinal tract (CST) sprouting, motor cortex muscle evoked potential (MEP) LTP-like plasticity, and their associated molecular underpinnings. Only iTBS produced CST sprouting after short-term neuromodulation (1 d of stimulation; 9-d survival for sprouting expression); both iTBS and rMPS produced sprouting with long-term (10-d) neuromodulation. Significant mTOR signaling activation and phosphatase and tensin homolog (PTEN) protein deactivation predicted axon growth across all neuromodulation conditions that produced significant sprouting. Both neuromodulation protocols, regardless of duration, were effective in producing MEP enhancement. However, persistent LTP-like enhancement of MEPs at 30 d was only produced by long-term iTBS. Statistical modeling suggests that Stat3 signaling is the key mediator of MEP enhancement. Cervical spinal cord injury (SCI) alone did not affect baseline molecular signaling. Whereas iTBS and rMPS after SCI produced strong mTOR activation and PTEN deactivation, only iTBS produced Stat3 activation. Our findings support differential molecular biomarkers for neuromodulation-dependent structural and physiological plasticity and show that motor cortex epidural neuromodulation produces molecular changes in neurons that support axonal growth after SCI. iTBS may be more suitable for repair after SCI because it promotes molecular signaling for both CST growth and MEP plasticity. [ABSTRACT FROM AUTHOR]

Heart rate has natural fluctuations that are typically ascribed to autonomic function. Recent evidence suggests that conscious processing can affect the timing of the heartbeat. We hypothesized that heart rate is modulated by conscious processing and therefore dependent on attentional focus. To test this, we leverage the observation that neural processes synchronize between subjects by presenting an identical narrative stimulus. As predicted, we find significant inter-subject correlation of heart rate (ISC-HR) when subjects are presented with an auditory or audiovisual narrative. Consistent with our hypothesis, we find that ISC-HR is reduced when subjects are distracted from the narrative, and higher ISC-HR predicts better recall of the narrative. Finally, patients with disorders of consciousness have lower ISC-HR, as compared to healthy individuals. We conclude that heart rate fluctuations are partially driven by conscious processing, depend on attentional state, and may represent a simple metric to assess conscious state in unresponsive patients. [Display omitted] • Narrative stimuli can synchronize fluctuations of heart rate between individuals • This interpersonal synchronization is modulated by attention and predicts memory • These effects on heart rate cannot be explained by modulation of respiratory patterns • Synchrony is lower in patients with disorders of consciousness Stories affect our hearts and bind us together. Pérez et al. show that attention to narratives can synchronize fluctuations of heart rate between individuals. Heart synchronization predicts memory and cannot be explained by respiration. Finally, synchrony is lower in patients with disorders of consciousness and might inform prognosis. [ABSTRACT FROM AUTHOR]

Music tends to be highly repetitive, both in terms of musical structure and in terms of listening behavior, yet little is known about how engagement changes with repeated exposure. Here we postulate that engagement with music affects the inter-subject correlation of brain responses during listening. We predict that repeated exposure to music will affect engagement and thus inter-subject correlation. Across repeated exposures to instrumental music, inter-subject correlation decreased for music written in a familiar style. Participants with formal musical training showed more inter-subject correlation, and sustained it across exposures to music in an unfamiliar style. This distinguishes music from other domains, where repetition has consistently been shown to decrease inter-subject correlation. Overall, the study suggests that listener engagement tends to decrease across repeated exposures of familiar music, but that unfamiliar musical styles can sustain an audience's interest, in particular in individuals with some musical training. Future work needs to validate the link proposed here between music engagement and inter-subject correlation of brain responses during listening. [ABSTRACT FROM AUTHOR]

