scholarly journals Microbiota links to neural dynamics supporting threat processing

2021 ◽  
Author(s):  
Caitlin V. Hall ◽  
Ben J. Harrison ◽  
Kartik K. Iyer ◽  
Hannah S. Savage ◽  
Martha Zakrzewski ◽  
...  
Keyword(s):  
Neural Dynamics ◽  
Threat Processing

scholarly journals Neural dynamics for facial threat processing as revealed by gamma band synchronization using MEG

NeuroImage ◽  
2007 ◽  
Vol 34 (2) ◽  
pp. 839-847 ◽  
Author(s):  
Qian Luo ◽  
Tom Holroyd ◽  
Matthew Jones ◽  
Talma Hendler ◽  
James Blair
Keyword(s):  
Gamma Band ◽  
Neural Dynamics ◽  
Threat Processing ◽  
Facial Threat

scholarly journals Microbiota links to neural dynamics supporting threat processing

2021 ◽  
Author(s):  
Caitlin V. Hall ◽  
Ben J. Harrison ◽  
Kartik K. Iyer ◽  
Hannah S. Savage ◽  
Martha Zakrzewski ◽  
...  
Keyword(s):  
Computational Modelling ◽  
Anterior Cingulate ◽  
Microbial Consortia ◽  
Neural Dynamics ◽  
Ecological Indices ◽  
Threat Processing ◽  
Neural Computations ◽  
Functional Inference ◽  
High Level ◽  
Healthy Participants

AbstractThere is growing recognition that the composition of the gut microbiota influences behaviour, including responses to threat. The cognitive-interoceptive appraisal of threat-related stimuli relies on dynamic neural computations between the anterior insular (AIC) and the dorsal anterior cingulate (dACC) cortices. If, to what extent, and how microbial consortia influence the activity of this cortical threat processing circuitry is unclear. We addressed this question by combining a threat processing task, neuroimaging, 16S rRNA profiling, and computational modelling in healthy participants. Results showed interactions between high-level ecological indices with threat-related AIC-dACC neural dynamics. At finer taxonomic resolutions, the abundance of Ruminococcus was differentially linked to connectivity between, and activity within the AIC and dACC during threat updating. Functional inference analysis provides a strong rationale to motivate future investigations of microbiota-derived metabolites in the observed relationship with threat-related brain processes.

Behavioural and EEG atypicalities during rest, visual perception, and cognitive control in autistic adults

2020 ◽  
Author(s):  
Amandine Lassalle ◽  
Michael X Cohen ◽  
Laura Dekkers ◽  
Elizabeth Milne ◽  
Rasa Gulbinaite ◽  
...  
Keyword(s):  
Visual Perception ◽  
Cognitive Control ◽  
Visual Processing ◽  
Autism Spectrum ◽  
Neural Dynamics ◽  
Simple Explanation ◽  
Control Task ◽  
Neural Structure ◽  
Eeg Power ◽  
Autistic Adults

Background: People with an Autism Spectrum Condition diagnosis (ASD) are hypothesized to show atypical neural dynamics, reflecting differences in neural structure and function. However, previous results regarding neural dynamics in autistic individuals have not converged on a single pattern of differences. It is possible that the differences are cognitive-set-specific, and we therefore measured EEG in autistic individuals and matched controls during three different cognitive states: resting, visual perception, and cognitive control.Methods: Young adults with and without an ASD (N=17 in each group) matched on age (range 20 to 30 years), sex, and estimated Intelligence Quotient (IQ) were recruited. We measured their behavior and their EEG during rest, a task requiring low-level visual perception of gratings of varying spatial frequency, and the “Simon task” to elicit activity in the executive control network. We computed EEG power and Inter-Site Phase Clustering (ISPC; a measure of connectivity) in various frequency bands.Results: During rest, there were no ASD vs. controls differences in EEG power, suggesting typical oscillation power at baseline. During visual processing, without pre-baseline normalization, we found decreased broadband EEG power in ASD vs. controls, but this was not the case during the cognitive control task. Furthermore, the behavioral results of the cognitive control task suggest that autistic adults were better able to ignore irrelevant stimuli.Conclusions: Together, our results defy a simple explanation of overall differences between ASD and controls, and instead suggest a more nuanced pattern of altered neural dynamics that depend on which neural networks are engaged.

Fast Neural Dynamics of Proactive Cognitive Control in a Task-Switching Analogue of the Wisconsin Card Sorting Test

2017 ◽  
Vol 31 (3) ◽  
pp. 407-418 ◽  
Author(s):  
Gema Díaz-Blancat ◽  
Juan García-Prieto ◽  
Fernando Maestú ◽  
Francisco Barceló
Keyword(s):  
Cognitive Control ◽  
Task Switching ◽  
Wisconsin Card Sorting Test ◽  
Neural Dynamics ◽  
Card Sorting ◽  
Wisconsin Card Sorting ◽  
Sorting Test

scholarly journals Correction to: Neural dynamics based complete grid coverage by single and multiple

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Arindam Singha ◽  
Anjan Kumar Ray ◽  
Arun Baran Samaddar
Keyword(s):  
Neural Dynamics

A correction to this paper has been published: https://doi.org/10.1007/s42452-021-04606-4

scholarly journals Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hamidreza Abbaspourazad ◽  
Mahdi Choudhury ◽  
Yan T. Wong ◽  
Bijan Pesaran ◽  
Maryam M. Shanechi
Keyword(s):  
Neural Control ◽  
Multiple Scales ◽  
Network Activity ◽  
Neural Dynamics ◽  
Population Activity ◽  
Reach And Grasp ◽  
Spatiotemporal Scales ◽  
Motor Cortical ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

AbstractMotor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode’s decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

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