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Objective: We aim to investigate the relationship between the core symptoms of autism, anxiety levels, and attention deficit hyperactivity disorder (ADHD) traits, and a non-autism-specific, neurophysiological metric, the Delta-Beta phase-amplitude coupling (PAC), extracted from the resting-state EEG for autistic and non-autistic populations across three different age groups (children, adolescents, and adults)., Methods: We analyze the eyes-open resting-state EEG of 371 individuals. We applied a phase de-biasing PAC algorithm expected to result in a more accurate PAC estimate than other PAC methodologies available in the literature., Results: In the adult group, we found a significant increase of the delta-beta PAC in the autistic subgroup who met the Autism Diagnostic Observation Schedule-2 (ADOS-2) Autism Diagnostic Interview-Revised (ADR-R) ADOS-2/ADI-R threshold compared to non-autistic individuals. The differences seem age-specific since we found no statistically significant differences in the children and adolescent populations. Moreover, we found a significant positive correlation with the restricted and repetitive behaviours score of the ADOS-2 diagnostic instrument and with ADHD hyperactivity/impulsivity in the entire autistic cohort., Conclusions: The neurophysiological differences we found only in the autistic individuals that meet the thresholds also point out the need for future studies that look for autistic neurodiverse subgroups beyond age., Significance: The delta-beta debiasing PAC (dPAC) may potentially serve as a severity biomarker in the autistic population., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

A significant amount of European basic and clinical neuroscience research includes the use of transcranial magnetic stimulation (TMS) and low intensity transcranial electrical stimulation (tES), mainly transcranial direct current stimulation (tDCS). Two recent changes in the EU regulations, the introduction of the Medical Device Regulation (MDR) (2017/745) and the Annex XVI have caused significant problems and confusions in the brain stimulation field. The negative consequences of the MDR for non-invasive brain stimulation (NIBS) have been largely overlooked and until today, have not been consequently addressed by National Competent Authorities, local ethical committees, politicians and by the scientific communities. In addition, a rushed bureaucratic decision led to seemingly wrong classification of NIBS products without an intended medical purpose into the same risk group III as invasive stimulators. Overregulation is detrimental for any research and for future developments, therefore researchers, clinicians, industry, patient representatives and an ethicist were invited to contribute to this document with the aim of starting a constructive dialogue and enacting positive changes in the regulatory environment., (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces., (© 2024. The Author(s).)

To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e.g. neuromodulatory regulation of spike-frequency adaptation during sleep-wake cycles or anesthetics). Using the Virtual Brain (TVB) environment to connect mean-field AdEx models, we have previously simulated the general properties of brain states, playing on spike-frequency adaptation, but have not yet performed detailed analyses of other parameters possibly also regulating transitions in brain-scale dynamics between different brain states. We performed a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing. We report a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the TVB-AdEx model reveals large-scale properties identified experimentally in sleep and anesthesia., (© 2023. The Author(s).)

Research has shown a prominent role for cortical hyperexcitability underlying aberrant perceptions, hallucinations, and distortions in human conscious experience - even in neurotypical groups. The rVLPFC has been identified as an important structure in mediating cognitive affective states / feeling conscious states. The current study examined the involvement of the rVLPFC in mediating cognitive affective states in those predisposed to aberrant experiences in the neurotypical population. Participants completed two trait-based measures: (i) the Cortical Hyperexcitability Index_II (CHi_II, a proxy measure of cortical hyperexcitability) and (ii) two factors from the Cambridge Depersonalisation Scale (CDS). An optimised 7-channel MtDCS montage for stimulation conditions (Anodal, Cathodal and Sham) was created targeting the rVLPFC in a single-blind study. At the end of each stimulation session, participants completed a body-threat task (BTAB) while skin conductance responses (SCRs) and psychological responses were recorded. Participants with signs of increasing cortical hyperexcitability showed significant suppression of SCRs in the Cathodal stimulation relative to the Anodal and sSham conditions. Those high on the trait-based measures of depersonalisation-like experiences failed to show reliable effects. Collectively, the findings suggest that baseline brain states can mediate the effects of neurostimulation which would be missed via sample level averaging and without appropriate measures for stratifying individual differences., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of clinical and research applications. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to time consuming and cost ineffective treatment development phases. We propose the combination of electroencephalography (EEG) and unsupervised learning for the stratification and prediction of individual responses to tDCS. A randomized, sham-controlled, double-blind crossover study design was conducted within a clinical trial for the development of pediatric treatments based on tDCS. The tDCS stimulation (sham and active) was applied either in the left dorsolateral prefrontal cortex or in the right inferior frontal gyrus. Following the stimulation session, participants performed 3 cognitive tasks to assess the response to the intervention: the Flanker Task, N-Back Task and Continuous Performance Test (CPT). We used data from 56 healthy children and adolescents to implement an unsupervised clustering approach that stratify participants based on their resting-state EEG spectral features before the tDCS intervention. We then applied a correlational analysis to characterize the clusters of EEG profiles in terms of participant's difference in the behavioral outcome (accuracy and response time) of the cognitive tasks when performed after a tDCS-sham or a tDCS-active session. Better behavioral performance following the active tDCS session compared to the sham tDCS session is considered a positive intervention response, whilst the reverse is considered a negative one. Optimal results in terms of validity measures was obtained for 4 clusters. These results show that specific EEG-based digital phenotypes can be associated to particular responses. While one cluster presents neurotypical EEG activity, the remaining clusters present non-typical EEG characteristics, which seem to be associated with a positive response. Findings suggest that unsupervised machine learning can be successfully used to stratify and eventually predict responses of individuals to a tDCS treatment., (© 2023. The Author(s).)

