Functional source separation and hand cortical representation for a brain-computer interface feature extraction

Personalized strategies of neurostimulation: from static biomarkers to dynamic closed-loop assessment of neural function

Despite considerable advancement of first choice treatment (pharmacological, physical therapy, etc.) over many decades, neurological disorders still represent a major portion of the worldwide disease burden. Particularly concerning, the trend is that this scenario will worsen given an ever expanding and aging population. The many different methods of brain stimulation (electrical, magnetic, etc.) are, on the other hand, one of the most promising alternatives to mitigate the suffering of patients and families when conventional treatment fall short of delivering efficacious treatment. With applications in virtually all neurological conditions, neurostimulation has seen considerable success in providing relief of symptoms. On the other hand, a large variability of therapeutic outcomes has also been observed, particularly in the usage of non-invasive brain stimulation (NIBS) modalities. Borrowing inspiration and concepts from its pharmacological counterpart and empowered by unprecedented neurotechnological advancement, the neurostimulation field has seen in recent years a widespread of methods aimed at the personalization of its parameters, based on biomarkers of the individuals being treated. The rationale is that, by taking into account important factors influencing the outcome, personalized stimulation can yield a much-improved therapy. Here, we review the literature to delineate the state-of-the-art of personalized stimulation, while also considering the important aspects of the type of informing parameter (anatomy, function, hybrid), invasiveness, and level of development (pre-clinical experimentation versus clinical trials). Moreover, by reviewing relevant literature on closed loop neuroengineering solutions in general and on activity dependent stimulation method in particular, we put forward the idea that improved personalization may be achieved when the method is able to track in real time brain dynamics and adjust its stimulation parameters accordingly. We conclude that such approaches have great potential of promoting the recovery of lost functions and enhance the quality of life for patients.

Functional balance at rest of hemispheric homologs assessed via normalized compression distance

Introduction

The formation and functioning of neural networks hinge critically on the balance between structurally homologous areas in the hemispheres. This balance, reflecting their physiological relationship, is fundamental for learning processes. In our study, we explore this functional homology in the resting state, employing a complexity measure that accounts for the temporal patterns in neurodynamics.

Methods

We used Normalized Compression Distance (NCD) to assess the similarity over time, neurodynamics, of the somatosensory areas associated with hand perception (S1). This assessment was conducted using magnetoencephalography (MEG) in conjunction with Functional Source Separation (FSS). Our primary hypothesis posited that neurodynamic similarity would be more pronounced within individual subjects than across different individuals. Additionally, we investigated whether this similarity is influenced by hemisphere or age at a population level.

Results

Our findings validate the hypothesis, indicating that NCD is a robust tool for capturing balanced functional homology between hemispheric regions. Notably, we observed a higher degree of neurodynamic similarity in the population within the left hemisphere compared to the right. Also, we found that intra-subject functional homology displayed greater variability in older individuals than in younger ones.

Discussion

Our approach could be instrumental in investigating chronic neurological conditions marked by imbalances in brain activity, such as depression, addiction, fatigue, and epilepsy. It holds potential for aiding in the development of new therapeutic strategies tailored to these complex conditions, though further research is needed to fully realize this potential.

Normalized compression distance to measure cortico-muscular synchronization

The neuronal functional connectivity is a complex and non-stationary phenomenon creating dynamic networks synchronization determining the brain states and needed to produce tasks. Here, as a measure that quantifies the synchronization between the neuronal electrical activity of two brain regions, we used the normalized compression distance (NCD), which is the length of the compressed file constituted by the concatenated two signals, normalized by the length of the two compressed files including each single signal. To test the NCD sensitivity to physiological properties, we used NCD to measure the cortico-muscular synchronization, a well-known mechanism to control movements, in 15 healthy volunteers during a weak handgrip. Independently of NCD compressor (Huffman or Lempel Ziv), we found out that the resulting measure is sensitive to the dominant-non dominant asymmetry when novelty management is required (p = 0.011; p = 0.007, respectively) and depends on the level of novelty when moving the non-dominant hand (p = 0.012; p = 0.024). Showing lower synchronization levels for less dexterous networks, NCD seems to be a measure able to enrich the estimate of functional two-node connectivity within the neuronal networks that control the body.

