Comparison of Performance for Cochlear-Implant Listeners Using Audio Processing Strategies Based on Short-Time Fast Fourier Transform or Spectral Feature Extraction.

OBJECTIVES: We compared sound quality and performance for a conventional cochlear-implant (CI) audio processing strategy based on short-time fast-Fourier transform (Crystalis) and an experimental strategy based on spectral feature extraction (SFE). In the latter, the more salient spectral features (acoustic events) were extracted and mapped into the CI stimulation electrodes. We hypothesized that (1) SFE would be superior to Crystalis because it can encode acoustic spectral features without the constraints imposed by the short-time fast-Fourier transform bin width, and (2) the potential benefit of SFE would be greater for CI users who have less neural cross-channel interactions. DESIGN: To examine the first hypothesis, 6 users of Oticon Medical Digisonic SP CIs were tested in a double-blind design with the SFE and Crystalis strategies on various aspects: word recognition in quiet, speech-in-noise reception threshold (SRT), consonant discrimination in quiet, listening effort, melody contour identification (MCI), and subjective sound quality. Word recognition and SRTs were measured on the first and last day of testing (4 to 5 days apart) to assess potential learning and/or acclimatization effects. Other tests were run once between the first and last testing day. Listening effort was assessed by measuring pupil dilation. MCI involved identifying a five-tone contour among five possible contours. Sound quality was assessed subjectively using the multiple stimulus with hidden reference and anchor (MUSHRA) paradigm for sentences, music, and ambient sounds. To examine the second hypothesis, cross-channel interaction was assessed behaviorally using forward masking. RESULTS: Word recognition was similar for the two strategies on the first day of testing and improved for both strategies on the last day of testing, with Crystalis improving significantly more. SRTs were worse with SFE than Crystalis on the first day of testing but became comparable on the last day of testing. Consonant discrimination scores were higher for Crystalis than for the SFE strategy. MCI scores and listening effort were not substantially different across strategies. Subjective sound quality scores were lower for the SFE than for the Crystalis strategy. The difference in performance with SFE and Crystalis was greater for CI users with higher channel interaction. CONCLUSIONS: CI-user performance was similar with the SFE and Crystalis strategies. Longer acclimatization times may be required to reveal the full potential of the SFE strategy.

Investigating the electrode-electrolyte interface modelling in cochlear implants.

OBJECTIVE: Proposing a good electrode-electrolyte interface (EEI) model and properly identifying relevant parameters may help designing safer and more optimized auditory nerve fiber stimulation and recording in cochlear implants (CI). However, in the literature EEI model parameter values exhibit large variability. We aim to explain some root causes of this variability using the Cole model and its simpler form, the Basic RC model. APPROACH: We use temporal and spectral methods and fit the models to stimulation pulse voltage response (SPVR) and electrochemical impedance spectroscopy (EIS) data. MAIN RESULTS: Temporal fittings show that there are multiple sets of model parameters that provide a good fit to the SPVR data. Therefore, small methodological differences in literature may result in different model fits. While these models share similar characteristics at high frequencies >500 Hz, the SPVR fitting is blind to low frequencies, thus it cannot correctly estimate the Faradaic resistor. Similarly, the polarization capacitor and its fractional order are not estimated robustly (capacitor variations in the nano- to micro-farad range) due to limited observation of mid-range frequencies. EIS provides a good model fit down to ~3Hz, and thus robust estimation for the polarization capacitor. At lower frequencies charge mechanisms may modify the EEI, requiring multi-compartment Cole model fitting to EIS to improve the estimation of Faradaic characteristics. Our EIS data measurements down to 0.05Hz show that a two-compartment Cole model is sufficient to explain the data. SIGNIFICANCE: Our study describes the scope and limitation of SPVR and EIS fitting methods, by which literature variability is explained among CI EEI models. The estimation of mid-to-low-frequency characteristics of the CI EEI is not in the scope of the SPVR method. EIS provides a better fit; however, its results should not be extrapolated to unobserved frequencies where new charge transfer mechanisms may emerge at the EEI.

A 15-day course of donepezil modulates spectral EEG dynamics related to target auditory stimuli in young, healthy adult volunteers.

