Good vibrations: Cortical oscillations modulated by sensory, environmental, internal, and volitional inputs

Relative strength of MS and DS stimulation to inhibitory neurons determines spiking activity oscillations power and frequency
Relative strength of MS and DS stimulation to inhibitory neurons determines the power and frequency of oscillations in spiking activity. (A) Schematic of local network and the monosynaptic (solid black) and disynaptic (dotted black) pathways for stimulating the local inhibitory neurons. The model network architecture featured both excitatory (E) and inhibitory (I) neurons, with recurrent connections between and within E and I populations. (B) Example evolution of population oscillatory activity (thick traces in green palette) from baseline (1) by increasing stimulation to MS (2) and DS (3) pathways in a model network oscillating in the gamma range (30–80Hz). The data shown are for first 200 ms of an example trial. The relative strengths of individual pathways are indicated at the top of each panel. Raster plots show the spike times of all inhibitory (inside rectangle) and excitatory neurons. (C) Power spectrum of average population activity for the three cases shown in B (mean across 10 trials of 2-s duration). Dotted lines indicate the peak power and corresponding frequency of narrowband oscillations. (D) Variation in peak power and frequency of narrowband oscillations with the strength of MS and DS stimulation of inhibitory neurons. The stimulation strengths are normalized to the range of interest. (E) Modulation of peak power and frequency of oscillations with the relative strength of MS-to-DS stimulation. The power and frequency data were normalized to the range of values shown in D. The MS-to-DS stimulation ratios were calculated from the absolute values of the two inputs used in the simulation experiments.Copyright © PNAS, doi:10.1073/pnas.1405300111

(Medical Xpress)—Cortical information is carried by axonal spike timing, which is also a key factor in synaptic plasticity. Spike timing, in turn, can be synchronized by cortical oscillations, thereby regulating cortical information processing. That said, oscillations in the cerebral cortex are the subject of much debate – and in the case of their regulatory mechanisms, not well understood. Addressing this problem, scientists at Howard Hughes Medical Institute and University of California at San Diego used a model cortical circuit to propose that such a regulatory mechanism links the dynamical state of the cortex to interactions between sensory and behavioral context during information processing. Moreover, their proposed regulatory mechanism explains a wide range of heretofore paradoxical empirical results.

Postdoctoral Fellow Monika P. Jadi discussed the paper that she and Prof. Terrence J. Sejnowski published in Proceedings of the National Academy of Sciences. "The cortex comprises multiple types of excitatory and . As a result, our first challenge of was to identify the key neuron populations to include in the model cortical circuit we were using to propose a regulatory mechanism," Jadi tells Specifically, the basic model discussed in the paper has one excitatory and one inhibitory neuronal population – so, Jadi relates, the scientists had to ask, Does this refer to a specific morphology of each kind of neuron in the cortical tissue, or does it represent neuron families in the cortex that express a specific type of calcium-binding protein? "We concluded that the two types of neural populations we use in our model cortical circuit are characterized not by morphology or specific proteins, but rather by the intra- and extra-population connectivity these populations exhibit in cortical tissue."

Moreover, Jadi continues, the researchers were interested in identifying the most parsimonious model that would capture the regulation of cortical oscillations as observed in electrophysiological experiments. "Therefore, an equally important challenge was evaluating the wealth of excellent computational work that has been done in the past to suggest mechanisms for the oscillatory phenomenon itself," Jadi adds. "While our model does provide an elegant explanation for the data as well as some interesting predictions for sensory processing, we await the next round of experiments that can actually test those predictions."

Another issue was linking the dynamical state of the cortex to interactions between sensory and behavioral context during information processing. "The main difficulty here was to synthesize the large quantity of high-quality data on cortical oscillations in the intact – and mostly alert – mammalian brain," Jadi explains. This data suggests that the cortical oscillation frequency or amplitude is determined by whether the subject attends to or away from sensory information in the environment, as well as that the presence of useful or distracting information influences oscillatory behavior. "We were motivated to explore a single model that would explain these findings from independent experiments in order to learn more about cortical integration of the external and internal worlds," she adds. "In that process, we could unravel a novel functional role for these oscillations in cortical electrical activity."

Inhibitory neurons are considered crucial to oscillatory neural activity in the cortex, so developing the model depended on understanding the activity balance of monosynaptic and disynaptic pathways to inhibitory neurons. "Even the simplest computational models have enough parameters that can be adjusted to capture a given phenomenon – but it's critical to vary as few of these settings as possible." Jadi points out. "Therefore, given what we know about the properties and dynamics of neurons and synapses and the phenomena to be modeled, the challenge here was to judge which of these parameters was best-suited to being adjusted at a timescale of tens to hundreds of milliseconds. "We identified the ratio of monosynaptic to disynaptic excitation provided to a subpopulation of inhibitory neurons as being the key parameter that could explain the variety of experimental data."

Contrast dependence of gamma in visual cortex in model and experiments
Contrast dependence of gamma in visual cortex in model and experiments. (A) Linear translation formulae used to map the contrast of visual input described in Fig. 4B to stimulation of monosynaptic (MS) and disynaptic (DS) pathways in the model local visual cortical circuit. (B) Average spiking and oscillatory activity of neuronal population for the three scenarios of visual stimulation (indicated by three symbols) described in Fig. 4B. (C) Stimulus-induced gamma-range oscillations in the primary visual cortex increase their frequency with increasing contrast of the classical receptive field and its surround [adapted from Ray and Maunsell (6)]. The figure shows power in the local field potential signal at different frequencies. Copyright © PNAS, doi:10.1073/pnas.1405300111

Finally, the researchers had to explain how a prominent set of otherwise paradoxical empirical results can be understood through this. "The challenge here was to understand the current state of our knowledge about how local population of inhibitory neurons is differentially activated by environmental and brain state changes," Jadi tells "The data suggests that mechanisms identified by our model are very much feasible – but again, it remains to be proven experimentally."

