scholarly journals A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

2018 ◽  
Vol 14 (10) ◽  
pp. e1006359 ◽  
Author(s):  
Maximilian Schmidt ◽  
Rembrandt Bakker ◽  
Kelly Shen ◽  
Gleb Bezgin ◽  
Markus Diesmann ◽  
...  
Keyword(s):  
Network Model ◽  
Resting State ◽  
Multi Scale ◽  
Cortical Areas ◽  
Scale Layer ◽  
Spiking Network ◽  
Visual Cortical

scholarly journals Assessing uncertainty in connective field estimations from resting state fMRI activity

2020 ◽  
Author(s):  
Azzurra Invernizzi ◽  
Nicolas Gravel ◽  
Koen V. Haak ◽  
Remco J. Renken ◽  
Frans W. Cornelissen
Keyword(s):  
Visual Field ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
List Type ◽  
Spatial Integration ◽  
Cortical Areas ◽  
Local Functional ◽  
Visual Cortical

AbstractConnective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modelling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data, although this is important since 3T scanners are much more abundant and more often used in clinical research than 7T ones. In this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans interleaved by the acquisition of visual stimulation in 12 healthy participants.Our results show that both approaches reveal good agreement between RS- and visual field (VF)-based maps. Moreover, the 3T observations were similar to those previously reported at 7T. In addition, to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.HighlightsLocal functional connectivity between visual cortical areas can be estimated from RS-fMRI data at 3T using both standard CF and Bayesian CF modelling.Bayesian CF modelling quantifies the model uncertainty associated with each CF parameter on RS and VF data, important in particular for future studies on clinical populations.3T observations were qualitatively similar to those previously reported at 7T.

Patterns of resting state connectivity in human primary visual cortical areas: A 7T fMRI study

NeuroImage ◽  
2014 ◽  
Vol 84 ◽  
pp. 911-921 ◽  
Author(s):  
Mathijs Raemaekers ◽  
Wouter Schellekens ◽  
Richard J.A. van Wezel ◽  
Natalia Petridou ◽  
Gert Kristo ◽  
...  
Keyword(s):  
Resting State ◽  
Cortical Areas ◽  
Fmri Study ◽  
Visual Cortical ◽  
Resting State Connectivity ◽  
7T Fmri

scholarly journals Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T

2021 ◽  
Vol 15 ◽  
Author(s):  
Azzurra Invernizzi ◽  
Nicolas Gravel ◽  
Koen V. Haak ◽  
Remco J. Renken ◽  
Frans W. Cornelissen
Keyword(s):  
Visual Field ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Parameter Estimates ◽  
Spatial Integration ◽  
External Stimulation ◽  
Cortical Areas ◽  
Local Functional ◽  
Visual Cortical

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modeling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data. This is important since 3T scanners are much more abundant and more often used in clinical research compared to 7T scanners. Therefore in this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans, which were separated by the acquisition of visual field mapping data in 12 healthy participants. Our results show good agreement between RS- and visual field (VF)- based maps using either the standard or Bayesian CF approach. In addition to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. Moreover, observations obtained using 3T scanners were qualitatively similar to those reported for 7T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.

Subcortical structures projecting to visual cortical areas in squirrel monkey

1982 ◽  
Vol 209 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Johannes Tigges ◽  
M. Tigges ◽  
N. A. Cross ◽  
R. L. McBride ◽  
W. D. Letbetter ◽  
...  
Keyword(s):  
Squirrel Monkey ◽  
Subcortical Structures ◽  
Cortical Areas ◽  
Visual Cortical

scholarly journals Functional Specialization of Seven Mouse Visual Cortical Areas

Neuron ◽  
2011 ◽  
Vol 72 (6) ◽  
pp. 1040-1054 ◽  
Author(s):  
James H. Marshel ◽  
Marina E. Garrett ◽  
Ian Nauhaus ◽  
Edward M. Callaway
Keyword(s):  
Functional Specialization ◽  
Cortical Areas ◽  
Visual Cortical

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

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