1,720,981 research outputs found
Brain network analysis of EEG functional connectivity during imagery hand movements
No full textThe characterization of human neural activity during imaginary movement tasks represent an important challenge in order to develop er effective applications that allow the control of a machine. Yet methods based on brain network analysis of functional connectivity have been scarcely investigated. As a result we use graph theoretic methods to investigate the functional connectivity and brain network measures in order to characterize imagery hand movements in a set of healthy subjects. The results of the present study show that functional connectivity analysis and minimum spanning tree (MST) parameters allow to successfully discriminate between imagery hand movements (both right and left) and resting state conditions. In conclusion, this paper shows that brain network analysis of EEG functional connectivity could represent an efficient alternative to more classical local activation based approaches. Furthermore, it also suggests the shift toward methods based on the characterization of a limited set of fundamental functional connections that disclose salient network topological features
Prospecting epilepsy surgery outcome using virtual resection paradigm. Computational-model validation
No full textEpilepsy surgery still lacks an operational method for finding the epileptogenic zone (EZ) or the minimal amount of tissue that has to be resected in order to leave the patient seizure-free. Here we propose a method for predicting the result of a resection from data collected before the eventual resection, therefore allowing an optimal surgical planning. Our major hypothesis is that focal generalized epilepsies are caused by sub-systems with excessive afferent connectivity to and from the rest of the neuronal tissue. To address the issue of delineating the EZ before the actual resection, we propose a paradigm performing "virtual surgery"on the matrix of connectivity between local EEG measurements. The virtual resection removes not only the nodes covered by the suspected EZ and their connections but also subtracts the influence of these nodes on the rest of connectivity. The residual connectivity is then compared to the original one and a significant decrease indicate that the resection contains the EZ or at least large part of it. We tested this approach on a computational model of spatially distributed bi-stable units that provides a generic model of focal epileptic neuronal system. In the modelled cases of epileptic system with spreading seizures, we found that the removal of the EZ can be predicted by significant decrease of the residual connectivity after performing virtual resection. This decrease commensurate with the increase of the epileptic threshold (the ground truth). The method also predicts the actual change of connectivity after removing the nodes from the model dynamics. In addition we tested our techniques against the "naïve"virtual resection which is based on simply removing the corresponding nodes from the connectivity measure. The findings in this work can be exploited to increase the efficiency and accuracy of pre-surgical epileptogenic zone localization in cases of focal epileptic seizure onsets
A comparison between power spectral density and network metrics: an EEG study
Full text linkPower spectral density (PSD) and network analysis performed on functional correlation (FC) patterns represent two common approaches used to characterize Electroencephalographic (EEG) data. Despite the two approaches are widely used, their possible association may need more attention. To investigate this question, we performed a comparison between PSD and some widely used nodal network metrics (namely strength, clustering coefficient and betweenness centrality), using two different publicly available resting-state EEG datasets, both at scalp and source levels, employing four different FC methods (PLV, PLI, AEC and AECC). Here we show that the two approaches may provide similar information and that their correlation depends on the method used to estimate FC. In particular, our results show a strong correlation between PSD and nodal network metrics derived from FC methods (pick at 0.736 for PLV and 0.530 for AEC) that do not limit the effects of volume conduction/signal leakage. The correlations are less relevant for more conservative FC methods (pick at 0.224 for AECC). These findings suggest that the results derived from the two different approaches may be not independent and should not be treated as distinct analyses. We conclude that it may represent good practice to report the findings from the two approaches in conjunction to have a more comprehensive view of the results
Validation of virtual resection on intraoperative interictal data acquired during epilepsy surgery
No full textObjective. A ‘Virtual resection’ consists of computationally simulating the effect of an actual resection on the brain. We validated two functional connectivity based virtual resection methods with the actual connectivity measured using post-resection intraoperative recordings. Approach. A non-linear association index was applied to pre-resection recordings from 11 extra-temporal focal epilepsy patients. We computed two virtual resection strategies: first, a ‘naive’ one obtained by simply removing from the connectivity matrix the electrodes that were resected; second, a virtual resection with partialization accounting for the influence of resected electrodes on not-resected electrodes. We validated the virtual resections with two analysis: (1) we tested with a Kolmogorov-Smirnov test if the distributions of connectivity values after the virtual resections differed from the actual post-resection connectivity distribution; (2) we tested if the overall effect of the resection measured by contrasting pre-resection and post-resection connectivity values is detectable with the virtual resection approach using a Kolmogorv-Smirnov test. Main results. The estimation of post-resection connectivity values did not succeed for both methods. In the second analysis, the naive method failed completely to detect the effect found between pre-resection and post-resection connectivity distributions, while the partialization method agreed with post-resection measurements in detecting a drop connectivity compared to pre-resection recordings. Our findings suggest that the partialization technique is superior to the naive method in detecting the overall effect after the resection. Significance. We pointed out how a realistic validation based on actual post-resection recordings reveals that virtual resection methods are not yet mature to inform the clinical decision-making
