Biology subjects

Bosch-Bayard, J.

Publications and source records attributed to Bosch-Bayard, J..

5 recordsLinked to original sources

bioRxiv2018

Identification negative BOLD responses using windkessel models

Alongside positive BOLD responses (PBR), a variety of negative BOLD responses (NBR) with distinct underlying mechanisms also occur. We identify five mechanisms of NBR: i) local/lateral/contralateral inhibition (LCI), ii) neuronal disruption of network activity (NDA), iii) altered balance of neuro-metabolic/vascular couplings (ANC), iv) arterial blood stealing (ABS), and v) venous blood backpressure (VBB). Detecting and classifying these mechanisms from BOLD signals is pivotal in understanding normal/pathological brain functions. This requires models and parameters with anatomical/functional interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a windkessel model with viscoelastic compliance as well as dynamics of both neuronal and tissue/blood O2 to investigate the generation, detection, classification and interpretation of the BOLD hemodynamic response functions (HRF) of above mechanisms. Firstly, we evaluated the use of the general linear model to detect simulated NBRs. Secondly, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying a new HRF. Crossvalidation indicates NDA and ANC can accurately be classified solely from fMRI BOLD signals; while LCI, ABS and VBB might require additional imaging modalities. Thirdly, we demonstrated that estimators of the model parameters determinant in the NBRs formation are accurate, and precise to certain resolutions. Finally, we successfully applied our detection/classification/estimation methodology to EEG-fMRI data in a clinical situation where several of these mechanisms could coexist. We believe that the proper identification and interpretation of NBR mechanisms have important clinical and cognitive implications in fMRI studies.

neuroscience↗

bioRxiv2018

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons

Electrophysiological Source Imaging (ESI) methods are hampered by the lack of "gold standards" for model comparison. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) recordings (namely EECoG) are considered gold standard to validating EEG generative models with primate models have the unique advantages of both flexibility and translational value in human research. However the severe EEG artifacts during such invasive experiments, the complexity of providing sufficiently detailed biophysical models, as well as lacking sound statistical connectivity comparison methods have hampered the availability and analysis of such datasets. In this paper, 1) we provide EECoG-Comp: an open source platform (https://github.com/Vincent-wq/EECoG-Comp) which encompasses the preprocessing, forward modeling, simulation and comparison module; 2) we take the simultaneous EECoG dataset from www.neurotycho.org as an example to illustrate the use of this platform and compare the source connectivity estimation performance of 4 popular ESI methods named MNE, LCMV, eLORETA and SSBL. The conclusion shows the limits of performance of these ESI connectivity estimators using both simulations and real data analysis. In fact, the use of this platform also suggests the need for both improved simultaneous EEG and ECoG experiments and ESI connectivity estimators.

bioinformatics↗

bioRxiv2018

Populational Super-Resolution Sparse M/EEG Sources and Connectivity Estimation

In this paper, we describe a novel methodology, BC-VARETA, for estimating the Inverse Solution (sources activity) and its Precision Matrix (connectivity parameters) in the frequency domain representation of Stationary Time Series. The aims of this method are three. First: Joint estimation of Source Activity and Connectivity as a frequency domain linear dynamical system identification approach. Second: Achieve super high resolution in the connectivity estimation through Sparse Hermitian Sources Graphical Model. Third: To be a populational approach, preventing the Inverse Solution and Connectivity statistical analysis across subjects as a postprocessing, by modeling population features of Source Activity and Connectivity. Our claims are supported by a wide simulation framework using realistic head models, realistic Sources Setup, and Inverse Crime effects evaluation. Also, a fair quantitative analysis is performed, based on a diversification of quality measures on which state of the art Inverse Solvers were tested.

neuroscience↗

bioRxiv2017

Measures of time series coupling based on generalized weighted multiple regression

1.The sharing and the transmission of information between cortical brain regions is carried out by mechanisms that are still not fully understood. A deeper understanding should shed light on how consciousness and cognition are implemented in the brain. Research activity in this field has recently been focusing on the discovery of non-conventional coupling mechanisms, such as all forms of cross-frequency couplings between diverse combinations of amplitudes and phases, applied to measured or estimated cortical signals of electric neuronal activity. However, all coupling measures that involve phase computation have poor statistical properties. In this work, the conventional estimators for the well-known phase-phase (phase synchronization or locking), phase-amplitude, and phaseamplitude-amplitude couplings are generalized by means of the weighted multiple regression model. The choice of appropriate weights produces estimators that bypass the need for computing the complex-valued phase. In addition, a new coupling, denoted as the inhibitory coupling (InhCo), is introduced and defined as the dependence of one complex-valued variable on the inverse and on the conjugate inverse of another complex-valued variable. A weighted version denoted as wInhCo is also introduced, bypassing the need for computing the inverse of a complex variable, which has very poor statistical properties. The importance of this form of inhibitory coupling is that it may capture well- known processes, such as the observed inverse alpha/gamma relation within the same cortical region, or the inverse alpha/alpha relation between distant cortical regions.

neuroscience↗

bioRxiv2017

Innovations orthogonalization: a solution to the major pitfalls of EEG/MEG "leakage correction"

1.The problem of interest here is the study of brain functional and effective connectivity based non-invasive EEG-MEG inverse solution time series. These signals generally have low spatial resolution, such that an estimated signal at any one site is an instantaneous linear mixture of the true, actual, unobserved signals across all cortical sites. False connectivity can result from analysis of these low-resolution signals. Recent efforts toward \"unmixing\" have been developed, under the name of \"leakage correction\". One recent noteworthy approach is that by Colclough et al (2015 NeuroImage, 117:439-448), which forces the inverse solution signals to have zero cross-correlation at lag zero. One goal is to show that Colcloughs method produces false human connectomes under very broad conditions. The second major goal is to develop a new solution, that appropriately \"unmixes\" the inverse solution signals, based on innovations orthogonalization. The new method first fits a multivariate autoregression to the inverse solution signals, giving the mixed innovations. Second, the mixed innovations are orthogonalized. Third, the mixed and orthogonalized innovations allow the estimation of the \"unmixing\" matrix, which is then finally used to \"unmix\" the inverse solution signals. It is shown that under very broad conditions, the new method produces proper human connectomes, even when the signals are not generated by an autoregressive model.

neuroscience↗