Comparison of beamformer and ICA for dynamic connectivity analysis: A simultaneous MEG-SEEG study.

Magnetoencephalography (MEG) is a powerful tool for estimating brain connectivity with both good spatial and temporal resolution. It is particularly helpful in epilepsy to characterize non-invasively the epileptic networks. However, using MEG to map brain networks requires solving a difficult inverse problem that introduces uncertainty in the activity localization and connectivity measures. Our goal here was to compare independent component analysis (ICA) followed by dipole source localization and the linearly constrained minimum-variance beamformer (LCMV-BF) for characterizing regions with interictal epileptic activity and their dynamic connectivity. After a simulation study, we compared ICA and LCMV-BF results with intracerebral EEG (stereotaxic EEG, SEEG) recorded simultaneously in 8 epileptic patients, which provide a unique 'ground truth' to which non-invasive results can be confronted. We compared the signal time courses extracted applying ICA and LCMV-BF on MEG data to that of SEEG, both for the actual signals and the dynamic connectivity computed using cross-correlation (evolution of links in time). With our simulations, we illustrated the different effect of the temporal and spatial correlation among sources on the two methods. While ICA was more affected by the temporal correlation but robust against spatial configurations, LCMV-BF showed opposite behavior. Moreover, ICA seems more suited to retrieve the simulated networks. In case of real patient data, good MEG/SEEG correlation and good localization were obtained in 6 out of 8 patients. In 4 of them ICA had the best performance (higher correlation, lower localization distance). In terms of dynamic connectivity, the evolution in time of the cross-correlation links could be retrieved in 5 patients out of 6, however, with more variable results in terms of correlation and distance. In two patients LCMV-BF had better results than ICA. In one patient the two methods showed equally good outcomes, and in the remaining two patients ICA performed best. In conclusion, our results obtained by exploiting simultaneous MEG/SEEG recordings suggest that ICA and LCMV-BF have complementary qualities for retrieving the dynamics of interictal sources and their network interactions.

Kinematic evaluation and reliability assessment of the Nine Hole Peg Test for manual dexterity.

BACKGROUND: The Nine Hole Peg Test (NHPT) is one of the most frequently used tools to assess manual dexterity. However, no kinematic parameters are provided to describe the quality of the motor performance, since time is the only score. PURPOSE: To investigate test-retest and intra-rater reliability, correlation with clinical test score, and discriminant validity of kinematic indexes during NHPT. STUDY DESIGN: A clinical measurement study. METHODS: Twenty-five healthy right-handed volunteers performed the NHPT. An experienced physiotherapist administered two sessions at a 6-hour interval with two trials for dominant and non-dominant upper limbs. An optoelectronic system was used to detect NHPT performance, which was divided into nine consecutive peg-grasp, peg-transfer, peg-in-hole, hand-return phases, and one final removing phase. Outcome measures were total and single phases times, normalized jerk, mean, peak and time-to-peak of velocity, curvature index during peg-grasp and hand-return phases, and trunk 3D displacement. The statistical analysis included Intraclass Correlation Coefficients (ICCs) for test-retest and intra-rater reliability, Pearson's coefficients for correlation with the NHPT score, and paired t-tests for discriminant validity. RESULTS: Test-retest reliability was excellent for trunk rotation (ICC: 0.91) and good to moderate for the other indexes (ICCs: 0.89-0.61). Intra-rater reliability was excellent for total and removing times (ICCs: 0.91 and 0.94) and good to moderate for the other indexes (ICCs: 0.84-0.66), except for trunk inclination (ICC: 0.37). NHPT phases, normalized jerk, mean velocity, peak of velocity, time-to-peak and curvature index correlated with total time (r-score: 0.8-0.3). NHPT phases and most kinematic indexes discriminated the dominant from non-dominant upper limb, with the greatest effect size for normalized jerk during hand-return (d = 1.16). CONCLUSIONS: Kinematic indexes during NHPT can be considered for manual dexterity assessment. These indexes may allow for the detection of kinematic changes responsible for NHPT score variations in healthy subjects or patients with upper limb impairments.

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates.

