How does the electric field induced by tDCS influence motor-related connectivity? Model-guided perspectives

Over the last decade, transcranial direct current stimulation (tDCS) has been applied not only to modulate local cortical activation, but also to address communication between functionally-related brain areas. Stimulation protocols based on simple two-electrode placements are being replaced by multi-electrode montages to target intra- and inter-hemispheric neural networks using multichannel/high definition paradigms.Objective. This study aims to investigate the characteristics of electric field (EF) patterns originated by tDCS experiments addressing changes in functional brain connectivity.Methods. A previous selection of tDCS experimental studies aiming to modulate motor-related connectivity in health and disease was conducted. Simulations of the EF induced in the cortex were then performed for each protocol selected. The EF magnitude and orientation are determined and analysed in motor-related cortical regions for five different head models to account for inter-subject variability. Functional connectivity outcomes obtained are qualitatively analysed at the light of the simulated EF and protocol characteristics, such as electrode position, number and stimulation dosing.Main findings. The EF magnitude and orientation predicted by computational models can be related with the ability of tDCS to modulate brain functional connectivity. Regional differences in EF distributions across subjects can inform electrode placements more susceptible to inter-subject variability in terms of brain connectivity-related outcomes.Significance. Neuronal facilitation/inhibition induced by tDCS fields may indirectly influence intra and inter-hemispheric connectivity by modulating neural components of motor-related networks. Optimization of tDCS using computational models is essential for adequate dosing delivery in specific networks related to clinically relevant connectivity outcomes.

The frontooccipital interaction mechanism of high-frequency acoustoelectric signal

Based on acoustoelectric effect, acoustoelectric brain imaging has been proposed, which is a high spatiotemporal resolution neural imaging method. At the focal spot, brain electrical activity is encoded by focused ultrasound, and corresponding high-frequency acoustoelectric signal is generated. Previous studies have revealed that acoustoelectric signal can also be detected in other non-focal brain regions. However, the processing mechanism of acoustoelectric signal between different brain regions remains sparse. Here, with acoustoelectric signal generated in the left primary visual cortex, we investigated the spatial distribution characteristics and temporal propagation characteristics of acoustoelectric signal in the transmission. We observed a strongest transmission strength within the frontal lobe, and the global temporal statistics indicated that the frontal lobe features in acoustoelectric signal transmission. Then, cross-frequency phase-amplitude coupling was used to investigate the coordinated activity in the AE signal band range between frontal and occipital lobes. The results showed that intra-structural cross-frequency coupling and cross-structural coupling co-occurred between these two lobes, and, accordingly, high-frequency brain activity in the frontal lobe was effectively coordinated by distant occipital lobe. This study revealed the frontooccipital long-range interaction mechanism of acoustoelectric signal, which is the foundation of improving the performance of acoustoelectric brain imaging.

Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system.Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model.Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals' local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices.Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.

A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-Computer Interfaces Assisted Rehabilitation

It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in [Formula: see text], revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.

Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking

Introduction

Brain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challenges that classical processing techniques do not solve, showing certain shortcomings in the real-time domain. Recent advances in deep-learning techniques open a window of opportunity to solve some of these problems. In this work, an interface able to detect the evoked potential that occurs when a person intends to stop due to the appearance of an unexpected obstacle has been developed.

Material and methods

First, the interface was tested on a treadmill with five subjects, in which the user stopped when an obstacle appeared (simulated by a laser). The analysis is based on two consecutive convolutional networks: the first one to discern the intention to stop against normal walking and the second one to correct false detections of the previous one.

Results and discussion

The results were superior when using the methodology of the two consecutive networks vs. only the first one in a cross-validation pseudo-online analysis. The false positives per min (FP/min) decreased from 31.8 to 3.9 FP/min and the number of repetitions in which there were no false positives and true positives (TP) improved from 34.9% to 60.3% NOFP/TP. This methodology was tested in a closed-loop experiment with an exoskeleton, in which the brain-machine interface (BMI) detected an obstacle and sent the command to the exoskeleton to stop. This methodology was tested with three healthy subjects, and the online results were 3.8 FP/min and 49.3% NOFP/TP. To make this model feasible for non-able bodied patients with a reduced and manageable time frame, transfer-learning techniques were applied and validated in the previous tests, and were then applied to patients. The results for two incomplete Spinal Cord Injury (iSCI) patients were 37.9% NOFP/TP and 7.7 FP/min.

