Dorso-Lateral Frontal Cortex of the Ferret Encodes Perceptual Difficulty during VisualDiscrimination

Visual discrimination requires sensory processing followed by a perceptual decision. Despite a growing understanding of visual areas in this behavior, it is unclear what role top-down signals from prefrontal cortex play, in particular as a function of perceptual difficulty. To address this gap, we investigated how neurons in dorso-lateral frontal cortex (dl-FC) of freely-moving ferrets encode task variables in a two-alternative forced choice visual discrimination task with high- and low-contrast visual input. About two-thirds of all recorded neurons in dl-FC were modulated by at least one of the two task variables, task difficulty and target location. More neurons in dl-FC preferred the hard trials; no such preference bias was found for target location. In individual neurons, this preference for specific task types was limited to brief epochs. Finally, optogenetic stimulation confirmed the functional role of the activity in dl-FC before target touch; suppression of activity in pyramidal neurons with the ArchT silencing opsin resulted in a decrease in reaction time to touch the target but not to retrieve reward. In conclusion, dl-FC activity is differentially recruited for high perceptual difficulty in the freely-moving ferret and the resulting signal may provide top-down behavioral inhibition

Frequency-band signatures of visual responses to naturalistic input in ferret primary visual cortex during free viewing

Neuronal firing responses reflect the statistics of visual input and emerge from the interaction with endogenous network dynamics. Artificial visual stimuli presented to animals in which the network dynamics were constrained by anesthetic agents or trained behavioral tasks have provided fundamental understanding of how individual neurons in primary visual cortex respond to input. In contrast, very little is known about the mesoscale network dynamics and their relationship to microscopic spiking activity in the awake animal during free viewing of naturalistic visual input. To address this gap in knowledge, we recorded local field potential (LFP) and multiunit activity (MUA) in all layers of primary visual cortex (V1) of awake, freely viewing ferrets presented with naturalistic visual input (nature movie clips). We found that naturalistic visual stimuli modulated the entire oscillation spectrum; low frequency oscillations were mostly suppressed whereas higher frequency oscillations were enhanced. In average across all cortical layers, stimulus-induced change in delta and alpha power negatively correlated with the MUA responses, whereas sensory-evoked increases in gamma power positively correlated with MUA responses. The time-course of the band-limited power in these frequency bands provided evidence for a model in which naturalistic visual input switched V1 between two distinct, endogenously present activity states defined by the power of low (delta, alpha) and high (gamma) frequency oscillatory activity. Therefore, the two mesoscale activity states delineated in this study may define the engagement of the circuit with processing sensory input at the level of spiking activity

Targeting the neurophysiology of cognitive systems with transcranial alternating current stimulation

Cognitive impairment represents one of the most debilitating and most difficult symptom to treat of many psychiatric illnesses. Human neurophysiology studies have suggested specific pathologies of cortical network activity correlate with cognitive impairment. However, we lack (1) demonstration of causal relationships between specific network activity patterns and cognitive capabilities and (2) treatment modalities that directly target impaired network dynamics of cognition. Transcranial alternating current stimulation (tACS), a novel non-invasive brain stimulation approach, may provide a crucial tool to tackle these challenges. We here propose that tACS can be used to elucidate the causal role of cortical synchronization in cognition and, eventually, to enhance pathologically weakened synchrony that may underlie cognitive deficits. To accelerate such development of tACS as a treatment for cognitive deficits, we discuss studies on tACS and cognition (all performed in healthy participants) according to the Research Domain Criteria (RDoC) of the National Institute of Mental Health

Transcranial Alternating Current Stimulation Modulates Large-Scale Cortical Network Activity by Network Resonance

Transcranial direct current stimulation (tDCS) has emerged as a potentially safe and effective brain stimulation modality that alters cortical excitability by passing a small, constant electric current through the scalp. tDCS creates an electric field that weakly modulates the membrane voltage of a large number of cortical neurons. Recent human studies have suggested that sine-wave stimulation waveforms [transcranial alternating current stimulation (tACS)] represent a more targeted stimulation paradigm for the enhancement of cortical oscillations. Yet, the underlying mechanisms of how periodic, weak global perturbations alter the spatiotemporal dynamics of large-scale cortical network dynamics remain a matter of debate. Here, we simulated large-scale networks of spiking neuron models to address this question in endogenously rhythmic networks. We identified distinct roles of the depolarizing and hyperpolarizing phases of tACS in entrainment, which entailed moving network activity toward and away from a strong nonlinearity provided by the local excitatory coupling of pyramidal cells. Together, these mechanisms gave rise to resonance dynamics characterized by an Arnold tongue centered on the resonance frequency of the network. We then performed multichannel extracellular recordings of multiunit firing activity during tACS in anesthetized ferrets

Breakdown of local information processing may underlie isoflurane anesthesia effects

