Multichannel Recordings Research Articles

Short Wavelengths Contribution to Light-induced Responses and Irradiance Coding in the Rat Dorsal Lateral Geniculate Nucleus – An In vivo Electrophysiological Approach

The dorsal lateral geniculate nucleus (dLGN) is the main neuronal station en route to higher visual areas. It receives information about environmental light from retinal photoreceptors whose sensitivity peaks are distributed across a visible spectrum. Here, using electrophysiological multichannel recordings in vivo combined with different light stimulations, we investigated short wavelength contribution to the dLGN responses to light and irradiance coding. The results showed that the majority of dLGN cells responded evenly to almost all wavelengths from the 340 to 490 nm spectrum; however, some cells representing extremes of unimodal distribution of Blue-UV index were specialised in the reception of blue or UV light. Moreover, by using alternate yellow and monochromatic light stimuli from blue - UV range, we also assessed the relative spectral contribution to rat dLGN responses to light. Finally, we observed no clear changes in the irradiance coding property of short wavelength-deficient light stimuli, however we noticed a distortion of the coding curves manifested by a significant drop in measure of fit after using short wavelength blocking filter. In conclusion, our data provide the first electrophysiological report on dLGN short wavelength-induced responses under changing light conditions and suggest the presence of colour opponent cells in the rat dLGN.

Exercise Improves Movement by Regulating the Plasticity of Cortical Function in Hemiparkinsonian Rats.

Aberrant cortical spike-local field potential (LFP) coupling leads to abnormal basal ganglia activity, disruption of cortical function, and impaired movement in Parkinson's disease (PD). Here, the primary motor cortex mediated plasticity mechanism underlying behavioral improvement by exercise intervention was investigated. Exercise alleviates motor dysfunction and induces neuroplasticity in PD. In this study, Sprague-Dawley (SD) rats were injected with 6-hydroxydopamine (6-OHDA) to induce unilateral nigrostriatal dopamine depletion. Two weeks later, a 4-week exercise intervention was initiated in the PD + exercise (Ex) group. Multichannel recording technology recorded spikes and LFPs in rat motor cortices, and balanced ability tests evaluated behavioral performance. The balanced ability test showed that the total crossing time/front leg error/input latency time was significantly lower in PD + Ex rats than in PD rats (P < 0.05). Scalograms and LFP power spectra indicated increased beta-range LFP power in lesioned hemispheres, with exercise reducing LFP power spectral density. Spike-triggered LFP waveform averages showed strong phase-locking in PD motor cortex cells, and exercise reduced spike-LFP synchronization. Our results suggest that exercise can suppress overexcitability of LFPs and minimize spike-LFP synchronization in the motor cortex, leading to motor-improving effects in PD.

Multiple synaptic connections into a single cortical pyramidal cell or interneuron in the anterior cingulate cortex of adult mice

The ACC is an important brain area for the processing of pain-related information. Studies of synaptic connections within the ACC provide an understanding of basic cellular and molecular mechanisms for brain functions such as pain, emotion and related cognitive functions. Previous study of ACC synaptic transmission mainly focused on presumably thalamic inputs into pyramidal cells. In the present study, we developed a new mapping technique by combining single neuron whole-cell patch-clamp recording with 64 multi-channel field potential recording (MED64) to examine the properties of excitatory inputs into a single neuron in the ACC. We found that a single patched pyramidal neuron or interneuron simultaneously received heterogeneous excitatory synaptic innervations from different subregions (ventral, dorsal, deep, and superficial layers) in the ACC. Conduction velocity is faster as stimulation distance increases in pyramidal neurons. Fast-spiking interneurons (FS-IN) show slower inactivation when compared to pyramidal neurons and regular-spiking interneurons (RS-IN) while pyramidal neurons displayed the most rapid activation. Bath application of non-competitive AMPA receptor antagonist GYKI 53655 followed by CNQX revealed that both FS-INs and RS-INs have AMPA and KA mediated components. Our studies provide a new strategy and technique for studying the network of synaptic connections.

Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD

This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed information transfer between EEG signals within δ, θ, α, β and γ-bands. The developed methodology is then used to study the properties of directed BNs in children with attention-deficit hyperactivity disorder (ADHD) and compare them with that of the healthy controls using both statistical and receiver operating characteristic (ROC) analyses. The results indicate that directed information transfer between scalp EEG electrodes in the ADHD subjects differs significantly compared to the healthy ones. The results of the statistical and ROC analyses of frequency-specific graph measures demonstrate their highly discriminative ability between the two groups. Specifically, the graph measures extracted from the estimated directed BNs in the β-band show the highest discrimination between the ADHD and control groups. These findings are in line with the fact that β-band reflects active concentration, motor activity, and anxious mental states. The reported results show that the developed methodology has the capacity to be used for investigating patterns of directed BNs in neuropsychiatric disorders.

A Study of Features and Deep Neural Network Architectures and Hyper-Parameters for Domestic Audio Classification

Recent methodologies for audio classification frequently involve cepstral and spectral features, applied to single channel recordings of acoustic scenes and events. Further, the concept of transfer learning has been widely used over the years, and has proven to provide an efficient alternative to training neural networks from scratch. The lower time and resource requirements when using pre-trained models allows for more versatility in developing system classification approaches. However, information on classification performance when using different features for multi-channel recordings is often limited. Furthermore, pre-trained networks are initially trained on bigger databases and are often unnecessarily large. This poses a challenge when developing systems for devices with limited computational resources, such as mobile or embedded devices. This paper presents a detailed study of the most apparent and widely-used cepstral and spectral features for multi-channel audio applications. Accordingly, we propose the use of spectro-temporal features. Additionally, the paper details the development of a compact version of the AlexNet model for computationally-limited platforms through studies of performances against various architectural and parameter modifications of the original network. The aim is to minimize the network size while maintaining the series network architecture and preserving the classification accuracy. Considering that other state-of-the-art compact networks present complex directed acyclic graphs, a series architecture proposes an advantage in customizability. Experimentation was carried out through Matlab, using a database that we have generated for this task, which composes of four-channel synthetic recordings of both sound events and scenes. The top performing methodology resulted in a weighted F1-score of 87.92% for scalogram features classified via the modified AlexNet-33 network, which has a size of 14.33 MB. The AlexNet network returned 86.24% at a size of 222.71 MB.

Estimation of PQ distance dispersion for atrial fibrillation detection

Background and objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. It is associated with significantly increased morbidity and mortality. Diagnosis of the disease can be based on the analysis of the electrical atrial activity, on quantification of the heart rate irregularity or on a mixture of the both approaches. Since the amplitude of the atrial waves is small, their analysis can lead to false results. On the other hand, the heart rate based analysis usually leads to many unnecessary warnings. Therefore, our goal is to develop a new method for effective AF detection based on the analysis of the electrical atrial waves.Methods: The proposed method employs the fact that there is a lack of repeatable P waves preceding QRS complexes during AF. We apply the operation of spatio-temporal filtering (STF) to magnify and detect the prominent spatio-temporal patterns (STP) within the P waves in multi-channel ECG recordings. Later we measure their distances (PQ) to the succeeding QRS complexes, and we estimate dispersion of the obtained PQ series. For signals with normal sinus rhythm, this dispersion is usually very low, and contrary, for AF it is much raised. This allows for effective discrimination of this cardiologic disorder.Results: Tested on an ECG database consisting of AF cases, normal rhythm cases and cases with normal rhythm restored by the use of cardioversion, the method proposed allowed for AF detection with the accuracy of 98.75% on the basis of both 8–channel and 2–channel signals of 12 s length. When the signals length was decreased to 6 s, the accuracy varied in the range of 95%−97.5% depending on the number of channels and the dispersion measure applied.Conclusions: Our approach allows for high accuracy of atrial fibrillation detection using the analysis of electrical atrial activity. The method can be applied to an early detection of the desease and can advantageously be used to decrease the number of false warnings in systems based on the analysis of the heart rate.