Attention-Based Convolutional Recurrent Deep Neural Networks for the Prediction of Response to Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder

Repetitive Transcranial Magnetic Stimulation (rTMS) is proposed as an effective treatment for major depressive disorder (MDD). However, because of the suboptimal treatment outcome of rTMS, the prediction of response to this technique is a crucial task. We developed a deep learning (DL) model to classify responders (R) and non-responders (NR). With this aim, we assessed the pre-treatment EEG signal of 34 MDD patients and extracted effective connectivity (EC) among all electrodes in four frequency bands of EEG signal. Two-dimensional EC maps are put together to create a rich connectivity image and a sequence of these images is fed to the DL model. Then, the DL framework was constructed based on transfer learning (TL) models which are pre-trained convolutional neural networks (CNN) named VGG16, Xception, and EfficientNetB0. Then, long short-term memory (LSTM) cells are equipped with an attention mechanism added on top of TL models to fully exploit the spatiotemporal information of EEG signal. Using leave-one subject out cross validation (LOSO CV), Xception-BLSTM-Attention acquired the highest performance with 98.86% of accuracy and 97.73% of specificity. Fusion of these models as an ensemble model based on optimized majority voting gained 99.32% accuracy and 98.34% of specificity. Therefore, the ensemble of TL-LSTM-Attention models can predict accurately the treatment outcome.

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

Closed-Loop Construction and Analysis of Cortico-Muscular-Cortical Functional Network After Stroke

Brain networks allow a topological understanding into the pathophysiology of stroke-induced motor deficits, and have been an influential tool for investigating brain functions. Unfortunately, currently applied methods generally lack in the recognition of the dynamic changes in the cortical networks related to muscle activity, which is crucial to clarify the alterations of the cooperative working patterns in the motor control system after stroke. In this study, we integrate corticomuscular and intermuscular interactions to cortico-cortical network and propose a novel closed-loop construction of cortico-muscular-cortical functional network, named closed-loop network (CLN). Directional characteristic in terms of differentiating causal interactions is endowed on basis of the CLN framework, further expanding the definition of functional connectivity (FC) and effective connectivity (EC) dedicated to CLN. Next, CLN is applied to stroke patients to reveal the underlying after-effects mechanism of low frequency repetitive transcranial magnetic stimulation (rTMS) induced alterations of cortical physiologic functions during movement. Results show that the short-term modulation of rTMS is reflected in the enhancement of information interaction within the interhemispheric primary motor regions and inhibition of the coupling between motor cortex and effector muscles. CLN provides a new perspective for the study of motor-related cortical networks with muscle activities involvement instead of being restricted to brain network analysis of behaviors.

Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity

Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.

Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum

This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications. The aim of this work is to analyze the specific characteristics of lower-limb stimulation, identifying the strengths and weaknesses of these works and framing them with the current knowledge of tDCS. The ultimate goal of this work is to propose areas of improvement to create more effective stimulation therapies with less variability.

tACS facilitates flickering driving by boosting steady-state visual evoked potentials

