doi: 10.3389/fncom.2015.00014 Cross-frequency transfer in a stochastically driven mesoscopic neur... more doi: 10.3389/fncom.2015.00014 Cross-frequency transfer in a stochastically driven mesoscopic neuronal model
POSTER PRESENTATION Open Access Stimulus induced resonance in a neural mass model driven with a temporally correlated noise
Power spectra of experimental recordings such as EEG and LFP exhibit a broadband nature character... more Power spectra of experimental recordings such as EEG and LFP exhibit a broadband nature characterized by a stable 1/fb background with embedded peaks. These peaks are associated with specific brain states and stimuli, and depending on conditions appear at different frequen-cies. Interactions between brain rhythms from different frequency bands are assumed to play an important role in brain functioning. Here we study theoretically a parti-cular cross-frequency interaction that has been observed experimentally [1]. That experiment examined the effect of slow rocking on sleep in human subjects, and showed that slow oscillatory stimulus related to rocking enhanced power in EEG measurements in low (0.6-4 Hz) and alpha frequency bands, thus revealing a cross-frequency trans-fer of power.
The brain is known to operate under the constant influence of noise arising from a variety of sou... more The brain is known to operate under the constant influence of noise arising from a variety of sources. It also organises its activity into rhythms spanning multiple frequency bands. These rhythms originate from neuronal oscillations which can be detected via measurements such as electroen-cephalography (EEG) and functional magnetic resonance (fMRI). Experimental evidence suggests that interactions between rhythms from distinct frequency bands play a key role in brain processing, but the dynamical mechanisms underlying this cross-frequency interactions are still under investigation. Some rhythms are pathological and harmful to brain function. Such is the case of epileptiform rhythms characterising epileptic seizures. Much has been learnt about the dynamics of the brain from computational modelling. Particularly relevant is mesoscopic scale modelling, which is concerned with spatial scales exceeding those of individual neurons and corresponding to processes and structures underlying t...
We present a mean-field formalism able to predict the collective dynamics of large networks of co... more We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the populations response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology.
The brain can be understood as a collection of interacting neuronal oscillators, but the extent t... more The brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we study the joint dynamics of two cortical columns described by Jansen-Rit neural mass models, and show that coupling between the columns gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events. For large enough coupling strengths, termination of these events results mainly from the emergence of synchronization between the columns, and thus is controlled by coupling instead of noise. Stochastic triggering and noise-independent durations are characteristic of excitable dynamics, and thus we interpret our results in terms of collective excitability.
Macroscopic models of brain networks typically incorporate assumptions regarding the characterist... more Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/f b spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands.
The brain is known to operate in multiple coexisting frequency bands. Increasing experimental evi... more The brain is known to operate in multiple coexisting frequency bands. Increasing experimental evidence suggests that interactions between those distinct bands play a crucial role in brain processes, but the dynamical mechanisms underlying this cross-frequency coupling are still under investigation. Two approaches have been proposed to address this issue. In the first one distinct nonlinear oscillators representing the brain rhythms involved are coupled actively (bidirectionally), whereas in the second one the oscillators are coupled unidirectionally and thus the driving between them is passive. Here we elaborate the latter approach by implementing a stochastically driven network of coupled neural mass models that operate in the alpha range. This model exhibits a broadband power spectrum with 1/f b form, similar to those observed experimentally. Our results show that such a model is able to reproduce recent experimental observations on the effect of slow rocking on the alpha activity associated with sleep. This suggests that passive driving can account for cross-frequency transfer in the brain, as a result of the complex nonlinear dynamics of its underlying oscillators.
Physica A: Statistical Mechanics and its Applications, 2002
Higher order clustering coefficients C(x) are introduced for random networks. The coefficients ex... more Higher order clustering coefficients C(x) are introduced for random networks. The coefficients express probabilities that the shortest distance between any two nearest neighbours of a certain vertex i equals x, when one neglects all paths crossing the node i. Using C(x) we found that in the Barabási-Albert (BA) model the average shortest path length in a node's neighbourhood is smaller than the equivalent quantity of the whole network and the remainder depends only on the network parameter m. Our results show that small values of the standard clustering coefficient in large BA networks are due to random character of the nearest neighbourhood of vertices in such networks.
