Chromatic and Achromatic Spatial Resolution of Local Field Potentials in Awake Cortex

Gamma Rhythm and Theta-gamma Coupling Alternation in Chronic Unpredictable Stress (CUS)-induced Depression Rats. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38082931 DOI: 10.1109/embc40787.2023.10340209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Depression is a debilitating disease, which, in severe cases, can lead to suicide. However, objective and reliable biomarker for the diagnosis of depression is lack. In this preclinical study, we recorded resting local field potentials (LFPs) from chronic unpredictable stress (CUS)-induced depressed (n =20) and control (n = 20) rats and then compared their gamma activities in terms of single-band and cross-frequency coupling patterns. Both theta-gamma coupling and relative power in total gamma band revealed significant abnormalities in gamma rhythm in the right auditory cortex of depressed rats. These findings implied that resting-state gamma rhythms may be a promising objective diagnostic biomarker for depression. Furthermore, our research provided direct evidence from the perspective of source signals in deep brain sites, which might be useful for clinical applications.Clinical Relevance- This research showed that resting gamma in the auditory cortex is a promising biomarker for depression diagnosis.

ROSE: A Neurocomputational Architecture for Syntax

A comprehensive model of natural language processing in the brain must accommodate four components: representations, operations, structures and encoding. It further requires a principled account of how these different components mechanistically, and causally, relate to each another. While previous models have isolated regions of interest for structure-building and lexical access, and have utilized specific neural recording measures to expose possible signatures of syntax, many gaps remain with respect to bridging distinct scales of analysis that map onto these four components. By expanding existing accounts of how neural oscillations can index various linguistic processes, this article proposes a neurocomputational architecture for syntax, termed the ROSE model (Representation, Operation, Structure, Encoding). Under ROSE, the basic data structures of syntax are atomic features, types of mental representations (R), and are coded at the single-unit and ensemble level. Elementary computations (O) that transform these units into manipulable objects accessible to subsequent structure-building levels are coded via high frequency broadband γ activity. Low frequency synchronization and cross-frequency coupling code for recursive categorial inferences (S). Distinct forms of low frequency coupling and phase-amplitude coupling (δ-θ coupling via pSTS-IFG; θ-γ coupling via IFG to conceptual hubs in lateral and ventral temporal cortex) then encode these structures onto distinct workspaces (E). Causally connecting R to O is spike-phase/LFP coupling; connecting O to S is phase-amplitude coupling; connecting S to E is a system of frontotemporal traveling oscillations; connecting E back to lower levels is low-frequency phase resetting of spike-LFP coupling. This compositional neural code has important implications for algorithmic accounts, since it makes concrete predictions for the appropriate level of study for psycholinguistic parsing models. ROSE is reliant on neurophysiologically plausible mechanisms, is supported at all four levels by a range of recent empirical research, and provides an anatomically precise and falsifiable grounding for the basic property of natural language syntax: hierarchical, recursive structure-building.

Neural oscillatory characteristics of feedback-associated activity in globus pallidus interna

Neural oscillatory activities in basal ganglia have prominent roles in cognitive processes. However, the characteristics of oscillatory activities during cognitive tasks have not been extensively explored in human Globus Pallidus internus (GPi). This study aimed to compare oscillatory characteristics of GPi between dystonia and Parkinson's Disease (PD). A dystonia and a PD patient performed the Intra-Extra-Dimension shift (IED) task during both on and off-medication states. During the IED task, patients had to correctly choose between two visual stimuli containing shapes or lines based on a hidden rule via trial and error. Immediate auditory and visual feedback was provided upon the choice to inform participants if they chose correctly. Bilateral GPi Local Field Potentials (LFP) activity was recorded via externalized DBS leads. Transient high gamma activity (~ 100-150 Hz) was observed immediately after feedback in the dystonia patient. Moreover, these bursts were phase synchronous between left and right GPi with an antiphase clustering of phase differences. In contrast, no synchronous high gamma activity was detected in the PD patient with or without dopamine administration. The off-med PD patient also displayed enhanced low frequency clusters, which were ameliorated by medication. The current study provides a rare report of antiphase homotopic synchrony in human GPi, potentially related to incorporating and processing feedback information. The absence of these activities in off and on-med PD patient indicates the potential presence of impaired medication independent feedback processing circuits. Together, these findings suggest a potential role for GPi's synchronized activity in shaping feedback processing mechanisms required in cognitive tasks.

