Bayesian estimation of synaptic physiology from the spectral responses of neural masses

Moran, Rosalyn J.; Stephan, Klaas Ε.; Kiebel, Stefan J.; Rombach, N.; O’Connor, W.T.; Murphy, Keith J.; Reilly, Richard B.; Friston, Karl J.

doi:10.1016/j.neuroimage.2008.01.025

Cited by 123 publications

(139 citation statements)

References 58 publications

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“…For example, Moran et al (2008) demonstrated that a DCM of steady-state electrophysiological responses correctly inferred changes in synaptic physiology, following a neurochemical manipulation, which were predicted from concurrent microdialysis measurements. In a next step, ongoing rodent studies test whether DCMs can infer selective changes in specific neuronal mechanisms that result from controlled experimental manipulations; e.g.…”

Section: Approximate Probabilistic (Bayesian) Inferencementioning

confidence: 99%

“…In these cases it is more efficient to summarize the measured time series in terms of their spectral profile. This is the approach developed by Moran et al (2007Moran et al ( , 2008Moran et al ( , 2009), which models local field potential (LFP) data based on the neural mass model described above, using a linearization of the evolution function f around its steady-state (Moran et al 2007). This approach is valid whenever brain activity can be assumed to consist of small perturbations around steady-state (background) activity.…”

Section: Dcm For Eeg/meg/lfpmentioning

confidence: 99%

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Dynamic causal modelling: A critical review of the biophysical and statistical foundations

2011

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The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs.

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Section: Approximate Probabilistic (Bayesian) Inferencementioning

confidence: 99%

Section: Dcm For Eeg/meg/lfpmentioning

confidence: 99%

Dynamic causal modelling: A critical review of the biophysical and statistical foundations

2011

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“…Bitan et al 2005;Ethofer et al 2006;Fairhall and Ishai 2007;Fan et. al 2007;Grol et al 2007;Mechelli et al 2003;Stephan et al 2007a) and has recently been applied to electromagnetic data as observed with EEG and MEG (Garrido et al, 2007;Kiebel et al, 2007) or invasively recorded local field potentials (Moran et al 2008). Even more recently, we have described a dynamic causal model for spectral responses as summarised by time-frequency representations of source-reconstructed EEG or MEG data (Chen et al 2008).…”

Section: Dynamic Causal Modelling For Induced Responsesmentioning

confidence: 99%

Forward and backward connections in the brain: A DCM study of functional asymmetries

et al. 2009

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We compared models with and without nonlinear (between-frequency) coupling in both forward and backward connections. Bayesian model comparison indicated that the best model had nonlinear forward and backward connections. Using the best model we then quantified frequency-specific causal influences mediating observed spectral responses. We found a striking asymmetry between forward and backward connections; in which high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This suppression was significantly greater than the homologous coupling in the forward connections. Furthermore, exactly the asymmetry was observed when we examined face-selective coupling (i.e., coupling under faces minus scrambled faces). These results highlight the importance of nonlinear coupling among brain regions and point to a functional asymmetry between forward and backward connections in the human brain that is consistent with anatomical and physiological evidence from animal studies. This asymmetry is also consistent with functional architectures implied by theories of perceptual inference in the brain, based on hierarchical generative models.

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“…The possibility for extracting physiological information on collective network activity from an EEG has been demonstrated [15][16][17][18]. Indeed, collective dynamics in complex systems that consist of interacting subunits can often be captured by a single or few macroscopic observables, which are named the order parameters [22,64,65].…”

Section: Relationship Between Local Eeg Short-term Sp Of Particular Tmentioning

confidence: 99%

“…It was demonstrated that neural activity patterns measurable at the macro-level by EEG are correlated with underlying neural computations [13][14][15][16][17][18]. Thus, EEG provides a direct measure of cortical activity with millisecond temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology~!2009-11-10~!2010-02-22~!2010-09-08~!

Fingelkurts¹

2010

TONIJ

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Spectral decomposition, to this day, still remains the main analytical paradigm for the analysis of EEG oscillations. However, conventional spectral analysis assesses the mean characteristics of the EEG power spectra averaged out over extended periods of time and/or broad frequency bands, thus resulting in a "static" picture which cannot reflect adequately the underlying neurodynamic. A relatively new promising area in the study of EEG is based on reducing the signal to elementary short-term spectra of various types in accordance with the number of types of EEG stationary segments instead of using averaged power spectrum for the whole EEG. It is suggested that the various perceptual and cognitive operations associated with a mental or behavioural condition constitute a single distinguishable neurophysiological state with a distinct and reliable spectral pattern. In this case, one type of short-term spectral pattern may be considered as a single event in EEG phenomenology. To support this assumption the following issues are considered in detail: (a) the relations between local EEG short-term spectral pattern of particular type and the actual state of the neurons in underlying network and a volume conduction; (b) relationship between morphology of EEG short-term spectral pattern and the state of the underlying neurodynamical system i.e. neuronal assembly; (c) relation of different spectral pattern components to a distinct physiological mechanism; (d) relation of different spectral pattern components to different functional significance; (e) developmental changes of spectral pattern components; (f) heredity of the variance in the individual spectral pattern and its components; (g) intra-individual stability of the sets of EEG short-term spectral patterns and their percent ratio; (h) discrete dynamics of EEG short-term spectral patterns. Functional relevance (consistency) of EEG short-term spectral patterns in accordance with the changes of brain functional state, cognitive task and with different neuropsychopathologies is demonstrated.

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Bayesian estimation of synaptic physiology from the spectral responses of neural masses

Cited by 123 publications

References 58 publications

Dynamic causal modelling: A critical review of the biophysical and statistical foundations

Dynamic causal modelling: A critical review of the biophysical and statistical foundations

Forward and backward connections in the brain: A DCM study of functional asymmetries

Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology~!2009-11-10~!2010-02-22~!2010-09-08~!

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