Role of the EEG Theta Network During Software Production: A Connectivity Study.

Software programming is an acquired evolutionary skill originating from consolidated cognitive functions (i.e., attentive, logical, coordination, mathematic calculation, and language comprehension), but the underlying neurophysiological processes are still not completely known. In the present study, we investigated and compared the brain activities supporting realistic programming, text and code reading tasks, analyzing Electroencephalographic (EEG) signals acquired from 11 experienced programmers. Multichannel spectral analysis and a phase-based effective connectivity study were carried out. Our results highlighted that both realistic programming and reading tasks are supported by modulations of the Theta fronto-parietal network, in which parietal areas behave as sources of information, while frontal areas behave as receivers. Nevertheless, during realistic programming, both an increase in Theta power and changes in network topology emerged, suggesting a task-related adaptation of the supporting network system. This reorganization mainly regarded the parietal area, which assumes a prominent role, increasing its hub functioning and its connectivity in the network in terms of centrality and degree.

Selecting a pre-processing pipeline for the analysis of EEG event-related rhythms modulation.

When deciding how to pre-process EEG data, researchers need to make a choice at each single step of the procedure among different possibilities, equally valid. Therefore, in this work, we illustrate how these decisions may affect the quality of the final cleaned data in an Action Observation/Motor Imagery protocol, using quantitative indices. In particular, we showed the effect of segmenting or not the data in epochs around the stimulus presentation time on the independent component analysis (ICA) used for artifact removal. For ICA analysis, we tested two algorithms (SOBI and Extended Infomax). Finally, three re-reference approaches (Common averaged reference-CAR, robust-CAR and reference electrode standardization technique - REST) were also applied and their effects compared. Results showed that the segmenting method has a prominent effect on the cleaning procedure and consequently on final EEG data quality. Extended Infomax is confirmed as the method of choice for the identification of the artifactual components and, finally, CAR and the REST re-referencing techniques led to similar good results.

Levodopa-dependent differences in the non-oscillatory activity of the subthalamic nucleus.

The study of local field potentials (LFP) recorded from the basal ganglia of patients with movement disorders led to significant advancement in the understanding the pathophysiology of Parkinson's disease (PD). The possibility of investigating possible changes in the activity of the brain caused by the levodopa administration may provide a useful tool to evaluate the influence or the side-effects of the treatment from patient to patient. The analysis was carried out through a systematic analysis of the fractal component of the subthalamic local field potentials (STN-LFP) that may reveal, with respect to the classical power spectrum analysis, novel important information about the dynamic modulation caused by the drug intake. Indeed, so far, much of what is known about that is related to the presence of a spectral peak in the beta frequency band then attenuated after the levodopa administration. The nonlinear power-law exponent goes beyond this feature, exploring differences that reflect the fractal (scale-free) behavior of the PD brain dynamics. Here, in order to demonstrate that the presence or absence of the peak has no effect on the computation of the power-law exponent, we used simulated LFP recordings. After that, we performed the fractal analysis in shorts epochs of STN LFPs recordings ( N=24 patients, 12 females and 12 males) before and after Levodopa administration. We found no differences in the nonlinear power-law exponent for simulated data, reinforcing the idea that the parameter was not influenced by the attenuation of the hallmark peak for PD patients. As regard real LFP time series, we found that pharmacological treatment for PD differently altered LFP power of non-oscillatory activity, as well as changed the level of fractal exponent in specific frequency bands. Particularly we observed an increase of the fractal exponent in condition of post-levodopa with significant differences related to the response to levodopa in Parkinson's disease. Clinical Relevance- This study points out a potentially novel non-oscillatory biomarker which could reflect intrinsic properties of complex biological systems thus constituting a potential target parameter for novel and alternative neuroprosthetic applications.

Action Observation Therapy Before Sleep Hours: An EEG Study.

Action Observation Therapy (AOT) is a rehabilitation method which aims at stimulating motor memory by means of the repetitive observation of motor tasks presented through video-clips. Since sleep seems to have a positive effect on learning processes, it is reasonable to hypothesize that the delivery of AOT immediately before sleep hours could enhance the effects of motor training. The objective of the present work was to test the effect of AOT delivered before the sleep hours in terms of improvements in manual dexterity and changes in cortical activity through Electroencephalography (EEG) on healthy subjects. Specifically, EEG traces acquired on a treatment and on a control group before and after three weeks of training during the execution of a Nine Hole Peg Test were analyzed. The spectral analysis of brain signals showed an increased activation of the motor cortex on a subgroup of the treatment subjects. Moreover, a significantly higher involvement of frontal areas was observed in the treatment group.