Hippocampal Interneurons are Required for Trace Eyeblink Conditioning in Mice

While the hippocampus has been implicated in supporting the association among time-separated events, the underlying cellular mechanisms have not been fully clarified. Here, we combined in vivo multi-channel recording and optogenetics to investigate the activity of hippocampal interneurons in freely-moving mice performing a trace eyeblink conditioning (tEBC) task. We found that the hippocampal interneurons exhibited conditioned stimulus (CS)-evoked sustained activity, which predicted the performance of conditioned eyeblink responses (CRs) in the early acquisition of the tEBC. Consistent with this, greater proportions of hippocampal pyramidal cells showed CS-evoked decreased activity in the early acquisition of the tEBC. Moreover, optogenetic suppression of the sustained activity in hippocampal interneurons severely impaired acquisition of the tEBC. In contrast, suppression of the sustained activity of hippocampal interneurons had no effect on the performance of well-learned CRs. Our findings highlight the role of hippocampal interneurons in the tEBC, and point to a potential cellular mechanism subserving associative learning.

Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography.

Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.

A Deep Learning Approach for Automatic Seizure Detection in Children With Epilepsy.

Over the last few decades, electroencephalogram (EEG) has become one of the most vital tools used by physicians to diagnose several neurological disorders of the human brain and, in particular, to detect seizures. Because of its peculiar nature, the consequent impact of epileptic seizures on the quality of life of patients made the precise diagnosis of epilepsy extremely essential. Therefore, this article proposes a novel deep-learning approach for detecting seizures in pediatric patients based on the classification of raw multichannel EEG signal recordings that are minimally pre-processed. The new approach takes advantage of the automatic feature learning capabilities of a two-dimensional deep convolution autoencoder (2D-DCAE) linked to a neural network-based classifier to form a unified system that is trained in a supervised way to achieve the best classification accuracy between the ictal and interictal brain state signals. For testing and evaluating our approach, two models were designed and assessed using three different EEG data segment lengths and a 10-fold cross-validation scheme. Based on five evaluation metrics, the best performing model was a supervised deep convolutional autoencoder (SDCAE) model that uses a bidirectional long short-term memory (Bi-LSTM) – based classifier, and EEG segment length of 4 s. Using the public dataset collected from the Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), this model has obtained 98.79 ± 0.53% accuracy, 98.72 ± 0.77% sensitivity, 98.86 ± 0.53% specificity, 98.86 ± 0.53% precision, and an F1-score of 98.79 ± 0.53%, respectively. Based on these results, our new approach was able to present one of the most effective seizure detection methods compared to other existing state-of-the-art methods applied to the same dataset.

Characterization of optogenetically-induced cortical spreading depression in awake mice using graphene micro-transistor arrays

Objective. The development of experimental methodology utilizing graphene micro-transistor arrays to facilitate and advance translational research into cortical spreading depression (CSD) in the awake brain. Approach. CSDs were reliably induced in awake nontransgenic mice using optogenetic methods. High-fidelity DC-coupled electrophysiological mapping of propagating CSDs was obtained using flexible arrays of graphene soultion-gated field-effect transistors (gSGFETs). Main results. Viral vectors targetted channelrhopsin expression in neurons of the motor cortex resulting in a transduction volume ⩾1 mm3. 5–10 s of continous blue light stimulation induced CSD that propagated across the cortex at a velocity of 3.0 ± 0.1 mm min−1. Graphene micro-transistor arrays enabled high-density mapping of infraslow activity correlated with neuronal activity suppression across multiple frequency bands during both CSD initiation and propagation. Localized differences in the CSD waveform could be detected and categorized into distinct clusters demonstrating the spatial resolution advantages of DC-coupled recordings. We exploited the reliable and repeatable induction of CSDs using this preparation to perform proof-of-principle pharmacological interrogation studies using NMDA antagonists. MK801 (3 mg kg−1) suppressed CSD induction and propagation, an effect mirrored, albeit transiently, by ketamine (15 mg kg−1), thus demonstrating this models’ applicability as a preclinical drug screening platform. Finally, we report that CSDs could be detected through the skull using graphene micro-transistors, highlighting additional advantages and future applications of this technology. Significance. CSD is thought to contribute to the pathophysiology of several neurological diseases. CSD research will benefit from technological advances that permit high density electrophysiological mapping of the CSD waveform and propagation across the cortex. We report an in vivo assay that permits minimally invasive optogenetic induction, combined with multichannel DC-coupled recordings enabled by gSGFETs in the awake brain. Adoption of this technological approach could facilitate and transform preclinical investigations of CSD in disease relevant models.

