Background:Cortical activity across the visual hierarchy has different oscillatory ranges.While 25-90 Hz gamma band influences the feedforward processing,6-13 Hz alpha band travels in the feedback direction.Furthermor...Background:Cortical activity across the visual hierarchy has different oscillatory ranges.While 25-90 Hz gamma band influences the feedforward processing,6-13 Hz alpha band travels in the feedback direction.Furthermore,gamma band acts in supragranular layers,whereas alpha range is localized in infragranular cortical layers.Is the pulvinar,the largest visual thalamic nucleus,mediating this oscillatory cortical coupling?We investigated this question by inactivating pharmacologically the pulvinar in cats and analyzing its impact on the oscillatory flow of neural activity in the visual cortex.Methods:Extracellular responses to full-field 100%contrast gratings were recorded in cortical areas 17 and 21a,from anesthetized cats using linear silicon probes before,during and after the pulvinar inactivation(injection of GABA solution).Visual stimuli were presented in one selected direction.Local field potentials(LFPs)were obtained from low-pass filtering of raw recordings.Wavelet and Granger causality analyses were performed on LFPs to determine the oscillatory coupling between cortical layers.Results:We found that cortical oscillatory activity was enhanced during LPl inactivation.These increases were observed for alpha and gamma bands in areas 17 and 21a.In area 17,alpha and gamma bands significantly increased in layers IV,V,and IV.In area 21a,this increase was observed in all layers except layer I,with a substantial increase of gamma activity in layer IV.Granger causality analysis showed that the pulvinar inactivation caused enhanced of feedforward gamma band signals from area 17(layer III)to area 21a(layer IV).For the feedback coupling,the alpha band rose from area 21a(layer V)to area 17(layers III,V,and VI).Conclusions:Our findings suggest that the pulvinar mediates the cortical oscillatory transmission between areas 17 and 21a.In particular,during the visual stimulation,the pulvinar mediates,to some extent,the bottom-up regulation from layer III of area 17 to layer IV in area 21a.Furthermore,the LPl regulates the feedback directionality of the alpha band from layer V in area 21a to layers II,V,and VI in area 17.These results contribute to our understanding of the mechanism underlying the oscillatory coupling of the feedforward and feedback processing throughout the visual cortical hierarchy.展开更多
Background:For years,studies using several animal models have highlighted the predominant role of the primary visual area in visual information processing.Its six cortical layers have morphological,hodological and phy...Background:For years,studies using several animal models have highlighted the predominant role of the primary visual area in visual information processing.Its six cortical layers have morphological,hodological and physiological differences,although their roles regarding the integration of visual contrast and the messages sent by the layers to other brain regions have been poorly explored.Given that cortical layers have distinct properties,this study aims to understand these differences and how they are affected by a changing visual contrast.Methods:A linear multi-channel electrode was placed in the primary visual cortex(V1)of the anesthetized mouse to record neuronal activity across the different cortical layers.The laminar position of the electrode was verified in real time by measuring the current source density(CSD)and the multi-unit activity(MUA),and confirmed post-mortem by histological analysis.Drifting gratings varying in contrast enabled the measurement of the firing rate of neurons throughout layers.We fitted this data to the Naka-Rushton equations,which generated the contrast response function(CRF)of neurons.Results:The analysis revealed that the baseline activity as well as the rate of change of neural discharges(the slope of the CRF)had a positive correlation across the cortical layers.In addition,we found a trend between the cortical position and the contrast evoking the semi-saturation of the activity.A significant difference in the maximum discharge rate was also found between layers II/III and IV,as well as between layers II/III and V.Conclusions:Since layers II/III and V process visual contrast differently,our results suggest that higher cortical visual areas,as well subcortical regions,receive different information regarding a change in visual contrast.Thus,a contrast may be processed differently throughout the different areas of the visual cortex.展开更多