The increasingly widespread diffusion of wearable devices makes possible the continuous monitoring of vital signs, such as heart rate (HR), heart rate variability (HRV), and breath signal. However, these devices usually do not record the "gold-standard" signals, namely the electrocardiography (ECG) and respiratory activity, but a single photoplethysmographic (PPG) signal, which can be exploited to estimate HR and respiratory activity. In addition, these devices employ low sampling rates to limit power consumption. Hence, proper methods should be adopted to compensate for the resulting increased discretization error, while diverse breath-extraction algorithms may be differently sensitive to PPG sampling rate. Here, we assessed the efficacy of parabola interpolation, cubic-spline, and linear regression methods to improve the accuracy of the inter-beat intervals (IBIs) extracted from PPG sampled at decreasing rates from 64 to 8 Hz. PPG-derived IBIs and HRV indices were compared with those extracted from a standard ECG. In addition, breath signals extracted from PPG using three different techniques were compared with the gold-standard signal from a thoracic belt. Signals were recorded from eight healthy volunteers during an experimental protocol comprising sitting and standing postures and a controlled respiration task. Parabola and cubic-spline interpolation significantly increased IBIs accuracy at 32, 16, and 8 Hz sampling rates. Concerning breath signal extraction, the method holding higher accuracy was based on PPG bandpass filtering. Our results support the efficacy of parabola and spline interpolations to improve the accuracy of the IBIs obtained from low-sampling rate PPG signals, and also indicate a robust method for breath signal extraction.

EEG connectivity in functional brain networks supporting visuomotor integration processes in dominant and non-dominant hand movements.

Objective.This study explores the changes in the organization of functional brain networks induced by performing a visuomotor integration task, as revealed by noninvasive spontaneous electroencephalographic traces (EEG).Approach.EEG data were acquired during the execution of the Nine Hole Peg Test (NHPT) with the dominant and non-dominant hands in a group of 44 right-handed volunteers. Both spectral analysis and phase-based connectivity analysis were performed in the theta (ϑ), mu (µ) and beta (ß) bands. Graph Theoretical Analysis (GTA) was also performed to investigate the topological reorganization induced by motor task execution.Main results.Spectral analysis revealed an increase of frontoparietal ϑ power and a spatially diffused reduction ofµand ß contribution, regardless of the hand used. GTA showed a significant increase in network integration induced by movement performed with the dominant limb compared to baseline in the ϑ band. Theµand ß bands were associated with a reduction in network integration during the NHPT. In theµrhythm, this result was more evident for the right-hand movement, while in the ß band, results did not show dependence on the laterality. Finally, correlation analysis highlighted an association between frequency-specific topology measures and task performance for both hands.Significance.Our results show that functional brain networks reorganize during visually guided movements in a frequency-dependent manner, differently depending on the hand used (dominant/non dominant).

Assessing cardiovascular stress based on heart rate variability in female shift workers: a multiscale-multifractal analysis approach.

Introduction: Sleep-wake cycle disruption caused by shift work may lead to cardiovascular stress, which is observed as an alteration in the behavior of heart rate variability (HRV). In particular, HRV exhibits complex patterns over different time scales that help to understand the regulatory mechanisms of the autonomic nervous system, and changes in the fractality of HRV may be associated with pathological conditions, including cardiovascular disease, diabetes, or even psychological stress. The main purpose of this study is to evaluate the multifractal-multiscale structure of HRV during sleep in healthy shift and non-shift workers to identify conditions of cardiovascular stress that may be associated with shift work. Methods: The whole-sleep HRV signal was analyzed from female participants: eleven healthy shift workers and seven non-shift workers. The HRV signal was decomposed into intrinsic mode functions (IMFs) using the empirical mode decomposition method, and then the IMFs were analyzed using the multiscale-multifractal detrended fluctuation analysis (MMF-DFA) method. The MMF-DFA was applied to estimate the self-similarity coefficients, α(q, τ), considering moment orders (q) between -5 and +5 and scales (τ) between 8 and 2,048 s. Additionally, to describe the multifractality at each τ in a simple way, a multifractal index, MFI(τ), was computed. Results: Compared to non-shift workers, shift workers presented an increase in the scaling exponent, α(q, τ), at short scales (τ < 64 s) with q < 0 in the high-frequency component (IMF1, 0.15-0.4 Hz) and low-frequency components (IMF2-IMF3, 0.04-0.15 Hz), and with q> 0 in the very low frequencies (IMF4, < 0.04 Hz). In addition, at large scales (τ> 1,024 s), a decrease in α(q, τ) was observed in IMF3, suggesting an alteration in the multifractal dynamic. MFI(τ) showed an increase at small scales and a decrease at large scales in IMFs of shift workers. Conclusion: This study helps to recognize the multifractality of HRV during sleep, beyond simply looking at indices based on means and variances. This analysis helps to identify that shift workers show alterations in fractal properties, mainly on short scales. These findings suggest a disturbance in the autonomic nervous system induced by the cardiovascular stress of shift work.

