1,721,142 research outputs found
Digital pre-processing of foetal magnetocardiographic signals for optimal extractions of foetal traces
No full textSpace division architectures for crosstalk reduction in optical interconnection networks
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The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks
No full textIn recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable. (C) 2011 American Institute of Physics. [doi:10.1063/1.3553181
A K-means multivariate approach for clustering independent components from magnetoencephalographic data
No full textIndependent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic
and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications,
ICA needs to be extended from single to multi‐session and multi‐subject studies for interpreting and
assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent
components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means
based (MAGMICK).
The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running
ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK
are: i) the implementation of an efficient set of “MEG fingerprints” designed to summarize properties of MEG
ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version
of the standard K-means procedure to improve its data-driven character. This algorithmgroups the obtained ICs
automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint
on the ICs independence, i.e. components coming from the same session (at subject level) or subject
(at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two
sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate
that the method can extract consistent patterns of spatial topography and spectral properties across sessions
and subjects that are in good agreement with the literature. In addition, these results are compared to those
from amodified version of affinity propagation clusteringmethod. The comparison, evaluated in terms of different
clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually,
these results are confirmed by a comparison with a MEG tailored version of the self-organizing group
ICA, which is largely used for fMRI IC clustering
Fetal cardiac time intervals: validation of an automatic tool for beat-to-beat detection on fetal magnetocardiograms
No full textFetal magnetocardiography (fMCG) allows the
non-invasive registration of fetal cardiac activity. This
technique, combined with the use of independent component
analysis (ICA) for signal processing, allows reconstructing of
reliable fetal cardiac traces. Low noise fetal signals can be used
to evaluate fetal cardiac time intervals (fCTI), useful to
monitor fetal heart function. In this work we present a method
for the automatic detection of cardiac waves (ACWD); it was
validated on 45 fMCG data sets of normal fetuses with
gestational age from 22 to 37 weeks. The outcomes of the
automatic procedure were compared with those of a manual
procedure performed by three independent operators on
rhythm strips of 100 consecutive cardiac cycles for each data
set. Distances between the wave boundaries detected with the
two methods were statistically estimated using confidence
intervals: differences were always comparable to those that
could be obtained from different investigators’ estimates.
Statistical correlation between fCTI quantified with ACWD
and with a manual procedure was assessed using the
parametric two-tailed Pearson’s correlation test, significance
level at a = 0.01. The automatic procedure showed a
computation time decrease in the ratio of approximately 1:600 with respect to the manual procedure performed on the same number of beats
Spatial localization of EEG electrodes using 3D scanning
No full textObjective. A reliable reconstruction of neural activity using high-density electroencephalography (EEG) requires an accurate spatial localization of EEG electrodes aligned to the structural magnetic resonance (MR) image of an individual's head. Current technologies for electrode positioning, such as electromagnetic digitization, are yet characterized by non-negligible localization and co-registration errors. In this study, we propose an automated method for spatial localization of EEG electrodes using 3D scanning, a non-invasive and easy-to-use technology with potential applications in clinical settings. Approach. Our method consists of three main steps: (1) the 3D scan is ambient light-corrected and spatially aligned to the head surface extracted from the anatomical MR image; (2) electrode positions are identified by segmenting the 3D scan based on predefined colour and topological properties; (3) electrode labelling is performed by aligning an EEG montage template to the electrode positions. The performance of the method was assessed on data collected in eight participants wearing high-density EEG caps with 128 sensors, from three different manufacturers. We estimated the co-registration error using the distance between the MR-based head shape and the closest 3D scan points. Also, we quantified the positioning error using the distance between the detected electrode positions and the corresponding locations manually selected on the 3D scan data. Main results. For all participants and EEG caps, we obtained a median error of co-registration below 3.0 mm and of spatial localization below 1.4 mm. The method based on 3D scanning data was significantly more precise compared to the electromagnetic digitization technique, and the total time required for obtaining electrode positions was reduced by about half. Significance. We have introduced a method to automatically detect EEG electrodes based on 3D scanning information. We believe that our work can contribute to a more effective, reliable and widespread use of high-density EEG as brain imaging tool
Independent component analysis and fetal magnetocardiography: a tool for the automatic classification of independent components
No full textFetal Magnetocardiography (fMCG) allows the
non-invasive recording of the weak magnetic field variations associated with the electrical activity of the fetal heart. We used Independent Component Analysis (ICA) for the separation of maternal and fetal signals from fMCG recordings. The identification of fetal components is essential to reconstruct fetal signals. In this work we present a tool for the automatic classification of independent components (ACCT). Its performances were assessed using 66 fMCG data sets of normal fetuses ranging between 22 and 37 weeks. ACCT, whose outcomes were compared with those manually obtained
by an expert investigator, showed to be an effective tool.
Moreover, ACCT implementation permitted the reconstruction of stable and reliable fetal traces in a completely automatic manner. The SNR of the obtained fetal signals was high, showing that this was a further step forward the use of fMCG in hospital settings
Automated detection and labeling of high-density EEG electrodes from structural MR images
Full text linkObjective. Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Approach. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Main results. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. Significance. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool
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