Measuring Synchronization between Spikes and Local Field Potential Based on the Kullback-Leibler Divergence

Neurophysiological studies have shown that there is a close relationship between spikes and local field potential (LFP), which reflects crucial neural coding information. In this paper, we used a new method to evaluate the synchronization between spikes and LFP. All possible phases of LFP from −π to π were first binned into a freely chosen number of bins; then, the probability of spikes falling in each bin was calculated, and the deviation degree from the uniform distribution based on the Kullback–Leibler divergence was calculated to define the synchronization between spikes and LFP. The simulation results demonstrate that the method is rapid, basically unaffected by the total number of spikes, and can adequately resist the noise of spike trains. We applied this method to the experimental data of patients with intractable epilepsy, and we observed the synchronization between spikes and LFP in the formation of memory. These results show that our proposed method is a powerful tool that can quantitatively measure the synchronization between spikes and LFP.

Adaptation Modulates Spike-Phase Coupling Tuning Curve in the Rat Primary Auditory Cortex

Adaptation is an important mechanism that causes a decrease in the neural response both in terms of local field potentials (LFP) and spiking activity. We previously showed this reduction effect in the tuning curve of the primary auditory cortex. Moreover, we revealed that a repeated stimulus reduces the neural response in terms of spike-phase coupling (SPC). In the current study, we examined the effect of adaptation on the SPC tuning curve. To this end, employing the phase-locking value (PLV) method, we estimated the spike-LFP coupling. The data was obtained by a simultaneous recording from four single-electrodes in the primary auditory cortex of 15 rats. We first investigated whether the neural system may use spike-LFP phase coupling in the primary auditory cortex to encode sensory information. Secondly, we investigated the effect of adaptation on this potential SPC tuning. Our data showed that the coupling between spikes’ times and the LFP phase in beta oscillations represents sensory information (different stimulus frequencies), with an inverted bell-shaped tuning curve. Furthermore, we showed that adaptation to a specific frequency modulates SPC tuning curve of the adapter and its neighboring frequencies. These findings could be useful for interpretation of feature representation in terms of SPC and the underlying neural mechanism of adaptation.

Multiple-target tracking in human and machine vision

Humans are able to track multiple objects at any given time in their daily activities—for example, we can drive a car while monitoring obstacles, pedestrians, and other vehicles. Several past studies have examined how humans track targets simultaneously and what underlying behavioral and neural mechanisms they use. At the same time, computer-vision researchers have proposed different algorithms to track multiple targets automatically. These algorithms are useful for video surveillance, team-sport analysis, video analysis, video summarization, and human–computer interaction. Although there are several efficient biologically inspired algorithms in artificial intelligence, the human multiple-target tracking (MTT) ability is rarely imitated in computer-vision algorithms. In this paper, we review MTT studies in neuroscience and biologically inspired MTT methods in computer vision and discuss the ways in which they can be seen as complementary.

Multiple-target tracking (MTT) is a challenging task vital for both a human’s daily life and for many artificial intelligent systems, such as those used for urban traffic control. Neuroscientists are interested in discovering the underlying neural mechanisms that successfully exploit cognitive resources, e.g., spatial attention or memory, during MTT. Computer-vision specialists aim to develop powerful MTT algorithms based on advanced models or data-driven computational methods. In this paper, we review MTT studies from both communities and discuss how findings from cognitive studies can inspire developers to construct higher performing MTT algorithms. Moreover, some directions have been proposed through which MTT algorithms could raise new questions in the cognitive science domain, and answering them can shed light on neural processes underlying MTT.