Background:Information about the visual world is processed by an ensemble of cortical visual areas,which follow a hierarchical organization.The primary visual cortex(V1)first receives most of this information through ...Background:Information about the visual world is processed by an ensemble of cortical visual areas,which follow a hierarchical organization.The primary visual cortex(V1)first receives most of this information through the lateral geniculate nucleus(LGN),before being conveyed to higher-order cortical areas.Aside from this connectional route,there is also a complex network of bilateral connections between areas of the visual cortex and the pulvinar,considered as the largest extrageniculate visual thalamic nucleus.Despite an increasing number of studies on pulvinar,the exact function of this thalamic complex remains unknown.In this study,we investigated the functional impact of the lateral posterior(LP)nucleus,the homologue of the primate pulvinar,on the activity of neurons in the primary visual cortex in mice using optogenetic stimulation.Methods:A channel rhodopsin-2 gene-carrying viral vector(AAV5.CaMKII.hChR2-eYFP.WPRE)was injected into the LP of wild-type(C57BL/6)mice.Extracellular recordings of the activity of V1 neurons were carried out using 16-and 32-channel silicon probes.The stimulation of LP was achieved with light pulses(470 nm,20 pulse trains of 5 ms each at 10 Hz)delivered by a 4-channel optrode,which also recorded the thalamic activity.Visual stimuli consisted on drifting sinewave gratings of varying parameters(direction,contrast,spatial or temporal frequency and size).Results:Our preliminary data shows that LP stimulation performed in conjunction with the visual stimulation decreases the amplitude of neuronal responses up to 50%.To date,results indicate that this inhibitory effect is only observed in neurons in the infragranular layers.The response profiles of V1 neurons to size-increasing stimuli were also affected.Conclusions:These findings suggest that the pulvinar nucleus can exert layer-dependent contextual modulation on the activity of neurons in the mouse primary visual cortex.展开更多
Background:In the visual system,one of the most explored neural behaviors is the response of cells to changes in visual contrast.This neural response to visual contrast,also known as the contrast response function(CRF...Background:In the visual system,one of the most explored neural behaviors is the response of cells to changes in visual contrast.This neural response to visual contrast,also known as the contrast response function(CRF),can be fitted with the Naka-Rushton equation(NRE).Assessing the CRF of many neurons at the same time is critical to establishing functional visual properties.However,maximizing the performance of neurons to fit the NRE,while minimizing their time acquisitions is a challenge.We present a method to accurately obtain reliable NRE fits from experimental data,that ensure a reasonable time of record acquisition.Methods:We simulated CRF of cortical neurons with a toy model based on the response of Poisson spike trains to varied levels of contrasts.We first tested whether mean values or the whole set of contrast responses fit better the NRE.Then,we analyzed what were the boundaries to optimize the fit of the NRE,and after we explore the consequences of fitting the NRE with single-or multi-units.With these outcomes,we varied experimental parameters such as the number of trials,number of input contrasts and length of time acquisition to calculate the errors of fitting CRFs.Those data sets that maximize the CRF fit but minimize the time of recording were selected.The selected data set was then evaluated in visual cortical neurons of anesthetized cats from areas 17,18 and 21a.Results:First,we found that is always better to fit the NRE with mean values rather than the whole set of points.Then,we noticed that either removing or imposing loose boundaries to the CRF parameters lead to an increase in the performance of the NRE fit.Afterward,we found that single units(SU)or assume multi-unit formed of several SUs(>30)adjusted considerably better the NRE fit.Finally,the experiments showed that specific sets of patterns(number of trials,number of input contrasts and length of time acquisition)satisfied our two constraints:minimize the error of the NRE fit while maximizing the acquisition time of recording.The most characteristic pattern was the one with 6 points,15 repetitions and 1 second of duration.However,cortical areas varied in the representation of the patterns.Conclusions:Theoretical simulations of many different sets of patterns and their following experimental validation suggest strongly that a particular set of patterns can satisfy the imposed constraints.With this approach,we provided a tool that allows an optimal design of stimuli to assess the CRF of large neuronal populations and guarantees the finest fit for each unit analyzed.展开更多
