An Investigation of Feasibility and Safety of Bi-Modal Stimulation for the Treatment of Tinnitus: An Open-Label Pilot Study.

OBJECTIVES: Tinnitus is the perception of sound in the absence of an external auditory stimulus. It is widely believed that tinnitus, in patients with associated hearing loss, is a neurological phenomenon primarily affecting the central auditory structures. However, there is growing evidence for the involvement of the somatosensory system in this form of tinnitus. For this reason it has been suggested that the condition may be amenable to bi-modal stimulation of the auditory and somatosensory systems. We conducted a pilot study to investigate the feasibility and safety of a device that delivers simultaneous auditory and somatosensory stimulation to treat the symptoms of chronic tinnitus. METHODS: A cohort of 54 patients used the stimulation device for 10 weeks. Auditory stimulation was delivered via headphones and somatosensory stimulation was delivered via electrical stimulation of the tongue. Patient usage, logged by the device, was used to classify patients as compliant or noncompliant. Safety was assessed by reported adverse events and changes in tinnitus outcome measures. Response to treatment was assessed using tinnitus outcome measures: Minimum Masking Level (MML), Tinnitus Loudness Matching (TLM), and Tinnitus Handicap Inventory (THI). RESULTS: The device was well tolerated by patients and no adverse events or serious difficulties using the device were reported. Overall, 68% of patients met the defined compliance threshold. Compliant patients (N = 30) demonstrated statistically significant improvements in mean outcome measures after 10 weeks of treatment: THI (-11.7 pts, p < 0.001), TLM (-7.5dB, p < 0.001), and MML (-9.7dB, p < 0.001). The noncompliant group (N = 14) demonstrated no statistical improvements. CONCLUSION: This study demonstrates the feasibility and safety of a new bi-modal stimulation device and supports the potential efficacy of this new treatment for tinnitus.

Dreams, mnemonics, and tuning for criticality.

According to the tuning-for-criticality theory, the essential role of sleep is to protect the brain from super-critical behaviour. Here we argue that this protective role determines the content of dreams and any apparent relationship to the art of memory is secondary to this.

Independent component analysis for brain FMRI does indeed select for maximal independence.

A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources.

A slow axon antidromic blockade hypothesis for tremor reduction via deep brain stimulation.

Parkinsonian and essential tremor can often be effectively treated by deep brain stimulation. We propose a novel explanation for the mechanism by which this technique ameliorates tremor: a reduction of the delay in the relevant motor control loops via preferential antidromic blockade of slow axons. The antidromic blockade is preferential because the pulses more rapidly clear fast axons, and the distribution of axonal diameters, and therefore velocities, in the involved tracts, is sufficiently long-tailed to make this effect quite significant. The preferential blockade of slow axons, combined with gain adaptation, results in a reduction of the mean delay in the motor control loop, which serves to stabilize the feedback system, thus ameliorating tremor. This theory, without any tuning, accounts for several previously perplexing phenomena, and makes a variety of novel predictions.

A new hypothesis for sleep: tuning for criticality.

We propose that the critical function of sleep is to prevent uncontrolled neuronal feedback while allowing rapid responses and prolonged retention of short-term memories. Through learning, the brain is tuned to react optimally to environmental challenges. Optimal behavior often requires rapid responses and the prolonged retention of short-term memories. At a neuronal level, these correspond to recurrent activity in local networks. Unfortunately, when a network exhibits recurrent activity, small changes in the parameters or conditions can lead to runaway oscillations. Thus, the very changes that improve the processing performance of the network can put it at risk of runaway oscillation. To prevent this, stimulus-dependent network changes should be permitted only when there is a margin of safety around the current network parameters. We propose that the essential role of sleep is to establish this margin by exposing the network to a variety of inputs, monitoring for erratic behavior, and adjusting the parameters. When sleep is not possible, an emergency mechanism must come into play, preventing runaway behavior at the expense of processing efficiency. This is tiredness.

Software platform for rapid prototyping of NIRS brain computer interfacing techniques.

This paper describes the control system of a next-generation optical brain-computer interface (BCI). Using functional near-infrared spectroscopy (fNIRS) as a BCI modality is a relatively new concept, and research has only begun to explore approaches for its implementation. It is necessary to have a system by which it is possible to investigate the signal processing and classification techniques available in the BCI community. Most importantly, these techniques must be easily testable in real-time applications. The system we describe was built using LABVIEW, a graphical programming language designed for interaction with National Instruments hardware. This platform allows complete configurability from hardware control and regulation, testing and filtering in a graphical interface environment.

Dissecting the cellular contributions to early visual sensory processing deficits in schizophrenia using the VESPA evoked response.