Intermodal consistency of whole-brain connectivity and signal propagation delays
Measuring the propagation of perturbations across the human brain and their transmission delays i... more Measuring the propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires cross-validation approaches. Here, we compare results from a recently introduced, non-invasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of epileptic pharmaco-resistant patients. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays. This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e. neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence ...
Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electro... more Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and th...
Communication between gray matter regions underpins all facets of brain function. To date, progre... more Communication between gray matter regions underpins all facets of brain function. To date, progress in understanding large-scale neural communication has been hampered by the inability of current neuroimaging techniques to track signaling at whole-brain, high-spatiotemporal resolution. Here, we use 2.77 million intracranial EEG recordings, acquired following 29,055 single-pulse electrical stimulations in a total of 550 individuals, to study inter-areal communication in the human brain. We found that network communication models—computed on structural connectivity inferred from diffusion MRI—can explain the propagation of direct, focal electrical stimulation through white matter, measured at millisecond time scales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (out-of-sample R2=54%). Our work contributes towards the biological v...
In tinnitus literature, researchers have increasingly been advocating for a clearer distinction b... more In tinnitus literature, researchers have increasingly been advocating for a clearer distinction between tinnitus perception and tinnitus-related distress. In non-bothersome tinnitus, the perception itself can be more specifically investigated: this has provided a body of evidence, based on resting-state and activation fMRI protocols, highlighting the involvement of regions outside the conventional auditory areas, such as the right parietal operculum. Here, we aim to conduct a review of available investigations of the human parietal operculo–insular subregions conducted at the microscopic, mesoscopic, and macroscopic scales arguing in favor of an auditory–somatosensory cross-talk. Both the previous literature and new results on functional connectivity derived from cortico–cortical evoked potentials show that these subregions present a dense tissue of interconnections and a strong connectivity with auditory and somatosensory areas in the healthy brain. Disrupted integration processes ...
Connectivity probability as well as features describing fibers biophysical properties, estimated ... more Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 315 patients, in the MarsAtlas, HCP-MMP1 and Lausanne2008 (resolutions 60, 125, 250) parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, integral, duration and the velocity estimated from the onset latency and the fibers distance between the parcels.
We present the first measurements of charge-dependent correlations on angular difference variable... more We present the first measurements of charge-dependent correlations on angular difference variables η1 − η2 (pseudorapidity) and φ1 − φ2 (azimuth) for primary charged hadrons with transverse momentum 0.15 ≤ pt ≤ 2 GeV/c and |η| ≤ 1.3 from Au-Au collisions at √ sNN = 130 GeV. We observe correlation structures not predicted by theory but consistent with evolution of hadron emission geometry with increasing centrality from one-dimensional fragmentation of color strings along the beam direction to an at least two-dimensional hadronization geometry along the beam and azimuth directions of a hadron-opaque bulk medium.
We report on p-, p-, p-, and pcorrelation functions constructed in central Au-Au collisions at √ ... more We report on p-, p-, p-, and pcorrelation functions constructed in central Au-Au collisions at √ s NN = 200 GeV by the STAR experiment at RHIC. The proton and lambda source size is inferred from the pand pcorrelation functions. It is found to be smaller than the pion source size also measured by the STAR experiment at smaller transverse masses, in agreement with a scenario of a strong universal collective flow. The pand pcorrelation functions, which are measured for the first time, exhibit a large anticorrelation. Annihilation channels and/or a negative real part of the spin-averaged scattering length must be included in the final-state interactions calculation to reproduce the measured correlation function.
