Toxicology study for magnetic injection of prednisolone into the rat cochlea

This paper investigates the safety of a novel 'magnetic injection' method of delivering therapy to the cochlea, in a rodent model. In this method of administration, a magnetic field is employed to actively transport drug-eluting superparamagnetic iron-oxide core nanoparticles into the cochlea, where they then release their drug payload (we delivered the steroid prednisolone). Our study design and selection of control groups was based on published regulatory guidance for safety studies that involve local drug delivery. We tested for both single and multiple delivery doses to the cochlea, and found that magnetic delivery did not harm hearing. There was no statistical difference in hearing between magnetically treated ears versus ears that received intra-tympanic steroid (a mimic of a standard-of-care for sudden sensorineural hearing loss), both 2 and 30 days after treatment. Since our treatment is local to the ear, the levels of steroid and iron circulating systemically after our treatment were low, below mass-spectrometry detection limits for the steroid and no different from normal for iron. No adverse findings were observed in ear tissue histopathology or in animal gross behavior. At 2 and 30 days after treatment, inflammatory changes examined in the ear were limited to the middle ear, were very mild in severity, and by day 90 there was ongoing and almost complete reversibility of these changes. There were no ear tissue scarring or hemorrhage trends associated with magnetic delivery. In summary, after conducting a pre-clinical safety study, no adverse safety issues were observed.

Dynamics of phase-independent spectro-temporal tuning in primary auditory cortex of the awake ferret

Tuning of cortical neurons is often measured as a static property, or during a steady-state regime, despite a number of studies suggesting that tuning depends on when it is measured during a neuron's response (e.g., onset vs. sustained vs. offset). We have previously shown that phase-locked tuning to feature transients evolves as a dynamic quantity from the onset of the sound. In this follow-up study, we examined the phase-independent tuning during feature transients. Based on previous results, we hypothesized phase-independent tuning should evolve on the same timescale as phase-locked tuning. We used stimuli of constant level, but alternating between flat spectro-temporal envelope and a modulated envelope with well-defined spectral density and temporal periodicity. This allowed the measure of changes in tuning to novel spectro-temporal content, as happens during running speech and other sounds with rapid transitions without a confounding change in sound level. For 95% of neurons, tuning changed significantly from the onset, over the course of the response. For a majority of these cells, the change occurred within the first 40ms following a feature onset, often even around 10-20ms. This solidifies the idea that tuning can change rapidly from onset tuning to the sustained, steady-state tuning.

Contrast tuned responses in primary auditory cortex of the awake ferret

Auditory neurons are often characterized by their spectro-temporal receptive field (STRF), a linear measure that captures overall trends of neural responses to modulations of the spectro-temporal envelopes of sounds. We have previously shown that primary auditory cortex neurons of the awake ferret are better characterized by STRFs followed by a non-trivial non-linearity. This non-linearity is a half-wave rectification followed by a squaring function, indicating that cortical neurons probably encode higher-order statistics of the spectrum of sounds. In this article, we introduce the concept of a contrast receptive field (CRF) and show that neurons in the auditory cortex encode quadratic statistics of the spectro-temporal envelope of sounds, which we call auditory contrast. We reveal phase-dependent contrast tuning in single units. Most units with a reliable STRF also possess a reliable CRF, such that the response to stimulus contrast complements the linear response described by the STRF. The relationship between the STRF and the CRF is analyzed in terms of orthogonality, co-localization in time-frequency, feature orientation selectivity, and output non-linearity interdependence. Our study shows that contrast can be used by auditory cortex neurons to sharpen, in a noise-resistant fashion, their responses to dynamic spectral profiles.

Nonlinearity of coding in primary auditory cortex of the awake ferret

Neural computation in sensory systems is often modeled as a linear system. This first order approximation is computed by reverse correlating a stimulus with the spike train it evokes. The spectro-temporal receptive field (STRF) is a generalization of this procedure which characterizes processing in the auditory pathway in both frequency and time. While the STRF performs well in predicting the overall course of the response to a novel stimulus, it is unable to account for aspects of the neural output which are inherently nonlinear (e.g. discrete events and non-negative spike rates). We measured the STRFs of neurons in the primary auditory cortex (AI) of the awake ferret using spectro-temporally modulated auditory gratings, or ripples. We quantified the degree of nonlinearity of these neurons by comparing their responses to the responses predicted from their respective STRFs. The responses of most cells in AI exhibited a squaring, nonlinear relation to the stimuli used to evoke them. Thus, the nonlinearity of these cells was nontrivial, that is it was not solely the result of spike rate rectification or saturation. By modeling the nonlinearity as a polynomial static output function, the predictive power of the STRF was significantly improved.