OBJECTIVE: To identify possible electroencephalographic (EEG) markers of donepezil's effect on cortical activity in young, healthy adult volunteers at the group level. METHODS: Thirty subjects were administered a daily dose of either 5mg donepezil or placebo for 15days in a double-blind, randomized, cross-over trial. The electroencephalogram during an auditory oddball paradigm was recorded from 58 scalp electrodes. Current source density (CSD) transformations were applied to EEG epochs. The event-related potential (ERP), inter-trial coherence (ITC: the phase consistency of the EEG spectrum) and event-related spectral perturbation (ERSP: the EEG power spectrum relative to the baseline) were calculated for the target (oddball) stimuli. RESULTS: The donepezil and placebo conditions differed in terms of the changes in delta/theta/alpha/beta ITC and ERSP in various regions of the scalp (especially the frontal electrodes) but not in terms of latency and amplitude of the P300-ERP component. CONCLUSION: Our results suggest that ITC and ERSP analyses can provide EEG markers of donepezil's effects in young, healthy, adult volunteers at a group level. SIGNIFICANCE: Novel EEG markers could be useful to assess the therapeutic potential of drug candidates in Alzheimer's disease in healthy volunteers prior to the initiation of Phase II/III clinical studies in patients.

Attention modulation during motor preparation in Parkinsonian freezers: A time-frequency EEG study.

OBJECTIVE: To investigate the cortical integration of attentional stimuli during motor preparation in parkinsonian patients with freezing of gait (FoG, n=12) or without freezing of gait (n=13), and in aged-matched healthy controls (n=13). We hypothesized that interference between attention and action in freezers would be revealed by differences in cortical modulation during this dual task. METHODS: Attention during step preparation was modulated by means of an auditory oddball discrimination task. EEG oscillations in different frequency bands were measured for the attentional stimulus and the motor stimulus. RESULTS: Over the 500ms following the sound, low-frequency power increased in all three groups. This was followed by a power decrease in mid-range frequencies after both target and standard sounds in the healthy controls and in the non-FoG group. In contrast, EEG oscillations in the beta band were impaired in the FoG group, who notably failed to display event-related desynchronization after perceiving the sound. CONCLUSIONS: An attentional stimulus was able to trigger event-related desynchronization before motor preparation in the non-FoG group but not in the FoG group. SIGNIFICANCE: In the FoG group, stimulus discrimination was maintained but the coupling between attention and motor preparation was impaired.

From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study.

Both biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.

Effects of transcranial Direct Current Stimulation (tDCS) on cortical activity: a computational modeling study.

Although it is well-admitted that transcranial Direct Current Stimulation (tDCS) allows for interacting with brain endogenous rhythms, the exact mechanisms by which externally-applied fields modulate the activity of neurons remain elusive. In this study a novel computational model (a neural mass model including subpopulations of pyramidal cells and inhibitory interneurons mediating synaptic currents with either slow or fast kinetics) of the cerebral cortex was elaborated to investigate the local effects of tDCS on neuronal populations based on an in-vivo experimental study. Model parameters were adjusted to reproduce evoked potentials (EPs) recorded from the somatosensory cortex of the rabbit in response to air-puffs applied on the whiskers. EPs were simulated under control condition (no tDCS) as well as under anodal and cathodal tDCS fields. Results first revealed that a feed-forward inhibition mechanism must be included in the model for accurate simulation of actual EPs (peaks and latencies). Interestingly, results revealed that externally-applied fields are also likely to affect interneurons. Indeed, when interneurons get polarized then the characteristics of simulated EPs become closer to those of real EPs. In particular, under anodal tDCS condition, more realistic EPs could be obtained when pyramidal cells were depolarized and, simultaneously, slow (resp. fast) interneurons became de- (resp. hyper-) polarized. Geometrical characteristics of interneurons might provide some explanations for this effect.

Transcranial current brain stimulation (tCS): models and technologies.

In this paper, we provide a broad overview of models and technologies pertaining to transcranial current brain stimulation (tCS), a family of related noninvasive techniques including direct current (tDCS), alternating current (tACS), and random noise current stimulation (tRNS). These techniques are based on the delivery of weak currents through the scalp (with electrode current intensity to area ratios of about 0.3-5 A/m2) at low frequencies (typically < 1 kHz) resulting in weak electric fields in the brain (with amplitudes of about 0.2-2 V/m). Here we review the biophysics and simulation of noninvasive, current-controlled generation of electric fields in the human brain and the models for the interaction of these electric fields with neurons, including a survey of in vitro and in vivo related studies. Finally, we outline directions for future fundamental and technological research.

Transcranial direct-current stimulation modulates synaptic mechanisms involved in associative learning in behaving rabbits.