Noninvasive brain stimulation techniques are used in experimental and clinical fields for their potential effects on brain network dynamics and behavior. Transcranial electrical stimulation (TES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), has gained popularity because of its convenience and potential as a chronic therapy. However, a mechanistic understanding of TES has lagged behind its widespread adoption. Here, we review data and modelling on the immediate neurophysiological effects of TES in vitro as well as in vivo in both humans and other animals. While it remains unclear how typical TES protocols affect neural activity, we propose that validated models of current flow should inform study design and artifacts should be carefully excluded during signal recording and analysis. Potential indirect effects of TES (e.g., peripheral stimulation) should be investigated in more detail and further explored in experimental designs. We also consider how novel technologies may stimulate the next generation of TES experiments and devices, thus enhancing validity, specificity, and reproducibility. Transcranial electrical stimulation techniques, such as tDCS and tACS, are popular tools for neuroscience and clinical therapy, but how low-intensity current might modulate brain activity remains unclear. In this review, the authors review the evidence on mechanisms of transcranial electrical stimulation. [ABSTRACT FROM AUTHOR]

The original version of this Article contained an error in the last sentence of the first paragraph of the Introduction, which incorrectly read ‘Correlation of brain activity is typically measured using functional magnetic resonance imaging (fMRI), and the correlation structure is often referred to as “fu’. The correct version states ‘referred to as “functional connectivity”2-6’ in place of ‘referred to as “fu’. This has been corrected in both the PDF and HTML versions of the Article. [ABSTRACT FROM AUTHOR]

A correction to the article "Origins of powerlaw degree distribution in the heterogeneity of human activity in social networks" is presented.

The presence of asymmetry in the misclassification costs or class prevalences is a common occurrence in the pattern classification domain. While much interest has been devoted to the study of cost-sensitive learning techniques, the relationship between cost-sensitive learning and the specification of the model set in a parametric estimation framework remains somewhat unclear. To that end, we differentiate between the case of the model including the true posterior, and that in which the model is misspecified. In the former case, it is shown that thresholding the maximum likelihood (ML) estimate is an asymptotically optimal solution to the risk minimization problem. On the other hand, under model misspecification, it is demonstrated that thresholded ML is suboptimal and that the risk-minimizing solution varies with the misclassification cost ratio. Moreover, we analytically show that the negative weighted log likelihood (Elkan, 2001) is a tight, convex upper bound of the empirical loss. Coupled with empirical results on several real-world data sets, we argue that weighted ML is the preferred cost-sensitive technique. [ABSTRACT FROM AUTHOR]

Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in the context of functional brain imaging data for which it seems ideally suited. The work suggests to identify a subspace projection which optimally separates classes while ensuring that each dimension in this space captures an independent contribution to the discrimination. [ABSTRACT FROM AUTHOR]

It is well established that neural responses to visual stimuli are enhanced at select locations in the visual field. Although spatial selectivity and the effects of spatial attention are well understood for discrete tasks (e.g. visual cueing), little is known for naturalistic experience that involves continuous dynamic visual stimuli (e.g. driving). Here, we assess the strength of neural responses across the visual space during a kart‐race game. Given the varying relevance of visual location in this task, we hypothesized that the strength of neural responses to movement will vary across the visual field, and it would differ between active play and passive viewing. To test this, we measure the correlation strength of scalp‐evoked potentials with optical flow magnitude at individual locations on the screen. We find that neural responses are strongly correlated at task‐relevant locations in visual space, extending beyond the focus of overt attention. Although the driver's gaze is directed upon the heading direction at the centre of the screen, neural responses were robust at the peripheral areas (e.g. roads and surrounding buildings). Importantly, neural responses to visual movement are broadly distributed across the scalp, with visual spatial selectivity differing across electrode locations. Moreover, during active gameplay, neural responses are enhanced at select locations in the visual space. Conventionally, spatial selectivity of neural response has been interpreted as an attentional gain mechanism. In the present study, the data suggest that different brain areas focus attention on different portions of the visual field that are task‐relevant, beyond the focus of overt attention. [ABSTRACT FROM AUTHOR]