Electrophysiological research has shown clear dysfunction of early visual processing mechanisms in patients with schizophrenia. In particular, the P1 component of the visual evoked potential (VEP) is substantially reduced in amplitude in patients. A novel visual evoked response known as the VESPA (Visual Evoked Spread Spectrum Analysis) was recently described. This response has a notably different scalp topography from that of the traditional VEP, suggesting preferential activation of a distinct subpopulation of cells. As such, this method constitutes a potentially useful candidate for investigating cellular contributions to early visual processing deficits. In this paper we compare the VEP and VESPA responses between a group of healthy control subjects and a group of schizophrenia patients. We also introduce an extension of the VESPA method to incorporate nonlinear processing in the visual system. A significantly reduced P1 component was found in patients using the VEP (with a large effect size; Cohen's d=1.6), while there was no difference whatsoever in amplitude between groups for either the linear or nonlinear VESPA. This pattern of results points to a highly specific cellular substrate of early visual processing deficits in schizophrenia, suggesting that these deficits are based on dysfunction of magnocellular pathways with parvocellular processing remaining largely intact.

Isolating endogenous visuo-spatial attentional effects using the novel visual-evoked spread spectrum analysis (VESPA) technique.

In natural visual environments, we use attention to select between relevant and irrelevant stimuli that are presented simultaneously. Our attention to objects in our visual field is largely controlled endogenously, but is also affected exogenously through the influence of novel stimuli and events. The study of endogenous and exogenous attention as separate mechanisms has been possible in behavioral and functional imaging studies, where multiple stimuli can be presented continuously and simultaneously. It has also been possible in electroencephalogram studies using the steady-state visual-evoked potential (SSVEP); however, it has not been possible in conventional event-related potential (ERP) studies, which are hampered by the need to present suddenly onsetting stimuli in isolation. This is unfortunate as the ERP technique allows for the analysis of human physiology with much greater temporal resolution than functional magnetic resonance imaging or the SSVEP. While ERP studies of endogenous attention have been widely reported, these experiments have a serious limitation in that the suddenly onsetting stimuli, used to elicit the ERP, inevitably have an exogenous, attention-grabbing effect. Recently we have shown that it is possible to derive separate event-related responses to concurrent, continuously presented stimuli using the VESPA (visual-evoked spread spectrum analysis) technique. In this study we employed an experimental paradigm based on this method, in which two pairs of diagonally opposite, non-contiguous disc-segment stimuli were presented, one pair to be ignored and the other to be attended. VESPA responses derived for each pair showed a strong modulation at 90-100 ms (during the visual P1 component), demonstrating the utility of the method for isolating endogenous visuo-spatial attention effects.

Illusory percepts from auditory adaptation.

Phenomena resembling tinnitus and Zwicker phantom tone are seen to result from an auditory gain adaptation mechanism that attempts to make full use of a fixed-capacity channel. In the case of tinnitus, the gain adaptation enhances internal noise of a frequency band otherwise silent due to damage. This generates a percept of a phantom sound as a consequence of hearing loss. In the case of Zwicker tone, a frequency band is temporarily silent during the presentation of a notched broadband sound, resulting in a percept of a tone at the notched frequency. The model suggests a link between tinnitus and the Zwicker tone percept, in that it predicts different results for normal and tinnitus subjects due to a loss of instantaneous nonlinear compression. Listening experiments on 44 subjects show that tinnitus subjects (11 of 44) are significantly more likely to hear the Zwicker tone. This psychoacoustic experiment establishes the first empirical link between the Zwicker tone percept and tinnitus. Together with the modeling results, this supports the hypothesis that the phantom percept is a consequence of a central adaptation mechanism confronted with a degraded sensory apparatus.

Optimal coding predicts attentional modulation of activity in neural systems.

Neuronal activity in response to a fixed stimulus has been shown to change as a function of attentional state, implying that the neural code also changes with attention. We propose an information-theoretic account of such modulation: that the nervous system adapts to optimally encode sensory stimuli while taking into account the changing relevance of different features. We show using computer simulation that such modulation emerges in a coding system informed about the uneven relevance of the input features. We present a simple feedforward model that learns a covert attention mechanism, given input patterns and coding fidelity requirements. After optimization, the system gains the ability to reorganize its computational resources (and coding strategy) depending on the incoming attentional signal, without the need of multiplicative interaction or explicit gating mechanisms between units. The modulation of activity for different attentional states matches that observed in a variety of selective attention experiments. This model predicts that the shape of the attentional modulation function can be strongly stimulus dependent. The general principle presented here accounts for attentional modulation of neural activity without relying on special-purpose architectural mechanisms dedicated to attention. This principle applies to different attentional goals, and its implications are relevant for all modalities in which attentional phenomena are observed.

The VESPA: a method for the rapid estimation of a visual evoked potential.

