An analysis of current source density profiles activated by local stimulation in the mouse auditory cortex in vitro

Inhibiting presynaptic calcium channel motility in the auditory cortex suppresses synchronized input processing

Introduction

The emergent coherent population activity from thousands of stochastic neurons in the brain is believed to constitute a key neuronal mechanism for salient processing of external stimuli and its link to internal states like attention and perception. In the sensory cortex, functional cell assemblies are formed by recurrent excitation and inhibitory influences. The stochastic dynamics of each cell involved is largely orchestrated by presynaptic CAV2.1 voltage-gated calcium channels (VGCCs). Cav2.1 VGCCs initiate the release of neurotransmitters from the presynaptic compartment and are therefore able to add variability into synaptic transmission which can be partly explained by their mobile organization around docked vesicles.

Methods

To investigate the relevance of Cav2.1 channel surface motility for the input processing in the primary auditory cortex (A1) in vivo, we make use of a new optogenetic system which allows for acute, reversable cross-linking Cav2.1 VGCCs via a photo-cross-linkable cryptochrome mutant, CRY2olig. In order to map neuronal activity across all cortical layers of the A1, we performed laminar current-source density (CSD) recordings with varying auditory stimulus sets in transgenic mice with a citrine tag on the N-terminus of the VGCCs.

Results

Clustering VGCCs suppresses overall sensory-evoked population activity, particularly when stimuli lead to a highly synchronized distribution of synaptic inputs.

Discussion

Our findings reveal the importance of membrane dynamics of presynaptic calcium channels for sensory encoding by dynamically adjusting network activity across a wide range of synaptic input strength.

A Multielectrode Array-Based Recording System for Analyzing Ultrasound-Driven Neural Responses in Brain Slices in vitro

Ultrasound stimulation is expected to be useful for transcranial local and deep stimulation of the brain, which is difficult to achieve using conventional electromagnetic stimulation methods. Previous ultrasound stimulation experiments have used various types of acute in vitro preparations, including hippocampus slices from rodents and Caenorhabditis elegans tissue. For in vivo preparations, researchers have used the cortices of rodents as targets for transcranial ultrasound stimulation. However, no previous studies have used in vitro ultrasound stimulation in rodent cortical slices to examine the mechanisms of ultrasound-driven central neural circuits. Here we demonstrate the optimal experimental conditions for an in vitro ultrasound stimulation system for measuring activity in brain slices using a multielectrode array substrate. We found that the peak amplitudes of the ultrasound-evoked cortical responses in the brain slices depend on the intensities and durations of the ultrasound stimulation parameters. Thus, our findings provide a new in vitro experimental setup that enables activation of a brain slice via ultrasound stimulation. Accordingly, our results indicate that choosing the appropriate ultrasound waveguide structure and stimulation parameters is important for producing the desired intensity distribution in a localized area within a brain slice. We expect that this experimental setup will facilitate future exploration of the mechanisms of ultrasound-driven neural activity.

Stimulus dependent transformations between synaptic and spiking receptive fields in auditory cortex

Auditory cortex neurons nonlinearly integrate synaptic inputs from the thalamus and cortex, and generate spiking outputs for simple and complex sounds. Directly comparing synaptic and spiking activity can determine whether this input-output transformation is stimulus-dependent. We employ in vivo whole-cell recordings in the mouse primary auditory cortex, using pure tones and broadband dynamic moving ripple stimuli, to examine properties of functional integration in tonal (TRFs) and spectrotemporal (STRFs) receptive fields. Spectral tuning in STRFs derived from synaptic, subthreshold and spiking responses proves to be substantially more selective than for TRFs. We describe diverse spectral and temporal modulation preferences and distinct nonlinearities, and their modifications between the input and output stages of neural processing. These results characterize specific processing differences at the level of synaptic convergence, integration and spike generation resulting in stimulus-dependent transformation patterns in the primary auditory cortex.

The authors compare receptive fields and nonlinearities of synaptic inputs, membrane potentials, and spiking activity in the auditory cortex for broadband stimuli revealing distinct differences, which lead to an increase in feature selectivity from neuron input to output. Frequency selectivity is distinctly higher for spectrotemporal receptive fields (STRFs) than for tonal receptive fields (TRFs).