Impaired inhibitory control accompanied by enhanced salience attributed to drug‐related cues, both associated with function of the dorsolateral prefrontal cortex (dlPFC), are hallmarks of drug addiction, contributing to worse symptomatology including craving. dlPFC modulation with transcranial direct current stimulation (tDCS) previously showed craving reduction in inpatients with cocaine use disorder (CUD). Our study aimed at assessing feasibility of a longer tDCS protocol in CUD (15 versus the common five/10 sessions) and replicability of previous results. In a randomized double‐blind sham‐controlled protocol, 17 inpatients with CUD were assigned to either a real‐tDCS (right anodal/left cathodal) or a sham‐tDCS condition for 15 sessions. Following the previous report, primary outcome measures were self‐reported craving, anxiety, depression, and quality of life. Secondary measures included sleepiness, readiness to change drug use, and affect. We also assessed cognitive function including impulsivity. An 88% retention rate demonstrated feasibility. Partially supporting the previous results, there was a trend for self‐reported craving to decrease in the real‐tDCS group more than the sham‐group, an effect that would reach significance with 15 subjects per group. Quality of life and impulsivity improved over time in treatment in both groups. Daytime sleepiness and readiness to change drug use showed significant Group × Time interactions whereby improvements were noted only in the real‐tDCS group. One‐month follow‐up suggested transient effects of tDCS on sleepiness and craving. These preliminary results suggest the need for including more subjects to show a unique effect of real‐tDCS on craving and examine the duration of this effect. After replication in larger sample sizes, increased vigilance and motivation to change drug use in the real‐tDCS group may suggest fortification of dlPFC‐supported executive functions. [ABSTRACT FROM AUTHOR]

Severe brain injuries can lead to long-lasting disorders of consciousness (DoC) such as vegetative state/unresponsive wakefulness syndrome (VS/UWS) or minimally conscious state (MCS). While behavioral assessment remains the gold standard to determine conscious state, EEG has proven to be a promising complementary tool to monitor the effect of new therapeutics. Encouraging results have been obtained with invasive electrical stimulation of the brain, and recent studies identified transcranial direct current stimulation (tDCS) as an effective approach in randomized controlled trials. This non-invasive and inexpensive tool may turn out to be the preferred treatment option. However, its mechanisms of action and physiological effects on brain activity remain unclear and debated. Here, we stimulated 60 DoC patients with the anode placed over left-dorsolateral prefrontal cortex in a prospective open-label study. Clinical behavioral assessment improved in twelve patients (20%) and none deteriorated. This behavioral response after tDCS coincided with an enhancement of putative EEG markers of consciousness: in comparison with non-responders, responders showed increases of power and long-range cortico-cortical functional connectivity in the theta-alpha band, and a larger and more sustained P300 suggesting improved conscious access to auditory novelty. The EEG changes correlated with electric fields strengths in prefrontal cortices, and no correlation was found on the scalp. Taken together, this prospective intervention in a large cohort of DoC patients strengthens the validity of the proposed EEG signatures of consciousness, and is suggestive of a direct causal effect of tDCS on consciousness. [ABSTRACT FROM AUTHOR]

Abstract Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method. Highlights • MCCA combines multiple data sets into a common representation. • MCCA can be used to summarize data across subjects. • MCCA can be used to denoise data, or reduce dimensionality, based on consistency across subjects. [ABSTRACT FROM AUTHOR]