Globus pallidus internus activity during simultaneous bilateral microelectrode recordings in status dystonicus.

Limited data are available regarding the electrophysiology of status dystonicus (SD). We report simultaneous microelectrode recordings (MERs) from the globus pallidus internus (GPi) of a patient with SD who was treated with bilateral deep brain stimulation (DBS). Mean neuronal discharge rate was of 30.1 ± 10.9 Hz and 38.5 Hz ± 11.1 Hz for the right and left GPi, respectively. On the right side, neuronal electrical activity was completely abolished at the target point, whereas the mean burst index values showed a predominance of bursting and irregular activity along trajectories on both sides. Our data are in line with previous findings of pallidal irregular hypoactivity as a potential electrophysiological marker of dystonia and thus SD, but further electrophysiological studies are needed to confirm our results.

An intra-operative feature-based classification of microelectrode recordings to support the subthalamic nucleus functional identification during deep brain stimulation surgery.

Objective. The subthalamic nucleus (STN) is the most selected target for the placement of the Deep Brain Stimulation (DBS) electrode to treat Parkinson's disease. Its identification is a delicate and challenging task which is based on the interpretation of the STN functional activity acquired through microelectrode recordings (MERs). Aim of this work is to explore the potentiality of a set of 25 features to build a classification model for the discrimination of MER signals belonging to the STN.Approach.We explored the use of different sets of spike-dependent and spike-independent features in combination with an ensemble trees classification algorithm on a dataset composed of 13 patients receiving bilateral DBS. We compared results from six subsets of features and two dataset conditions (with and without standardization) using performance metrics on a leave-one-patient-out validation schema.Main results.We obtained statistically better results (i.e. higher accuracyp-value = 0.003) on the RAW dataset than on the standardized one, where the selection of seven features using a minimum redundancy maximum relevance algorithm provided a mean accuracy of 94.1%, comparable with the use of the full set of features. In the same conditions, the spike-dependent features provided the lowest accuracy (86.8%), while a power density-based index was shown to be a good indicator of STN activity (92.3%).Significance.Results suggest that a small and simple set of features can be used for an efficient classification of MERs to implement an intraoperative support for clinical decision during DBS surgery.

A novel spatiotemporal tool for the automatic classification of fMRI noise based on Independent Component Analysis.

In this study, a semi-automatic, easy-to-use classification method for the identification and removal of fMRI noise is proposed and tested. The method relies on subject-level spatial independent component analysis (ICA) of fMRI data. Starting from a reference set of labeled independent components (ICs), novel ICs are classified as physiological/artefactual by combining a spatial correlation (SC) analysis with the reference ICs and relative power spectral (PS) analysis. Here, ICs from a task-based fMRI dataset were used as reference. SC and SP thresholds were set using a test dataset (5 subjects, same fMRI protocol) based on Receiving Operating Characteristic curves. The tool performance and versatility were measured on a resting-state fMRI dataset (5 subjects). Our results show that the method can automatically identify noise-related ICs with accuracy, specificity and sensitivity higher than 80% across different fMRI protocols. These findings also suggest that the reference set provided in the present study might be used to mark ICs coming from independent taskrelated or resting-state fMRI datasets.Clinical relevance- The new method will be included in a userfriendly, open-source tool for removal of noisy contributions from fMRI datasets to be used in clinical and research practices.

Characterization of Microelectrode Recordings for the Subthalamic Nucleus identification in Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, when the pharmacological approach has no more effect. DBS efficacy strongly depends on the accurate localization of the STN and the adequate positioning of the stimulation electrode during DBS stereotactic surgery. During this procedure, the analysis of microelectrode recordings (MER) is fundamental to assess the correct localization. Therefore, in this work, we explore different signal feature types for the characterization of the MER signals associated to STN from NON-STN structures. We extracted a set of spike-dependent (action potential domain) and spike-independent features in the time and frequency domain to evaluate their usefulness in distinguishing the STN from other structures. We discuss the results from a physiological and methodological point of view, showing the superiority of features having a direct electrophysiological interpretation.Clinical Relevance- The identification of a simple, clinically interpretable, and powerful set of features for the STN localization would support the clinical positioning of the DBS electrode, improving the treatment outcome.