The disruption of coupling between brain areas has been suggested as the mechanism underlying loss of consciousness in anesthesia. This hypothesis has been tested previously by measuring the information transfer between brain areas, and by taking reduced information transfer as a proxy for decoupling. Yet, information transfer is a function of the amount of information available in the information source—such that transfer decreases even for unchanged coupling when less source information is available. Therefore, we reconsidered past interpretations of reduced information transfer as a sign of decoupling, and asked whether impaired local information processing leads to a loss of information transfer. An important prediction of this alternative hypothesis is that changes in locally available information (signal entropy) should be at least as pronounced as changes in information transfer. We tested this prediction by recording local field potentials in two ferrets after administration of isoflurane in concentrations of 0.0%, 0.5%, and 1.0%. We found strong decreases in the source entropy under isoflurane in area V1 and the prefrontal cortex (PFC)—as predicted by our alternative hypothesis. The decrease in source entropy was stronger in PFC compared to V1. Information transfer between V1 and PFC was reduced bidirectionally, but with a stronger decrease from PFC to V1. This links the stronger decrease in information transfer to the stronger decrease in source entropy—suggesting reduced source entropy reduces information transfer. This conclusion fits the observation that the synaptic targets of isoflurane are located in local cortical circuits rather than on the synapses formed by interareal axonal projections. Thus, changes in information transfer under isoflurane seem to be a consequence of changes in local processing more than of decoupling between brain areas. We suggest that source entropy changes must be considered whenever interpreting changes in information transfer as decoupling

Early Development of Network Oscillations in the Ferret Visual Cortex

Abstract Although oscillations during development have been characterized in a wide range of neural systems, little is known about the interaction between these network oscillations and neuronal spiking, and the interactions among different oscillation frequencies. Here we recorded the spontaneous and visual-elicited local field potential (LFP) and multi-unit activity (MUA) in the visual cortex of freely-moving juvenile ferrets before and after eye-opening. We found that both the spontaneous and visually-elicited LFP power was increased after eye-opening, especially in higher frequency bands (>30 Hz). Spike LFP phase coupling was decreased for lower frequency bands (theta and alpha) but slightly increased for higher frequencies (high-gamma band). A similar shift towards faster frequencies also occurred for phase-amplitude coupling; with maturation, the coupling of the theta/alpha/beta band amplitude to the delta phase was decreased and the high-gamma amplitude coupling to theta/alpha phase was increased. This shift towards higher frequencies was also reflected in the visual responses; the LFP oscillation became more entrained by visual stimulation with higher frequencies (>10 Hz). Taken together, these results suggest gamma oscillation as a signature of the maturation of cortical circuitry

Dynamics analysis of neural univariate time series by recurrence plots

International audienceTransients in non-linear biological signals (e.g., population dynamics or physiological signals) encode an intrinsic behaviour of system dynamics. We study the problem of detecting dynamical transients given a set of signal trials. In general case, different biological signals emerge from different origins and hence exhibit distinct properties that are hard to grasp. For example, to analyze sleep recordings. one considers rhythms of the brain, the cardio-vascular and the respiratory systems. The synchronous analysis of the corresponding time series is an unsolved problem and extracting information from such signals and their trial statistics is challenging. In addition, measurement noise and time jitters between trials may corrupt signals. To attack this demanding problem, we start by a preliminary study of extracting features in multiple trials from univariate time series of the same origin, but without taking into account the common origin. The new method jointly analyzes neural signals by extracting statistical properties, obtained by exploiting the fundamental feature of dynamical systems, the recurrence structure

Structural and functional connectivity between the lateral posterior-pulvinar complex and primary visual cortex in the ferret

The role of higher-order thalamic structures in sensory processing remains poorly understood. Here, we used the ferret (Mustela putorius furo) as a novel model species for the study of the lateral posterior-pulvinar complex (LP/pulvinar) and its structural and functional connectivity with area 17 (primary visual cortex, V1). We found reciprocal anatomical connections between the lateral part of the Lateral Posterior Nucleus of the LP/pulvinar (LPl) and V1. In order to investigate the role of this feedback loop between LPl and V1 in shaping network activity, we determined the functional interactions between LPl and supragranular, granular, and infragranular layers of V1 by recording multiunit activity (MUA) and local field potential (LFP). Coherence was strongest between LPl and supragranular V1 with the most distinct peaks in the delta and alpha frequency bands. Inter-area interaction measured by spike-phase coupling identified the delta frequency band dominated by infragranular V1 and multiple frequency bands that were most pronounced in supragranular V1. This inter-area coupling was differentially modulated by full-field synthetic and naturalistic visual stimulation. We also found that visual responses in LPl were distinct from the ones in V1 in terms of their reliability. Together, our data support a model of multiple communication channels between the LPl and layers of V1 that are enabled by oscillations in different frequency bands. This demonstration of anatomical and functional connectivity between LPl and V1 in ferrets provides a roadmap for studying the interaction dynamics during behavior and a template for identifying activity dynamics of other thalamic feedback loops

Resting state network topology of the ferret brain

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4 tesla MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function