Abstract Online imaging and neuromodulation is invalid if stimulation distorts measurements beyond the point of accurate measurement. In theory, combining transcranial Direct Current Stimulation (tDCS) with electroencephalography (EEG) is compelling, as both use non-invasive electrodes and image-guided dose can be informed by the reciprocity principle. To distinguish real changes in EEG from stimulation artifacts, prior studies applied conventional signal processing techniques (e.g. high-pass filtering, ICA). Here, we address the assumptions underlying the suitability of these approaches. We distinguish physiological artifacts - defined as artifacts resulting from interactions between the stimulation induced voltage and the body and so inherent regardless of tDCS or EEG hardware performance – from methodology-related artifacts - arising from non-ideal experimental conditions or non-ideal stimulation and recording equipment performance. Critically, we identify inherent physiological artifacts which are present in all online EEG-tDCS: 1) cardiac distortion and 2) ocular motor distortion. In conjunction, non-inherent physiological artifacts which can be minimized in most experimental conditions include: 1) motion and 2) myogenic distortion. Artifact dynamics were analyzed for varying stimulation parameters (montage, polarity, current) and stimulation hardware. Together with concurrent physiological monitoring (ECG, respiration, ocular, EMG, head motion), and current flow modeling, each physiological artifact was explained by biological source-specific body impedance changes, leading to incremental changes in scalp DC voltage that are significantly larger than real neural signals. Because these artifacts modulate the DC voltage and scale with applied current, they are dose specific such that their contamination cannot be accounted for by conventional experimental controls (e.g. differing stimulation montage or current as a control). Moreover, because the EEG artifacts introduced by physiologic processes during tDCS are high dimensional (as indicated by Generalized Singular Value Decomposition- GSVD), non-stationary, and overlap highly with neurogenic frequencies, these artifacts cannot be easily removed with conventional signal processing techniques. Spatial filtering techniques (GSVD) suggest that the removal of physiological artifacts would significantly degrade signal integrity. Physiological artifacts, as defined here, would emerge only during tDCS, thus processing techniques typically applied to EEG in the absence of tDCS would not be suitable for artifact removal during tDCS. All concurrent EEG-tDCS must account for physiological artifacts that are a) present regardless of equipment used, and b) broadband and confound a broad range of experiments (e.g. oscillatory activity and event related potentials). Removal of these artifacts requires the recognition of their non-stationary, physiology-specific dynamics, and individualized nature. We present a broad taxonomy of artifacts (non/stimulation related), and suggest possible approaches and challenges to denoising online EEG-tDCS stimulation artifacts. Highlights • We identify inherent physiological artifacts during concurrent (online) tDCS-EEG. • Physiological artifacts include cardiac, ocular, motion, and myogenic artifacts. • Dose response and current source independence of physiological artifacts are examined. • FEM models explain physiological artifacts based on specific impedance changes. • Typical signal processing methods cannot reliably remove physiological artifacts. [ABSTRACT FROM AUTHOR]

Highlights • Neural engagement with videos is measured by the inter-subject correlation of EEG. • Memory of factual information in educational videos is predicted by neural engagement. • Neural engagement is stronger for intentional learning than incidental learning. • Neural engagement is modulated by attention. • These results are consistent with the known link between attention and memory. Abstract Online educational materials are largely disseminated through videos, and yet there is little understanding of how these videos engage students and fuel academic success. We hypothesized that components of the electroencephalogram (EEG), previously shown to reflect video engagement, would be predictive of academic performance in the context of educational videos. Two groups of subjects watched educational videos in either an intentional learning paradigm, in which they were aware of an upcoming test, or in an incidental learning paradigm, in which they were unaware that they would be tested. "Neural engagement" was quantified by the inter-subject correlation (ISC) of the EEG that was evoked by the videos. In both groups, students with higher neural engagement retained more information. Neural engagement also discriminated between attentive and inattentive video viewing. These results suggest that this EEG metric is a marker of the stimulus-related attentional mechanisms necessary to retain information. In the future, EEG may be used as a tool to design and assess online educational content. [ABSTRACT FROM AUTHOR]