Functional Source Separation-Identified Epileptic Network: Analysis Pipeline

This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.

Dynamics of the "Cognitive" Brain Wave P3b at Rest for Alzheimer Dementia Prediction in Mild Cognitive Impairment

Alzheimer's disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient's autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the 'cognitive brain wave' P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

Neuronal Electrical Ongoing Activity as Cortical Areas Signature: An Insight from MNI Intracerebral Recording Atlas

The time course of the neuronal activity in the brain network, the neurodynamics, reflects the structure and functionality of the generating neuronal pools. Here, using the intracranial stereo-electroencephalographic (sEEG) recordings of the public Montreal Neurological Institute (MNI) atlas, we investigated the neurodynamics of primary motor (M1), somatosensory (S1) and auditory (A1) cortices measuring power spectral densities (PSD) and Higuchi fractal dimension (HFD) in the same subject (M1 vs. S1 in 16 subjects, M1 vs. A1 in 9, S1 vs. A1 in 6). We observed specific spectral features in M1, which prevailed above beta band, S1 in the alpha band, and A1 in the delta band. M1 HFD was higher than S1, both higher than A1. A clear distinction of neurodynamics properties of specific primary cortices supports the efforts in cortical parceling based on this expression of the local cytoarchitecture and connectivity. In this perspective, we selected within the MNI intracortical database a first set of primary motor, somatosensory and auditory cortices' representatives to query in recognizing ongoing patterns of neuronal communication. Potential clinical impact stands primarily in exploiting such exchange patterns to enhance the efficacy of neuromodulation intervention to cure symptoms secondary to neuronal activity unbalances.

A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface

BACKGROUND AND OBJECTIVES

An Error related Potential (ErrP) can be noninvasively and directly measured from the scalp through electroencephalography (EEG), as response, when a person realizes they are making an error during a task (as a consequence of a cognitive error performed from the user). It has been shown that ErrPs can be automatically detected with time-discrete feedback tasks, which are widely applied in the Brain-Computer Interface (BCI) field for error correction or adaptation. In this work, a semi-supervised algorithm, namely the Functional Source Separation (FSS), is proposed to estimate a spatial filter for learning the ErrPs and to enhance the evoked potentials.

METHODS

EEG data recorded on six subjects were used to evaluate the proposed method based on FFS algorithm in comparison with the xDAWN algorithm. FSS- and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.) RESULTS: The results presented using the Bayesian Linear Discriminant Analysis (BLDA) classifier, show that FSS (accuracy 0.92, sensitivity 0.95, specificity 0.81, F1-score 0.95) overcomes the other methods (Cz - accuracy 0.72, sensitivity 0.74, specificity 0.63, F1-score 0.74; FCz - accuracy 0.72, sensitivity 0.75, specificity 0.61, F1-score 0.75; xDAWN - accuracy 0.75, sensitivity 0.79, specificity 0.61, F1-score 0.79) in terms of single-trial classification.

CONCLUSIONS

The proposed FSS-based method increases the single-trial detection accuracy of ErrPs with respect to both single channel (Cz, FCz) and xDAWN spatial filter.

Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia

Neurodegenerative processes of various types of dementia start years before symptoms, but the presence of a "neural reserve", which continuously feeds and supports neuroplastic mechanisms, helps the aging brain to preserve most of its functions within the "normality" frame. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal brain aging. About 50% of MCI subjects are already in a stage that is prodromal-to-dementia and during the following 3 to 5 years will develop clinically evident symptoms, while the other 50% remains at MCI or returns to normal. If the risk factors favoring degenerative mechanisms are modified during early stages (i.e., in the prodromal), the degenerative process and the loss of abilities in daily living activities will be delayed. It is therefore extremely important to have biomarkers able to identify-in association with neuropsychological tests-prodromal-to-dementia MCI subjects as early as possible. MCI is a large (i.e., several million in EU) and substantially healthy population; therefore, biomarkers should be financially affordable, largely available and non-invasive, but still accurate in their diagnostic prediction. Neurodegeneration initially affects synaptic transmission and brain connectivity; methods exploring them would represent a 1st line screening. Neurophysiological techniques able to evaluate mechanisms of synaptic function and brain connectivity are attracting general interest and are described here. Results are quite encouraging and suggest that by the application of artificial intelligence (i.e., learning-machine), neurophysiological techniques represent valid biomarkers for screening campaigns of the MCI population.