Early sleep after action observation and motor imagery training boosts improvements in manual dexterity.

The systematic observation and imagination of actions promotes acquisition of motor skills. Furthermore, studies demonstrated that early sleep after practice enhances motor learning through an offline stabilization process. Here, we investigated behavioral effects and neurodynamical correlates of early sleep after action observation and motor imagery training (AO + MI-training) on motor learning in terms of manual dexterity. Forty-five healthy participants were randomized into three groups receiving a 3 week intervention consisting of AO + MI-training immediately before sleeping or AO + MI-training at least 12 h before sleeping or a control stimulation. AO + MI-training implied the observation and motor imagery of transitive manual dexterity tasks, whereas the control stimulation consisted of landscape video-clips observation. Manual dexterity was assessed using functional tests, kinematic and neurophysiological outcomes before and after the training and at 1-month follow-up. AO + MI-training improved manual dexterity, but subjects performing AO + MI-training followed by early sleep had significantly larger improvements than those undergoing the same training at least 12 h before sleeping. Behavioral findings were supported by neurodynamical correlates during motor performance and additional sleep-dependent benefits were also detected at 1 month follow-up. These findings introduce a new approach to enhance the acquisition of new motor skills or facilitate recovery in patients with motor impairments.

Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease.

Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.

Bicoherence Interpretation in EEG Requires Signal to Noise Ratio Quantification: An Application to Sensorimotor Rhythms.

OBJECTIVE: In the electroencephalogram (EEG) the quadratic phase coupling (QPC) phenomenon indicates the presence of non-linear interactions among brain rhythms that could affect the interpretation of their physiological meaning. We propose the use of the bicoherence as a QPC quantification method to understand the nature of brain rhythm interplay. METHODS: We firstly provide a simulation study to show under which condition of signal to noise ratio (SNR) the bicoherence is a reliable QPC quantifier and how to interpret the results. Secondly, in the light of the simulation results, we applied the bicoherence analysis to real EEG data acquired on thirteen volunteers during a cue-paced reaching motor task to quantify coupling and decoupling between mu and beta rhythms. An inter-trial averaging procedure was adopted in order to allow the correct calculation of the bicoherence during a motor task. RESULTS: Simulations demonstrated that SNR has a strong impact on the correct quantification of bicoherence and that a reliable detection of QPC is possible when the SNR is favorable (>-5 dB). Results from EEG data demonstrated a QPC between mu and beta rhythms during the resting state and its fading during movement planning and execution, providing valuable information for the interpretation of their dynamics. CONCLUSION: The bicoherence was proven to be an effective tool for the investigation of coupling between the sensorimotor rhythms during all the phases of a motor task. This was assessed in relation to the physiological changing of the SNR characterizing the frequency components of interest.

Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia With Sleep Hypermotor Epilepsy.

OBJECTIVE: A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks during different sleep stages and in relation with the occurrence of seizures. METHOD: Stereo-electroencephalographic signals from seven pharmaco-resistant epileptic patients (focal cortical dysplasia type II) were analyzed. The discrete wavelet transform provided a multiscale framework on which a data-driven functional clustering procedure was applied, based on multivariate measures of integration and mutual information. The most interacting functional clusters (FCs) within the sampled brain areas were extracted. RESULTS: FCs characterized by strongly integrated activity were observed mostly in the beta and alpha frequency bands, immediately before seizure onset and in deep sleep stages. These FCs generally included the electrodes from the epileptogenic zone. Furthermore, repeatable patterns were found across ictal events in the same patient. CONCLUSION: In line with previous studies, our findings provide evidence of the important role of beta and alpha activity in seizures generation and support the relation between epileptic activity and sleep stages. SIGNIFICANCE: Despite the small number of subjects included in the study, the present results suggest that the proposed multiscale functional clustering approach is a useful tool for the identification of the frequency-dependent mechanisms underlying seizure generation.

Higher order spectral analysis of scalp EEG activity reveals non-linear behavior during rhythmic visual stimulation.