Background:It is well known that the pulvinar establishes reciprocal connections with areas of the visual cortex,allowing the transfer of cortico-cortical signals through transthalamic pathways.However,the exact funct...Background:It is well known that the pulvinar establishes reciprocal connections with areas of the visual cortex,allowing the transfer of cortico-cortical signals through transthalamic pathways.However,the exact function of these signals in coordinating activity across the visual cortical hierarchy remains largely unknown.In anesthetized cats,we have explored whether pulvinar inactivation affects the dynamic of interactions between the primary visual cortex(a17)and area 21a,a higher visual cortical area,as well as between layers within each cortical area.We found that pulvinar inactivation modifies the local field potentials(LFPs)coherence between a17 and 21a during a visual stimulation.In addition,the Granger causality analysis showed that the functional connectivity changed across visual areas and between cortical layers during pulvinar inactivation,the effects being stronger in layers of the same area.We observed that the effects of pulvinar inactivation arise at two different epochs of the visual response,i.e.,at the early and late components.The proportion of feedback and feedforward functional events was higher during the early and the late phases of the responses,respectively.We also found that pulvinar inactivation facilitates the feedback propagation of gamma oscillations from 21a to a17.This feedback transmission was predominant during the late response.At the temporal level,pulvinar inactivation also delayed the signals from a17 and 21a,depending on the source and the target of the cortical layer.Thus,the pulvinar can not only modify the functional connectivity between intra and inter cortical layers but may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.Methods:In vivo electrophysiological recordings of visual cortical areas,area 17 and 21a,in anesthetized cats,were then explored with temporal serial analysis(i.e.,Fourier analysis,Coherence,Cross-correlation and Granger causality)of the local field potential.Results:Inactivation of the thalamic nucleus modifies the dynamics of areas 17 and 21a.The changes observed depends on the source and the target of the cortical layer.The pulvinar inactivation arise at two different epochs of visual response.Conclusions:The pulvinar modifies the functional connectivity between intra and inter cortical layers and may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.展开更多
Background:All neurons of the visual system exhibit response to differences in luminance.This neural response to visual contrast,also known as the contrast response function(CRF),follows a characteristic sigmoid shape...Background:All neurons of the visual system exhibit response to differences in luminance.This neural response to visual contrast,also known as the contrast response function(CRF),follows a characteristic sigmoid shape that can be fitted with the Naka-Rushton equation.Four parameters define the CRF,and they are often used in different visual research disciplines,since they describe selective variations of neural responses.As novel technologies have grown,the capacity to record thousands of neurons simultaneously brings new challenges:processing and robustly analyzing larger amounts of data to maximize the outcomes of our experimental measurements.Nevertheless,current guidelines to fit neural activity based on the Naka-Rushton equation have been poorly discussed in depth.In this study,we explore several methods of boundary-setting and least-square curve-fitting for the CRF in order to avoid the pitfalls of blind curve-fitting.Furthermore,we intend to provide recommendations for experimenters to better prepare a solid quantification of CRF parameters that also minimize the time of the data acquisition.For this purpose,we have created a simplified theoretical model of spike-response dynamics,in which the firing rate of neurons is generated by a Poisson process.The spike trains generated by the theoretical model depending on visual contrast intensities were then fitted with the Naka-Rushton equation.This allowed us to identify combinations of parameters that were more important to adjust before performing experiments,to optimize the precision and efficiency of curve fitting(e.g.,boundaries of CRF parameters,number of trials,number of contrast tested,metric of contrast used and the effect of including multi-unit spikes into a single CRF,among others).Several goodness-of-fit methods were also examined in order to achieve ideal fits.With this approach,it is possible to anticipate the minimal requirements to gather and analyze data in a more efficient way in order to build stronger functional models.Methods:Spike-trains were randomly generated following a Poisson distribution in order to draw both an underlying theoretical curve and an empirical one.Random noise was added to the fit to simulate empirical conditions.The correlation function was recreated on the simulated data and re-fit using the Naka-Rushton equation.The two curves were compared:the idea being to determine the most advantageous boundaries and conditions for the curve-fit to be optimal.Statistical analysis was performed on the data to determine those conditions for experiments.Experiments were then conducted to acquire data from mice and cats to verify the model.Results:Results were obtained successfully and a model was