Cortical Balance Between ON and OFF Visual Responses Is Modulated by the Spatial Properties of the Visual Stimulus

The primary visual cortex of carnivores and primates is dominated by the OFF visual pathway and responds more strongly to dark than light stimuli. Here, we demonstrate that this cortical OFF dominance is modulated by the size and spatial frequency of the stimulus in awake primates and we uncover a main neuronal mechanism underlying this modulation. We show that large grating patterns with low spatial frequencies drive five times more OFF-dominated than ON-dominated neurons, but this pronounced cortical OFF dominance is strongly reduced when the grating size decreases and the spatial frequency increases, as when the stimulus moves away from the observer. We demonstrate that the reduction in cortical OFF dominance is not caused by a selective reduction of visual responses in OFF-dominated neurons but by a change in the ON/OFF response balance of neurons with diverse receptive field properties that can be ON or OFF dominated, simple, or complex. We conclude that cortical OFF dominance is continuously adjusted by a neuronal mechanism that modulates ON/OFF response balance in multiple cortical neurons when the spatial properties of the visual stimulus change with viewing distance and/or optical blur.

Stimulus-Specific Adaptation Decreases the Coupling of Spikes to LFP Phase

Stimulus repetition suppresses the neural activity in different sensory areas of the brain. This mechanism of so-called stimulus-specific adaptation (SSA) has been observed in both spiking activity and local field potential (LFP) responses. However, much remains to be known about the effect of SSA on the spike–LFP relation. In this study, we approached this issue by investigating the spike-phase coupling (SPC) in control and adapting paradigms. For the control paradigm, pure tones were presented in a random unbiased sequence. In the adapting paradigm, the same stimuli were presented in a random pattern but it was biased to an adapter stimulus. In fact, the adapter occupied 80% of the adapting sequence. During the tasks, LFP and multi-unit activity were recorded simultaneously from the primary auditory cortex of 15 anesthetized rats. To clarify the effect of adaptation on the relation between spike and LFP responses, the SPC of the adapter stimulus in these two paradigms was evaluated. Here, we employed phase locking value method for calculating the SPC. Our data show a strong coupling of spikes to LFP phase most prominently in beta band. This coupling was observed to decrease in the adapting condition compared to the control one. Importantly, we found that adaptation reduces spikes dominantly from the preferred phase of LFP in which spikes are more likely to be present there. As a result, the preferred phase of LFP may play a key role in coordinating neuronal spiking activity in neural adaptation mechanism. This finding is important for interpretation of the underlying neural mechanism of adaptation and also can be used in the context of the network and related connectivity models.

Multifractal detrended fluctuation analysis of continuous neural time series in primate visual cortex

BACKGROUND

Local field potential (LFP) recordings have become an important tool to study the activity of populations of neurons. The functional activity of LFPs is usually compared with the activity of neighboring single spike neurons with sampling rates much higher than those of the continuous field potential channel (5 kHz). However, comparison of these signals generated with the lower sampling rate technique is important.

NEW METHOD

In this study, we provide an analysis of extracellular field potential time series using the sophisticated nonlinear multifractal detrended fluctuation analysis (MF-DFA). Using the MF-DFA, we demonstrate that the integral of the singularity spectrum is a powerful new method to measure the response tuning of spikes in the continuous field potential channel.

RESULTS

Results show that the spikes in the continuous channel at frequency ranges above the LFP component signals were consistently tuned similar to those in the spike channel. Our results also show that using a low-pass filter (<250 Hz), which is commonly applied as a preprocessing step to insulate LFPs from spikes, significantly influences the nonlinearity of the multifractal time series.

COMPARISON WITH EXISTING METHODS AND CONCLUSIONS

Our approach for inferring the tuning curve of spiking activity from the continuous channel has some advantages compared to conventional methods such as spike trains. The MF-DFA does not require any preprocessing of the raw signal data and makes no assumptions about the time series characteristics. This method is robust and can be applied to short time series of continuous raw signals.