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry., Competing Interests: Declaration of Competing Interest Roser Sanchez-Todo, Edmundo Lopez-Sola, and Borja Mercadal work at Neuroelectrics, a company developing brain stimulation solutions. Giulio Ruffini works at and is a co-founder of Neuroelectrics., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

A topic of growing interest in computational neuroscience is the discovery of fundamental principles underlying global dynamics and the self-organization of the brain. In particular, the notion that the brain operates near criticality has gained considerable support, and recent work has shown that the dynamics of different brain states may be modeled by pairwise maximum entropy Ising models at various distances from a phase transition, i.e., from criticality. Here we aim to characterize two brain states (psychedelics-induced and placebo) as captured by functional magnetic resonance imaging (fMRI), with features derived from the Ising spin model formalism (system temperature, critical point, susceptibility) and from algorithmic complexity. We hypothesized, along the lines of the entropic brain hypothesis, that psychedelics drive brain dynamics into a more disordered state at a higher Ising temperature and increased complexity. We analyze resting state blood-oxygen-level-dependent (BOLD) fMRI data collected in an earlier study from fifteen subjects in a control condition (placebo) and during ingestion of lysergic acid diethylamide (LSD). Working with the automated anatomical labeling (AAL) brain parcellation, we first create "archetype" Ising models representative of the entire dataset (global) and of the data in each condition. Remarkably, we find that such archetypes exhibit a strong correlation with an average structural connectome template obtained from dMRI (r = 0.6). We compare the archetypes from the two conditions and find that the Ising connectivity in the LSD condition is lower than in the placebo one, especially in homotopic links (interhemispheric connectivity), reflecting a significant decrease of homotopic functional connectivity in the LSD condition. The global archetype is then personalized for each individual and condition by adjusting the system temperature. The resulting temperatures are all near but above the critical point of the model in the paramagnetic (disordered) phase. The individualized Ising temperatures are higher in the LSD condition than in the placebo condition (p = 9 × 10-5). Next, we estimate the Lempel-Ziv-Welch (LZW) complexity of the binarized BOLD data and the synthetic data generated with the individualized model using the Metropolis algorithm for each participant and condition. The LZW complexity computed from experimental data reveals a weak statistical relationship with condition (p = 0.04 one-tailed Wilcoxon test) and none with Ising temperature (r(13) = 0.13, p = 0.65), presumably because of the limited length of the BOLD time series. Similarly, we explore complexity using the block decomposition method (BDM), a more advanced method for estimating algorithmic complexity. The BDM complexity of the experimental data displays a significant correlation with Ising temperature (r(13) = 0.56, p = 0.03) and a weak but significant correlation with condition (p = 0.04, one-tailed Wilcoxon test). This study suggests that the effects of LSD increase the complexity of brain dynamics by loosening interhemispheric connectivity-especially homotopic links. In agreement with earlier work using the Ising formalism with BOLD data, we find the brain state in the placebo condition is already above the critical point, with LSD resulting in a shift further away from criticality into a more disordered state., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: authors with a Neuroelectrics affiliation (GR, GiD, DL-S and ND) work for a company creating brain stimulation solutions. GR is a co-founder of Neuroelectrics. The other authors have declared that no competing interests exist., (Copyright: © 2023 Ruffini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

An early, extensive, accurate, and cost-effective clinical diagnosis of neurocognitive disorders will have advantages for older people and their families, but also for the health and care systems sustainability and performance. BRAINCODE is a technology that assesses cognitive impairment in older people, differentiating normal from pathologic brain condition, based in an EEG biomarkers evaluation. This paper will address BRAINCODE's pilot design, which intends to validate its efficacy, to provide guidelines for future studies and to allow its integration on the SHAPES platform. It is expected that BRAINCODE confirms a regular clinical diagnosis and neuropsychologic tests to discriminate 'normal' from pathologic cognitive decline and differentiates mild cognitive impairment from dementia in older adults with/without subjective cognitive complains.