proposed to assess the goodness of the fit of the contrast response function.Various parametres and their influence of the model were tested.Other similar models were proposed and their performance was assessed and compared to the previous ones.The fit was optimized to give semi-strict guidelines for scientists to follow in order to maximize their efficiency while obtaining the contrast tuning of a neuron.Conclusions:The aim of the study was to assess the optimal testing parametres of the neuronal response to visual gratings with various luminance,also called the CRF.As technology gets more powerful and potent,one must make choices when experimenting.With a strong model,robust boundaries,and strong experimental conditioning,the best fit to a function can lead to more efficient analysis and stronger cognitive models.展开更多
文摘Background:Cortical activity across the visual hierarchy has different oscillatory ranges.While 25-90 Hz gamma band influences the feedforward processing,6-13 Hz alpha band travels in the feedback direction.Furthermore,gamma band acts in supragranular layers,whereas alpha range is localized in infragranular cortical layers.Is the pulvinar,the largest visual thalamic nucleus,mediating this oscillatory cortical coupling?We investigated this question by inactivating pharmacologically the pulvinar in cats and analyzing its impact on the oscillatory flow of neural activity in the visual cortex.Methods:Extracellular responses to full-field 100%contrast gratings were recorded in cortical areas 17 and 21a,from anesthetized cats using linear silicon probes before,during and after the pulvinar inactivation(injection of GABA solution).Visual stimuli were presented in one selected direction.Local field potentials(LFPs)were obtained from low-pass filtering of raw recordings.Wavelet and Granger causality analyses were performed on LFPs to determine the oscillatory coupling between cortical layers.Results:We found that cortical oscillatory activity was enhanced during LPl inactivation.These increases were observed for alpha and gamma bands in areas 17 and 21a.In area 17,alpha and gamma bands significantly increased in layers IV,V,and IV.In area 21a,this increase was observed in all layers except layer I,with a substantial increase of gamma activity in layer IV.Granger causality analysis showed that the pulvinar inactivation caused enhanced of feedforward gamma band signals from area 17(layer III)to area 21a(layer IV).For the feedback coupling,the alpha band rose from area 21a(layer V)to area 17(layers III,V,and VI).Conclusions:Our findings suggest that the pulvinar mediates the cortical oscillatory transmission between areas 17 and 21a.In particular,during the visual stimulation,the pulvinar mediates,to some extent,the bottom-up regulation from layer III of area 17 to layer IV in area 21a.Furthermore,the LPl regulates the feedback directionality of the alpha band from layer V in area 21a to layers II,V,and VI in area 17.These results contribute to our understanding of the mechanism underlying the oscillatory coupling of the feedforward and feedback processing throughout the visual cortical hierarchy.
文摘Background:For years,studies using several animal models have highlighted the predominant role of the primary visual area in visual information processing.Its six cortical layers have morphological,hodological and physiological differences,although their roles regarding the integration of visual contrast and the messages sent by the layers to other brain regions have been poorly explored.Given that cortical layers have distinct properties,this study aims to understand these differences and how they are affected by a changing visual contrast.Methods:A linear multi-channel electrode was placed in the primary visual cortex(V1)of the anesthetized mouse to record neuronal activity across the different cortical layers.The laminar position of the electrode was verified in real time by measuring the current source density(CSD)and the multi-unit activity(MUA),and confirmed post-mortem by histological analysis.Drifting gratings varying in contrast enabled the measurement of the firing rate of neurons throughout layers.We fitted this data to the Naka-Rushton equations,which generated the contrast response function(CRF)of neurons.Results:The analysis revealed that the baseline activity as well as the rate of change of neural discharges(the slope of the CRF)had a positive correlation across the cortical layers.In addition,we found a trend between the cortical position and the contrast evoking the semi-saturation of the activity.A significant difference in the maximum discharge rate was also found between layers II/III and IV,as well as between layers II/III and V.Conclusions:Since layers II/III and V process visual contrast differently,our results suggest that higher cortical visual areas,as well subcortical regions,receive different information regarding a change in visual contrast.Thus,a contrast may be processed differently throughout the different areas of the visual cortex.
