O objetivo deste trabalho é apresentar os métodos originais em Bioinformática desenvolvidos para a análise estatística na interpretação dos dados de duas técnicas baseadas em imagens: a técnica de microarranjos de DNA e a técnica de ressonância magnética funcional. O interesse principal é abordar essas técnicas experimentais quando enfrenta-se uma situação clara de amostras escassas, isto é, quando existem relativamente poucas observações experimentais do fenômeno estudado, sendo a análise individual/personalizada o representante extremo desta situação, que tem que ser resolvida. Para tanto, opta-se pelo uso da Inferência Bayesiana no contexto da Teoria da Decisão sob Incerteza, implementada computacionalmente sob o arcabouço dos Sistemas de Suporte à Decisão. Ambas as tecnologias estudadas produzem dados complexos, baseados na interpretação das diferenças entre imagens obtidas da resposta do sistema a um estímulo e da resposta numa situação controle. O resultado deste trabalho é o desenvolvimento de dois sistemas de suporte à decisão, chamados HTself e Dotslashen, para a análise de dados de microarranjos e ressonância magnética funcional, respectivamente; e de seus métodos matemáticos/computacionais subjacentes. Os sistemas desenvolvidos extraem conhecimento racional de bancos-de-dados normativos, através de modelos matemáticos específicos, contornando então o problema de amostras escassas. Finalmente, neste trabalho são descritas aplicações a problemas reais, para destacar a utilidade dos sistemas de suporte à decisão desenvolvidos nas áreas de Biologia Molecular e Neuroimagem Funcional. / The goal of this work is to present the novel Bioinformatics methods that were developed aiming the statistical analysis of two image-based techniques: DNA microarrays and functional magnetic resonance imaging. The main interest is to approach these experimental techniques in small sample size situations, i.e., when there are relatively few experimental observations of the phenomena of interest, for which the case of single subject/datum analysis is its most extreme. In order to approach these problems we chose to use Bayesian Inference in the context of the Decision Theory under Uncertainty, computationally implemented under the Decision Support Systems framework. Both technologies produce complex data, based on the interpretation of differences between images from the response to a given stimulus and the control situation. The result of this work is the development of two decision support systems, called HTself and Dotslashen, to analyze microarray and functional magnetic resonance imaging data, respectively; and the underling mathematical and computational methods. These systems use the rational knowledge from normative databases implemented in specific mathematical models, overcoming the problem of small sample size. Finally, in this work it is described applications to real problems in order to stress the utility for Molecular Biology and Functional Neuroimaging of the developed decision support systems.

Infinite-Dimensional Systems (IDSs) which have been made possible by recent advances in mathematical and computational tools can be used to model complex real phenomena. However, due to physical, economic, or stringent non-invasive constraints on real systems, the underlying characteristics for mathematical models in general (and IDSs in particular) are often missing or subject to uncertainty. Therefore, developing efficient estimation techniques to extract missing pieces of information from available measurements is essential. The human brain is an example of IDSs with severe constraints on information collection from controlled experiments and invasive sensors. Investigating the intriguing modeling potential of the brain is, in fact, the main motivation for this work. Here, we will characterize the hemodynamic behavior of the brain using functional magnetic resonance imaging data. In this regard, we propose efficient estimation methods for two classes of IDSs, namely Partial Differential Equations (PDEs) and Fractional Differential Equations (FDEs). This work is divided into two parts. The first part addresses the joint estimation problem of the state, parameters, and input for a coupled second-order hyperbolic PDE and an infinite-dimensional ordinary differential equation using sampled-in-space measurements. Two estimation techniques are proposed: a Kalman-based algorithm that relies on a reduced finite-dimensional model of the IDS, and an infinite-dimensional adaptive estimator whose convergence proof is based on the Lyapunov approach. We study and discuss the identifiability of the unknown variables for both cases. The second part contributes to the development of estimation methods for FDEs where major challenges arise in estimating fractional differentiation orders and non-smooth pointwise inputs. First, we propose a fractional high-order sliding mode observer to jointly estimate the pseudo-state and input of commensurate FDEs. Second, we propose a modulating function-based algorithm for the joint estimation of the parameters and fractional differentiation orders of non-commensurate FDEs. Sufficient conditions ensuring the local convergence of the proposed algorithm are provided. Subsequently, we extend the latter technique to estimate smooth and non-smooth pointwise inputs. The performance of the proposed estimation techniques is illustrated on a neurovascular-hemodynamic response model. However, the formulations are efficiently generic to be applied to a wide set of additional applications.