doi: 10.3389/fncom.2015.00014 Cross-frequency transfer in a stochastically driven mesoscopic neur... more doi: 10.3389/fncom.2015.00014 Cross-frequency transfer in a stochastically driven mesoscopic neuronal model
POSTER PRESENTATION Open Access Stimulus induced resonance in a neural mass model driven with a temporally correlated noise
Power spectra of experimental recordings such as EEG and LFP exhibit a broadband nature character... more Power spectra of experimental recordings such as EEG and LFP exhibit a broadband nature characterized by a stable 1/fb background with embedded peaks. These peaks are associated with specific brain states and stimuli, and depending on conditions appear at different frequen-cies. Interactions between brain rhythms from different frequency bands are assumed to play an important role in brain functioning. Here we study theoretically a parti-cular cross-frequency interaction that has been observed experimentally [1]. That experiment examined the effect of slow rocking on sleep in human subjects, and showed that slow oscillatory stimulus related to rocking enhanced power in EEG measurements in low (0.6-4 Hz) and alpha frequency bands, thus revealing a cross-frequency trans-fer of power.
The brain is known to operate under the constant influence of noise arising from a variety of sou... more The brain is known to operate under the constant influence of noise arising from a variety of sources. It also organises its activity into rhythms spanning multiple frequency bands. These rhythms originate from neuronal oscillations which can be detected via measurements such as electroen-cephalography (EEG) and functional magnetic resonance (fMRI). Experimental evidence suggests that interactions between rhythms from distinct frequency bands play a key role in brain processing, but the dynamical mechanisms underlying this cross-frequency interactions are still under investigation. Some rhythms are pathological and harmful to brain function. Such is the case of epileptiform rhythms characterising epileptic seizures. Much has been learnt about the dynamics of the brain from computational modelling. Particularly relevant is mesoscopic scale modelling, which is concerned with spatial scales exceeding those of individual neurons and corresponding to processes and structures underlying t...
We present a mean-field formalism able to predict the collective dynamics of large networks of co... more We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the populations response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology.
The brain can be understood as a collection of interacting neuronal oscillators, but the extent t... more The brain can be understood as a collection of interacting neuronal oscillators, but the extent to which its sustained activity is due to coupling among brain areas is still unclear. Here we study the joint dynamics of two cortical columns described by Jansen-Rit neural mass models, and show that coupling between the columns gives rise to stochastic initiations of sustained collective activity, which can be interpreted as epileptic events. For large enough coupling strengths, termination of these events results mainly from the emergence of synchronization between the columns, and thus is controlled by coupling instead of noise. Stochastic triggering and noise-independent durations are characteristic of excitable dynamics, and thus we interpret our results in terms of collective excitability.
Macroscopic models of brain networks typically incorporate assumptions regarding the characterist... more Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/f b spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands.
The brain is known to operate in multiple coexisting frequency bands. Increasing experimental evi... more The brain is known to operate in multiple coexisting frequency bands. Increasing experimental evidence suggests that interactions between those distinct bands play a crucial role in brain processes, but the dynamical mechanisms underlying this cross-frequency coupling are still under investigation. Two approaches have been proposed to address this issue. In the first one distinct nonlinear oscillators representing the brain rhythms involved are coupled actively (bidirectionally), whereas in the second one the oscillators are coupled unidirectionally and thus the driving between them is passive. Here we elaborate the latter approach by implementing a stochastically driven network of coupled neural mass models that operate in the alpha range. This model exhibits a broadband power spectrum with 1/f b form, similar to those observed experimentally. Our results show that such a model is able to reproduce recent experimental observations on the effect of slow rocking on the alpha activity associated with sleep. This suggests that passive driving can account for cross-frequency transfer in the brain, as a result of the complex nonlinear dynamics of its underlying oscillators.
