Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation

David, Olivier; Guillemain, Isabelle; Saillet, Sandrine; Reyt, Sebastien; Deransart, Colin; Segebarth, Christoph; Depaulis, Antoine

doi:10.1371/journal.pbio.0060315

Cited by 495 publications

(533 citation statements)

References 77 publications

(118 reference statements)

Supporting

Mentioning

517

Contrasting

Unclassified

Order By: Relevance

“…In other words, the foci will be responsible for inducing the plastic changes into the circuit, phenotypically generating and initiating SWDs and driving and maintaining these bilateral synchronous SWDs in this absence model. It is most likely that similar processes occur in other rodent genetic models such as GAERS as well (David et al, 2008).…”

mentioning

confidence: 87%

Upholding WAG/Rij rats as a model of absence epileptogenesis: Hidden mechanisms and a new theory on seizure development

Russo

Citraro

Constanti³

et al. 2016

Neuroscience & Biobehavioral Reviews

128

View full text Add to dashboard Cite

Upholding WAG/Rij rats as a model of absence epileptogenesis: Hidden mechanisms and a new theory on seizure development.Neuroscience and Biobehavioral Reviews http://dx.doi.org/10. 1016/j.neubiorev.2016.09.017 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractThe WAG/Rij rat model has recently gathered attention as a suitable animal model of absence epileptogenesis. This latter term has a broad definition encompassing any possible cause that determines the development of spontaneous seizures; however, most of, if not all, preclinical knowledge on epileptogenesis is confined to the study of post-brain insult models such as traumatic brain injury or post-status epilepticus models. WAG/Rij rats, but also synapsin 2 knockout, Kv7 current-deficient mice represent the first examples of genetic models where an efficacious antiepileptogenic treatment (ethosuximide) was started before seizure onset. In this review, we have critically reconsidered all articles published regarding WAG/Rij rats, from the perspective that the period before SWD onset is considered as the latent period. In our new theory on seizure development, it is proposed that genes might be considered as the initial "insult" responsible for all plastic changes underpinning the development of spontaneous seizures. According to this idea, in WAG/Rij rats, genetic predisposition would lead to the development of abnormal bilateral cortical epileptic foci, which would then nongenetically stimulate the rest of the brain to rearrange networks in order to phenotypically develop seizures similarly to what happens during electrical kindling.

show abstract

mentioning

confidence: 87%

Upholding WAG/Rij rats as a model of absence epileptogenesis: Hidden mechanisms and a new theory on seizure development

Russo

Citraro

Constanti³

et al. 2016

Neuroscience & Biobehavioral Reviews

128

View full text Add to dashboard Cite

show abstract

“…Application of G-causality to fMRI BOLD data has been highly controversial for apparently good reasons; e.g., David et al (2008); Valdes-Sosa et al (2011). First, the BOLD signal (as captured by the hemodynamic response function, HRF) is an indirect, sluggish, and variable (inter-regionally and inter-subjectively), transformation of underlying neural activity (Handwerker et al, 2012).…”

Section: Application To Fmri Bold Datamentioning

confidence: 99%

“…Second, typical fMRI protocols involve severe downsampling with sample intervals (repetition times, TRs) normally ranging from 1 -3 sec, substantially longer than typical inter-neuron delays. Inter-regional HRF variation has been argued to be particularly devastating for G-causality analysis: if X is causally driving Y at the neural level, but if the BOLD response to X peaks later than the BOLD response to Y, the suspicion is that G-causality analysis would falsely infer that Y is driving X (David et al, 2008). Perhaps surprisingly, this is not the case.…”

Section: Application To Fmri Bold Datamentioning

confidence: 99%

The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference

Barnett

Seth

2014

Journal of Neuroscience Methods

883

874

View full text Add to dashboard Cite

Background: Wiener-Granger causality ("G-causality") is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling.New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy.Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s):The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions:The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference.

show abstract

“…Another consequence is the potential detection of a group effect due to a systematic HRF difference, which would then be incorrectly interpreted as a neuronal effect. Moreover, when attempting to infer causality within brain networks from BOLD data, differences in HRF latency across brain regions can potentially confound the directionality of information flow (David et al, 2008;Smith et al, 2011;Murta et al, 2012;Jorge et al, 2014). On the other hand, HRF variability may be an object of interest on its own, potentially reflecting physiological changes associated with the effects of drugs, aging or pathology, for example (Iadecola, 2004).…”

Section: Significance Of Hrf Estimationmentioning

confidence: 99%

On the distinguishability of HRF models in fMRI

Silvestre¹,

Figueiredo²,

Rosa³

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

View full text Add to dashboard Cite

Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.

show abstract

Identifying Neural Drivers with Functional MRI: An Electrophysiological Validation

Cited by 495 publications

References 77 publications

Upholding WAG/Rij rats as a model of absence epileptogenesis: Hidden mechanisms and a new theory on seizure development

Upholding WAG/Rij rats as a model of absence epileptogenesis: Hidden mechanisms and a new theory on seizure development

The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference

On the distinguishability of HRF models in fMRI

Contact Info

Product

Resources

About