Optimization of the Cortical Traveling Wave Analysis framework for feasibility in Stereo-Electroencephalography.

The study of brain waves propagation is of interest to understand the neural involvement in both physiological and pathological events, such as interictal epileptic spikes (IES). The possibility to track the trajectory of IESs could be useful to better characterize the role of the involved structures in the epileptic network, adding valuable information to the epileptic focus localization. Methods for the cortical traveling wave analysis (CTWA) have been proposed to trace the preferred propagation path of sleep slow waves, using scalp high-density EEG and reconstructing the trajectories both in the sensors and in the sources space. In this work, we propose a feasibility study of the application of these concepts to Stereo-EEG (SEEG) data for the analysis of IES. Through simulations, we selected the best performing Electrical Source Imaging inverse solution for our purpose and illustrate the CTWA procedure. We further show an exemplary application on real data and discuss advantages and pitfalls of the application of CTWA in SEEG.

Functional Clustering approach for the analysis of Stereo-EEG activity patterns in correspondence of epileptic seizures.

In this study, a functional clustering approach is proposed and tested for the identification of brain functional networks emerging during sleep-related seizures. Stereo-EEG signals recorded in patients with Type II Focal Cortical Dysplasia (FCD type II), were analyzed. This novel approach is able to identify the network configuration changes in pre-ictal and early ictal periods, by grouping Stereo-EEG signals on the basis of the Cluster Index, after wavelet multiscale decomposition. Results showed that the proposed method is able to detect clusters of interacting leads, mainly overlapped on the Epileptogenic Zone (EZ) identified by a clinical expert, with distinctive configurations related to analyzed frequency ranges. This suggested the presence of coupling activities between the elements of the epileptic system at different frequency scales.

The putative role of neuronal network synchronization as a potential biomarker for bipolar disorder: A review of EEG studies.

Impaired intra-hemispheric and inter-hemispheric communication play a major role in the pathophysiology and cognitive disturbances of bipolar disorder (BD). Brain connectivity in BD has been largely investigated using magnetic resonance imaging (MRI) techniques, which have found alterations in prefronto-limbic coupling. In contrast, evidence for functional neural circuitry abnormalities in BD is less consistent. Indeed, just a few studies employing the electroencephalographic (EEG) technique, enabling the exploration of oscillatory brain dynamics, addressed this issue. Therefore, in the present review we summarize the results from EEG studies examining connectivity in patients with BD, to further clarify the putative role of neuronal network synchronization as a potential biomarker of this disabling mental illness.

Healthy and tumoral tissue resistivity in wild-type and sparc-/- animal models.

Despite the technological improvement of radiologic, endoscopic and nuclear imaging, the accuracy of diagnostic procedures for tumors can be limited whenever a mass-forming lesion is identified. This is true also because bioptical sampling cannot be properly guided into the lesions so as to puncture neoplastic tissue and to avoid necrotic areas. Under these circumstances, invasive and expensive procedures are still required to obtain diagnosis which is mandatory to plan the most appropriate therapeutic strategy. In order to test if electrical impedance spectroscopy may be helpful in providing further evidence for cancer detection, resistivity measurements were taken on 22 mice, 11 wild-type and 11 sparc-/- (knock out for the protein SPARC: secreted protein acidic and rich in cysteine), bearing mammary carcinomas, by placing a needle-probe into tumor, peritumoral and contralateral healthy fat areas. Tumor resistivity was significantly lower than both peritumoral fat and contralateral fat tissues. Resistivity in sparc-/- mice was lower than wild-type animals. A significant frequency dependence of resistivity was present in tissues analyzed. We conclude that accurate measurements of resistivity may allow to discriminate between tissues with different pathological and/or structural characteristics. Therefore, resistivity measurements could be considered for in vivo detection and differential diagnosis of tumor masses.

The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.

The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.