Physica A: Statistical Mechanics and its Applications, 2002
Higher order clustering coefficients C(x) are introduced for random networks. The coefficients ex... more Higher order clustering coefficients C(x) are introduced for random networks. The coefficients express probabilities that the shortest distance between any two nearest neighbours of a certain vertex i equals x, when one neglects all paths crossing the node i. Using C(x) we found that in the Barabási-Albert (BA) model the average shortest path length in a node's neighbourhood is smaller than the equivalent quantity of the whole network and the remainder depends only on the network parameter m. Our results show that small values of the standard clustering coefficient in large BA networks are due to random character of the nearest neighbourhood of vertices in such networks.
Intermodal consistency of whole-brain connectivity and signal propagation delays
Measuring the propagation of perturbations across the human brain and their transmission delays i... more Measuring the propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires cross-validation approaches. Here, we compare results from a recently introduced, non-invasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of epileptic pharmaco-resistant patients. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays. This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e. neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence ...
Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electro... more Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and th...
Communication between gray matter regions underpins all facets of brain function. To date, progre... more Communication between gray matter regions underpins all facets of brain function. To date, progress in understanding large-scale neural communication has been hampered by the inability of current neuroimaging techniques to track signaling at whole-brain, high-spatiotemporal resolution. Here, we use 2.77 million intracranial EEG recordings, acquired following 29,055 single-pulse electrical stimulations in a total of 550 individuals, to study inter-areal communication in the human brain. We found that network communication models—computed on structural connectivity inferred from diffusion MRI—can explain the propagation of direct, focal electrical stimulation through white matter, measured at millisecond time scales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (out-of-sample R2=54%). Our work contributes towards the biological v...
In tinnitus literature, researchers have increasingly been advocating for a clearer distinction b... more In tinnitus literature, researchers have increasingly been advocating for a clearer distinction between tinnitus perception and tinnitus-related distress. In non-bothersome tinnitus, the perception itself can be more specifically investigated: this has provided a body of evidence, based on resting-state and activation fMRI protocols, highlighting the involvement of regions outside the conventional auditory areas, such as the right parietal operculum. Here, we aim to conduct a review of available investigations of the human parietal operculo–insular subregions conducted at the microscopic, mesoscopic, and macroscopic scales arguing in favor of an auditory–somatosensory cross-talk. Both the previous literature and new results on functional connectivity derived from cortico–cortical evoked potentials show that these subregions present a dense tissue of interconnections and a strong connectivity with auditory and somatosensory areas in the healthy brain. Disrupted integration processes ...
Connectivity probability as well as features describing fibers biophysical properties, estimated ... more Connectivity probability as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 315 patients, in the MarsAtlas, HCP-MMP1 and Lausanne2008 (resolutions 60, 125, 250) parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, integral, duration and the velocity estimated from the onset latency and the fibers distance between the parcels.
We present the first measurements of charge-dependent correlations on angular difference variable... more We present the first measurements of charge-dependent correlations on angular difference variables η1 − η2 (pseudorapidity) and φ1 − φ2 (azimuth) for primary charged hadrons with transverse momentum 0.15 ≤ pt ≤ 2 GeV/c and |η| ≤ 1.3 from Au-Au collisions at √ sNN = 130 GeV. We observe correlation structures not predicted by theory but consistent with evolution of hadron emission geometry with increasing centrality from one-dimensional fragmentation of color strings along the beam direction to an at least two-dimensional hadronization geometry along the beam and azimuth directions of a hadron-opaque bulk medium.
We report on p-, p-, p-, and pcorrelation functions constructed in central Au-Au collisions at √ ... more We report on p-, p-, p-, and pcorrelation functions constructed in central Au-Au collisions at √ s NN = 200 GeV by the STAR experiment at RHIC. The proton and lambda source size is inferred from the pand pcorrelation functions. It is found to be smaller than the pion source size also measured by the STAR experiment at smaller transverse masses, in agreement with a scenario of a strong universal collective flow. The pand pcorrelation functions, which are measured for the first time, exhibit a large anticorrelation. Annihilation channels and/or a negative real part of the spin-averaged scattering length must be included in the final-state interactions calculation to reproduce the measured correlation function.
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Papers by Maciej Jedynak