文摘Background:Information about the visual world is processed by an ensemble of cortical visual areas,which follow a hierarchical organization.The primary visual cortex(V1)first receives most of this information through the lateral geniculate nucleus(LGN),before being conveyed to higher-order cortical areas.Aside from this connectional route,there is also a complex network of bilateral connections between areas of the visual cortex and the pulvinar,considered as the largest extrageniculate visual thalamic nucleus.Despite an increasing number of studies on pulvinar,the exact function of this thalamic complex remains unknown.In this study,we investigated the functional impact of the lateral posterior(LP)nucleus,the homologue of the primate pulvinar,on the activity of neurons in the primary visual cortex in mice using optogenetic stimulation.Methods:A channel rhodopsin-2 gene-carrying viral vector(AAV5.CaMKII.hChR2-eYFP.WPRE)was injected into the LP of wild-type(C57BL/6)mice.Extracellular recordings of the activity of V1 neurons were carried out using 16-and 32-channel silicon probes.The stimulation of LP was achieved with light pulses(470 nm,20 pulse trains of 5 ms each at 10 Hz)delivered by a 4-channel optrode,which also recorded the thalamic activity.Visual stimuli consisted on drifting sinewave gratings of varying parameters(direction,contrast,spatial or temporal frequency and size).Results:Our preliminary data shows that LP stimulation performed in conjunction with the visual stimulation decreases the amplitude of neuronal responses up to 50%.To date,results indicate that this inhibitory effect is only observed in neurons in the infragranular layers.The response profiles of V1 neurons to size-increasing stimuli were also affected.Conclusions:These findings suggest that the pulvinar nucleus can exert layer-dependent contextual modulation on the activity of neurons in the mouse primary visual cortex.
文摘Background:In the visual system,one of the most explored neural behaviors is the response of cells to changes in visual contrast.This neural response to visual contrast,also known as the contrast response function(CRF),can be fitted with the Naka-Rushton equation(NRE).Assessing the CRF of many neurons at the same time is critical to establishing functional visual properties.However,maximizing the performance of neurons to fit the NRE,while minimizing their time acquisitions is a challenge.We present a method to accurately obtain reliable NRE fits from experimental data,that ensure a reasonable time of record acquisition.Methods:We simulated CRF of cortical neurons with a toy model based on the response of Poisson spike trains to varied levels of contrasts.We first tested whether mean values or the whole set of contrast responses fit better the NRE.Then,we analyzed what were the boundaries to optimize the fit of the NRE,and after we explore the consequences of fitting the NRE with single-or multi-units.With these outcomes,we varied experimental parameters such as the number of trials,number of input contrasts and length of time acquisition to calculate the errors of fitting CRFs.Those data sets that maximize the CRF fit but minimize the time of recording were selected.The selected data set was then evaluated in visual cortical neurons of anesthetized cats from areas 17,18 and 21a.Results:First,we found that is always better to fit the NRE with mean values rather than the whole set of points.Then,we noticed that either removing or imposing loose boundaries to the CRF parameters lead to an increase in the performance of the NRE fit.Afterward,we found that single units(SU)or assume multi-unit formed of several SUs(>30)adjusted considerably better the NRE fit.Finally,the experiments showed that specific sets of patterns(number of trials,number of input contrasts and length of time acquisition)satisfied our two constraints:minimize the error of the NRE fit while maximizing the acquisition time of recording.The most characteristic pattern was the one with 6 points,15 repetitions and 1 second of duration.However,cortical areas varied in the representation of the patterns.Conclusions:Theoretical simulations of many different sets of patterns and their following experimental validation suggest strongly that a particular set of patterns can satisfy the imposed constraints.With this approach,we provided a tool that allows an optimal design of stimuli to assess the CRF of large neuronal populations and guarantees the finest fit for each unit analyzed.