Can home-monitoring of sleep predict depressive episodes in bipolar patients?

The aim of this study is the evaluation of the autonomic regulations during depressive stages in bipolar patients in order to test new quantitative and objective measures to detect such events. A sensorized T-shirt was used to record ECG signal and body movements during the night, from which HRV data and sleep macrostructure were estimated and analyzed. 9 out of 20 features extracted resulted to be significant (p<;0.05) in discriminating among depressive and non-depressive states. Such features are representation of HRV dynamics in both linear and non-linear domain and parameters linked to sleep modulations.

Clusters of mu rhythm from EEG data: A comparative study between 61 and 19 channel datasets.

The EEG mu rhythm is a sensorimotor oscillation which is desynchronized by voluntary movement execution. Independent Component Analysis (ICA) allows the decomposition of recorded scalp EEG data into temporally, functionally, and spatially independent source signals. Clustering techniques applied to independent sources resolved with ICA have been proven to be successful in the identification of clusters of sensorimotor mu rhythm across different subjects. The present work deals with the issue regarding the minimum number of data channels that is recommended to find reliable clusters of mu rhythm. Left and right mu clusters were identified from high-density EEG recordings (61 channels) belonging to a publicly available EEG database. A second dataset was created by selecting a small subset of the same high-density EEG recordings. Specifically, only the 19 channels belonging to the standard 10-20 International System were used for the identification of left and right mu clusters. Quantitative parameters computed from mu clusters obtained from both the 61-channel and the 19-channel datasets were statistically compared. The obtained results suggest that clusters of mu rhythm in sensorimotor areas can be reliably found from a lower number of EEG channels compared to high-density electrodes configuration.

Hemodynamic and EEG Time-Courses During Unilateral Hand Movement in Patients with Cortical Myoclonus. An EEG-fMRI and EEG-TD-fNIRS Study.

Multimodal human brain mapping has been proposed as an integrated approach capable of improving the recognition of the cortical correlates of specific neurological functions. We used simultaneous EEG-fMRI (functional magnetic resonance imaging) and EEG-TD-fNIRS (time domain functional near-infrared spectroscopy) recordings to compare different hemodynamic methods with changes in EEG in ten patients with progressive myoclonic epilepsy and 12 healthy controls. We evaluated O2Hb, HHb and Blood oxygen level-dependent (BOLD) changes and event-related desynchronization/synchronization (ERD/ERS) in the α and ß bands of all of the subjects while they performed a simple motor task. The general linear model was used to obtain comparable fMRI and TD-fNIRS activation maps. We also analyzed cortical thickness in order to evaluate any structural changes. In the patients, the TD-NIRS and fMRI data significantly correlated and showed a significant lessening of the increase in O2Hb and the decrease in BOLD. The post-movement ß rebound was minimal or absent in patients. Cortical thickness was moderately reduced in the motor area of the patients and correlated with the reduction in the hemodynamic signals. The fMRI and TD-NIRS results were consistent, significantly correlated and showed smaller hemodynamic changes in the patients. This finding may be partially attributable to mild cortical thickening. However, cortical hyperexcitability, which is known to generate myoclonic jerks and probably accounts for the lack of EEG ß-ERS, did not reflect any increased energy requirement. We hypothesize that this is due to a loss of inhibitory neuronal components that typically fire at high frequencies.

Heart rate variability and cardiorespiratory coupling in obstructive sleep apnea: elderly compared with young.

INTRODUCTION: Aging is known to be a major contributing factor to the increased risk of obstructive sleep apnea (OSA). With aging, breathing undergoes significant changes during sleep, increasing the prevalence of apnea events, which affects heart rate variability (HRV) and cardiorespiratory coupling (CRC). OBJECTIVES: To compare HRV and CRC during wakefulness and sleep between young and elderly patients with and without OSA; and to determine whether the presence of OSA in young and elderly patients has a different impact on HRV and CRC during sleep. METHODS: One hundred subjects, 50 young (mean age, 27 ± 9; 20 normal and 30 OSA) and 50 elderly (mean age, 65 ± 7; 20 normal and 30 OSA), underwent polysomnography. Spectral, cross-spectrum, and HRV parameters were analyzed during wakefulness and sleep. RESULTS: The spectral analysis indicated that age affected HRV, with higher values of low frequency (P < 0.05) in elderly subjects during wakefulness and an interaction between the presence of OSA and age. OSA influenced HRV during sleep with lower LF/HF ratios during stage 2 (S2) and rapid eye movement (REM) sleep (P <0.05), with an interaction between the presence of OSA and age in REM sleep. Elderly patients had significantly lower percent tachogram power coherent with respiration (%TPCR) during wakefulness (P < 0.05), and OSA led to lower %TPCR during S2. CONCLUSIONS: Age and OSA have an unfavorable impact on HRV, with reduced autonomic modulation during wakefulness, S2, and REM sleep. Age affects CRC during wakefulness and the presence of OSA affects CRC during sleep.

