Improving the stimulation selectivity in the human cochlea by strategic selection of the current return electrode

The hearing quality provided by cochlear implants are poorly predicted by computer simulations. A realistic cochlear anatomy is crucial for the accuracy of predictions. In this study, the standard multipolar stimulation paradigms are revisited and Rattay’s Activating Function is evaluated in a finite element model of a realistic cochlear geometry that is based on µ-CT images and a commercial lead. The stimulation thresholds across the cochlear fibers were investigated for monopolar, bipolar, tripolar, and a novel (distant) bipolar electrode configuration using an active compartmental nerve model based on Schwartz-Eikhof-Frijns membrane dynamics. The results suggest that skipping of the stimulation point from the vicinity of the cathodic electrode to distant fibers, especially to the low frequency (apical) region of the basilar membrane that is most critical to hearing, occurs more often with monopolar stimulation than other electrode configurations. Bipolar and tripolar electrodes near the apical region did not provide a large threshold margin either before the stimulation skips over distant fibers. On the other hand, the threshold margin could be improved by proper selection of the electrode for the return current with bipolar stimulation, a technique named here as distant bipolar. The results also demonstrate the significance of having a realistic cochlear geometry in computer models for accurate interpretation for multipolar stimulation paradigms. More selective and focal stimulation may be possible by designing the electrode carrier shape and positioning of the current return electrodes more strategically. This is needed particularly in the apical turn of the cochlea where the current stimulation methods are the least selective

DABS-LS: Deep Atlas-Based Segmentation Using Regional Level Set Self-Supervision

Cochlear implants (CIs) are neural prosthetics used to treat patients with severe-to-profound hearing loss. Patient-specific modeling of CI stimulation of the auditory nerve fiber (ANFs) can help audiologists improve the CI programming. These models require localization of the ANFs relative to surrounding anatomy and the CI. Localization is challenging because the ANFs are so small they are not directly visible in clinical imaging. In this work, we hypothesize the position of the ANFs can be accurately inferred from the location of the internal auditory canal (IAC), which has high contrast in CT, since the ANFs pass through this canal between the cochlea and the brain. Inspired by VoxelMorph, in this paper we propose a deep atlas-based IAC segmentation network. We create a single atlas in which the IAC and ANFs are pre-localized. Our network is trained to produce deformation fields (DFs) mapping coordinates from the atlas to new target volumes and that accurately segment the IAC. We hypothesize that DFs that accurately segment the IAC in target images will also facilitate accurate atlas-based localization of the ANFs. As opposed to VoxelMorph, which aims to produce DFs that accurately register the entire volume, our novel contribution is an entirely self-supervised training scheme that aims to produce DFs that accurately segment the target structure. This self-supervision is facilitated using a regional level set (LS) inspired loss function. We call our method Deep Atlas Based Segmentation using Level Sets (DABS-LS). Results show that DABS-LS outperforms VoxelMorph for IAC segmentation. Tests with publicly available datasets for trachea and kidney segmentation also show significant improvement in segmentation accuracy, demonstrating the generalizability of the method

Translational Modeling of Non-Invasive Electrical Stimulation

Seminal work in the early 2000’s demonstrated the effect of low amplitude non-invasive electrical stimulation in people using neurophysiological measures (motor evoked potentials, MEPs). Clinical applications of transcranial Direct Current Stimulation (tDCS) have since proliferated, though the mechanisms are not fully understood. Efforts to refine the technique to improve results are on-going as are mechanistic studies both in vivo and in vitro. Volume conduction models are being applied to these areas of research, especially in the design and analysis of clinical montages. However, additional research on the parameterization of models remains. In this dissertation, Finite Element Method (FEM) models of current flow were developed for clinical applications. The first image-derived models of obese subjects were developed to assess the relative impact of fat delineation from skin. Body mass index and more broadly inter-individual differences were considered. The effect of incorporating the meninges was predicted from CAD-based (Computer Aided Design) models before being translated into image-derived head models as an “emulated” CSF conductivity. These predictions were tested in a recently validated database of head models. Multi-scale models of transcutaneous vagus nerve stimulation (tVNS) were developed by coupling image-derived volume conduction models with physiological compartment modeling. The impact of local tissue inhomogeneities on fiber activation were considered