Transcranial direct current stimulation (tDCS) applied over the prefrontal cortex has been shown to improve behavioral responsiveness in patients with disorders of consciousness following severe brain injury, especially those in minimally conscious state (MCS). However, one potential barrier of clinical response to tDCS is the timing of stimulation with regard to the fluctuations of vigilance that characterize this population. Indeed, a previous study showed that the vigilance of MCS patients has periodic average cycles of 70 min (range 57-80 min), potentially preventing them to be in an optimal neural state to benefit from tDCS when applied randomly. To tackle this issue, we propose a new protocol to optimize the application of tDCS by selectively stimulating at high and low vigilance states. Electroencephalography (EEG) real-time spectral entropy will be used as a marker of vigilance and to trigger tDCS, in a closed-loop fashion. We will conduct a randomized controlled crossover clinical trial on 16 patients in prolonged MCS who will undergo three EEG-tDCS sessions 5 days apart (1. tDCS applied at high vigilance; 2. tDCS applied at low vigilance; 3. tDCS applied at a random moment). Behavioral effects will be assessed using the Coma Recovery Scale-Revised at baseline and right after the stimulations. EEG will be recorded throughout the session and for 30 min after the end of the stimulation. This unique and novel approach will provide patients' tailored treatment options, currently lacking in the field of disorders of consciousness., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Little is known about how transcranial alternating current stimulation (tACS) interacts with brain activity. Here, we investigate the effects of tACS using an intermittent tACS-EEG protocol and use, in addition to classical metrics, Lempel-Ziv-Welch complexity (LZW) to characterize the interactions between task, endogenous and exogenous oscillations. In a cross-over study, EEG was recorded from thirty participants engaged in a change-of-speed detection task while receiving multichannel tACS over the visual cortex at 10 Hz, 70 Hz and a control condition. In each session, tACS was applied intermittently during 5 s events interleaved with EEG recordings over multiple trials. We found that, with respect to control, stimulation at 10 Hz ([Formula: see text]) enhanced both [Formula: see text] and [Formula: see text] power, [Formula: see text]-LZW complexity and [Formula: see text] but not [Formula: see text] phase locking value with respect to tACS onset ([Formula: see text]-PLV, [Formula: see text]-PLV), and increased reaction time (RT). [Formula: see text] increased RT with little impact on other metrics. As trials associated with larger [Formula: see text]-power (and lower [Formula: see text]-LZW) predicted shorter RT, we argue that [Formula: see text] produces a disruption of functionally relevant fast oscillations through an increase in [Formula: see text]-band power, slowing behavioural responses and increasing the complexity of gamma oscillations. Our study highlights the complex interaction between tACS and endogenous brain dynamics, and suggests the use of algorithmic complexity inspired metrics to characterize cortical dynamics in a behaviorally relevant timescale.

Several decades of research suggest that weak electric fields may influence neural processing, including those induced by neuronal activity and proposed as a substrate for a potential new cellular communication system, i.e., ephaptic transmission. Here we aim to model mesoscopic ephaptic activity in the human brain and explore its trajectory during aging by characterizing the electric field generated by cortical dipoles using realistic finite element modeling. Extrapolating from electrophysiological measurements, we first observe that modeled endogenous field magnitudes are comparable to those in measurements of weak but functionally relevant self-generated fields and to those produced by noninvasive transcranial brain stimulation, and therefore possibly able to modulate neuronal activity. Then, to evaluate the role of these fields in the human cortex in large MRI databases, we adapt an interaction approximation that considers the relative orientation of neuron and field to estimate the membrane potential perturbation in pyramidal cells. We use this approximation to define a simplified metric (EMOD1) that weights dipole coupling as a function of distance and relative orientation between emitter and receiver and evaluate it in a sample of 401 realistic human brain models from healthy subjects aged 16-83. Results reveal that ephaptic coupling, in the simplified mesoscopic modeling approach used here, significantly decreases with age, with higher involvement of sensorimotor regions and medial brain structures. This study suggests that by providing the means for fast and direct interaction between neurons, ephaptic modulation may contribute to the complexity of human function for cognition and behavior, and its modification across the lifespan and in response to pathology., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: GR is cofounder of Neuroelectrics; RS and RS-T work at Neuroelectrics, a company that designs and produces EEG and tES solutions.