Monitoring nocturnal heart rate with bed sensor.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Studying Cardiovascular and Respiratory Systems". OBJECTIVES: The aim of this study is to assess the reliability of the estimated Nocturnal Heart Rate (HR), recorded through a bed sensor, compared with the one obtained from standard electrocardiography (ECG). METHODS: Twenty-eight sleep deprived patients were recorded for one night each through matrix of piezoelectric sensors, integrated into the mattress, through polysomnography (PSG) simultaneously. The two recording methods have been compared in terms of signal quality and differences in heart beat detection. RESULTS: On average, coverage of 92.7% of the total sleep time was obtained for the bed sensor, testifying the good quality of the recordings. The average beat-to-beat error of the inter-beat intervals was 1.06%. These results suggest a good overall signal quality, however, considering fast heart rates (HR > 100 bpm), performances were worse: in fact, the sensitivity in the heart beat detection was 28.4% while the false positive rate was 3.8% which means that a large amount of fast beats were not detected. CONCLUSIONS: The accuracy of the measurements made using the bed sensor has less than 10% of failure rate especially in periods with HR lower than 70 bpm. For fast heart beats the uncertainty increases. This can be explained by the change in morphology of the bed sensor signal in correspondence of a higher HR.

Heart rate variability and cardio-respiratory coupling during sleep in patients prior to bariatric surgery.

Obesity is associated with increased cardiac risk of morbidly and mortality and for the development and progression of obstructive sleep apnea (OSA). Severity of obesity negatively affects the heart rate variability (HRV) in patients with indication for bariatric surgery (BS). The purpose of this study is to determine if the severity of obesity alters the autonomic cardiac regulation and the cardio-respiratory coupling during sleep using spectral analysis of HRV and respiration variability signals (RS) in patients prior to BS. Twenty-nine consecutive preoperative BS and ten subjects (controls) underwent polysomnography. The spectral and cross-spectral parameters of the HRV and RS were computed during different sleep stages (SS). Spectral analysis of the HRV and RV indicated lower respiration regularity during sleep and a lower HRV in obese patients (OP) during all SS when compared with controls (p < 0.05). Severely (SO) and super-obese patients (SOP) presented lower values of low frequency/high frequency (LF/HF) ratio and LF power during REM sleep and higher HF power (p < 0.05), while morbidly obese (MO) patients presented lower LF/HF ratio and LF power in SS-S2 and higher HF power when compared to controls (p < 0.05). The cross-spectral parameters showed that SOP presented lower percentage of tachogram power coherent with respiration in SS-S3 when compared to controls (p < 0.05). Patients prior to BS presented altered HRV and RV in all SS. SO, MO, and SOP presented altered cardio-respiratory coupling during sleep, and these alterations are related with severity of obesity and OSA parameters.

ERP and Adaptive Autoregressive identification with spectral power decomposition to study rapid auditory processing in infants.

The ability to process rapidly-occurring auditory stimuli plays an important role in the mechanisms of language acquisition. For this reason, the research community has begun to investigate infant auditory processing, particularly using the Event Related Potentials (ERP) technique. In this paper we approach this issue by means of time domain and time-frequency domain analysis. For the latter, we propose the use of Adaptive Autoregressive (AAR) identification with spectral power decomposition. Results show EEG delta-theta oscillation enhancement related to the processing of acoustic frequency and duration changes, suggesting that, as expected, power modulation encodes rapid auditory processing (RAP) in infants and that the time-frequency analysis method proposed is able to identify this modulation.