On the similarity of functional connectivity between neurons estimated across timescales (original) (raw)
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Relating Macroscopic Measures of Brain Activity to Fast, Dynamic Neuronal Interactions
Neural Computation, 2001
In this article we used biologically plausible simulations of coupled neuronal populations to address the relationship between phasic and fast coherent neuronal interactions and macroscopic measures of activity that are integrated over time, such as the BOLD response in functional magnetic resonance imaging. Event-related, dynamic correlations were assessed using joint peristimulus time histograms and, in particular , the mutual information between stimulus-induced transients in two populations. This mutual information can be considered as an index of functional connectivity . Our simulations showed that functional connectivity or dynamic integration between two populations increases with mean background activity and stimulus-related rate modulation. Furthermore, as the background activity increases, the populations become increasingly sensitive to the intensity of the stimulus in terms of a predisposition to transient phase locking. This re ects an interaction between background activity and stimulus intensity in producing dynamic correlations, in that background activity augments stimulus-induced coherence modulation. This is interesting from a computational perspective because background activity establishes a context that may have a profound effect on eventrelated interactions or functional connectivity between neuronal populations. Finally, total ring rates, which subsume both background activity and stimulus-related rate modulation, were almost linearly related to the expression of dynamic correlations over large ranges of activities. These observations show that under the assumptions implicit in our model, ratespeci c metrics based on rate or coherence modulation may be different perspectives on the same underlying dynamics. This suggests that activity (averaged over all peristimulus times), as measured in neuroimaging, may be tightly coupled to the expression of dynamic correlations.
Dynamics of neuronal firing correlation: modulation of "effective connectivity
Journal of neurophysiology, 1989
1. We reexamine the possibilities for analyzing and interpreting the time course of correlation in spike trains simultaneously and separably recorded from two neurons. 2. We develop procedures to quantify and properly normalize the classical joint peristimulus time scatter diagram. These allow separation of the "raw" correlation into components caused by direct stimulus modulations of the single-neuron firing rates and those caused by various types of interaction between the two neurons. 3. A newly developed significance test ("surprise") is applied to evaluate such inferences. 4. Application of the new procedures to simulated spike trains allowed the recovery of the known circuitry. In particular, it proved possible to recover fast stimulus-locked modulations of "effective connectivity," even if they were masked by strong direct stimulus modulations of individual firing rates. These procedures thus present a clearly superior alternative to the commonly...
2019
Information processing by neural circuits is widely understood to depend on correlation in the activity of upstream neurons. However, whether correlation is favorable or not is contentious. Correlated activity can facilitate information transmission but also increases redundancy. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using high-density 512-channel electrode arrays we recorded the spiking activity of hundreds of well-isolated neurons in organotypic cultures of mouse somatosensory cortex and asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related when examined at synaptic timescales (0.05-14 ms), where mutual information values were generally low. This effect was mediated by increased information transmission that resulted as mut...
Functional connectivity dynamics among cortical neurons: a dependence analysis
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2012
This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100-1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quant...
Timescales of Multineuronal Activity Patterns Reflect Temporal Structure of Visual Stimuli
PLoS ONE, 2011
The investigation of distributed coding across multiple neurons in the cortex remains to this date a challenge. Our current understanding of collective encoding of information and the relevant timescales is still limited. Most results are restricted to disparate timescales, focused on either very fast, e.g., spike-synchrony, or slow timescales, e.g., firing rate. Here, we investigated systematically multineuronal activity patterns evolving on different timescales, spanning the whole range from spike-synchrony to mean firing rate. Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space. Patterns evolve on a continuum of coexisting timescales that strongly relate to the temporal properties of stimuli. Timescales consistent with the time constants of neuronal membranes and fast synaptic transmission (5-20 ms) play a particularly salient role in encoding a large amount of stimulus-related information. Thus, to faithfully encode the properties of visual stimuli the brain engages multiple neurons into activity patterns evolving on multiple timescales.
Frontiers in Systems Neuroscience, 2012
Over the last two decades numerous functional imaging studies have shown that higher order cognitive functions are crucially dependent on the formation of distributed, large-scale neuronal assemblies (neurocognitive networks), often for very short durations. This has fueled the development of a vast number of functional connectivity measures that attempt to capture the spatiotemporal evolution of neurocognitive networks. Unfortunately, interpreting the neural basis of goal directed behavior using connectivity measures on neuroimaging data are highly dependent on the assumptions underlying the development of the measure, the nature of the task, and the modality of the neuroimaging technique that was used. This paper has two main purposes. The first is to provide an overview of some of the different measures of functional/effective connectivity that deal with high temporal resolution neuroimaging data. We will include some results that come from a recent approach that we have developed to identify the formation and extinction of task-specific, large-scale neuronal assemblies from electrophysiological recordings at a ms-by-ms temporal resolution. The second purpose of this paper is to indicate how to partially validate the interpretations drawn from this (or any other) connectivity technique by using simulated data from large-scale, neurobiologically realistic models. Specifically, we applied our recently developed method to realistic simulations of MEG data during a delayed match-to-sample (DMS) task condition and a passive viewing of stimuli condition using a large-scale neural model of the ventral visual processing pathway. Simulated MEG data using simple head models were generated from sources placed in V1, V4, IT, and prefrontal cortex (PFC) for the passive viewing condition. The results show how closely the conclusions obtained from the functional connectivity method match with what actually occurred at the neuronal network level.
One of the challenges in neuroscience is the detection of directionality between signals reflecting neural activity. To reveal the directionality of coupling and time delays between interacting multi-scale signals, we use a combination of a data-driven technique called empirical mode decomposition (EMD) and partial directed coherence (PDC) together with the instantaneous causality test (ICT). EMD is used to separate multiple processes associated with different frequency bands, while PDC and ICT allow to explore directionality and characteristic time delays, respectively. We computationally validate our approach for the cases of both stochastic and chaotic oscillatory systems with different types of coupling. Moreover, we apply our approach to the analysis of the connectivity in different frequency bands between local field potentials (LFPs) bilaterally recorded from the left and right of subthalamic nucleus (STN) in patients with Parkinson’s disease (PD). We reveal a bidirectional coupling between the left and right STN in the beta-band (10–30 Hz) for an akinetic PD patient and in the tremor band (3–5 Hz) for a tremor-dominant PD patient. We detect a short time delay, most probably reflecting the inter-hemispheric transmission time. Additionally, in both patients we observe a long time delay of approximately a mean period of the beta-band activity in the akinetic PD patient or the tremor band activity in the tremor-dominant PD patient. These long delays may emerge in subcortico-thalamic loops or longer pathways, comprising reflex loops, respectively. We show that the replacement of EMD by conventional bandpass filtering complicates the detection of directionality and leads to a spurious detection of time delays.
2019
ABSTRACTNeural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how correlated activity and information processing are related in cortical circuits. Using recordings of hundreds of spiking neurons in organotypic cultures of mouse neocortex, we asked whether mutual information between neurons that feed into a common third neuron increased synergistic information processing by the receiving neuron. We found that mutual information and synergistic processing were positively related at synaptic timescales (0.05-14 ms), where mutual information values were low. This effect was mediated by the increase in information transmission—of which synergistic processing is a component—that resulted as mutual information grew. However, at extrasynaptic windows (up to 3000 ms), where mutual information values were high, the relationship between mutual information a...
Dynamical interactions reconfigure the gradient of cortical timescales
Network Neuroscience
The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) is also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when nonlocal interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.