Objective: The objective of this study was to test the feasibility of a combined intervention involving transcranial direct current stimulation (tDCS) on the dorsolateral prefrontal cortex (dlPFC) and cognitive training (CT). Short-term effects on food consumption, cognition, endocannabinoid (eCB) levels, and electroencephalogram (EEG) markers of future weight loss were explored., Methods: Eighteen healthy volunteers with morbid obesity were randomized in a double-blind, placebo-controlled, parallel trial. Participants received sham or active tDCS plus CT for four consecutive days. Cognitive performance, daily food intake, and eCB blood samples were collected before and after the intervention; EEG data were gathered before and after daily training., Results: The active tDCS + CT group reversed left-dominant frontal asymmetry and increased frontal coherence (FC) in the γ-band (30-45 Hz) after the intervention. The strength of the latter predicted BMI reduction. Additionally, a large intervention effect on food intake was shown in the active tDCS + CT group at follow-up (-339.6 ± 639 kcal on average), and there was a decrease of plasma eCB concentrations., Conclusions: dlPFC modulation through tDCS + CT is an effective tool to restore right dominance of the dlPFC and enhance FC in patients with morbid obesity. Moreover, the effect of the strength of FC on BMI suggests that the interhemispheric FC at the dlPFC is functionally relevant for the efficient regulation of food choice., (© 2020 The Obesity Society.)

Background: Transcranial direct current stimulation (tDCS) may promote the recovery of severely brain-injured patients with disorders of consciousness (DOC). Prior tDCS studies targeted single brain regions rather than brain networks critical for consciousness recovery., Objective: Investigate the behavioral and electrophysiological effects of multifocal tDCS applied over the frontoparietal external awareness network in patients with chronic acquired DOC., Methods: Forty-six patients were included in this randomized double-blind sham-controlled crossover trial (median [interquartile range]: 46 [35 - 59] years old; 12 [5 - 47] months post injury; 17 unresponsive wakefulness syndrome, 23 minimally conscious state (MCS) and 6 emerged from the MCS). Multifocal tDCS was applied for 20 min using 4 anodes and 4 cathodes with 1 mA per electrode. Coma Recovery Scale-Revised (CRS-R) assessment and 10 min of resting state electroencephalogram (EEG) recordings were acquired before and after the active and sham sessions., Results: At the group level, there was no tDCS behavioral treatment effect. However, following active tDCS, the EEG complexity significantly increased in low frequency bands (1-8 Hz). CRS-R total score improvement was associated with decreased baseline complexity in those bands. At the individual level, after active tDCS, new behaviors consistent with conscious awareness emerged in 5 patients. Conversely, 3 patients lost behaviors consistent with conscious awareness., Conclusion: The behavioral effect of multifocal frontoparietal tDCS varies across patients with DOC. Electrophysiological changes were observed in low frequency bands but not translated into behavioral changes at the group level., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

REM Behavior Disorder (RBD) is now recognized as the prodromal stage of α-synucleinopathies such as Parkinson's disease (PD). In this paper, we describe deep learning models for diagnosis/prognosis derived from a few minutes of eyes-closed resting electroencephalography data (EEG) collected at baseline from idiopathic RBD patients ( n = 121) and healthy controls (HC, n = 91). A few years after the EEG acquisition (4±2 years), a subset of the RBD patients were eventually diagnosed with either PD ( n = 14) or Dementia with Lewy bodies (DLB, n = 13), while the rest remained idiopathic RBD. We describe first a simple deep convolutional neural network (DCNN) with a five-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG spectrograms from idiopathic patients and healthy controls. We treat the data as in audio or image classification problems where deep networks have proven successful by exploiting invariances and compositional features in the data. For comparison, we study a simple deep recurrent neural network (RNN) model using three stacked Long Short Term Memory network (LSTM) cells or gated-recurrent unit (GRU) cells-with very similar results. The performance of these networks typically reaches 80% (±1%) classification accuracy in the balanced HC vs. PD-conversion classification problem. In particular, using data from the best single EEG channel, we obtain an area under the curve (AUC) of 87% (±1%)-while avoiding spectral feature selection. The trained classifier can also be used to generate synthetic spectrograms using the DeepDream algorithm to study what time-frequency features are relevant for classification. We find these to be bursts in the theta band together with a decrease of bursting in the alpha band in future RBD converters (i.e., converting to PD or DLB in the follow up) relative to HCs. From this first study, we conclude that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, offering physiological insights and enabling the identification of clinically relevant biomarkers.