Dynamics of spectro-temporal tuning in primary auditory cortex of the awake ferret

We previously characterized the steady-state spectro-temporal tuning properties of cortical cells with respect to broadband sounds by using sounds with sinusoidal spectro-temporal modulation envelope where spectral density and temporal periodicity were constant over several seconds. However, since speech and other natural sounds have spectro-temporal features that change substantially over milliseconds, we study the dynamics of tuning by using stimuli of constant overall intensity, but alternating between a flat spectro-temporal envelope and a modulated envelope with well defined spectral density and temporal periodicity. This allows us to define the tuning of cortical cells to speech-like and other rapid transitions, on the order of milliseconds, as well as the time evolution of this tuning in response to the appearance of new features in a sound. Responses of 92 cells in AI were analyzed based on the temporal evolution of the following measures of tuning after a rapid transition in the stimulus: center of mass and breadth of tuning; separability and direction selectivity; temporal and spectral asymmetry. We find that tuning center of mass increased in 70% of cells for spectral density and in 68% of cells for temporal periodicity, while roughly half of cells (47%) broadened their tuning, with the other half (53%) sharpening tuning. The majority of cells (73%) were initially not direction selective, as measured by an inseparability index, which had an initial low value that then increased to a higher steady state value. Most cells were characterized by temporal symmetry, while spectral symmetry was initially high and then progressed to low steady-state values (61%). We demonstrate that cortical neurons can be characterized by a lag-dependent modulation transfer function. This characterization, when measured through to steady-state, becomes equivalent to the classical spectro-temporal receptive field.

Stability of spectro-temporal tuning over several seconds in primary auditory cortex of the awake ferret

The steady-state spectro-temporal tuning of auditory cortical cells has been studied using a variety of broadband stimuli that characterize neurons by their steady-state responses to long duration stimuli, lasting from about a second to several minutes. Central sensory stations are thought to adapt in their response to stimuli presented over extended periods of time. For instance, we have previously shown that auditory cortical neurons display a second order of adaptation, whereby the rate of their adaptation to the repeated presentation of fixed alternating stimuli decreases with each presentation. The auditory grating (or ripple) method of characterizing central auditory neurons, and its extensions, have proven very effective. But these stimuli are typically used with spectro-temporal content held fixed over time-scales of seconds, introducing the possibility of rapid adaptation while the receptive field is being measured, whereas the neural response used to compute a spectro-temporal receptive field (STRF) assumes stationarity in the neural input/output function. We demonstrate dynamic changes in some parameters during the measurement of the STRF over a period of seconds, even absent of a relevant behavioral task. Specifically, we find in the primary auditory cortex of the awake ferret, small but systematic changes in duration and breadth of tuning of STRFs when comparing the early (0.25-1.75 s) and late (4.5-6 s) segments of the responses to these stimuli.

Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex

To understand the neural representation of broadband, dynamic sounds in primary auditory cortex (AI), we characterize responses using the spectro-temporal response field (STRF). The STRF describes, predicts, and fully characterizes the linear dynamics of neurons in response to sounds with rich spectro-temporal envelopes. It is computed from the responses to elementary "ripples," a family of sounds with drifting sinusoidal spectral envelopes. The collection of responses to all elementary ripples is the spectro-temporal transfer function. The complex spectro-temporal envelope of any broadband, dynamic sound can expressed as the linear sum of individual ripples. Previous experiments using ripples with downward drifting spectra suggested that the transfer function is separable, i.e., it is reducible into a product of purely temporal and purely spectral functions. Here we measure the responses to upward and downward drifting ripples, assuming reparability within each direction, to determine if the total bidirectional transfer function is fully separable. In general, the combined transfer function for two directions is not symmetric, and hence units in AI are not, in general, fully separable. Consequently, many AI units have complex response properties such as sensitivity to direction of motion, though most inseparable units are not strongly directionally selective. We show that for most neurons, the lack of full separability stems from differences between the upward and downward spectral cross-sections but not from the temporal cross-sections; this places strong constraints on the neural inputs of these AI units.