Experimental study on temperature drop during roller quenching process of large-section ultra-heavy steel plate.

Large-section ultra-heavy steel plates feature low cooling rate of central part during quenching process, as well as great differences in texture and performance along thickness direction. Wet jet impingement quenching test apparatus and multi-channel temperature recorder were developed to explore the temperature drop characteristics and its influential factors of ultra-heavy steel plate. The temperature drop curves of plates with different thickness (160, 220, and 300 mm) and weight (23-43 tons) were recorded during the quenching process, accompanied with the exploration on distribution rules of temperature gradient, heat flux density and cooling rate along thickness direction under different water volumes (9000-9400 m3/h) and pressures (0.4-0.8 MPa), upon which the three-dimensional inverse heat conduction model, surface heat transfer coefficient model and thermophysical parameter model were established using finite element and optimization methods. The overall results indicate the symmetric heat transfer between the upper and lower plate surfaces can be realized, when the water flow ratios of the upper and lower surfaces were 1:1.25 and 1:1.4 under pressures of 0.8 MPa and 0.4 MPa, respectively. The "temperature return" phenomenon was accompanied with changing heat flux density of wall surface of 220 mm- and 300 mm-thick plates. The lowest cooling rate along thickness direction appeared at 1/4 thickness position for the 160 mm- and 220 mm- thick plates during quenching, which was attributable to the synergy between heat flux density and temperature gradient. This work may pave a way for improving the cooling rate and quenching uniformity of ultra-heavy steel plates along thickness direction.

Walsh-Hadamard-Based Orthogonal Sampling Technique for Parallel Neural Recording Systems

Walsh-Hadamard based orthogonal sampling of signals is studied in this paper, and an application of this technique is presented. Using orthogonal sampling, a single analog-to-digital converter (ADC) only is sufficient to perform parallel (simultaneous) recording from the sensors. Furthermore, employing Walsh functions as modulation signals, the required bandwidth of the ADC in the proposed system is equal to the bandwidth of a time-multiplexed ADC in a system with identical number of recording channels. Therefore, the bandwidth of the ADC in the proposed system is effectively employed and shared among all the channels. The efficient usage of the ADC bandwidth leads to saving power at the ADC stage and reducing the data-rate of the output signal compared to state-of-the-art recording systems based on frequency-division multiplexing. This paper presents the orthogonal sampling technique for neural recording in multi-channel recording systems which is implemented with four recording channels using a $0.18~\mu \text{m}$ technology which results in a power consumption of $1.26~\mu \text{W}$ /channel at a 0.8 V supply.

A 30 μW Embedded Real-Time Cetacean Smart Detector

Cetacean monitoring is key to their protection. Understanding their behavior relies on multi-channel and high-sampling-rate underwater acoustic recordings for identifying and tracking them in a passive way. However, a lot of energy and data storage is required, requiring frequent human maintenance operations. To cope with these constraints, an ultra-low power mixed-signal always-on wake-up is proposed. Based on pulse-pattern analysis, it can be used for triggering a multi-channel high-performance recorder only when cetacean clicks are detected, thus increasing autonomy and saving storage space. This detector is implemented as a mixed architecture making the most of analog and digital primitives: this combination drastically improves power consumption by processing high-frequency data using analog features and lower-frequency ones in a digital way. Furthermore, a bioacoustic expert system is proposed for improving detection accuracy (in ultra-low-power) via state machines. Power consumption of the system is lower than 30 μW in always-on mode, allowing an autonomy of 2 years on a single CR2032 battery cell with a high detection accuracy. The receiver operating characteristic (ROC) curve obtained has an area under curve of 85% using expert rules and 75% without it. This implementation provides an excellent trade-off between detection accuracy and power consumption. Focused on sperm whales, it can be tuned to detect other species emitting pulse trains. This approach facilitates biodiversity studies, reducing maintenance operations and allowing the use of lighter, more compact and portable recording equipment, as large batteries are no longer required. Additionally, recording only useful data helps to reduce the dataset labeling time.