A Practical Workflow for Organizing Clinical Intraoperative and Long-term iEEG Data in BIDS
Full text linkThe neuroscience community increasingly uses the Brain Imaging Data Structure (BIDS) to organize data, extending from MRI to electrophysiology data. While automated tools and workflows are developed that help organize MRI data from the scanner to BIDS, these workflows are lacking for clinical intracranial EEG (iEEG data). We present a practical workflow on how to organize full clinical iEEG epilepsy data into BIDS. We present electrophysiological datasets recorded from twelve subjects who underwent intracranial monitoring followed by resective epilepsy surgery at the University Medical Center Utrecht, the Netherlands, and became seizure-free after surgery. These data include intraoperative electrocorticography recordings from six patients, long-term electrocorticography recordings from three patients and stereo-encephalography recordings from three patients. We describe the 6 steps in the pipeline that are essential to structure the data from these clinical iEEG recordings into BIDS and the challenges during this process. These proposed workflow enable centers performing clinical iEEG recordings to structure their data to improve accessibility, reusability and interoperability of clinical data
An EEG-based biometric system using eigenvector centrality in resting state brain networks
No full textRecently, there has been a growing interest in the use of brain activity for biometric systems. However, so far these studies have focused mainly on basic features of the Electroencephalography. In this study we propose an approach based on phase synchronization, to investigate personal distinctive brain network organization. To this end, the importance, in terms of centrality, of different regions was determined on the basis of EEG recordings. We hypothesized that nodal centrality enables the accurate identification of individuals. EEG signals from a cohort of 109 64-channels EEGs were band-pass filtered in the classical frequency bands and functional connectivity between the sensors was estimated using the Phase Lag Index. The resulting connectivity matrix was used to construct a weighted network, from which the nodal Eigenvector Centrality was computed. Nodal centrality was successively used as feature vector. Highest recognition rates were observed in the gamma band (equal error rate ( EER) = 0.044 ) and high beta band (EER= 0.102). Slightly lower recognition rate was observed in the low beta band (EER = 0.144), while poor recognition rates were observed for the others frequency bands. The reported results show that resting-state functional brain network topology provides better classification performance than using only a measure of functional connectivity, and may represent an optimal solution for the design of next generation EEG based biometric systems. This study also suggests that results from biometric systems based on high-frequency scalp EEG features should be interpreted with caution
Minimum spanning tree and k-core decomposition as measure of subject-specific EEG traits
No full textThe identification of subject-specific traits extracted from patterns of brain activity still represents an important challenge. The need to detect distinctive brain features, which is relevant for biometric and brain computer interface systems, has been also emphasized in monitoring the effect of clinical treatments and in evaluating the progression of brain disorders. In this study we propose an approach which aims to investigate the existence of a distinctive functional core (sub-network) using an unbiased reconstruction of network topology. Brain signals from a public and freely available EEG dataset were analysed using a phase synchronization based measure, minimum spanning tree and k -core decomposition. The analysis was performed for each classical brain rhythm separately. Highest classification rates from k -core decomposition were obtained in the gamma (EER = 0.130, AUC = 0.943) and high beta (EER = 0.172, AUC = 0.905) frequency bands. These results confirm that EEG analysis may represent an effective tool to identify subject-specific characteristics that may be of great impact for several bioengineering applications. However, despite the widespread use of these techniques, critical aspects should be considered when dealing with results from high-frequency scalp EEG
A comparison between scalp- and source-reconstructed EEG networks
Full text linkEEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use diferent assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to fnd an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in diferent experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the efects of volume conduction/signal leakage. These fndings suggest that these efects alter the estimated EEG network organization, limiting the interpretation of results of scalp analysis. Finally, this study also suggests
that the use of metrics that address the problem of zero lag correlations may give more reliable estimates of the underlying network topology
LESSICI E GRAMMATICHE NELLA DIDATTICA DELL’ITALIANO TRA OTTOCENTO E NOVECENTO
No full textConvegno Internazionale
Università degli Studi di Milano, 22-23 novembre 2016
a cura di
Massimo Prada e Giuseppe Polimeni
contributi di
Dalila Bachis, Monica Barsi, Lucia Berti, Paola Cantoni, Sandra Covino, Margherita De Blasi, Nicola De Blasi, Silvia Demartini, Cecilia Demuru, Matteo Grassano, Beatriz Hernan Gomez Prieto, Giovanni Iamartino, Ludovica Maconi, Elisa Marazzi, Claudio Marazzini, Carla Marello, Giuseppe Patota, Massimo Prada, Luisa Revelli, Alessio Ricci, Paolo Silvestri, Francesco Vaucher De La Croix Joel
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