To address these challenges, the scientists developed feasible models of oscillations in neuronal networks by leveraging past efforts by computational neuroscientists. "Our key insight was to explore nonlinear behavior inherent to individual neurons and subpopulations comprising the cortical network," Jadi notes, "which we address in an accompanying publication1. The innovation here was to pick the right computational model that would allow us to more or less cleanly explore that aspect." Jadi acknowledges that while a biophysically detailed model would have been more realistic in some sense, the complex dynamics in these models would have made it very hard to test their hypothesis. "The technique we therefore used is not our novel contribution, and in fact has been previously developed by other groups." She underscores that their novel contributions were identifying (1) the key parameter of the local cortical network and (2) the appropriate computational modeling technique to test their hypothesis.

In the paper, the researchers concluded that the explored in their study explains a wide range of data on the regulation of oscillations in the alert cortex interacting with the sensory world. "In studying sensory processing in the brain," Jadi points out, "the visual system is the most widely explored perceptual system. Moreover, there's extensive literature on the effect of changes in the sensory environment and internal brain states, such as arousal and visual attention, on cortical oscillations. Many of the circuits and mechanisms involved in visual processing inferred from these studies are assumed, or at least hoped, to be canonical – that is, typical to at least sensory processing, if not other cortical functions." Along similar lines, she adds that they expect that a model explaining a range of data on oscillation regulation in the visual cortex could serve as a template for understanding the regulation and potential functional role for oscillations in other aspects of cortical sensory processing.

The paper also emphasizes the importance of sensory and behavioral contextual information, as well as the signals driving the classical receptive fields, in shaping cerebral cortex oscillations. "Previous studies on gamma-range (30-80 Hz) oscillations in the visual cortex focused on hypothesizing and testing specific functional role for these oscillations. However, newer data suggests that some of the simplifying assumptions in these hypotheses such as uniform frequency under different stimulus and behavioral conditions might not hold. While this is not necessarily sufficient to dismiss the theoretical ideas developed around a role for these oscillations, it is important to have a model of how the oscillations are regulated going forward. Our study provides the model that could underlie how the integration of contextual and behavioral information can influence these oscillations. For example, experimental studies have shown that when the visual stimulus increases in size, gamma oscillations detected in the primary visual cortex grow more powerful but become slower. Is this always true? What determines that a larger visual stimulus will slow down the oscillations in cortical activity? Our model can now answer these questions in the form of predictions to be tested experimentally.

The study's findings suggest that the oscillations reflect the integration of bottom-up, lateral, and top-down information as related to gamma-range oscillations being modulated by sensory input, contextual information in the environment, internal brain states, and volitional control. "Processing sensory information in the brain involves integration of multiple information channels – inter-areal, or multiple brain region, feedforward information, inter-areal feedback, and intra-areal feedback - and our model proposes a way for this multi-channel integration to influence oscillatory activity," Jadi tells "To what extent these feedforward and feedback pathways are activated is determined by several factors, such as the contextual information in the visual environment, volition, motivation and attentive state of the subject. Hence we infer that the oscillations reflect the integration of external sensory context and internal brain states – but if and how this has a causal effect remains to be seen."

Moving forward, Jadi acknowledges that while their model provides an economical explanation for several experimental observations, there are some – for example, the differential effect of spatial attention on gamma oscillations when recorded from different areas of the – which the model cannot explain in its current form. "We're following a few hypotheses about how attention influences information processing in relation to differences between cortical areas, and that could potentially explain these differences within the framework of our model," she says. "This could also shed light on how attention influences different stages of visual processing." Jadi adds that in its current state, their model ignores some single neuron level biophysical details that can influence network dynamics in a significant way – and that this remains to be explored.

Looking ahead, Jadi notes that oscillations detected in humans using electrode-based recording techniques such as electroencephalography (EEG) and electrocorticography (ECoG) have been extensively studied to characterize the differences between neurotypical brains and those of people with disorders such as ADHD, autism and schizophrenia. (In electrocorticography – also referred to as intracranial EEG, or (iEEG) – electrodes are placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex.) In these studies, narrowband oscillation power or frequency abnormalities have been empirically shown to elucidate the differences between the neurotypical and disordered brains.

"Assuming that the signals detected using surface electrodes reflect filtered version of the local network dynamics in the cortex, such as the data modeled in our study, we can start thinking about the implications of the abnormal findings in the diseased brains in terms of cortical function," Jadi concludes. " "Going further, assuming that the underlying mechanism of oscillatory activity is similar to what we propose in our study, we can start looking into the actual neurostructural deficits that can cause abnormalities observed in the disordered brain."

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More information: Cortical oscillations arise from contextual interactions that regulate sparse coding, Proceedings of the National Academy of Sciences, Published online before print on April 17, 2014, doi:10.1073/pnas.1405300111

1Regulating Cortical Oscillations in an Inhibition-Stabilized Network, Proceedings of the IEEE, Published 21 April 2014, doi:10.1109/JPROC.2014.2313113 (PDF)

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