Faster and less obtrusive means for measuring a Visual Evoked Potential would be valuable in clinical testing and basic neuroscience research. This study presents a method for accomplishing this by smoothly modulating the luminance of a visual stimulus using a stochastic process. Despite its visually unobtrusive nature, the rich statistical structure of the stimulus enables rapid estimation of the visual system's impulse response. The profile of these responses, which we call VESPAs, correlates with standard VEPs, with r=0.91, p<10(-28) for the group average. The time taken to obtain a VESPA with a given signal-to-noise ratio compares favorably to that required to obtain a VEP with a similar level of certainty. Additionally, we show that VESPA responses to two independent stimuli can be obtained simultaneously, which could drastically reduce the time required to collect responses to multiple stimuli. The new method appears to provide a useful alternative to standard VEP methods, and to have potential application both in clinical practice and to the study of sensory and perceptual functions.

Sparse representations for the cocktail party problem.

A striking feature of many sensory processing problems is that there appear to be many more neurons engaged in the internal representations of the signal than in its transduction. For example, humans have approximately 30,000 cochlear neurons, but at least 1000 times as many neurons in the auditory cortex. Such apparently redundant internal representations have sometimes been proposed as necessary to overcome neuronal noise. We instead posit that they directly subserve computations of interest. Here we provide an example of how sparse overcomplete linear representations can directly solve difficult acoustic signal processing problems, using as an example monaural source separation using solely the cues provided by the differential filtering imposed on a source by its path from its origin to the cochlea [the head-related transfer function (HRTF)]. In contrast to much previous work, the HRTF is used here to separate auditory streams rather than to localize them in space. The experimentally testable predictions that arise from this model, including a novel method for estimating the optimal stimulus of a neuron using data from a multineuron recording experiment, are generic and apply to a wide range of sensory computations.

A spectrum of colors: investigating the temporal frequency characteristics of the human visual system using a system identification approach.

Noise input signals are commonly used in both linear and nonlinear system identification of physiological systems. This method can be applied to electrophysiological analysis of the human visual system by controlling the modulation of the contrast of a checkerboard stimulus using a pre-computed noise waveform. In this study we describe how one can obtain an estimate of the linear impulse response of the visual system using noise waveforms. Furthermore, we examine the impulse responses obtained using noise signals with different frequency characteristics, in an attempt to investigate the temporal frequency characteristics of the human visual system. We show that noise signals with frequency content greater than 15 Hz are more effective at evoking these responses than those with little or no power at high frequencies.

Fast robust subject-independent magnetoencephalographic source localization using an artificial neural network.

We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually assisted commercial software.

MEG source localization using an MLP with a distributed output representation.

We present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) which takes the sensor measurements as inputs, uses one hidden layer, and generates as outputs the amplitudes of receptive fields holding a distributed representation of the dipole location. We trained this Soft-MLP on dipolar sources with real brain noise and converted the network's output into an explicit Cartesian coordinate representation of the dipole location using two different decoding strategies. The proposed Soft-MLPs are much more accurate than previous networks which output source locations in Cartesian coordinates. Hybrid Soft-MLP-start-LM systems, in which the Soft-MLP output initializes Levenberg-Marquardt, retained their accuracy of 0.28 cm with a decrease in computation time from 36 ms to 30 ms. We apply the Soft-MLP localizer to real MEG data separated by a blind source separation algorithm, and compare the Soft-MLP dipole locations to those of a conventional system.

Independent components of magnetoencephalography: single-trial response onset times.

Werecently demonstrated that second-order blind identification (SOBI), an independent component analysis (ICA) method, can separate the mixture of neuronal and noise signals in magnetoencephalographic (MEG) data into neuroanatomically and neurophysiologically meaningful components. When the neuronal signals had relatively higher trial-to-trial variability, SOBI offered a particular advantage in identifying and localizing neuronal source activations with increased source detectability (A. C. Tang et al., 2002, Neural Comput. 14, 1827-1858). Here, we explore the utility of SOBI in the analysis of temporal aspects of neuromagnetic signals from MEG data. From SOBI components, we were able to measure single-trial response onset times of neuronal populations in visual, auditory, and somatosensory modalities during cognitive and sensory activation tasks, with a detection rate as high as 96% under optimal conditions. Comparing the SOBI-aided detection results with those obtained directly from the sensors, we found that with SOBI preprocessing, we were able to measure, among a greater proportion of trials, single-trial response onset times that are above background neuronal activity. We suggest that SOBI ICA can improve our current capability in measuring single-trial responses from human subjects using the noninvasive brain imaging method MEG.

Independent components of magnetoencephalography: localization.

We applied second-order blind identification (SOBI), an independent component analysis method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneficial for MEG studies of higher-level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios than sensory activation tasks.

Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise.

Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLP-start-LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trained with noise perform better than the MLP trained without noise, and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location.