Working memory (WM) for facial identity and WM for facial expressions of emotions is important in everyday functioning and seems to have different neurobiological correlates. We investigated the level of neural activation in three regions of interest (ROI): the fusiform face area (FFA), dorsolateral prefrontal cortex (dlPFC), and amygdala; and how they are related to behavioral performance during an n-back task involving face stimuli with a complex background figure within an fMRI-paradigm. Participants performed three different 2-back tasks, one for facial expressions of emotions (EMO), one for the facial identity (ID), and one for a background figure presented behind the face (FIG). We hypothesized that the FFA would activate more in ID, the amygdala would activate more during EMO, and that the dlPFC would activate in all n-back tasks. An ROI analysis was done to extract mean activation values from the participants (N = 32) in the fusiform gyrus area 2 (FG2), the laterobasal amygdala (amygdala lb), and dlPFC in the different tasks. A one way repeated measures ANOVA revealed a similar activation in FG2 and amygdala lb in both ID and EMO. During the FIG task higher activation in FG2 was shown in comparison with ID and EMO, and lower activation in amygdala lb was shown in comparison to ID. dlPFC was activated in all tasks. Furthermore, there was a negative correlation between amygdala lb activation and reaction time in the FIG task, where an abstract figure was kept in WM and facial information was to be ignored. These results indicate that the activation in FG2 and amygdala lb might not differ between WM for facial identity and WM for facial expressions of emotions, which is unexpected in comparison to perception studies where a difference in these nodes has been reported for processing these two different types of information. This might suggest that the role of these neural nodes differ depending on WM load and task irrelevant features.

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate analysis frameworks. Two most prominent MVPA methods for information mapping are searchlight decoding and classifier weight mapping. The new MVPA brain mapping methods, however, have also posed new challenges for analysis and statistical inference on the group level. In this thesis, I discuss why the usual procedure of performing t-tests on MVPA derived information maps across subjects in order to produce a group statistic is inappropriate. I propose a fully nonparametric solution to this problem, which achieves higher sensitivity than the most commonly used t-based procedure. The proposed method is based on resampling methods and preserves the spatial dependencies in the MVPA-derived information maps. This enables to incorporate a cluster size control for the multiple testing problem. Using a volumetric searchlight decoding procedure and classifier weight maps, I demonstrate the validity and sensitivity of the new approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, the new results showed a higher sensitivity and spatial specificity. The second goal of this thesis is the comparison of the two widely used information mapping approaches -- the searchlight technique and classifier weight mapping. Both methods take into account the spatially distributed patterns of activation in order to predict stimulus conditions, however the searchlight method solely operates on the local scale. The searchlight decoding technique has furthermore been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. In this thesis, I compare searchlight decoding with linear classifier weight mapping, both using the formerly proposed non-parametric statistical framework using a simulation and ultra-high-field 7T experimental data. It was found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, the weight mapping method was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, such global multivariate methods provide a substantial improvement for characterizing structure-function relationships.

Background: Software is an integral part of today's world with an outlook of ever-increasing importance. Maintaining all of these software artifacts is a major challenge for software engineering. A future with robust software primarily relies on programmers' ability to understand existing source code, because they spend most of their time on it. Program comprehension is the cognitive process of understanding source code. Since program comprehension is an internal cognitive process, it is inherently difficult to observe and measure reliably. Decades of research have developed fundamental models of program comprehension, but there still are substantial knowledge gaps in our understanding of program comprehension. Novel psycho-physiological and neuroimaging measures provide an additional perspective on program comprehension which promise new insights to program comprehension. Recently, these measures have been permeating software engineering research. The measures include eye tracking and physiological sensors, but also neuroimaging measures, such as functional magnetic resonance imaging (fMRI), which allow researchers to more objectively observe cognitive processes. Aims: This dissertation aims to advance software engineering by better understanding program comprehension. We apply and refine the use of psycho-physiological and neuroimaging measures. The goals are twofold: First, we develop a framework for studying program comprehension with neuroimaging, psycho-physiological, eye tracking, and behavioral methods. For neuroimaging, we focus on functional magnetic resonance imaging (fMRI), as it allows researchers to unravel cognitive processes in high detail. Our framework offers a detailed, multi-modal view on program comprehension that allows us to examine even small effects. Second, we shed light on the underlying cognitive process of program comprehension by applying our experiment framework. One major focus is to understand experienced programmers' efficient top-down comprehension. We also