Modelling event-related responses in the brain
| Autor: | Olivier David, Lee M. Harrison, Karl J. Friston |
|---|---|
| Přispěvatelé: | David, Olivier, Wellcome Department of Imaging Neuroscience, Functional Imaging Laboratory, Institute of Neurology, This work was supported by the Wellcome Trust. |
| Rok vydání: | 2005 |
| Předmět: |
MESH: Neurons
Neocortex Electroencephalography MESH: Signal Processing Computer-Assisted Synaptic Transmission MESH: Magnetic Resonance Imaging MESH: Neocortex 0302 clinical medicine MESH: Animals Attention MESH: Neuronal Plasticity MESH: Oscillometry Evoked Potentials Neurons education.field_of_study Neuronal Plasticity medicine.diagnostic_test Pyramidal Cells 05 social sciences Signal Processing Computer-Assisted MESH: Interneurons Magnetic Resonance Imaging MESH: Nonlinear Dynamics MESH: Evoked Potentials Neurology MESH: Stochastic Processes [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] Arousal Psychology Cognitive Neuroscience Population Stimulus (physiology) 050105 experimental psychology MESH: Electromyography MESH: Neural Networks (Computer) 03 medical and health sciences Superposition principle Interneurons Oscillometry MESH: Electroencephalography MESH: Synaptic Transmission medicine Animals Humans 0501 psychology and cognitive sciences [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] Dominance Cerebral education Stochastic Processes MESH: Attention MESH: Humans Electromyography Stochastic process business.industry MESH: Arousal MESH: Pyramidal Cells Magnetoencephalography MESH: Dominance Cerebral Phase synchronization Nonlinear system Nonlinear Dynamics Neural Networks Computer Artificial intelligence business Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | NeuroImage NeuroImage, Elsevier, 2005, 25 (3), pp.756-70. ⟨10.1016/j.neuroimage.2004.12.030⟩ |
| ISSN: | 1053-8119 1095-9572 |
| Popis: | International audience; The aim of this work was to investigate the mechanisms that shape evoked electroencephalographic (EEG) and magneto-encephalographic (MEG) responses. We used a neuronally plausible model to characterise the dependency of response components on the models parameters. This generative model was a neural mass model of hierarchically arranged areas using three kinds of inter-area connections (forward, backward and lateral). We investigated how responses, at each level of a cortical hierarchy, depended on the strength of connections or coupling. Our strategy was to systematically add connections and examine the responses of each successive architecture. We did this in the context of deterministic responses and then with stochastic spontaneous activity. Our aim was to show, in a simple way, how event-related dynamics depend on extrinsic connectivity. To emphasise the importance of nonlinear interactions, we tried to disambiguate the components of event-related potentials (ERPs) or event-related fields (ERFs) that can be explained by a linear superposition of trial-specific responses and those engendered nonlinearly (e.g., by phase-resetting). Our key conclusions were; (i) when forward connections, mediating bottom-up or extrinsic inputs, are sufficiently strong, nonlinear mechanisms cause a saturation of excitatory interneuron responses. This endows the system with an inherent stability that precludes nondissipative population dynamics. (ii) The duration of evoked transients increases with the hierarchical depth or level of processing. (iii) When backward connections are added, evoked transients become more protracted, exhibiting damped oscillations. These are formally identical to late or endogenous components seen empirically. This suggests that late components are mediated by reentrant dynamics within cortical hierarchies. (iv) Bilateral connections produce similar effects to backward connections but can also mediate zero-lag phase-locking among areas. (v) Finally, with spontaneous activity, ERPs/ERFs can arise from two distinct mechanisms: For low levels of (stimulus related and ongoing) activity, the systems response conforms to a quasi-linear superposition of separable responses to the fixed and stochastic inputs. This is consistent with classical assumptions that motivate trial averaging to suppress spontaneous activity and disclose the ERP/ERF. However, when activity is sufficiently high, there are nonlinear interactions between the fixed and stochastic inputs. This interaction is expressed as a phase-resetting and represents a qualitatively different explanation for the ERP/ERF. |
| Databáze: | OpenAIRE |
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