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.

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.