Early Visual Processing of Feature Saliency Tasks: A Review of Psychophysical Experiments

The visual system is constantly bombarded with information originating from the outside world, but it is unable to process all the received information at any given time. In fact, the most salient parts of the visual scene are chosen to be processed involuntarily and immediately after the first glance along with endogenous signals in the brain. Vision scientists have shown that the early visual system, from retina to lateral geniculate nucleus (LGN) and then primary visual cortex, selectively processes the low-level features of the visual scene. Everything we perceive from the visual scene is based on these feature properties and their subsequent combination in higher visual areas. Different experiments have been designed to investigate the impact of these features on saliency and understand the relative visual mechanisms. In this paper, we review the psychophysical experiments which have been published in the last decades to indicate how the low-level salient features are processed in the early visual cortex and extract the most important and basic information of the visual scene. Important and open questions are discussed in this review as well and one might pursue these questions to investigate the impact of higher level features on saliency in complex scenes or natural images.

Long-wavelength (reddish) hues induce unusually large gamma oscillations in the primate primary visual cortex

Electrophysiological signals recorded from the brain exhibit prominent gamma oscillations (∼30–80 Hz) under sensory stimulation. These oscillations are modulated by stimulus properties and behavioral state, and implicated to play a role in cognitive functions, such as attention and object-binding. In visual areas, gamma oscillations have mainly been studied using achromatic gratings/gabors. Here we show that color stimuli generate unprecedented levels of gamma oscillations in the local field potentials recorded from primate area V1. The strongest oscillations are induced by long-wavelength (reddish) hues. Importantly, their strength depends on color saturation but not on luminance, and is strongly correlated with the L−M cone contrast produced by stimuli, suggesting that gamma oscillations may represent key components of the processing of visual chromatic information.

Gamma oscillations (∼30–80 Hz) are a prominent signature of electrophysiological signals, with a purported role in natural vision. Previous studies in the primary visual cortex (area V1) have shown that achromatic gratings or gabor stimuli generate salient gamma oscillations, whose strength and frequency depend on stimulus properties such as their size, contrast, and orientation. Surprisingly, although natural images are rarely achromatic, the effect of chromatic input on gamma has not been thoroughly investigated. Recording from primate V1, we show that gamma oscillations of extremely high magnitude (peak increase of ∼300-fold in some cases), far exceeding the gamma generated by optimally tuned achromatic gratings, are induced in the local field potentials by full-field color stimuli of different hues. Furthermore, gamma oscillations are sensitive to the hue of the chromatic input, with the strongest oscillations for long-wavelength (reddish) hues and another, smaller gamma response peak for hues in the short-wavelength (bluish) range, which lie approximately on the two cardinal chromatic response axes of the upstream lateral geniculate nucleus neurons. These oscillations depended critically on the purity of the hue, decreasing with hue desaturation, but remained robust for pure hue stimuli even at reduced luminance. Importantly, the magnitude of gamma oscillations was highly correlated with positive L−M cone contrast produced by the stimuli, suggesting that gamma could be a marker of the specific mechanisms underlying this computation. These findings provide insights into the generation of gamma oscillations, as well as the processing of color along the visual pathway.

Nonselective Wiring Accounts for Red-Green Opponency in Midget Ganglion Cells of the Primate Retina

In primate retina, "red-green" color coding is initiated when signals originating in long (L) and middle (M) wavelength-sensitive cone photoreceptors interact antagonistically. The center-surround receptive field of "midget" ganglion cells provides the neural substrate for L versus M cone-opponent interaction, but the underlying circuitry remains unsettled, centering around the longstanding question of whether specialized cone wiring is present. To address this question, we measured the strength, sign, and spatial tuning of L- and M-cone input to midget receptive fields in the peripheral retina of macaque primates of either sex. Consistent with previous work, cone opponency arose when one of the cone types showed a stronger connection to the receptive field center than to the surround. We implemented a difference-of-Gaussians spatial receptive field model, incorporating known biology of the midget circuit, to test whether physiological responses we observed in real cells could be captured entirely by anatomical nonselectivity. When this model sampled nonselectively from a realistic cone mosaic, it accurately reproduced key features of a cone-opponent receptive field structure, and predicted both the variability and strength of cone opponency across the retina. The model introduced here is consistent with abundant anatomical evidence for nonselective wiring, explains both local and global properties of the midget population, and supports a role in their multiplexing of spatial and color information. It provides a neural basis for human chromatic sensitivity across the visual field, as well as the maintenance of normal color vision despite significant variability in the relative number of L and M cones across individuals.SIGNIFICANCE STATEMENT Red-green color vision is a hallmark of the human and nonhuman primate that starts in the retina with the presence of long (L)- and middle (M)-wavelength sensitive cone photoreceptor types. Understanding the underlying retinal mechanism for color opponency has focused on the broad question of whether this characteristic can emerge from nonselective wiring, or whether complex cone-type-specific wiring must be invoked. We provide experimental and modeling support for the hypothesis that nonselective connectivity is sufficient to produce the range of red-green color opponency observed in midget ganglion cells across the retina. Our nonselective model reproduces the diversity of physiological responses of midget cells while also accounting for systematic changes in color sensitivity across the visual field.

