Biology subjects

Fraschini, M.

Publications and source records attributed to Fraschini, M..

7 recordsLinked to original sources

bioRxiv2024

The influence of the head model on magnetoencephalography-derived functional connectivity fingerprinting

Functional connectivity (FC)-based neural fingerprinting is an approach that promises to identify and/or differentiate subjects within a cohort on the basis of the patterns of statistical dependencies between time series recorded mostly if not always noninvasively, with electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). The message is that brain activity is what differentiates subjects, or what makes a neural fingerprint "unique". In EEG- and MEG-derived FC fingerprinting, the activity recorded at the sensors is usually projected back into cortical sources by means of an inverse model depending on head and brain shapes, sensor locations, and tissue conductivity, and further reduced in dimension to obtain time series of regional activity, used to compute FC. We think that the role of the head model in fingerprinting has been so far dismissed by means of suboptimal or incomplete tests. Here we employed a set of experiments aimed to decouple recorded activity and head model for each subject, and we found that the head model has a strong influence on both identification and differentiation.

neuroscience↗

bioRxiv2020

Scorepochs: a computer-aided scoring tool for resting-state M/EEG epochs

M/EEG resting-state analysis often requires the definition of the epoch length and the criteria to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thus hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64-channel system.

bioengineering↗

bioRxiv2019

Subject, session and task effects on power, connectivity and network centrality: a source-based EEG study

Inter-subjects variability in functional brain networks has been extensively investigated in the last few years. In this context, unveiling subject-specific characteristics of EEG features may play an important role for both clinical (e.g., biomarkers) and bio-engineering purposes (e.g., biometric systems and brain computer interfaces). Nevertheless, the effects induced by multi-sessions and task-switching are not completely understood and considered. In this work, we aimed to investigate how the variability due to subject, session and task affects EEG power, connectivity and network features estimated using source-reconstructed EEG time-series. Our results point out a remarkable ability to identify subject-specific EEG traits within a given task together with striking independence from the session. The results also show a relevant effect of task-switching, which is comparable to individual variability. This study suggests that power and connectivity EEG features may be adequate to detect stable (over-time) individual properties within predefined and controlled tasks.

bioengineering↗

bioRxiv2019

A comparison between power spectral density and network metrics: an EEG study

Power 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 (PLV and AEC) that do not limit the effects of volume conduction/signal leakage. The correlations are less relevant for more conservative FC methods (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.

bioengineering↗

bioRxiv2019

Removing zero-lag functional connections can alter EEG source space networks at rest

Electro-encephalography (EEG) source connectivity is an emerging approach to estimate brain networks with high time/space resolution. Here, we aim to evaluate the effect of different functional connectivity (FC) methods on the EEG-source space networks at rest. The two main families of FC methods tested are: i) the FC methods that do not remove the zero-lag connectivity including the Phase Locking Value (PLV) and the Amplitude Envelope Correlation (AEC) and ii) the FC methods that remove the zero-lag connections such as the Phase Lag Index (PLI) and orthogonalisation approach combined with PLV (PLVorth) and AEC (AECorth). Methods are evaluated on resting state dense-EEG signals recorded from 20 healthy participants. Networks obtained by each FC method are compared with fMRI networks at rest (from the Human Connectome Project -HCP-, N=487). Results show low correlations for all the FC methods, however PLV and AEC networks are significantly correlated with fMRI networks ({rho} = 0.12, p = 1.93x10-8 and {rho} = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for each EEG frequency bands and for different FC matrices threshold. Furthermore, the effect of electrode density was also tested using four EEG montages (dense-EEG 256 electrodes, 128, 64 and 32 electrodes). Results show no significant differences between the four EEG montages in terms of correlations with the fMRI networks. Our main message here is to be careful when selecting the FC methods and mainly those that remove the zero-lag connections as they can affect the network characteristics. More comparative studies (based on simulation and real data) are still needed in order to make EEG source connectivity a mature technique to address questions in cognitive and clinical neuroscience.

neuroscience↗

bioRxiv2018

EEG-based personal identification: comparison of different functional connectivity metrics

Growing interest is devoted to understanding how brain signals recorded from scalp electroencephalography (EEG) may represent unique fingerprints of individual neural activity. In this context, the present paper aims to investigate the impact of some of the most commonly used techniques to estimate functional connectivity on the ability to unveil personal distinctive patterns of inter-regional interactions. Different metrics, commonly used to estimate functional connectivity and derived centrality measures, were compared in terms of equal error rate. It is widely accepted that each metric carries specific information in respect to the underlying interactions network. Nevertheless, the reason why these metrics convey different subject specific information has not been investigated yet. Experimental results on two publicly available datasets suggest that different functional connectivity metrics define a peculiar subjective profile of connectivity and have different mechanisms to detect subject-specific patterns of inter-channel interactions. It is important to consider the effects that frequency content and spurious connectivity values may play in determining subject-specific characteristics.

bioengineering↗

bioRxiv2017

A Comparison Between Scalp- And Source-Reconstructed EEG Networks

EEG 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 different 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 find 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 different 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 effects of volume conduction/signal leakage. These findings suggest that these effects 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.

neuroscience↗