Cathodic-leading pulses are more effective than anodic-leading pulses in intracortical microstimulation of the auditory cortex

OBJECTIVE

Intracortical microstimulation (ICMS) is widely used in neuroscientific research. Earlier work from our lab showed the possibility to combine ICMS with neuronal recordings on the same shank of multi-electrode arrays and consequently inside the same cortical column in vivo. The standard stimulus pulse shape for ICMS is a symmetric, biphasic current pulse. Here, we investigated the role of the leading-phase polarity (cathodic- versus anodic-leading) of such single ICMS pulses on the activation of the cortical network.

APPROACH

Local field potentials (LFPs) and multi-unit responses were recorded in the primary auditory cortex (A1) of adult guinea pigs (n  =  15) under ketamine/xylazine anesthesia using linear multi-electrode arrays. Physiological responses of A1 were recorded during acoustic stimulation and ICMS. For the ICMS, the leading-phase polarity, the stimulated electrode and the stimulation current where varied systematically on any one of the 16 electrodes while recording at the same time with the 15 remaining electrodes.

MAIN RESULTS

Cathodic-leading ICMS consistently led to higher response amplitudes. In superficial cortical layers and for a given current amplitude, cathodic-leading and anodic-leading ICMS showed comparable activation patterns, while in deep layers only cathodic-leading ICMS reliably generated local neuronal activity. ICMS had a significantly smaller dynamic range than acoustic stimulation regardless of leading-phase polarity.

SIGNIFICANCE

The present study provides in vivo evidence for a differential neuronal activation mechanism of the different leading-phase polarities, with cathodic-leading stimulation being more effective, and suggests that the waveform of the stimulus should be considered systematically for cortical neuroprosthesis development.

A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks

Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling

OBJECTIVE

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model.

APPROACH

We herein introduce an adaptive framework in which optimal input design is integrated with square root cubature Kalman filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification.

MAIN RESULTS

For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 ms in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes.

SIGNIFICANCE

Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications.

Salicylate-Induced Suppression of Electrically Driven Activity in Brain Slices from the Auditory Cortex of Aging Mice

The prevalence of tinnitus is known to increase with age. The age-dependent mechanisms of tinnitus may have important implications for the development of new therapeutic treatments. High doses of salicylate can be used experimentally to induce transient tinnitus and hearing loss. Although accumulating evidence indicates that salicylate induces tinnitus by directly targeting neurons in the peripheral and central auditory systems, the precise effect of salicylate on neural networks in the auditory cortex (AC) is unknown. Here, we examined salicylate-induced changes in stimulus-driven laminar responses of AC slices with salicylate superfusion in young and aged senescence-accelerated-prone (SAMP) and -resistant (SAMR) mice. Of the two strains, SAMP1 is known to be a more suitable model of presbycusis. We recorded stimulus-driven laminar local field potential (LFP) responses at multi sites in AC slice preparations. We found that for all AC slices in the two strains, salicylate always reduced stimulus-driven LFP responses in all layers. However, for the amplitudes of the LFP responses, the two senescence-accelerated mice (SAM) strains showed different laminar properties between the pre- and post-salicylate conditions, reflecting strain-related differences in local circuits. As for the relationships between auditory brainstem response (ABR) thresholds and the LFP amplitude ratios in the pre- vs. post-salicylate condition, we found negative correlations in layers 2/3 and 4 for both older strains, and in layer 5 (L5) in older SAMR1. In contrast, the GABAergic agonist muscimol (MSC) led to positive correlations between ABR thresholds and LFP amplitude ratios in the pre- vs. post-MSC condition in younger SAM mice from both strains. Further, in younger mice, salicylate decreased the firing rate in AC L4 pyramidal neurons. Thus, salicylate can directly reduce neural excitability of L4 pyramidal neurons and thereby influence AC neural circuit activity. That we observed age-dependent effects of salicylate and varied GABAergic sensitivity in the AC among mouse strains with hearing loss implies that potential therapeutic mechanisms for tinnitus may operate differently in young vs. aged subjects. Therefore, scientists developing new therapeutic modalities for tinnitus treatment should consider using both aged and young animals.