State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that is produced by a repetitive visual stimulus. SSVEPs have long been considered a steady-state response resulting from purely oscillatory components phase locked with the stimulation source, matching the stimulation frequency and its harmonics. Here we explore the dynamical character of the SSVEP response by proposing a novel non-stationary methodology for SSVEP detection based on an ensemble of Echo State Networks (ESN). The performance of this dynamical approach is compared to stationary canonical correlation analysis (CCA) for the detection of 6 visual stimulation frequencies ranging from 12 to 22 Hz. ESN-based methodology outperformed CCA, achieving an average information transfer rate of 47 bits/minute when simulating a BCI system of 6 degrees of freedom. However, for some subjects and stimulation frequencies the detection accuracy of CCA exceeds that of ESN. The comparison suggests that each methodology captures different features of the SSVEP response: while CCA captures purely stationary patterns, the ESN-based approach presented here is capable of detecting the non-stationary nature of the SSVEP., Competing Interests: The authors have the following interests: This study was funded in part by Starlab Barcelona. Co-authors D. Ibáñez-Soria, A. Soria-Frisch and G. Ruffini were employed by Starlab Barcelona during these studies. Co-author G. Ruffini is employed by Neuroelectrics Corporation. Authors affiliated to Starlab or Neuroelectrics have a commercial interest in promoting the EEG system used in the paper (Enobio). This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

Idiopathic rapid eye movement sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinson's disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis. We analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from ~ 3 min recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression. Complexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels. Poor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude algorithmic coupling. Algorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.

Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved with unsynchronized ones. Correctly tuned, echo state networks are able to efficiently discriminate between unsynchronized and synchronized sequences even in the presence of relatively high levels of noise. Compared to other state-of-the-art techniques of synchronization detection, the online capabilities of the proposed Echo State Network based methodology make it a promising choice for real-time applications aiming to monitor dynamical synchronization changes in continuous signals.

Transcranial direct current stimulation (tDCS) is currently being used for research and treatment of some neurological and neuropsychiatric disorders, as well as for improvement of cognitive functions. In order to better understand cerebral response to the stimulation and to redefine protocols and dosage, its effects must be monitored. To this end, we have used functional diffuse correlation spectroscopy (fDCS) and time-resolved functional near-infrared spectroscopy (TR-fNIRS) together with electroencephalography (EEG) during and after stimulation of the frontal cortex. Twenty subjects participated in two sessions of stimulation with two different polarity montages and twelve also underwent a sham session. Cerebral blood flow and oxyhemoglobin concentration increased during and after active stimulation in the region under the stimulation electrode while deoxyhemoglobin concentration decreased. The EEG spectrum displayed statistically significant power changes across different stimulation sessions in delta (2 to 4 Hz), theta (4 to 8 Hz), and beta (12 to 18 Hz) bands. Results suggest that fDCS and TR-fNIRS can be employed as neuromonitors of the effects of transcranial electrical stimulation and can be used together with EEG.

Providing objective metrics of conscious state is of great interest across multiple research and clinical fields-from neurology to artificial intelligence. Here we approach this challenge by proposing plausible mechanisms for the phenomenon of structured experience. In earlier work, we argued that the experience we call reality is a mental construct derived from information compression. Here we show that algorithmic information theory provides a natural framework to study and quantify consciousness from neurophysiological or neuroimaging data, given the premise that the primary role of the brain is information processing. We take as an axiom that "there is consciousness" and focus on the requirements for structured experience: we hypothesize that the existence and use of compressive models by cognitive systems, e.g. in biological recurrent neural networks, enables and provides the structure to phenomenal experience. Self-awareness is seen to arise naturally (as part of a better model) in cognitive systems interacting bidirectionally with the external world. Furthermore, we argue that by running such models to track data, brains can give rise to apparently complex (entropic but hierarchically organized) data. We compare this theory, named KT for its basis on the mathematical theory of Kolmogorov complexity, to other information-centric theories of consciousness. We then describe methods to study the complexity of the brain's output streams or of brain state as correlates of conscious state: we review methods such as (i) probing the brain through its input streams (e.g. event-related potentials in oddball paradigms or mutual algorithmic information between world and brain), (ii) analyzing spontaneous brain state, (iii) perturbing the brain by non-invasive transcranial stimulation, and (iv) quantifying behavior (e.g. eye movements or body sway).