Abstract In neuroscience, stimulus-response relationships have traditionally been analyzed using either encoding or decoding models. Here we propose a hybrid approach that decomposes neural activity into multiple components, each representing a portion of the stimulus. The technique is implemented via canonical correlation analysis (CCA) by temporally filtering the stimulus (encoding) and spatially filtering the neural responses (decoding) such that the resulting components are maximally correlated. In contrast to existing methods, this approach recovers multiple correlated stimulus-response pairs, and thus affords a richer, multidimensional analysis of neural representations. We first validated the technique’s ability to recover multiple stimulus-driven components using electroencephalographic (EEG) data simulated with a finite element model of the head. We then applied the technique to real EEG responses to auditory and audiovisual narratives experienced identically across subjects, as well as uniquely experienced video game play. During narratives, both auditory and visual stimulus-response correlations (SRC) were modulated by attention and tracked inter-subject correlations. During video game play, SRC varied with game difficulty and the presence of a dual task. Interestingly, the strongest component extracted for visual and auditory features of film clips had nearly identical spatial distributions, suggesting that the predominant encephalographic response to naturalistic stimuli is supramodal. The diversity of these findings demonstrates the utility of measuring multidimensional SRC via hybrid encoding-decoding. Highlights • A technique that combines stimulus encoding and neural decoding was proposed. • The technique decomposes the stimulus-response relationship into multiple independent dimensions. • Multidimensional stimulus-response correlation is modulated by attentional states and task demands. • The technique is applicable to the study of neural decoding with unique stimuli. [ABSTRACT FROM AUTHOR]

Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (N EEG  = 45, N fMRI  = 11, N ECoG  = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing. [ABSTRACT FROM AUTHOR]

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4−7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. [ABSTRACT FROM AUTHOR]

The objective of this review is to summarize the contribution of animal research using direct current stimulation (DCS) to our understanding of the physiological effects of transcranial direct current stimulation (tDCS). We comprehensively address experimental methodology in animal studies, broadly classified as: (1) transcranial stimulation; (2) direct cortical stimulation in vivo and (3) in vitro models. In each case advantages and disadvantages for translational research are discussed including dose translation and the overarching “quasi-uniform” assumption, which underpins translational relevance in all animal models of tDCS. Terminology such as anode, cathode, inward current, outward current, current density, electric field, and uniform are defined. Though we put key animal experiments spanning decades in perspective, our goal is not simply an exhaustive cataloging of relevant animal studies, but rather to put them in context of ongoing efforts to improve tDCS. Cellular targets, including excitatory neuronal somas, dendrites, axons, interneurons, glial cells, and endothelial cells are considered. We emphasize neurons are always depolarized and hyperpolarized such that effects of DCS on neuronal excitability can only be evaluated within subcellular regions of the neuron. Findings from animal studies on the effects of DCS on plasticity (LTP/LTD) and network oscillations are reviewed extensively. Any endogenous phenomena dependent on membrane potential changes are, in theory, susceptible to modulation by DCS. The relevance of morphological changes (galvanotropy) to tDCS is also considered, as we suggest microscopic migration of axon terminals or dendritic spines may be relevant during tDCS. A majority of clinical studies using tDCS employ a simplistic dose strategy where excitability is singularly increased or decreased under the anode and cathode, respectively. We discuss how this strategy, itself based on classic animal studies, cannot account for the complexity of normal and pathological brain function, and how recent studies have already indicated more sophisticated approaches are necessary. One tDCS theory regarding “functional targeting” suggests the specificity of tDCS effects are possible by modulating ongoing function (plasticity). Use of animal models of disease are summarized including pain, movement disorders, stroke, and epilepsy. [ABSTRACT FROM AUTHOR]

Abstract: Functional magnetic resonance imaging (fMRI) of brain activation during transcranial electrical stimulation is used to provide insight into the mechanisms of neuromodulation and targeting of particular brain structures. However, the passage of current through the body may interfere with the concurrent detection of blood oxygen level-dependent (BOLD) signal, which is sensitive to local magnetic fields. To test whether these currents can affect concurrent fMRI recordings we performed conventional gradient echo-planar imaging (EPI) during transcranial direct current (tDCS) and alternating current stimulation (tACS) on two post-mortem subjects. tDCS induced signals in both superficial and deep structures. The signal was specific to the electrode montage, with the strongest signal near cerebrospinal fluid (CSF) and scalp. The direction of change relative to non-stimulation reversed with tDCS stimulation polarity. For tACS there was no net effect of the MRI signal. High-resolution individualized modeling of current flow and induced static magnetic fields suggested a strong coincidence of the change EPI signal with regions of large current density and magnetic fields. These initial results indicate that (1) fMRI studies of tDCS must consider this potentially confounding interference from current flow and (2) conventional MRI imaging protocols can be potentially used to measure current flow during transcranial electrical stimulation. The optimization of current measurement and artifact correction techniques, including consideration of the underlying physics, remains to be addressed. [Copyright &y& Elsevier]