Impact of neuroanatomical variations and electrode orientation on stimulus current in a device for migraine: a computational study

Objective. Conventional treatment methods for migraine often have side effects. One treatment involves a wearable neuromodulator targeting frontal nerves. Studies based on this technique have shown limited efficacy and the existing setting can cause pain. These may be associated with neuroanatomical variations which lead to high levels of required stimulus current. The aim of this paper is to study the effect of such variations on the activation currents of the Cefaly neuromodulator. Also, using a different electrode orientation, the possibility of reducing activation current levels to avoid painful side-effects and improve efficacy, is explored. Approach. This paper investigates the effect of neuroanatomical variations and electrode orientation on the stimulus current thresholds using a computational hybrid model involving a volume conductor and an advanced nerve model. Ten human head models are developed considering statistical variations of key neuroanatomical features, to model a representative population. Main results. By simulating the required stimulus current level in the head models, it is shown that neuroanatomical variations have a significant impact on the outcome, which is not solely a function of one specific neuroanatomical feature. The stimulus current thresholds based on the conventional Cefaly system vary from 4.4 mA to 25.1 mA across all head models. By altering the electrode orientation to align with the nerve branches, the stimulus current thresholds are substantially reduced to between 0.28 mA and 15 mA, reducing current density near pain-sensitive structures which may lead to a higher level of patient acceptance, further improving the efficacy. Significance. Computational modeling based on statistically valid neuroanatomical parameters, covering a representative adult population, offers a powerful tool for quantitative comparison of the effect of the position of stimulating electrodes which is otherwise not possible in clinical studies