Nonlinear dynamics of cortical responses to color in the human cVEP

The main finding of this paper is that the human visual cortex responds in a very nonlinear manner to the color contrast of pure color patterns. We examined human cortical responses to color checkerboard patterns at many color contrasts, measuring the chromatic visual evoked potential (cVEP) with a dense electrode array. Cortical topography of the cVEPs showed that they were localized near the posterior electrode at position Oz, indicating that the primary cortex (V1) was the major source of responses. The choice of fine spatial patterns as stimuli caused the cVEP response to be driven by double-opponent neurons in V1. The cVEP waveform revealed nonlinear color signal processing in the V1 cortex. The cVEP time-to-peak decreased and the waveform's shape was markedly narrower with increasing cone contrast. Comparison of the linear dynamics of retinal and lateral geniculate nucleus responses with the nonlinear dynamics of the cortical cVEP indicated that the nonlinear dynamics originated in the V1 cortex. The nature of the nonlinearity is a kind of automatic gain control that adjusts cortical dynamics to be faster when color contrast is greater.

Salience of unique hues and implications for color theory

The unique hues--blue, green, yellow, red--form the fundamental dimensions of opponent-color theories, are considered universal across languages, and provide useful mental representations for structuring color percepts. However, there is no neural evidence for them from neurophysiology or low-level psychophysics. Tapping a higher prelinguistic perceptual level, we tested whether unique hues are particularly salient in search tasks. We found no advantage for unique hues over their nonunique complementary colors. However, yellowish targets were detected faster, more accurately, and with fewer saccades than their complementary bluish targets (including unique blue), while reddish-greenish pairs were not significantly different in salience. Similarly, local field potentials in primate V1 exhibited larger amplitudes and shorter latencies for yellowish versus bluish stimuli, whereas this effect was weaker for reddish versus greenish stimuli. Consequently, color salience is affected more by early neural response asymmetries than by any possible mental or neural representation of unique hues.

Learning to integrate contradictory multisensory self-motion cue pairings

Humans integrate multisensory information to reduce perceptual uncertainty when perceiving the world and self. Integration fails, however, if a common causality is not attributed to the sensory signals, as would occur in conditions of spatiotemporal discrepancies. In the case of passive self-motion, visual and vestibular cues are integrated according to statistical optimality, yet the extent of cue conflicts that do not compromise this optimality is currently underexplored. Here, we investigate whether human subjects can learn to integrate two arbitrary, but co-occurring, visual and vestibular cues of self-motion. Participants made size comparisons between two successive whole-body rotations using only visual, only vestibular, and both modalities together. The vestibular stimulus provided a yaw self-rotation cue, the visual a roll (Experiment 1) or pitch (Experiment 2) rotation cue. Experimentally measured thresholds in the bimodal condition were compared with theoretical predictions derived from the single-cue thresholds. Our results show that human subjects combine and optimally integrate vestibular and visual information, each signaling self-motion around a different rotation axis (yaw vs. roll and yaw vs. pitch). This finding suggests that the experience of two temporally co-occurring but spatially unrelated self-motion cues leads to inferring a common cause for these two initially unrelated sources of information about self-motion. We discuss our results in terms of specific task demands, cross-modal adaptation, and spatial compatibility. The importance of these results for the understanding of bodily illusions is also discussed.