Collective Dynamics of Ion Channels on Bilayer Lipid Membranes.

Ion channels self-organize on cellular and organelle membranes as clusters and mutually modulate their gating behavior. It has been reported that the efficient information transfer is achieved by cooperative clustering of ion channels. To address the origin and nature of collective dynamics in ion channel clusters, a statistical mechanical model, namely, the Zimm–Bragg-type model in two dimensions with unequal weight distribution in channel–channel interactions, has been proposed. Nearest neighbor interaction along with next-nearest neighbor interaction has been considered, assuming symmetric spatial organization. The multichannel bilayer electrophysiology recordings of the voltage-dependent anion channel (VDAC) from rat brain mitochondria have been analyzed in order to test and further extend the model. The model successfully describes the multichannel gating behavior and self-organization of the VDAC cluster.

Testing Jump-Diffusion in Epileptic Brain Dynamics: Impact of Daily Rhythms.

Stochastic approaches to complex dynamical systems have recently provided broader insights into spatial-temporal aspects of epileptic brain dynamics. Stochastic qualifiers based on higher-order Kramers-Moyal coefficients derived directly from time series data indicate improved differentiability between physiological and pathophysiological brain dynamics. It remains unclear, however, to what extent stochastic qualifiers of brain dynamics are affected by other endogenous and/or exogenous influencing factors. Addressing this issue, we investigate multi-day, multi-channel electroencephalographic recordings from a subject with epilepsy. We apply a recently proposed criterion to differentiate between Langevin-type and jump-diffusion processes and observe the type of process most qualified to describe brain dynamics to change with time. Stochastic qualifiers of brain dynamics are strongly affected by endogenous and exogenous rhythms acting on various time scales—ranging from hours to days. Such influences would need to be taken into account when constructing evolution equations for the epileptic brain or other complex dynamical systems subject to external forcings.

A 2.53 NEF 8-bit 10 kS/s 0.5 μm CMOS Neural Recording Read-Out Circuit with High Linearity for Neuromodulation Implants

This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. The synthetic neural signal is generated using a LabVIEW myDAQ device and processed through a LabVIEW GUI. The read-out circuit is designed and fabricated in the standard 0.5 μμm CMOS process. The proposed amplifier uses a fully differential two-stage topology with a reconfigurable capacitive-resistive feedback network. The amplifier achieves 49.26 dB and 60.53 dB gain within the frequency bandwidth of 0.57–301 Hz and 0.27–12.9 kHz to record the local field potentials (LFPs) and the action potentials (APs), respectively. The amplifier maintains a noise–power tradeoff by reducing the noise efficiency factor (NEF) to 2.53. The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the ADC. The SAR-ADC achieves a signal-to-noise ratio (SNR) of 45.8 dB, with a resolution of 8 bits. The ADC exhibits an effective number of bits of 7.32 at a low sampling rate of 10 ksamples/s. The total power consumption of the chip is 26.02 μμW, which makes it highly suitable for a multi-channel neural signal recording system.

Neural circuits and activity dynamics underlying sex-specific effects of chronic social isolation stress

Exposure to prolonged stress in critical developmental periods induces heightened vulnerability to psychiatric disorders, which may have sex-specific consequences. Here we investigate the neuronal circuits mediating behavioral changes in mice after chronic adolescent social isolation stress. Escalated aggression is exhibited in stressed males, while social withdrawal is shown in stressed females. Invivo multichannel recordings of free-moving animals indicate that pyramidal neurons in prefrontal cortex (PFC) from stressed males exhibit the significantly decreased spike activity during aggressive attacks, while PFC pyramidal neurons from stressed females show a blunted increase of discharge rates during sociability tests. Chemogenetic and electrophysiological evidence shows that PFC hypofunctioning and BLA principal neuron hyperactivity contribute to the elevated aggression in stressed males, while PFC hypofunctioning and VTA dopamine neuron hypoactivity contribute to the diminished sociability in stressed females. These results establish a framework for understanding the circuit and physiological mechanisms underlying sex-specific divergent effects of stress.