Scientists and clinicians have traditionally targeted single brain regions with stimulation to modulate brain function and disease. However, brain regions do not operate in isolation, but interact with other regions through networks. As such, stimulation of one region may impact and be impacted by other regions in its network. Here we test whether the effects of brain stimulation can be enhanced by simultaneously targeting a region and its network, identified with resting state functional connectivity MRI. Fifteen healthy participants received two types of transcranial direct current stimulation (tDCS): a traditional two-electrode montage targeting a single brain region (left primary motor cortex [M1]) and a novel eight-electrode montage targeting this region and its associated resting state network. As a control, 8 participants also received multifocal tDCS mismatched to this network. Network-targeted tDCS more than doubled the increase in left M1 excitability over time compared to traditional tDCS and the multifocal control. Modeling studies suggest these results are unlikely to be due to tDCS effects on left M1 itself, however it is impossible to completely exclude this possibility. It also remains unclear whether multifocal tDCS targeting a network selectively modulates this network and which regions within the network are most responsible for observed effects. Despite these limitations, network-targeted tDCS appears to be a promising approach for enhancing tDCS effects beyond traditional stimulation targeting a single brain region. Future work is needed to test whether these results extend to other resting state networks and enhance behavioral or therapeutic effects., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.

Stroke is a leading cause of serious long-term disability worldwide. Functional outcome depends on stroke location, severity, and early intervention. Conventional rehabilitation strategies have limited effectiveness, and new treatments still fail to keep pace, in part due to a lack of understanding of the different stages in brain recovery and the vast heterogeneity in the poststroke population. Innovative methodologies for restorative neurorehabilitation are required to reduce long-term disability and socioeconomic burden. Neuroplasticity is involved in poststroke functional disturbances and also during rehabilitation. Tackling poststroke neuroplasticity by non-invasive brain stimulation is regarded as promising, but efficacy might be limited because of rather uniform application across patients despite individual heterogeneity of lesions, symptoms, and other factors. Transcranial direct current stimulation (tDCS) induces and modulates neuroplasticity, and has been shown to be able to improve motor and cognitive functions. tDCS is suited to improve poststroke rehabilitation outcomes, but effect sizes are often moderate and suffer from variability. Indeed, the location, extent, and pattern of functional network connectivity disruption should be considered when determining the optimal location sites for tDCS therapies. Here, we present potential opportunities for neuroimaging-guided tDCS-based rehabilitation strategies after stroke that could be personalized. We introduce innovative multimodal intervention protocols based on multichannel tDCS montages, neuroimaging methods, and real-time closed-loop systems to guide therapy. This might help to overcome current treatment limitations in poststroke rehabilitation and increase our general understanding of adaptive neuroplasticity leading to neural reorganization after stroke.

The use of brain-derived signals for controlling external devices has long attracted the attention from neuroscientists and engineers during last decades. Although much effort has been dedicated to establishing effective brain-to-computer communication, computer-to-brain communication feedback for "closing the loop" is now becoming a major research theme. While intracortical microstimulation of the sensory cortex has already been successfully used for this purpose, its future application in humans partly relies on the use of non-invasive brain stimulation technologies. In the present study, we explore the potential use of transcranial alternating-current stimulation (tACS) for synthetic tactile perception in alert behaving animals. More specifically, we determined the effects of tACS on sensory local field potentials (LFPs) and motor output and tested its capability for inducing tactile perception using classical eyeblink conditioning in the behaving animal. We demonstrated that tACS of the primary somatosensory cortex vibrissa area could indeed substitute natural stimuli during training in the associative learning paradigm.