Bio-motivated features and deep learning for robust speech recognition

Mención Internacional en el título de doctorIn spite of the enormous leap forward that the Automatic Speech Recognition (ASR) technologies has experienced over the last five years their performance under hard environmental condition is still far from that of humans preventing their adoption in several real applications. In this thesis the challenge of robustness of modern automatic speech recognition systems is addressed following two main research lines. The first one focuses on modeling the human auditory system to improve the robustness of the feature extraction stage yielding to novel auditory motivated features. Two main contributions are produced. On the one hand, a model of the masking behaviour of the Human Auditory System (HAS) is introduced, based on the non-linear filtering of a speech spectro-temporal representation applied simultaneously to both frequency and time domains. This filtering is accomplished by using image processing techniques, in particular mathematical morphology operations with an specifically designed Structuring Element (SE) that closely resembles the masking phenomena that take place in the cochlea. On the other hand, the temporal patterns of auditory-nerve firings are modeled. Most conventional acoustic features are based on short-time energy per frequency band discarding the information contained in the temporal patterns. Our contribution is the design of several types of feature extraction schemes based on the synchrony effect of auditory-nerve activity, showing that the modeling of this effect can indeed improve speech recognition accuracy in the presence of additive noise. Both models are further integrated into the well known Power Normalized Cepstral Coefficients (PNCC). The second research line addresses the problem of robustness in noisy environments by means of the use of Deep Neural Networks (DNNs)-based acoustic modeling and, in particular, of Convolutional Neural Networks (CNNs) architectures. A deep residual network scheme is proposed and adapted for our purposes, allowing Residual Networks (ResNets), originally intended for image processing tasks, to be used in speech recognition where the network input is small in comparison with usual image dimensions. We have observed that ResNets on their own already enhance the robustness of the whole system against noisy conditions. Moreover, our experiments demonstrate that their combination with the auditory motivated features devised in this thesis provide significant improvements in recognition accuracy in comparison to other state-of-the-art CNN-based ASR systems under mismatched conditions, while maintaining the performance in matched scenarios. The proposed methods have been thoroughly tested and compared with other state-of-the-art proposals for a variety of datasets and conditions. The obtained results prove that our methods outperform other state-of-the-art approaches and reveal that they are suitable for practical applications, specially where the operating conditions are unknown.El objetivo de esta tesis se centra en proponer soluciones al problema del reconocimiento de habla robusto; por ello, se han llevado a cabo dos líneas de investigación. En la primera líınea se han propuesto esquemas de extracción de características novedosos, basados en el modelado del comportamiento del sistema auditivo humano, modelando especialmente los fenómenos de enmascaramiento y sincronía. En la segunda, se propone mejorar las tasas de reconocimiento mediante el uso de técnicas de aprendizaje profundo, en conjunto con las características propuestas. Los métodos propuestos tienen como principal objetivo, mejorar la precisión del sistema de reconocimiento cuando las condiciones de operación no son conocidas, aunque el caso contrario también ha sido abordado. En concreto, nuestras principales propuestas son los siguientes: Simular el sistema auditivo humano con el objetivo de mejorar la tasa de reconocimiento en condiciones difíciles, principalmente en situaciones de alto ruido, proponiendo esquemas de extracción de características novedosos. Siguiendo esta dirección, nuestras principales propuestas se detallan a continuación: • Modelar el comportamiento de enmascaramiento del sistema auditivo humano, usando técnicas del procesado de imagen sobre el espectro, en concreto, llevando a cabo el diseño de un filtro morfológico que captura este efecto. • Modelar el efecto de la sincroní que tiene lugar en el nervio auditivo. • La integración de ambos modelos en los conocidos Power Normalized Cepstral Coefficients (PNCC). La aplicación de técnicas de aprendizaje profundo con el objetivo de hacer el sistema más robusto frente al ruido, en particular con el uso de redes neuronales convolucionales profundas, como pueden ser las redes residuales. Por último, la aplicación de las características propuestas en combinación con las redes neuronales profundas, con el objetivo principal de obtener mejoras significativas, cuando las condiciones de entrenamiento y test no coinciden.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Javier Ferreiros López.- Secretario: Fernando Díaz de María.- Vocal: Rubén Solera Ureñ

Human thalamocortical connections and their involvement in language systems.

139 p.During evolution the expansion of the neocortex has been linked with the emergence of higher level cognitive functions, such as reasoning, abstract thinking, or language in human beings. Current research on cognitive neuroscience is mainly focused on the cerebral cortex. Whereas the thalamus is a structure that has extensive white-matter connections with the cerebral cortex, its expansion during evolution is parallel to the expansion of the neocortex. The thalamocortical connections are involved in communication between cortical areas. Thus, to fully understand the neural basis of cognition, a better understanding of the role of the thalamus in cortical function is necessary. The present doctoral dissertation is focused on the structure and function of the thalamus: the first study proposes a reproducible protocol to reconstruct the first-order thalamic white-matter tracts from diffusion-weighted imaging data; the second study investigates the higher-order thalamic white-matter tracts and a similar protocol is proposed to reconstruction those tracts; the third study uses task-based fMRI to examine the involvement of first-order thalamic nuclei in the main language systems.the current dissertation successfully reconstructed first-order and higher-order thalamic white-matter tracts from DWI data, and has proved high reproducibility of the reconstruction protocol. This protocol could benefit the tractography community to better understand the structural connectivity of the thalamus with cortical and subcortical structures and facilitate the research on thalamocortical pathways in humans. We also found evidence for differences in the processing of linguistic and nonlinguistic stimuli in first-order thalamic nuclei through a task-based fMRI study. These results suggest that the first-order thalamic nuclei play roles in human language that are beyond relaying sensory information from periphery to cerebral cortex. These findings are important to push forward our understanding on the role of subcortical structures, such as the thalamus, in human language functions, and to urge a revisitation of existing language models taking the thalamus into consideration