Conditional Entropy: A Potential Digital Marker for Stress.

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

Visuo-Motor Feedback Modulates Neural Activities in the Medulla of the Honeybee, Apis mellifera.

Behavioral and internal-state modulation of sensory processing has been described in several organisms. In insects, visual neurons in the optic lobe are modulated by locomotion, but the degree to which visual-motor feedback modulates these neurons remains unclear. Moreover, it also remains unknown whether self-generated and externally generated visual motion are processed differently. Here, we implemented a virtual reality system that allowed fine-scale control over visual stimulation in relation to animal motion, in combination with multichannel recording of neural activity in the medulla of a female honeybee (Apis mellifera). We found that this activity was modulated by locomotion, although, in most cases, only when the bee had behavioral control over the visual stimulus (i.e., in a closed-loop system). Moreover, closed-loop control modulated a third of the recorded neurons, and the application of octopamine (OA) evoked similar changes in neural responses that were observed in a closed loop. Additionally, in a subset of modulated neurons, fixation on a visual stimulus was preceded by an increase in firing rate. To further explore the relationship between neuromodulation and adaptive control of the visual environment of the bee, we modified motor gain sensitivity while locally injecting an OA receptor antagonist into the medulla. Whereas female honeybees were tuned to a motor gain of -2 to 2 (between the heading of the bee and its visual feedback), local disruption of the OA pathway in the medulla abolished this tuning, resulting in similar low levels of response across levels of motor gain. Our results show that behavioral control modulates neural activity in the medulla and ultimately impacts behavior.SIGNIFICANCE STATEMENT When moving, an animal generates the motion of the visual scene over its retina. We asked whether self-generated and externally generated optic flow are processed differently in the insect medulla. Our results show that closed-loop control of the visual stimulus modulates neural activity as early as the medulla and ultimately impacts behavior. Moreover, blocking octopaminergic modulation further disrupted object-tracking responses. Our results suggest that the medulla is an important site for context-dependent processing of visual information and that placing the animal in a closed-loop environment may be essential to understanding its visual cognition and processing.

Odour Retrieval Processing in Mice: Cholinergic Modulation of Oscillatory Coupling in Olfactory Bulb-Piriform Networks

Background/Aims: Olfactory dysfunction can provide valuable insight into early pathophysiological processes of brain disorders. Olfactory processing of chemosensory and odour sensitivity relies on segregating salient odours from background odours cues. Odour-evoked fast oscillations in the olfactory bulb (OB) are hypothesized to be an important index of odour quality coding. The present preclinical work aimed at better understanding connectivity associated with odour coding and behavioural odour discrimination. Methods: Network oscillations and functional connectivity (FC) were measured in C57BL/6 mice performing the olfactory associative odour learning (OL) test, using multichannel local field potential recordings in key olfactory networks. Cholinergic modulation of odour processing was investigated using the muscarinic antagonist scopolamine. Results: At the behavioural level, olfactory memory, which refers to the acquisition and recollection of a reference odour by reduced exploration time, was observed in animals that correctly learned the task. Significant decrease in mean investigation and retrieval time of the associated odour-food reward was observed between trials. At the network level, the associated odour during sniffing behaviour was associated with enhanced coherence in the β and γ frequency oscillations across the olfactory pathway, with marked changes observed between the OB and anterior piriform cortex (PC). The enhanced phase-amplitude cross-frequency coupling in the OB and the weak coupling index in the hippocampal CA1 suggests a role of the OB network in olfaction encoding and processing. Scopolamine impaired behavioural and FC underlying recall and retrieval of the associated odour. Conclusion: The results suggest that the acquisition and formation of odour reference memory rely primarily on FC at the OB-PC network and confirm the role of muscarinic receptors in olfactory retrieval processing.