Abstract: Transcranial direct current stimulation (tDCS) is being investigated as an adjunctive technique to behavioral rehabilitation treatment after stroke. The conventional “dosage”, consisting of a large (25cm2) anode over the target with the cathode over the contralateral hemisphere, has been previously shown to yield broadly distributed electric fields whose intensities at the target region are less than maximal. Here, we report the results of a systematic targeting procedure with small “high-definition” electrodes that was used in preparation for a pilot study on 8 stroke patients with chronic aphasia. We employ functional and anatomical magnetic resonance imagery (fMRI/MRI) to define a target and optimize (with respect to the electric field magnitude at the target) the electrode configuration, respectively, and demonstrate that electric field strengths in targeted cortex can be substantially increased (63%) over the conventional approach. The optimal montage exhibits significant variation across subjects as well as when perturbing the target location within a subject. However, for each displacement of the target co-ordinates, the algorithm is able to determine a montage which delivers a consistent amount of current to that location. These results demonstrate that MRI-based models of current flow yield maximal stimulation of target structures, and as such, may aid in reliably assessing the efficacy of tDCS in neuro-rehabilitation. [Copyright &y& Elsevier]

Abstract: EEG (electroencephalography) has been used for decades in thousands of research studies and is today a routine clinical tool despite the small magnitude of measured scalp potentials. It is widely accepted that the currents originating in the brain are strongly influenced by the high resistivity of skull bone, but it is less well known that the thin layer of CSF (cerebrospinal fluid) has perhaps an even more important effect on EEG scalp magnitude by spatially blurring the signals. Here it is shown that brain shift and the resulting small changes in CSF layer thickness, induced by changing the subject''s position, have a significant effect on EEG signal magnitudes in several standard visual paradigms. For spatially incoherent high-frequency activity the effect produced by switching from prone to supine can be dramatic, increasing occipital signal power by several times for some subjects (on average 80%). MRI measurements showed that the occipital CSF layer between the brain and skull decreases by approximately 30% in thickness when a subject moves from prone to supine position. A multiple dipole model demonstrated that this can indeed lead to occipital EEG signal power increases in the same direction and order of magnitude as those observed here. These results suggest that future EEG studies should control for subjects'' posture, and that some studies may consider placing their subjects into the most favorable position for the experiment. These findings also imply that special consideration should be given to EEG measurements from subjects with brain atrophy due to normal aging or neurodegenerative diseases, since the resulting increase in CSF layer thickness could profoundly decrease scalp potential measurements. [Copyright &y& Elsevier]

Traditional analysis methods for single-trial classification of electro-encephalography (EEG) focus on two types of paradigms: phase-locked methods, in which the amplitude of the signal is used as the feature for classification, that is, event related potentials; and second-order methods, in which the feature of interest is the power of the signal, that is, event related (de)synchronization. The process of deciding which paradigm to use is ad hoc and is driven by assumptions regarding the underlying neural generators. Here we propose a method that provides an unified framework for the analysis of EEG, combining first and second-order spatial and temporal features based on a bilinear model. Evaluation of the proposed method on simulated data shows that the technique outperforms state-of-the art techniques for single-trial classification for a broad range of signal-to-noise ratios. Evaluations on human EEG--including one benchmark data set from the Brain Computer Interface BCI) competition--show statistically significant gains in classification accuracy, with a reduction in overall classification error from 26%-28% to 19%. [ABSTRACT FROM AUTHOR]