Final publication is available at http://journals.lww.com/otology-neurotology/Pages/default.aspx / 京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19574号 / 医博第4081号 / 新制||医||1013(附属図書館) / 32610 / 京都大学大学院医学研究科医学専攻 / (主査)教授 渡邉 大, 教授 伊佐 正, 教授 一山 智 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(人間健康科学) / 甲第19640号 / 人健博第32号 / 新制||人健||3(附属図書館) / 32676 / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 黒木 裕士, 教授 坪山 直生, 教授 黒田 知宏 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

This dissertation argues how motivic saturation on the musical surface complicates a conventional harmonic interpretation in Fauré's late chamber works. Using motivic segmentation and linear analysis, I illustrate how the abundance of foreground motives has far-reaching implications for tonal voice leading and overall coherence. The outcomes of motivic saliency are twofold, influencing harmonic progressions by 1) altering traditional syntax or 2) replacing traditional syntax to provide the primary form of tonal coherence. I unpack the voice-leading consequences of stratifying motives over one another and bring in two larger, emerging concepts: 1) key duality as disjunction between melody and bass and 2) tonal coherence from the tonal profile of motives. In the first case, either the melody or the bass projects its own center or key separate from the other parts, producing a sensation of key duality. In the second, a single motive furnishes the main source of tonal grounding by unfolding a structural harmony that the surface sonorities obscure. While motivic saliency is a consistent trait across Fauré's late repertoire, the two phenomena above increase over time.

This paper presents analytic expressions for the wave structure function, frequency spread of the temporal frequency spectrum, and the temporal frequency spectrum of optical signals propagating through a random medium, specifically the Earth’s atmosphere. The results are believed to be valid for all optical turbulence conditions. These expressions are developed using the Rytov approximation method. Generally, the validity of statistical quantities obtained via this method is restricted to conditions of weak optical turbulence. However, in this work, by using a modification of the effective atmospheric spectral model presented by Andrews et al. for scintillation index, wave structure function expressions have been derived that are valid in all turbulence conditions as evidenced by comparison to experimental data. Analytic wave structure function results are developed for plane, spherical, and Gaussian-beam waves for one-way propagation. For the special case of a spherical wave, comparisons are made with experimental data. The double pass case is also considered. Analytic expressions for the wave structure function are given that incorporate reflection from a smooth target for an incident spherical wave. Additionally, analytic expressions for the frequency spread of the temporal frequency spectrum and the temporal frequency spectrum itself, after one-way propagation for horizontal and slant paths, are derived for plane and spherical waves. These results are also based on the Rytov perturbation method . Expressions that are believed to be valid in all turbulence conditions are also developed by use of the effective atmospheric spectral model used in the wave structure function development. Finally, double pass frequency spread expressions are also presented. As in the case of the wave structure function, reflection from a smooth target with an incident spherical wave is considered.

Tremor affects millions of people and many patients desire alternative treatment options to medication or neural surgery. Peripheral suppression techniques are gaining greater use, but are currently applied in a trial-and-error method. To optimize these techniques, the muscles most responsible for an individual patient's tremor need to be identified. In this dissertation, I explored two parallel paths that both could aid in identifying muscles responsible for tremor. The first method utilizies measured data and a technique (coherence) that quantifies the frequency dependent correlation between two signals. Using coherence to identify muscles contributing to tremor requires at least two parts: an analysis of how tremor content is shared between muscles, and an analyis between muscle activity and joint/hand motion. The interpretation of the second analysis depends on the results of the first. The second method of identifying responsible muscles uses a mathematical model of the upper limb. With a validated model established techniques can be used to quantify the contribution to the output from each input. However, the accuracy of the model that has been previously used in the Neuromechanics Research Group had not been quantified. To evaluate the accuracy of this model, I used measured muscle activity as the input to generate simulated tremor and compared that to the measured tremor. From the first method, I found that synergistic muscles tend to share tremor content and do so in phase with each other. Therefore, tremor is likely due to a group of muscles rather than a single muscle. Additionally, I observed that the elbow flexor and wrist extensor muscles tended to be most correlated with tremor and should therefore be considered in peripheral suppression techniques. The second method revealed that while this upper-limb model shows potential to predict cases of severe tremor, improved model parameters must be identified through measurement or estimation techniques before the model should be used as it currently over-predicts the tremor.

No current imaging technology can directly and without significant distortion visualize the defining microscopic features of the human brain. Ex vivo histological techniques yield exquisite planar images, but the cutting, mounting and staining they require induce slice-specific distortions, introducing cross-slice differences that prohibit true 3D analysis. Clearing techniques have proven difficult to apply to large blocks of human tissue and cause dramatic distortions as well. Thus, we have only a poor understanding of human brain structures that occur at a scale of 1–100 μm, in which neurons are organized into functional cohorts. To date, mesoscopic features which are critical components of this spatial context, have only been quantified in studies of 2D histologic images acquired in a small number of subjects and/or over a small region of the brain, typically in the coronal orientation, implying that features that are oblique or orthogonal to the coronal plane are difficult to properly analyze. A serial sectioning optical coherence tomography (OCT) imaging infrastructure will be developed and utilized to obtain images of cyto- and myelo-architectural features and microvasculature network of post-mortem human brain tissue. Our imaging infrastructure integrates vibratome with imaging head along with pre and post processing algorithms to construct volumetric OCT images of cubic centimeters of brain tissue blocks. Imaging is performed on tissue block-face prior to sectioning, which preserves the 3D information. Serial sections cut from the block can be subsequently treated with multiplexed histological staining of multiple molecular markers that will facilitate cellular classification or imaged with high-resolution transmission birefringence microscope. The successful completion of this imaging infrastructure enables the automated reconstruction of undistorted volume of human tissue brain blocks and permits studying the pathological alternations arising from diseases. Specifically, the mesoscopic and microscopic pathological alternations, as well as the optical properties and cortical morphological alternations of the dorsolateral prefrontal cortical region of two difference neurodegeneration diseases, Chronic Traumatic Encephalopathy (CTE) and Alzheimer’s Disease (AD), were evaluated using this imaging infrastructure.

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