Robust spectrotemporal reverse correlation for the auditory system: optimizing stimulus design

The spectrotemporal receptive field (STRF) is a functional descriptor of the linear processing of time-varying acoustic spectra by the auditory system. By cross-correlating sustained neuronal activity with the dynamic spectrum of a spectrotemporally rich stimulus ensemble, one obtains an estimate of the STRF. In this article, the relationship between the spectrotemporal structure of any given stimulus and the quality of the STRF estimate is explored and exploited. Invoking the Fourier theorem, arbitrary dynamic spectra are described as sums of basic sinusoidal components--that is, moving ripples. Accurate estimation is found to be especially reliant on the prominence of components whose spectral and temporal characteristics are of relevance to the auditory locus under study and is sensitive to the phase relationships between components with identical temporal signatures. These and other observations have guided the development and use of stimuli with deterministic dynamic spectra composed of the superposition of many temporally orthogonal moving ripples having a restricted, relevant range of spectral scales and temporal rates. The method, termed sum-of-ripples, is similar in spirit to the white-noise approach but enjoys the same practical advantages--which equate to faster and more accurate estimation--attributable to the time-domain sum-of-sinusoids method previously employed in vision research. Application of the method is exemplified with both modeled data and experimental data from ferret primary auditory cortex (AI).

Analysis of dynamic spectra in ferret primary auditory cortex. I. Characteristics of single-unit responses to moving ripple spectra

1. Auditory stimuli referred to as moving ripples are used to characterize the responses of both single and multiple units in the ferret primary auditory cortex. Moving ripples are broadband complex sounds with a sinusoidal spectral profile that drift along the logarithmic frequency axis at a constant velocity. 2. Neuronal responses to moving ripples are locked to the phase of the ripple, i.e., they exhibit the same periodicity as that of the moving ripple profile. Neural responses are characterized as a function of ripple velocity (temporal property) and ripple frequency (spectral property). Transfer functions describing the response to these temporal and spectral modulations are constructed. Temporal transfer functions are inverse Fourier transformed to obtain impulse response functions that reflect the cell's temporal characteristics. Ripple transfer functions are inverse Fourier transformed to obtain the response field, a measure analogous to the cell's response area. These operations assume linearity in the cell's response to moving ripples. 3. Transfer functions and other response functions are shown to be fairly independent on the overall level or depth of modulation of the ripple stimuli. Only downward moving ripples were used in this study. 4. The temporal and ripple transfer functions are found to be separable, in that their shapes remain unchanged for different test parameters. Thus ripple transfer functions and response fields remain statistically similar in shape (to within an overall scale factor) regardless of the ripple velocity or whether stationary or moving ripples are used in the measurement. The same stability in shape holds for the temporal transfer functions and the impulse response functions measured with different ripple frequencies. Separability implies that the combined spectrotemporal transfer function of a cell can be written as the product of a purely ripple and a purely temporal transfer functions, and thus that the neuron can be computationally modeled as processing spectral and temporal information in two separate and successive stages. 5. The ripple parameters that characterize cortical cells are distributed somewhat evenly, with the characteristic ripple frequencies ranging from 0.2 to > 2 cycles/octave and the characteristic angular frequency typically ranging from 2 to 20 Hz. 6. Many responses exhibit periodicities in the spectral envelope of the stimulus. These periodicities are of two types. Slow rebounds, not found in the spectral envelope, and with a period of approximately 150 ms, appear with various strengths in approximately 30% of the cells. Fast regular firings with interspike intervals of approximately 10 ms are much less common and appear to correspond to interactions between the component tones that make up a ripple.

Analysis of dynamic spectra in ferret primary auditory cortex. II. Prediction of unit responses to arbitrary dynamic spectra

1. Responses of single units and multiunit clusters were recorded in the ferret primary auditory cortex (AI) with the use of broadband complex dynamic spectra. Previous work has demonstrated that simpler spectra consisting of single moving ripples (i.e., sinusoidally modulated spectral profiles that travel at a constant velocity along the logarithmic frequency axis) could be used effectively to characterize the response fields and transfer functions of AI cells. 2. A complex dynamic spectral profile can be thought of as being the sum of moving ripple spectra. Such a decomposition can be computed from a two-dimensional spectrotemporal Fourier transform of the dynamic spectral profile with moving ripples as the basis function. 3. Therefore, if AI units were essentially linear, satisfying the superposition principle, then their responses to arbitrary dynamic spectra could be predicted from the responses to single moving ripples, i.e., from the units' response fields and transfer functions (spectral and temporal impulse response functions, respectively). 4. This conjecture was tested and confirmed with data from 293 combinations of moving ripples, involving complex spectra composed of up to 15 moving ripples of different ripple frequencies and velocities. For each case, response predictions based on the unit transfer functions were compared with measured responses. The correlation between predicted and measured responses was found to be consistently high (84% with rho > 0.6). 5. The distribution of response parameters suggests that AI cells may encode the profile of a dynamic spectrum by performing a multiscale spectrotemporal decomposition of the dynamic spectral profile in a largely linear manner.