Objective.There has become of increasing interest in transcranial alternating current stimulation (tACS) since its inception nearly a decade ago. tACS in modulating brain state is an active area of research and has been demonstrated effective in various neuropsychological and clinical domains. In the visual domain, much effort has been dedicated to brain rhythms and rhythmic stimulation, i.e. tACS. However, less is known about the interplay between the rhythmic stimulation and visual stimulation.Approach.Here, we used steady-state visual evoked potential (SSVEP), induced by flickering driving as a widely used technique for frequency-tagging, to investigate the aftereffect of tACS in healthy human subjects. Seven blocks of 64-channel electroencephalogram were recorded before and after the administration of 20min 10Hz tACS, while subjects performed several blocks of SSVEP tasks. We characterized the physiological properties of tACS aftereffect by comparing and validating the temporal, spatial, spatiotemporal and signal-to-noise ratio (SNR) patterns between and within blocks in real tACS and sham tACS.Main results.Our result revealed that tACS boosted the 10Hz SSVEP significantly. Besides, the aftereffect on SSVEP was mitigated with time and lasted up to 5 min.Significance.Our results demonstrate the feasibility of facilitating the flickering driving by external rhythmic stimulation and open a new possibility to alter the brain state in a direction by noninvasive transcranial brain stimulation.

Brain Networks Modulation in Young and Old Subjects During Transcranial Direct Current Stimulation Applied on Prefrontal and Parietal Cortex

Evidence indicates that the transcranial direct current stimulation (tDCS) has the potential to transiently modulate cognitive function, including age-related changes in brain performance. Only a small number of studies have explored the interaction between the stimulation sites on the scalp, task performance, and brain network connectivity within the frame of physiological aging. We aimed to evaluate the spread of brain activation in both young and older adults in response to anodal tDCS applied to two different scalp stimulation sites: Prefrontal cortex (PFC) and posterior parietal cortex (PPC). EEG data were recorded during tDCS stimulation and evaluated using the Small World (SW) index as a graph theory metric. Before and after tDCS, participants performed a behavioral task; a performance accuracy index was computed and correlated with the SW index. Results showed that the SW index increased during tDCS of the PPC compared to the PFC at higher EEG frequencies only in young participants. tDCS at the PPC site did not exert significant effects on the performance, while tDCS at the PFC site appeared to influence task reaction times in the same direction in both young and older participants. In conclusion, studies using tDCS to modulate functional connectivity and influence behavior can help identify suitable protocols for the aging brain.

Control of Transcranial Direct Current Stimulation Duration by Assessing Functional Connectivity of Near-Infrared Spectroscopy Signals

Transcranial direct current stimulation (tDCS) has been shown to create neuroplasticity in healthy and diseased populations. The control of stimulation duration by providing real-time brain state feedback using neuroimaging is a topic of great interest. This study presents the feasibility of a closed-loop modulation for the targeted functional network in the prefrontal cortex. We hypothesize that we cannot improve the brain state further after reaching a specific state during a stimulation therapy session. A high-definition tDCS of 1[Formula: see text]mA arranged in a ring configuration was applied at the targeted right prefrontal cortex of 15 healthy male subjects for 10[Formula: see text]min. Functional near-infrared spectroscopy was used to monitor hemoglobin chromophores during the stimulation period continuously. The correlation matrices obtained from filtered oxyhemoglobin were binarized to form subnetworks of short- and long-range connections. The connectivity in all subnetworks was analyzed individually using a new quantification measure of connectivity percentage based on the correlation matrix. The short-range network in the stimulated hemisphere showed increased connectivity in the initial stimulation phase. However, the increase in connection density reduced significantly after 6[Formula: see text]min of stimulation. The short-range network of the left hemisphere and the long-range network gradually increased throughout the stimulation period. The connectivity percentage measure showed a similar response with network theory parameters. The connectivity percentage and network theory metrics represent the brain state during the stimulation therapy. The results from the network theory metrics, including degree centrality, efficiency, and connection density, support our hypothesis and provide a guideline for feedback on the brain state. The proposed neuro-feedback scheme is feasible to control the stimulation duration to avoid overdosage.

Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI

One of the primary objectives of the brain-computer interface (BCI) is to obtain a command with higher classification accuracy within the shortest possible time duration. Therefore, this study evaluates several stimulation durations to propose a duration that can yield the highest classification accuracy. Furthermore, this study aims to address the inherent delay in the hemodynamic responses (HRs) for the command generation time. To this end, HRs in the sensorimotor cortex were evaluated for the functional near-infrared spectroscopy (fNIRS)-based BCI. To evoke brain activity, right-hand-index finger poking and tapping tasks were used. In this study, six different stimulation durations (i.e., 1, 3, 5, 7, 10, and 15 s) were tested on 10 healthy male subjects. Upon stimulation, different temporal features and multiple time windows were utilized to extract temporal features. The extracted features were then classified using linear discriminant analysis. The classification results using the main HR showed that a 5 s stimulation duration could yield the highest classification accuracy, i.e., 74%, with a combination of the mean and maximum value features. However, the results were not significantly different from the classification accuracy obtained using the 15 s stimulation. To further validate the results, a classification using the initial dip was performed. The results obtained endorsed the finding with an average classification accuracy of 73.5% using the features of minimum peak and skewness in the 5 s window. The results based on classification using the initial dip for 5 s were significantly different from all other tested stimulation durations (p < 0.05) for all feature combinations. Moreover, from the visual inspection of the HRs, it is observed that the initial dip occurred as soon as the task started, but the main HR had a delay of more than 2 s. Another interesting finding is that impulsive stimulation in the sensorimotor cortex can result in the generation of a clearer initial dip phenomenon. The results reveal that the command for the fNIRS-based BCI can be generated using the 5 s stimulation duration. In conclusion, the use of the initial dip can reduce the time taken for the generation of commands and can be used to achieve a higher classification accuracy for the fNIRS-BCI within a 5 s task duration rather than relying on longer durations.

Data-Driven Approach to the Analysis of Real-Time FMRI Neurofeedback Data: Disorder-Specific Brain Synchrony in PTSD

Brain-computer interfaces (BCIs) can be used in real-time fMRI neurofeedback (rtfMRI NF) investigations to provide feedback on brain activity to enable voluntary regulation of the blood-oxygen-level dependent (BOLD) signal from localized brain regions. However, the temporal pattern of successful self-regulation is dynamic and complex. In particular, the general linear model (GLM) assumes fixed temporal model functions and misses other dynamics. We propose a novel data-driven analyses approach for rtfMRI NF using intersubject covariance (ISC) analysis. The potential of ISC was examined in a reanalysis of data from 21 healthy individuals and nine patients with post-traumatic stress-disorder (PTSD) performing up-regulation of the anterior cingulate cortex (ACC). ISC in the PTSD group differed from healthy controls in a network including the right inferior frontal gyrus (IFG). In both cohorts, ISC decreased throughout the experiment indicating the development of individual regulation strategies. ISC analyses are a promising approach to reveal novel information on the mechanisms involved in voluntary self-regulation of brain signals and thus extend the results from GLM-based methods. ISC enables a novel set of research questions that can guide future neurofeedback and neuroimaging investigations.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain-computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets ([Formula: see text]), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

On the functional connectivity between heart, muscle, and frontal brain cortex during exercise fatigability

We analyze the functional connectivity of the cortico-cardiac-muscular network during muscle fatigability due to exercise. For our experiments, we recruited ten volunteers who performed two cycling routines. Our volunteers were classified according to physical activity level as active or sedentary. First, we measured the electromyography (EMG) signal of the vastus lateralis muscle to find the volunteer's individualized physical workload capacity at the fatigue threshold. Two to three days later, we measured the volunteers' EMG, electrocardiography, and electroencephalography (EEG) data during the development of muscle fatigue with the individualized pedaling load previously obtained. We analyzed the EEG to identify the brain rhythm with highest power, then functional connectivity was assessed by computing the pairwise partial directed coherence of our measurements at such rhythm. Finally, we assessed significant changes in functional connectivity from start to finish of the exercise. Our results indicate that the sedentary group showed a significant decrease in connectivity, while the active group showed an increase in left-frontal connectivity. This might be explained in terms of frontal asymmetry phenomena which, for the case of active volunteers, may be a reflection of positive emotional states. Additionally, the active group showed increased self-regulatory processes in heart and muscle, while the sedentary group showed a decreased connectivity from heart to muscle together with a reduction in the muscle's self-regulation. These last results may be explained by a preference of the body in the sedentary group to attend vital requirements instead of exercise.