Objective: To investigate the effects of anodal transcranial direct current stimulation (a-tDCS) intensity on corticospinal excitability and affected muscle activation in individuals with chronic spinal cord injury (SCI)., Design: Single-blind, randomized, sham-controlled, crossover study., Setting: Medical research institute and rehabilitation hospital., Participants: Volunteers (N = 9) with chronic SCI and motor dysfunction in wrist extensor muscles., Interventions: Three single session exposures to 20 minutes of a-tDCS (anode over the extensor carpi radialis [ECR] muscle representation on the left primary motor cortex, cathode over the right supraorbital area) using 1 mA, 2 mA, or sham stimulation, delivered at rest, with at least 1 week between sessions., Main Outcome Measures: Corticospinal excitability was assessed with motor-evoked potentials (MEPs) from the ECR muscle using surface electromyography after transcranial magnetic stimulation. Changes in spinal excitability, sensory threshold, and muscle strength were also investigated., Results: Mean MEP amplitude significantly increased by approximately 40% immediately after 2mA a-tDCS (pre: 0.36 ± 0.1 mV; post: 0.47 ± 0.11 mV; P = .001), but not with 1 mA or sham. Maximal voluntary contraction measures remained unaltered across all conditions. Sensory threshold significantly decreased over time after 1mA (P = .002) and 2mA (P = .039) a-tDCS and did not change with sham. F-wave persistence showed a nonsignificant trend for increase (pre: 32% ± 12%; post: 41% ± 10%; follow-up: 46% ± 12%) after 2 mA stimulation. No adverse effects were reported with any of the experimental conditions., Conclusions: The a-tDCS can transiently raise corticospinal excitability to affected muscles in patients with chronic SCI after 2 mA stimulation. Sensory perception can improve with both 1 and 2 mA stimulation. This study gives support to the safe and effective use of a-tDCS using small electrodes in patients with SCI and highlights the importance of stimulation intensity., (Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Background: The International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st) Project is a population-based, longitudinal study describing early growth and development in an optimally healthy cohort of 4607 mothers and newborns. At 24 months, children are assessed for neurodevelopmental outcomes with the INTERGROWTH-21st Neurodevelopment Package. This paper describes neurodevelopment tools for preschoolers and the systematic approach leading to the development of the Package., Methods: An advisory panel shortlisted project-specific criteria (such as multi-dimensional assessments and suitability for international populations) to be fulfilled by a neurodevelopment instrument. A literature review of well-established tools for preschoolers revealed 47 candidates, none of which fulfilled all the project's criteria. A multi-dimensional assessment was, therefore, compiled using a package-based approach by: (i) categorizing desired outcomes into domains, (ii) devising domain-specific criteria for tool selection, and (iii) selecting the most appropriate measure for each domain., Results: The Package measures vision (Cardiff tests); cortical auditory processing (auditory evoked potentials to a novelty oddball paradigm); and cognition, language skills, behavior, motor skills and attention (the INTERGROWTH-21st Neurodevelopment Assessment) in 35-45 minutes. Sleep-wake patterns (actigraphy) are also assessed. Tablet-based applications with integrated quality checks and automated, wireless electroencephalography make the Package easy to administer in the field by non-specialist staff. The Package is in use in Brazil, India, Italy, Kenya and the United Kingdom., Conclusions: The INTERGROWTH-21st Neurodevelopment Package is a multi-dimensional instrument measuring early child development (ECD). Its developmental approach may be useful to those involved in large-scale ECD research and surveillance efforts.

Human sensory and motor systems provide the natural means for the exchange of information between individuals, and, hence, the basis for human civilization. The recent development of brain-computer interfaces (BCI) has provided an important element for the creation of brain-to-brain communication systems, and precise brain stimulation techniques are now available for the realization of non-invasive computer-brain interfaces (CBI). These technologies, BCI and CBI, can be combined to realize the vision of non-invasive, computer-mediated brain-to-brain (B2B) communication between subjects (hyperinteraction). Here we demonstrate the conscious transmission of information between human brains through the intact scalp and without intervention of motor or peripheral sensory systems. Pseudo-random binary streams encoding words were transmitted between the minds of emitter and receiver subjects separated by great distances, representing the realization of the first human brain-to-brain interface. In a series of experiments, we established internet-mediated B2B communication by combining a BCI based on voluntary motor imagery-controlled electroencephalographic (EEG) changes with a CBI inducing the conscious perception of phosphenes (light flashes) through neuronavigated, robotized transcranial magnetic stimulation (TMS), with special care taken to block sensory (tactile, visual or auditory) cues. Our results provide a critical proof-of-principle demonstration for the development of conscious B2B communication technologies. More fully developed, related implementations will open new research venues in cognitive, social and clinical neuroscience and the scientific study of consciousness. We envision that hyperinteraction technologies will eventually have a profound impact on the social structure of our civilization and raise important ethical issues.