link programmers' cognition to common code complexity metrics. Method and Results: To fulfill our goals, we conduct a series of empirical studies on program comprehension. In these studies, we use and combine fMRI, psycho-physiological, and eye-tracking measures. Throughout the experiments, we develop and refine a multi-modal experiment framework to shed light onto program comprehension with a neuro-cognitive perspective. We demonstrate that the framework provides a reliable approach to quantify and to investigate programmers' cognitive processes. We explore the neuro-cognitive perspective of program comprehension to validate and extend established program-comprehension models. We show that programmers using top-down comprehension require less cognitive effort, but use the same network of brain areas. We also demonstrate how our developed experiment framework and fMRI as a measure can be used in software engineering to provide objective data in long-standing debates. For example, we show that commonly used, but criticized code complexity metrics indeed only have a limited predictive power on the required cognitive effort to understand source code. Conclusion: In our interdisciplinary research, we show how neuroimaging methods, such as fMRI, in combination with psycho-physiological, eye tracking and behavioral measures, is beneficial to software-engineering research. This dissertation provides a foundation to further investigate the neuro-cognitive perspective to programmers' brains, which is a critical contribution to the future of software engineering. / Hintergrund: Software ist ein fester Bestandteil der heutigen Welt mit einer immer wichtiger werdenden Bedeutung. Das moderne Leben ist infolgedessen zunehmend von funktionierender und möglichst fehlerfreier Software abhängig. Deshalb ist die Pflege aller Software-Artefakte eine wichtige und große Herausforderung für das Software-Engineering. Eine Zukunft mit robuster Software hängt in erster Linie von der Fähigkeit ab, den vorhandenen Quellcode zu verstehen, da damit die meiste Zeit verbracht wird. Programmverständnis ist der kognitive Prozess des Verstehens von Quellcode. Da dieser kognitive Prozess intern abläuft, ist ein zuverlässiges Beobachten und ein genaues Messen mit erheblichen Schwierigkeiten verbunden. Jahrzehntelange Forschung hat zwar grundlegende Modelle des Programmverständnisses entwickelt, aber das Bild von Programmverständnis weist noch immer erhebliche Wissenslücken auf. Neuartige psychophysiologische und nicht-invasive human-bildgebende Verfahren bieten zusätzliche Perspektiven auf das Programmverständnis, die neue Erkenntnisse versprechen. In den letzten Jahren haben diese Erfassungsmöglichkeiten die Software-Engineering-Forschung durchdrungen. Zu den Messverfahren gehören Eyetracking und physiologische Sensoren, aber auch nicht-invasive Human-Bildgebung, wie die funktionelle Magnetresonanztomographie (fMRT). Diese innovativen Messverfahren ermöglichen es Forschenden, kognitive Prozesse objektiver und genauer zu verfolgen und auszuwerten. Ziele: Diese Dissertation zielt darauf ab, Software-Engineering durch ein besseres Erfassen des Programmverständnisses voranzubringen. Dafür werden psychophysiologische und nicht-invasive human-bildgebende Verfahren angewendet und verfeinert. Es werden zwei Ziele verfolgt: Zum einen wird ein Framework für Experimente zum Programmverständnis, die mit Human-Bildgebung, Psychophysiologie, Eyetracking und Verhaltensmethoden durchgeführt werden, entwickelt. Bei der Human-Bildgebung erfolgt die Konzentration auf die funktionelle Magnetresonanztomographie (fMRT), da sie kognitive Prozesse mit hoher Detailschärfe entschlüsseln kann. Das entwickelte Framework bietet eine detaillierte, multimodale Sicht auf das Programmverständnis, die es ermöglicht, auch kleine Effekte zu untersuchen. Zum anderen wird der zugrunde liegende kognitive Prozess des Programmverständnisses durch den Einsatz des aufgestellten Frameworks analysiert. Ein Hauptaugenmerk liegt dabei auf dem Erfassen des effizienten Top-Down-Verstehens von Quellcode. Zusätzlich wird die Kognition beim Programmieren mit gängigen Komplexitätsmetriken von Quellcode verknüpft und im Zusammenhang ausgewertet. Methodik und Ergebnisse: Um die Ziele zu erreichen, werden eine Reihe empirischer Studien zum Programmverständnis durchgeführt. In diesen Studien werden fMRT, Psychophysiologie sowie Eyetracking verwendet und miteinander kombiniert. Während der Experimente erfolgt eine Entwicklung und Verfeinerung eines multimodalen Experimentframework, um das Programmverständnis mit einer neurokognitiven Perspektive zu beleuchten. Es wird dokumentiert, dass das entwickelte Framework einen zuverlässigen Ansatz bietet, um kognitive Prozesse beim Programmieren zu quantifizieren und zu untersuchen. Weiterhin wird die neurokognitive Perspektive des Programmverständnisses erforscht, um etablierte Programmverständnismodelle zu validieren und zu erweitern. Im Kontext dessen wird belegt, dass das Top-Down-Verständnis das gleiche Netzwerk von Gehirnbereichen aktiviert, aber zu geringerer kognitiver Last führt. Es wird demonstriert, wie das entwickelte Experimentframework und fMRT als Messverfahren im Software-Engineering verwendet werden können, um in langjährigen Debatten objektive Daten zu bieten. Dabei wird insbesondere gezeigt, weshalb gängige, aber in Frage gestellte Komplexitätsmetriken von Quellcode tatsächlich nur eine begrenzte Vorhersagekraft auf die erforderliche kognitive Last beim Verstehen von Quellcode haben. Schlussfolgerung: In interdisziplinärer Forschung wird nachgewiesen, dass nicht-invasive human-bildgebende Verfahren wie die fMRT, kombiniert mit Psychophysiologie, Eyetracking sowie Verhaltensmethoden für die Software-Engineering-Forschung von erheblichem Vorteil sind. Diese Dissertation bietet eine belastbare Grundlage für die weitere Untersuchung der neurokognitiven Perspektive auf das Gehirn von Programmierern. Damit wird ein entscheidender Beitrag für ein erfolgreiches Software-Engineering geleistet.