文摘Background:It is well known that the pulvinar establishes reciprocal connections with areas of the visual cortex,allowing the transfer of cortico-cortical signals through transthalamic pathways.However,the exact function of these signals in coordinating activity across the visual cortical hierarchy remains largely unknown.In anesthetized cats,we have explored whether pulvinar inactivation affects the dynamic of interactions between the primary visual cortex(a17)and area 21a,a higher visual cortical area,as well as between layers within each cortical area.We found that pulvinar inactivation modifies the local field potentials(LFPs)coherence between a17 and 21a during a visual stimulation.In addition,the Granger causality analysis showed that the functional connectivity changed across visual areas and between cortical layers during pulvinar inactivation,the effects being stronger in layers of the same area.We observed that the effects of pulvinar inactivation arise at two different epochs of the visual response,i.e.,at the early and late components.The proportion of feedback and feedforward functional events was higher during the early and the late phases of the responses,respectively.We also found that pulvinar inactivation facilitates the feedback propagation of gamma oscillations from 21a to a17.This feedback transmission was predominant during the late response.At the temporal level,pulvinar inactivation also delayed the signals from a17 and 21a,depending on the source and the target of the cortical layer.Thus,the pulvinar can not only modify the functional connectivity between intra and inter cortical layers but may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.Methods:In vivo electrophysiological recordings of visual cortical areas,area 17 and 21a,in anesthetized cats,were then explored with temporal serial analysis(i.e.,Fourier analysis,Coherence,Cross-correlation and Granger causality)of the local field potential.Results:Inactivation of the thalamic nucleus modifies the dynamics of areas 17 and 21a.The changes observed depends on the source and the target of the cortical layer.The pulvinar inactivation arise at two different epochs of visual response.Conclusions:The pulvinar modifies the functional connectivity between intra and inter cortical layers and may also control the temporal dynamics of neuronal activity across the visual cortical hierarchy.
文摘Background:All neurons of the visual system exhibit response to differences in luminance.This neural response to visual contrast,also known as the contrast response function(CRF),follows a characteristic sigmoid shape that can be fitted with the Naka-Rushton equation.Four parameters define the CRF,and they are often used in different visual research disciplines,since they describe selective variations of neural responses.As novel technologies have grown,the capacity to record thousands of neurons simultaneously brings new challenges:processing and robustly analyzing larger amounts of data to maximize the outcomes of our experimental measurements.Nevertheless,current guidelines to fit neural activity based on the Naka-Rushton equation have been poorly discussed in depth.In this study,we explore several methods of boundary-setting and least-square curve-fitting for the CRF in order to avoid the pitfalls of blind curve-fitting.Furthermore,we intend to provide recommendations for experimenters to better prepare a solid quantification of CRF parameters that also minimize the time of the data acquisition.For this purpose,we have created a simplified theoretical model of spike-response dynamics,in which the firing rate of neurons is generated by a Poisson process.The spike trains generated by the theoretical model depending on visual contrast intensities were then fitted with the Naka-Rushton equation.This allowed us to identify combinations of parameters that were more important to adjust before performing experiments,to optimize the precision and efficiency of curve fitting(e.g.,boundaries of CRF parameters,number of trials,number of contrast tested,metric of contrast used and the effect of including multi-unit spikes into a single CRF,among others).Several goodness-of-fit methods were also examined in order to achieve ideal fits.With this approach,it is possible to anticipate the minimal requirements to gather and analyze data in a more efficient way in order to build stronger functional models.Methods:Spike-trains were randomly generated following a Poisson distribution in order to draw both an underlying theoretical curve and an empirical one.Random noise was added to the fit to simulate empirical conditions.The correlation function was recreated on the simulated data and re-fit using the Naka-Rushton equation.The two curves were compared:the idea being to determine the most advantageous boundaries and conditions for the curve-fit to be optimal.Statistical analysis was performed on the data to determine those conditions for experiments.Experiments were then conducted to acquire data from mice and cats to verify the model.Results:Results were obtained successfully and a model was proposed to assess the goodness of the fit of the contrast response function.Various parametres and their influence of the model were tested.Other similar models were proposed and their performance was assessed and compared to the previous ones.The fit was optimized to give semi-strict guidelines for scientists to follow in order to maximize their efficiency while obtaining the contrast tuning of a neuron.Conclusions:The aim of the study was to assess the optimal testing parametres of the neuronal response to visual gratings with various luminance,also called the CRF.As technology gets more powerful and potent,one must make choices when experimenting.With a strong model,robust boundaries,and strong experimental conditioning,the best fit to a function can lead to more efficient analysis and stronger cognitive models.