Cortical neurodynamics changes mediate the efficacy of a personalized neuromodulation against multiple sclerosis fatigue

The people with multiple sclerosis (MS) often report that fatigue restricts their life. Nowadays, pharmacological treatments are poorly effective accompanied by relevant side effects. A 5-day transcranial direct current stimulation (tDCS) targeting the somatosensory representation of the whole body (S1) delivered through an electrode personalized based on the brain MRI was efficacious against MS fatigue (FaReMuS treatment). This proof of principle study tested whether possible changes of the functional organization of the primary sensorimotor network induced by FaReMuS partly explained the effected fatigue amelioration. We measured the brain activity at rest through electroencephalography equipped with a Functional Source Separation algorithm and we assessed the neurodynamics state of the primary somatosensory (S1) and motor (M1) cortices via the Fractal Dimension and their functional connectivity via the Mutual Information. The dynamics of the neuronal electric activity, more distorted in S1 than M1 before treatment, as well as the network connectivity, altered maximally between left and right M1 homologs, reverted to normal after FaReMuS. The intervention-related changes explained 48% of variance of fatigue reduction in the regression model. A personalized neuromodulation tuned in on specific anatomo-functional features of the impaired regions can be effective against fatigue.

Brain Functional Connectivity Changes After Transcranial Direct Current Stimulation in Epileptic Patients

Focal epilepsy is a network pathology, where the brain connectivity of the epileptic focus (EF) influences seizure frequency and cortical dysfunction. Growing evidence supports a clinical efficacy of cathodal transcranial direct current stimulation (ctDCS) in drug-resistant epilepsy (DRE). ctDCS effects can be merely attributed to the inhibition of cortical excitability, which is abnormally increased in epilepsy, but its effect on brain network of DRE patients has never been reported. We aimed at exploring the hypothesis that functional connectivity (FC) changes may explain part of ctDCS clinical effects in DRE patients. We assessed the ctDCS-induced changes of electroencephalography-derived brain FC of a group of six temporal lobe DRE patients receiving a seizure reduction after ctDCS. By a single-subject eLORETA analysis, we compared the FC among the EF region and other nine bilateral macro-regions, before and after Real and Sham ctDCS in a double-blind Sham-controlled crossover design. FC changed after Real ctDCS in all patients despite no appreciable changes occurred after Sham. Most of FC changes (73%) involved the EF region. The epileptic seizure reduction correlated with the increase of the EF FC, in the whole frequency band and in the theta band. This small-sample analysis clearly revealed that ctDCS induced FC changes in the brain network of temporal lobe DRE patients. Our data support the hypothesis that FC changes may contribute to explain the effects of ctDCS in epilepsy, offering a new scenario in the personalization of neuromodulation interventions in epileptic people.

Functional Semi-Blind Source Separation Identifies Primary Motor Area Without Active Motor Execution