Recently, multifocal transcranial current stimulation (tCS) devices using several relatively small electrodes have been used to achieve more focal stimulation of specific cortical targets. However, it is becoming increasingly recognized that many behavioral manifestations of neurological and psychiatric disease are not solely the result of abnormality in one isolated brain region but represent alterations in brain networks. In this paper we describe a method for optimizing the configuration of multifocal tCS for stimulation of brain networks, represented by spatially extended cortical targets. We show how, based on fMRI, PET, EEG or other data specifying a target map on the cortical surface for excitatory, inhibitory or neutral stimulation and a constraint on the maximal number of electrodes, a solution can be produced with the optimal currents and locations of the electrodes. The method described here relies on a fast calculation of multifocal tCS electric fields (including components normal and tangential to the cortical boundaries) using a five layer finite element model of a realistic head. Based on the hypothesis that the effects of current stimulation are to first order due to the interaction of electric fields with populations of elongated cortical neurons, it is argued that the optimization problem for tCS stimulation can be defined in terms of the component of the electric field normal to the cortical surface. Solutions are found using constrained least squares to optimize current intensities, while electrode number and their locations are selected using a genetic algorithm. For direct current tCS (tDCS) applications, we provide some examples of this technique using an available tCS system providing 8 small Ag/AgCl stimulation electrodes. We demonstrate the approach both for localized and spatially extended targets defined using rs-fcMRI and PET data, with clinical applications in stroke and depression. Finally, we extend these ideas to more general stimulation protocols, such as alternating current tCS (tACS)., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Modeling and rendering realistic ocean scenes have been thoroughly investigated for many years. Its appearance has been studied and it is possible to find very detailed simulations where a high degree of realism is achieved. Nevertheless, among the solutions to ocean rendering, real-time management of the huge heightmaps that are necessary for rendering an ocean scene is still not solved. We propose a new technique for simulating the ocean surface on GPU. This technique is capable of offering view-dependent approximations of the mesh while maintaining coherence among the extracted approximations. This feature is very important as most solutions previously presented must retessellate from the initial mesh. Our solution is able to use the latest extracted approximation when refining or coarsening the mesh.

Virtual Reality (VR) is increasingly being used in combination with psycho-physiological measures to improve assessment of distress in mental health research and therapy. However, the analysis and interpretation of multiple physiological measures is time consuming and requires specific skills, which are not available to most clinicians. To address this issue, we designed and developed a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments. The DSS integrates different biosensor data (ECG, breathing rate, EEG) and behavioral data (body gestures correlated with stress), following a training process in which self-rated and clinical-rated stress levels are used as ground truth. Detected stress events for each VR session are reported to the therapist as an aggregated value (ranging from 0 to 1) and graphically displayed on a diagram accessible by the therapist through a web-based interface.

In this paper, we provide a broad overview of models and technologies pertaining to transcranial current brain stimulation (tCS), a family of related noninvasive techniques including direct current (tDCS), alternating current (tACS), and random noise current stimulation (tRNS). These techniques are based on the delivery of weak currents through the scalp (with electrode current intensity to area ratios of about 0.3-5 A/m2) at low frequencies (typically < 1 kHz) resulting in weak electric fields in the brain (with amplitudes of about 0.2-2 V/m). Here we review the biophysics and simulation of noninvasive, current-controlled generation of electric fields in the human brain and the models for the interaction of these electric fields with neurons, including a survey of in vitro and in vivo related studies. Finally, we outline directions for future fundamental and technological research.

In this work we describe the performance evaluation of a system for stress detection. The analysed data is acquired by following an experimental protocol designed to induce cognitive stress to the subjects. The experimental set-up included the recording of electroencephalography (EEG) and facial (corrugator and zygomatic) electromyography (EMG). In a preliminary analysis we are able to correlate EEG features (alpha asymmetry and alpha/beta ratio using only 3 channels) with the stress level of the subjects statistically (by using averages over subjects) but also on a subject-to-subject basis by using computational intelligence techniques reaching classification rates up to 79% when classifying 3 minutes takes. On a second step, we apply fusion techniques to the overall multi-modal feature set fusing the formerly mentioned EEG features with EMG energy. We show that the results improve significantly providing a more robust stress index every second. Given the achieved performance the system described in this work can be successfully applied for stress therapy when combined with virtual reality.

This paper presents the results of the first human trials with the ENOBIO electrophysiology electrode prototype plus the initial results of a new wireless prototype with flexible electrodes based on the same platform. The results indicate that a dry active electrode that employs a CNT array as the electrode interface can perform on a par with traditional "wet" electrodes for the recording of EEG, ECG, EOG and EMG. We also demonstrate a new platform combining wireless technology plus flexible electrodes for improved comfort for applications that take advantage of the dry electrode concept.

We describe the development and first tests of ENOBIO, a dry electrode sensor concept for biopotential applications. In the proposed electrodes the tip of the electrode is covered with a forest of multi-walled carbon nanotubes (CNTs) that can be coated with Ag/AgCl to provide ionic-electronic transduction. The CNT brush-like structure is to penetrate the outer layers of the skin improving electrical contact as well as increase the contact surface area. In this paper we report the results of the first tests of this concept--immersion on saline solution and pig skin signal detection. These indicate performance on a par with state of the art research-oriented wet electrodes.