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 ◽  
...  
NeuroImage ◽  
2007 ◽  
Vol 34 (2) ◽  
pp. 839-847 ◽  
Author(s):  
Qian Luo ◽  
Tom Holroyd ◽  
Matthew Jones ◽  
Talma Hendler ◽  
James Blair

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

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.


2020 ◽  
Author(s):  
Amandine Lassalle ◽  
Michael X Cohen ◽  
Laura Dekkers ◽  
Elizabeth Milne ◽  
Rasa Gulbinaite ◽  
...  

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.


2017 ◽  
Vol 31 (3) ◽  
pp. 407-418 ◽  
Author(s):  
Gema Díaz-Blancat ◽  
Juan García-Prieto ◽  
Fernando Maestú ◽  
Francisco Barceló

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

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


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hamidreza Abbaspourazad ◽  
Mahdi Choudhury ◽  
Yan T. Wong ◽  
Bijan Pesaran ◽  
Maryam M. Shanechi

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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