High time resolution techniques are crucial for investigating the brain in action. Here, we propose a method to identify a section of the upper-limb motor area representation (FS_M1) by means of electroencephalographic (EEG) signals recorded during a completely passive condition (FS_M1bySS). We delivered a galvanic stimulation to the median nerve and we applied to EEG the semi-Blind Source Separation (s-BSS) algorithm named Functional Source Separation (FSS). In order to prove that FS_M1bySS is part of FS_M1, we also collected EEG in a motor condition, i.e. during a voluntary movement task (isometric handgrip) and in a rest condition, i.e. at rest with eyes open and closed. In motor condition, we show that the cortico-muscular coherence (CMC) of FS_M1bySS does not differ from FS_ M1 CMC (0.04 for both sources). Moreover, we show that the FS_M1bySS’s ongoing whole band activity during Motor and both rest conditions displays high mutual information and time correlation with FS_M1 (above 0.900 and 0.800, respectively) whereas much smaller ones with the primary somatosensory cortex [Formula: see text] (about 0.300 and 0.500, [Formula: see text]). FS_M1bySS as a marker of the upper-limb FS_M1 representation obtainable without the execution of an active motor task is a great achievement of the FSS algorithm, relevant in most experimental, neurological and psychiatric protocols.

A New, High-Efficacy, Noninvasive Transcranial Electric Stimulation Tuned to Local Neurodynamics

In this paper, we pose the following working hypothesis: in humans, transcranial electric stimulation (tES) with a time course that mimics the endogenous activity of its target is capable of altering the target's excitability. In our case, the target was the primary motor cortex (M1). We identified the endogenous neurodynamics of hand M1's subgroups of pyramidal neuronal pools in each of our subjects by applying Functional Source Separation (FSS) to their EEG recordings. We then tested whether the corticospinal excitability of the hand representation under the above described stimulation, which we named transcranial individual neurodynamics stimulation (tIDS), was higher than in the absence of stimulation (baseline). As a check, we compared tIDS with the most efficient noninvasive facilitatory corticospinal tES known so far, which is 20 Hz transcranial alternating current stimulation (tACS). The control conditions were as follows: (1) sham, (2) transcranial random noise stimulation (tRNS) in the same frequency range as tIDS (1-250 Hz), and (3) a low current tIDS (tIDS_low). Corticospinal excitability was measured with motor-evoked potentials under transcranial magnetic stimulation. The mean motor-evoked potential amplitude increase was 31% of the baseline during tIDS (p < 0.001), and it was 15% during tACS (p = 0.096). tRNS, tIDS_low, and sham induced no effects. Whereas tACS did not produce an enhancement in any subject at the individual level, tIDS was successful in producing an enhancement in 8 of the 16 subjects. The results of the present proof-of-principle study showed that proper exploitation of local neurodynamics can enhance the efficacy of personalized tES.SIGNIFICANCE STATEMENT This study demonstrated that, in humans, transcranial individual neurodynamics stimulation (tIDS), which mimics the endogenous dynamics of the target neuronal pools, effectively changes the excitability of these pools. tIDS holds promise for high-efficacy personalized neuromodulations based on individual local neurodynamics.

Neuronal electrical ongoing activity as a signature of cortical areas

Brodmann's pioneering work resulted in the classification of cortical areas based on their cytoarchitecture and topology. Here, we aim at documenting that diverse cortical areas also display different neuronal electric activities. We investigated this notion in the hand-controlling sections of the primary somatosensory (S1) and motor (M1) areas, in both hemispheres. We identified S1 and M1 in 20 healthy volunteers by applying functional source separation (FSS) to their recorded electroencephalograms (EEG). Our results show that S1 and M1 can be clearly differentiated by their neuroelectric activities in both hemispheres and independently of the subject's state (i.e., at rest or performing movements or receiving external stimulations). In particular, S1 displayed higher relative power than M1 in the alpha and low beta frequency ranges (8-25 Hz, p < .003), whereas the opposite occurred in the high gamma band (52-90 Hz, p = .006). In addition, S1's activity had a smaller Higuchi's fractal dimensions (HFD) than M1's (p < .00001) in all subjects, permitting a reliable classification of the two areas. Moreover, HFD of M1's activity resulted correlated with the hand's fine motor control, as expressed by the 9-hole peg test scores. The present work is a first step toward the identification and classification of brain cortical areas based on neuronal dynamics rather than on cytoarchitectural features. We deem this step to be an improvement of our knowledge of the brain's structural-functional unity.