Transcranial direct-current stimulation (tDCS) is a noninvasive brain stimulation technique that has been successfully applied for modulation of cortical excitability. tDCS is capable of inducing changes in neuronal membrane potentials in a polarity-dependent manner. When tDCS is of sufficient length, synaptically driven after-effects are induced. The mechanisms underlying these after-effects are largely unknown, and there is a compelling need for animal models to test the immediate effects and after-effects induced by tDCS in different cortical areas and evaluate the implications in complex cerebral processes. Here we show in behaving rabbits that tDCS applied over the somatosensory cortex modulates cortical processes consequent to localized stimulation of the whisker pad or of the corresponding area of the ventroposterior medial (VPM) thalamic nucleus. With longer stimulation periods, poststimulation effects were observed in the somatosensory cortex only after cathodal tDCS. Consistent with the polarity-specific effects, the acquisition of classical eyeblink conditioning was potentiated or depressed by the simultaneous application of anodal or cathodal tDCS, respectively, when stimulation of the whisker pad was used as conditioned stimulus, suggesting that tDCS modulates the sensory perception process necessary for associative learning. We also studied the putative mechanisms underlying immediate effects and after-effects of tDCS observed in the somatosensory cortex. Results when pairs of pulses applied to the thalamic VPM nucleus (mediating sensory input) during anodal and cathodal tDCS suggest that tDCS modifies thalamocortical synapses at presynaptic sites. Finally, we show that blocking the activation of adenosine A1 receptors prevents the long-term depression (LTD) evoked in the somatosensory cortex after cathodal tDCS.

Computational modeling of high-frequency oscillations at the onset of neocortical partial seizures: from 'altered structure' to 'dysfunction'.

In this paper, a neural mass model is proposed to analyze some mechanisms underlying the generation of fast oscillations (80 Hz and beyond) at the onset of seizures. This model includes one sub-population of pyramidal cells and one sub-population of interneurons targeting the perisomatic region of pyramidal cells where fast GABAergic currents are mediated. We identified some conditions for which the model can reproduce the features of high-frequency, chirp-like (from approximately 100 to approximately 70 Hz) signatures observed in real depth-EEG signals recorded in epileptic patients at seizure onset ("fast onset activity"). These conditions included appropriate alterations in (i) the strengths of GABAergic and glutamatergic connections, and (ii) the amplitude of average EPSPs/IPSPs. Results revealed that a subtle balance between excitatory and inhibitory feedbacks is required in the model for reproducing a 'realistic' fast activity, i.e., showing a reduction of frequency with a simultaneous increase in amplitude, as actually observed in epileptogenic cerebral cortex. Results also demonstrated that the number of scenarios (variation, in time, of model parameters) leading to chirp-like signatures was rather limited. First, to produce high-frequency output signals, the model should operate in a "resonance" region, at the frontier between a stable and an unstable region. Second both EPSP and IPSP amplitudes should decrease with time in order to obey the frequency/amplitude constraint. These scenarios obtained through a mathematical analysis of the model show how some alteration in the structure of neural networks can lead to dysfunction. They also provide insights into potentially important mechanisms for high-frequency epileptic activity generation.

Delta waves differently modulate high frequency components of EEG oscillations in various unconsciousness levels.

In this paper we investigate the modulation properties of high frequency EEG activities by delta waves during various depth of anesthesia. We show that slow and fast delta waves (0-2 Hz and 2-4 Hz respectively) and high frequency components of the EEG (8-20 Hz) are correlated with each other and there is a kind of phase locking between them that varies with depth of anesthesia. Our analyses show that maximum amplitudes of high frequency components of the EEG signal are appeared in different phases of slow and fast delta waves when the concentration of Desflurane and Propofol anesthetic agents varies in a patient. There are some slight differences in using slow and fast components of delta waves. For instance, when depth of anesthesia changes, biphasic responses of the EEG have more influences on results of the fast delta wave method. In addition, this method obtains more robust and less noisy results compared with the slow delta wave method. Since phase angle between fast EEG oscillations and delta waves indicates the status of information processing in the brain and it changes in various unconsciousness levels, it may improve the performance of other classic methods of determining depth of anesthesia.

Designing a planar vector field to investigate the role of a slow variable in an enhanced mean-field model during general anesthesia.

Local mean-field models (MFMs) describe regional brain activities by some connected differential equations. In an overall view, constituting variables of these differential equations can be divided to very fast, fast and slow variables. In this article we propose a method that can be used to determine role of a slow variable in behavior of MFMs. Very fast variables can be adiabatically removed from the equations. Isoclines of fast and slow variables and their corresponding vector field can provide valuable information about model behavior and role of the slow variable in it. The vector field of our interested MFM that is an enhanced MFM designed specially for general anesthesia, is a 3D field (one slow and two fast variables) and it is not so convenient for visually inspecting the role of the slow variable in this model. To afford this problem we design a 2D (planar) vector filed that only considers the slow variable and one of the fast variables.