OBJECTIVE: Flickering visual stimulation is known to evoke rhythmic oscillations in the electroencephalographic (EEG) activity, called steady-state visually evoked potentials (SSVEP). The presence of harmonic components in the EEG signals during SSVEP suggests the non-linearity of the visual-system response to rhythmic stimulation, but the nature of this behavior has not been deeply understood. The aim of this study is the quantitative evaluation and characterization of this non-linear phenomenon and its interference with the physiological alpha rhythm by means of spectral and higher order spectral analysis. APPROACH: EEG signals were acquired in a group of 12 healthy subjects during a pattern-reversal stimulation protocol at three different driving frequencies (7.5 Hz, 15 Hz and 24 Hz). Spectral power values were estimated, after Laplacian spatial filtering, to quantitatively evaluate the changes in the power of the individual alpha and stimulation frequencies related harmonic components. Bicoherence measure were employed to assess the presence of quadratic phase coupling (QPC) at each channel location. MAIN RESULTS: Our analysis confirmed a strong non-linear response to the rhythmic stimulus principally over the parieto-occipital channel locations and a simultaneous significant alpha power suppression during 7.5 Hz and 15 Hz stimulation. A prominent sub-harmonic component characterized the resonance behavior of the 24 Hz stimulation. SIGNIFICANCE: The findings presented suggest that bicoherence is a useful tool for the identification of QPC interactions between stimulus-related frequency components within the same signal and the characterization of the non-linearity of SSVEP-induced harmonics generation. In addition, the applied methodology demonstrates the presence of coupled EEG rhythms (harmonics of the main oscillation) both in resting condition and during stimulation, with different characteristics in the distinct brain areas.

EEG indices correlate with sustained attention performance in patients affected by diffuse axonal injury.

The aim of this study is to assess the ability of EEG-based indices in providing relevant information about cognitive engagement level during the execution of a clinical sustained attention (SA) test in healthy volunteers and DAI (diffused axonal injury)-affected patients. We computed three continuous power-based engagement indices (P ß /P α , 1/P α , and P ß / (P α + P θ )) from EEG recordings in a control group (n = 7) and seven DAI-affected patients executing a 10-min Conners' "not-X" continuous performance test (CPT). A correlation analysis was performed in order to investigate the existence of relations between the EEG metrics and behavioral parameters in both the populations. P ß /P α and 1/P α indices were found to be correlated with reaction times in both groups while P ß / (P α + P θ ) and P ß /P α also correlated with the errors rate for DAI patients. In line with previous studies, time course fluctuations revealed a first strong decrease of attention after 2 min from the beginning of the test and a final fading at the end. Our results provide evidence that EEG-derived indices extraction and evaluation during SA tasks are helpful in the assessment of attention level in healthy subjects and DAI patients, offering motivations for including EEG monitoring in cognitive rehabilitation practice. Graphical abstract Three EEG-derived indices were computed from four electrodes montages in a population of seven healthy volunteers and a group of seven DAI-affected patients. Results show a significant correlation between the time course of the indices and behavioral parameters, thus demonstrating their usefulness in monitoring mental engagement level during a sustained attention task.

EEG Analysis During Active and Assisted Repetitive Movements: Evidence for Differences in Neural Engagement.

Two key ingredients of a successful neuro-rehabilitative intervention have been identified as intensive and repetitive training and subject's active participation, which can be coupled in an active robot-assisted training. To exploit these two elements, we recorded electroencephalography, electromyography and kinematics signals from nine healthy subjects performing a 2×2 factorial design protocol, with subject's volitional intention and robotic glove assistance as factors. We quantitatively evaluated primary sensorimotor, premotor and supplementary motor areas activation during movement execution by computing event-related desynchronization (ERD) patterns associated to mu and beta rhythms. ERD patterns showed a similar behavior for all investigated regions: statistically significant ERDs began earlier in conditions requiring subject's volitional contribution; ERDs were prolonged towards the end of movement in conditions in which the robotic assistance was present. Our study suggests that the combination between subject volitional contribution and movement assistance provided by the robotic device (i.e., active robot-assisted modality) is able to provide early brain activation (i.e., earlier ERD) associated with stronger proprioceptive feedback (i.e., longer ERD). This finding might be particularly important for neurological patients, where movement cannot be completed autonomously and passive/active robot-assisted modalities are the only possibilities of execution.

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis.

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

EEG-based index for engagement level monitoring during sustained attention.

This paper investigates the relation between mental engagement level and sustained attention in 9 healthy adults performing a Conners' "not-X" continuous performance test (CPT), while their electroencephalographic (EEG) activity was simultaneously acquired. Spectral powers were estimated and extracted in the classical EEG frequency bands. The engagement index (ß/α) was calculated employing four different cortical montages suggested by the literature. Results show the efficacy of the estimated measures in detecting changes in mental state and its correlation with subject reaction times throughout the test. Moreover, the influence of the recording sites was proved underling the role of frontal cortex in maintaining a constant sustained attention level.