Impaired brainstem and thalamic high-frequency oscillatory EEG activity in migraine between attacks

Introduction

We investigated whether interictal thalamic dysfunction in migraine without aura (MO) patients is a primary determinant or the expression of its functional disconnection from proximal or distal areas along the somatosensory pathway.

Methods

Twenty MO patients and twenty healthy volunteers (HVs) underwent an electroencephalographic (EEG) recording during electrical stimulation of the median nerve at the wrist. We used the functional source separation algorithm to extract four functionally constrained nodes (brainstem, thalamus, primary sensory radial, and primary sensory motor tangential parietal sources) along the somatosensory pathway. Two digital filters (1–400 Hz and 450–750 Hz) were applied in order to extract low- (LFO) and high- frequency (HFO) oscillatory activity from the broadband signal.

Results

Compared to HVs, patients presented significantly lower brainstem (BS) and thalamic (Th) HFO activation bilaterally. No difference between the two cortical HFO as well as in LFO peak activations between the two groups was seen. The age of onset of the headache was positively correlated with HFO power in the right brainstem and thalamus.

Conclusions

This study provides evidence for complex dysfunction of brainstem and thalamocortical networks under the control of genetic factors that might act by modulating the severity of migraine phenotype.

Electroencephalographic Fractal Dimension in Healthy Ageing and Alzheimer's Disease

Brain activity is complex; a reflection of its structural and functional organization. Among other measures of complexity, the fractal dimension is emerging as being sensitive to neuronal damage secondary to neurological and psychiatric diseases. Here, we calculated Higuchi’s fractal dimension (HFD) in resting-state eyes-closed electroencephalography (EEG) recordings from 41 healthy controls (age: 20–89 years) and 67 Alzheimer’s Disease (AD) patients (age: 50–88 years), to investigate whether HFD is sensitive to brain activity changes typical in healthy aging and in AD. Additionally, we considered whether AD-accelerating effects of the copper fraction not bound to ceruloplasmin (also called “free” copper) are reflected in HFD fluctuations. The HFD measure showed an inverted U-shaped relationship with age in healthy people (R² = .575, p < .001). Onset of HFD decline appeared around the age of 60, and was most evident in central-parietal regions. In this region, HFD decreased with aging stronger in the right than in the left hemisphere (p = .006). AD patients demonstrated reduced HFD compared to age- and education-matched healthy controls, especially in temporal-occipital regions. This was associated with decreasing cognitive status as assessed by mini-mental state examination, and with higher levels of non-ceruloplasmin copper. Taken together, our findings show that resting-state EEG complexity increases from youth to maturity and declines in healthy, aging individuals. In AD, brain activity complexity is further reduced in correlation with cognitive impairment. In addition, elevated levels of non-ceruloplasmin copper appear to accelerate the reduction of neural activity complexity. Overall, HDF appears to be a proper indicator for monitoring EEG-derived brain activity complexity in healthy and pathological aging.

Multiple linear regression to estimate time-frequency electrophysiological responses in single trials

Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLR_d) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical oscillations, obtaining single-trial estimate of response latency, frequency, and magnitude. This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI.

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ERP/ERD/ERS are reliably isolated using PCA + Varimax rotation on single-trial TFDs.

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TF-MLR_d enhances the SNR of ERP/ERD/ERS in single trials.

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TF-MLR_d provides an unbiased estimation of single-trial parameters of ERP/ERD/ERS.

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Availability of single-trial estimates permits within-subject statistical comparison.

A constrained ICA approach for real-time cardiac artifact rejection in magnetoencephalography

Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

Neurophysiological techniques in the study of the excitability, connectivity, and plasticity of the human brain

There is increasing evidence to support the concept that brain plasticity involves distinct functional and structural components, each requiring several cellular mechanisms operating at different time scales, synaptic loci, and developmental phases within an extremely complex framework. However, the precise relationship between functional and structural components of brain plasticity/connectivity phenomena is still unclear and its explanation represents a major challenge within modern neuroscience. The key feature of neurophysiological techniques described in this review paper is their pivotal role in tracking temporal dynamics and inner hierarchies of brain functional and effective connectivities, possibly clarifying some crucial issues underlying brain plasticity. Taken together, the findings presented in this review open an intriguing new field in neuroscience investigation and are important for the adoption of neurophysiological techniques as a tool for basic research and, in future, even for clinical diagnostics purposes.

