Biology subjects

Jerbi, K.

Publications and source records attributed to Jerbi, K..

4 recordsLinked to original sources

bioRxiv2018

Estimation of task-based modulations in functional connectivity with MEG: a comparison of methods

For the assessment of functional interactions between distinct brain regions there is a great variety of mathematical techniques, with well-known properties, relative merits and shortcomings; however, the methods that deal specifically with task-based fluctuations in interareal coupling are scarce, and their relative performance is unclear. In the present article, we compare two approaches used in the estimation of correlation changes between the envelope amplitudes of narrowband brain activity obtained from magnetoencephalography (MEG) recordings. One approach is an implementation of semipartial canonical correlation analysis (SP-CCA), which is formally equivalent to the psychophysiological interactions technique successfully applied to functional magnetic resonance data. The other approach, which has been used in recent electrophysiology studies, consists of simply computing linear correlation coefficients of signals from two experimental conditions and taking their differences. We compared the two approaches with simulations and with multi-subject MEG signals acquired during a visuomotor coordination study. The analyses with simulated activity showed that computing differences in correlation coefficients (DCC) provided better discrimination between true coupling changes and spurious effects; on the other hand, SP-CCA resulted in significant effects around the reference location which were not found with DCC, and which may be due to field spread. Based on our findings, we recommend the use of DCC for the detection of task-based changes in connectivity, as it provided better performance than SP-CCA.

neuroscience↗

bioRxiv2018

Estimation of narrowband amplitude and phase from electrophysiology signals for phase-amplitude coupling studies: a comparison of methods

Many functional connectivity studies based on electrophysiological measurements, such as electro- and magnetoencephalography (EEG/MEG), start their investigations by extracting a narrowband representation of brain activity time series, and then computing their envelope amplitudes and instantaneous phases, which serve as inputs to subsequent data processing. The two most popular approaches for obtaining these narrowband amplitudes and phases are: bandpass filtering followed by Hilbert transform (we call this the Hilbert approach); and convolution with wavelet kernels (the wavelet approach). In this work, we investigate how these two approaches perform in detecting the phenomenon of phase-amplitude coupling (PAC), whereby the amplitude of a high-frequency signal is driven by the phase of a low-frequency signal. The comparison of both approaches is carried out by means of simulated brain activity, from which we run receiver operating characteristic (ROC) analyses, and of experimental MEG data from a visuomotor coordination study. The ROC analyses show that both approaches have comparable accuracy, except in the presence of interfering signals with frequencies near the high-frequency band. As for the visuomotor data, the most noticeable impact of the choice of approach was observed when evaluating task-based changes in PAC between the delta (2-5 Hz) and the high-gamma (60-90 Hz) frequency bands, as we were able to identify widespread brain areas with statistically significant effects only with the Hilbert approach. These results provide preliminary evidence of the advantages of the Hilbert approach over the wavelet approach, at least in the context of PAC estimates.

neuroscience↗

bioRxiv2017

Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures

When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or \"ghost\" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations.\n\nHighlights{checkmark} Reliable estimation of neuronal coupling with MEG and EEG is challenged by signal mixing\n{checkmark}A number of coupling techniques attempt to overcome this limitation by excluding zero-lag interactions\n{checkmark}Contrary to what is commonly admitted, our simulations illustrate that such interaction metrics will still yield false positives\n{checkmark}Spurious, or \"ghost\", interactions are generally detected between sources in the vicinity of true phase-lagged interacting sources\n{checkmark}Signal mixing also severely affects the mutual separability of phase and amplitude correlations

neuroscience↗

bioRxiv2017

Power and shift invariant detection of dynamically coupled networks (PSIICOS) from non-invasive MEG data

MEG reflects electrical activity of neuronal assemblies that coalesce and decoalesce in time providing for massively parallel and dynamic flow of information exchange in the brain. There is a growing evidence that our behavior is mediated by simultaneous activity of several interacting and simultaneously active cortical networks. Not only the location of network nodes but also the temporal profiles of such interaction are of interest. The existing methods are primarily concerned with proposing novel measures of synchrony to be applied to the cortical region activity signals extracted from the MEG data using beamforming or other spatial filtering approaches.\n\nIn this work we propose to render the task of estimating network topology and temporal profiles as a source estimation problem but in the space of interacting topographies. The proposed point of view has a number of benefits as it allows to use the wealth of techniques and intuition accumulated in the community when dealing with source estimation problems. Operating in the interacting topographies space we first propose subspace projection method to significantly reduce the spatial leakage effect in the cross-spectral matrix. We then use subspace matching metrics to extract a set of networks with their synchronicity profiles that explain the variance in the sensor space cross-spectral matrix, much like regular dipoles and their activations explain the variance in the regular evoked responses.\n\nThe preliminary results of application of this method to a simple self-paced voluntary finger movement paradigm comparing allowed to observe functional coupling between primary motor and supplementary motor area and establish that this coupling can be characterized with near-zero phase delay between the oscillatory activity of these two regions. This observed synchrony pattern is physiologically plausible and is in agreement with the current hypothesis about the neural mechanics of the motor acts.

neuroscience↗