Physiological aging impacts the hemispheric balances of resting state primary somatosensory activities

To hone knowledge of sensorimotor cerebral organization changes with physiological aging, we focused on the primary somatosensory cortical area (S1). S1 neuronal pools (FS_S1) were identified by the functional source separation (FSS) algorithm applied to magnetoencephalographic recordings during median nerve stimulation. Age-dependence of FS_S1 was then studied at rest separately in the left and right hemispheres of 26 healthy, right-handed subjects between the ages of 24 and 95 years. The resting state FS_S1 spectral features changed with increasing age: (1) alpha activity slowed down; (2) total power increased only in the right hemisphere; (3) right>left interhemispheric asymmetry increased in the whole spectrum; (4) spectral entropy increased with age selectively in the left hemisphere. The present FSS-enriched electrophysiological procedure provided measures of resting state hand representation area sensitive to changes with age. Alterations were stronger in the right hemisphere. Relationships between resting state S1 activity and its responsiveness to external stimuli, revealed that the interhemispheric unbalances which emerged with age were conceivably due to an increased excitability within the right thalamocortical circuit impacting left versus right unbalances of spontaneous firing rates and of local inhibitory-excitatory networks.

The relationship between the visual evoked potential and the gamma band investigated by blind and semi-blind methods

Gamma Band Activity (GBA) is increasingly studied for its relation with attention, change detection, maintenance of working memory and the processing of sensory stimuli. Activity around the gamma range has also been linked with early visual processing, although the relationship between this activity and the low frequency visual evoked potential (VEP) remains unclear. This study examined the ability of blind and semi-blind source separation techniques to extract sources specifically related to the VEP and GBA in order to shed light on the relationship between them. Blind (Independent Component Analysis—ICA) and semi-Blind (Functional Source Separation—FSS) methods were applied to dense array EEG data recorded during checkerboard stimulation. FSS was performed with both temporal and spectral constraints to identify specifically the generators of the main peak of the VEP (P100) and of the GBA. Source localisation and time-frequency analyses were then used to investigate the properties and co-dependencies between VEP/P100 and GBA. Analysis of the VEP extracted using the different methods demonstrated very similar morphology and localisation of the generators. Single trial time frequency analysis showed higher GBA when a larger amplitude VEP/P100 occurred. Further examination indicated that the evoked (phase-locked) component of the GBA was more related to the P100, whilst the induced component correlated with the VEP as a whole. The results suggest that the VEP and GBA may be generated by the same neuronal populations, and implicate this relationship as a potential mediator of the correlation between the VEP and the Blood Oxygenation Level Dependent (BOLD) effect measured with fMRI.

Primary sensory and motor cortex activities during voluntary and passive ankle mobilization by the SHADE orthosis

This study investigates cortical involvement during ankle passive mobilization in healthy subjects, and is part of a pilot study on stroke patient rehabilitation. Magnetoencephalographic signals from the primary sensorimotor areas devoted to the lower limb were collected together with simultaneous electromyographic activities from tibialis anterior (TA). This was done bilaterally, on seven healthy subjects (aged 29 ± 7), during rest, left and right passive ankle dorsiflexion (imparted through the SHADE orthosis, O-PM, or neuromuscular electrical stimulation, NMES-PM), and during active isometric contraction (IC-AM). The effects of focussing attention on ankle passive movements were considered. Primary sensory (FS(S1)) and motor (FS(M1)) area activities were discriminated by the Functional Source Separation algorithm. Only contralateral FS(S1) was recruited by common peroneal nerve stimulation and only contralateral FS(M1) displayed coherence with TA muscular activity. FS(M1) showed higher power of gamma rhythms (33-90 Hz) than FS(S1). Both sources displayed higher beta (14-32 Hz) and gamma powers in the left than in the right hemisphere. Both sources displayed a bilateral reduction of beta power during IC-AM with respect to rest. Only FS(S1) beta band power reduced during O-PM. No beta band modulation was observed of either source during NMES-PM. Mutual FS(S1)-FS(M1) coherence in gamma2 band (61-90 Hz) showed a slight trend towards an increase when focussing attention during O-PM. Somatosensory and motor counterparts of lower limb cortical representations were discriminated in both hemispheres. SHADE was effective in generating repeatable dorsiflexion and inducing primary sensory involvement similarly to voluntary movement.

Voxel-wise information theoretic EEG-fMRI feature integration

We have recently proposed the evaluation of a set of information theoretic quantities (ITQs) for the integration of simultaneously acquired EEG-fMRI data (Ostwald, D., Porcaro, C., Bagshaw, A.P., 2010. An information theoretic approach to EEG-fMRI integration of visually evoked responses. Neuroimage. 49, 498-516). In our previous experimental evaluation of the information theoretic framework, we defined the data subsets from which to calculate the ITQs using a priori constraints. In the case of EEG, this meant that data were extracted from a single electrode, while for fMRI the analysed data came from voxels contained within a sphere surrounding the most responsive voxel of visual cortex. While this approach was a natural starting point for the evaluation of the framework in the application to combined EEG-fMRI data sets, a more principled approach to data selection is desirable. Here, we propose to combine standard fMRI data pre-processing and low-resolution electromagnetic tomography (LORETA) for the evaluation of ITQs across the entire three-dimensional brain space. We apply the proposed method to a simultaneous EEG-fMRI data set acquired during checkerboard stimulation and assess the topographical informativeness of EEG (time and frequency domain) and fMRI features with respect to the stimulus and each other. The resulting information theoretic effect size maps are supplemented with a statistical evaluation based on Gaussian null model simulations using a false-discovery rate procedure. Given the contamination of EEG recordings by artefacts induced by the MR scanning environment we further assessed the influence of different advanced EEG pre-processing methods (independent component analysis and functional source separation) on the information topography. The results of this analysis provide evidence for the topographically focussed informativeness of both EEG and fMRI features with respect to the stimulus, but for the current feature selection do not detect EEG-fMRI activity dependence. More advanced EEG data pre-processing rendered the feature distributions more stimulus-informative, but did not alter the EEG-fMRI activity and conditional dependencies.

Synchronous with your feelings: sensorimotor {gamma} band and empathy for pain

Neuroscience studies on the social sharing of observed or imagined pain focused on whether empathic pain resonance is linked to affective or sensory nodes of the pain matrix. However, empathy, like other complex cognitive processes, is inherently linked to the activation of functional networks rather than of separate brain areas. Here, we used magnetoencephalography (MEG) to explore the relationship between empathy and functional coupling of neuronal activity in primary somatosensory (SI) and motor (MI) cortices. MEG recording was performed while healthy participants observed movie-clips depicting the static hand of a stranger model, the same hand deeply penetrated by a needle, or gently touched by a Q-tip. Subjects were asked to rate the movie-derived sensations attributed to self or to the model. For each type of clip observation, we analyzed spectral power and coherence values in alpha, beta, and gamma frequency bands. While spectral power indexes separate neural activity in SI and MI, coherence values index functional cross-talk between these two areas. No power changes of SI or MI sources were induced by observation conditions in any of the frequency bands. Crucially, gamma-band coherence values were significantly higher during needle-in-hand than touch and static hand observation and correlated with self-and other-referred pain ratings derived from needle-in-hand movies observation. Thus, observation of others' pain increases neuronal synchronization and cross-talk between the onlookers' sensory and motor cortices, indicating that empathic resonance relies upon the activity of functional networks more than of single areas.

Hand somatosensory subcortical and cortical sources assessed by functional source separation: an EEG study

We propose a novel electroencephalographic application of a recently developed cerebral source extraction method (Functional Source Separation, FSS), which starts from extracranial signals and adds a functional constraint to the cost function of a basic independent component analysis model without requiring solutions to be independent. Five ad-hoc functional constraints were used to extract the activity reflecting the temporal sequence of sensory information processing along the somatosensory pathway in response to the separate left and right median nerve galvanic stimulation. Constraints required only the maximization of the responsiveness at specific latencies following sensory stimulation, without taking into account that any frequency or spatial information. After source extraction, the reliability of identified FS was assessed based on the position of single dipoles fitted on its retroprojected signals and on a discrepancy measure. The FS positions were consistent with previously reported data (two early subcortical sources localized in the brain stem and thalamus, the three later sources in cortical areas), leaving negligible residual activity at the corresponding latencies. The high-frequency component of the oscillatory activity (HFO) of the extracted component was analyzed. The integrity of the low amplitude HFOs was preserved for each FS. On the basis of our data, we suggest that FSS can be an effective tool to investigate the HFO behavior of the different neuronal pools, recruited at successive times after median nerve galvanic stimulation. As FSs are reconstructed along the entire experimental session, directional and dynamic HFO synchronization phenomena can be studied.

Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects

Brain-computer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development have grown explosively over the past two decades. Efforts have recently begun to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this review, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology, and identify potential users and potential applications. Finally, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.

Choice of multivariate autoregressive model order affecting real network functional connectivity estimate

OBJECTIVE

A realistic simulation exploiting real cortical sources identified from non-invasive extra-cranial recordings in healthy subjects has been considered in order to select the most robust procedure for choosing the correct order of multivariate autoregressive (MVAR) models. Different signal-to-noise ratios filter settings and sampling rates were also tested on the estimate of functional connectivity among the network nodes, in simulated and real cases.

METHODS

Starting from magnetoencephalographic recordings, cortical sources in primary sensorimotor areas of the hand were obtained by functional source separation (FSS). Different criteria for the choice of the model order were compared in the simulated network constructed through one of the FSS-extracted sources and its noise-added delayed copies. In two real cases, a validation of the model order (not known a priori) choice was obtained by comparing the time-frequency properties as depicted by classical non-parametric and MVAR methods at rest, during isometric contraction (stationary states) and while dynamically responding to a sensory stimulation (transient state). For completeness, the whole set of MVAR functional connectivity measures was taken into account, to assess the most suitable for our network description.

RESULTS

That the use of an incorrect model order distorts network functional connectivity estimate was documented both in the realistic simulation and in the two real cases. The Minimal Description Length and Schwartz Bayesian Criterion were selected as the most robust for MVAR model order choice. Partial directed coherence (PDC) was the most suitable method for time-frequency connectivity estimate in the simulated as well as in the real cases, both in stationary and transient states. Moreover, the results of MVAR-based connectivity estimate depend on filter setting in the real case.

CONCLUSIONS

The most robust procedure for choosing the correct MVAR model order was provided. The adjunctive comparison of MVAR to classical methods is recommended to validate the choice in the real case.

SIGNIFICANCE

Correct MVAR model order choice and band filtering play an important role for the correct network connectivity estimate.

Repairing the human brain after stroke. II. Restorative therapies

Spontaneous behavioral recovery is usually limited after stroke, making stroke a leading source of disability. A number of therapies in development aim to improve patient outcomes not by acutely salvaging threatened tissue, but instead by promoting repair and restoration of function in the subacute or chronic phase after stroke. Examples include small molecules, growth factors, cell-based therapies, electromagnetic stimulation, device-based strategies, and task-oriented and repetitive training-based interventions. Stage of development across therapies varies widely, from preclinical to late-phase clinical trials. The optimal methods to prescribe such therapies require further studies, for example, to best identify appropriate patients or to guide features of dosing. Likely, anatomic, functional, and behavioral measures of brain state, as well as measures of injury, will each be useful in this regard. Considerations for clinical trials of restorative therapies are provided, emphasizing both similarities and points of divergence with acute stroke clinical trial design.

Brain-computer interface systems: progress and prospects

Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.