In general, highly hypnotizable individuals ("highs") have exhibited greater abilities to focus attention and inhibit pain than low hypnotizable individuals ("lows"). Furthermore, highs appear to have faster neural processing than lows. The present study investigated differences between lows and highs in morphological volume of some brain structures associated with inhibitory and excitatory neural processing, particularly the corpus callosum (CC). Participants were 18 healthy university students, aged 18 to 29, with no history of concussion or medical disorders. They were in a functional Magnetic Resonance Image (fMRI) study examining the neurophysiology of pain and hypnotic analgesia (Crawford, Horton, Harrington, et al., 1998; Downs et al., 1998). As assessed by the group version (Crawford & Allen, 1982) of the Stanford Hypnotic Susceptibility Scale, Form C (SHSS:C; Weitzenhoffer & Hilgard, 1962), there were eight highs (four women and four men; SHSS:C M = 11.0) and 10 lows (five men and five women; SHSS:C M = 2.1). Highs were able to successfully eliminate perception of pain and distress to experimental noxious stimuli. Their anatomical MRIs were measured to assess relationships between brain structure volume (CC, medial cortex, anterior brain regions) and hypnotizability. In comparison to lows, highs had a significantly larger CC volume in the rostrum and isthmus, inferred to reflect larger transcallosal axon diameter or greater axon myelination. For highs, but not lows, there were significant relationships between forebrain volume and the total CC, rostrum, and splenium. Findings provide support for the neuropsychophysiological model of Crawford and her associates (e.g. Crawford, 1994a, 1994b; Crawford & Gruzelier, 1992) proposing a more effective attentional system of inhibitory processes in highs than lows. Furthermore, the data suggest that the more effective systems of attentional and inhibitory processes enhanced neural processing speed, and interhemispheric transfer times seen in highs than lows, may be associated with morphological differences in certain anterior and posterior CC regions. These regions are known to be involved in the allocation of inhibitory and excitatory transfer of information between hemispheres. / Ph. D.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18227号 / 工博第3819号 / 新制||工||1585(附属図書館) / 31085 / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 小林 哲生, 教授 引原 隆士, 教授 小山田 耕二 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(教育学) / 甲第20127号 / 教博第204号 / 新制||教||167(附属図書館) / 京都大学大学院教育学研究科教育科学専攻 / (主査)准教授 野村 理朗, 教授 齊藤 智, 教授 Emmanuel MANALO / 学位規則第4条第1項該当 / Doctor of Philosophy (Education) / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20664号 / 医博第4274号 / 新制||医||1024(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 髙橋 良輔, 教授 伊佐 正, 教授 井上 治久 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

The purpose of this systematic review is to provide insight into the impact traumatic brain injury (TBI) has on the executive function known as the working memory. TBI is a damage to the brain that occurs when the brain is critically injured to the degree that it impacts several brain regions and functions such as the hippocampus, its surrounding areas, the prefrontal cortex, and the performance of the working memory ability. TBI may occur from bleeding or infraction (stroke), lack of oxygen after cardiac arrest (anoxic brain injury), or diseases such as brain tumours or infections in the brain (encephalitis/meningitis). Working memory is the ability that maintains and manipulates information such as judgment and decision-making. TBI impacts several cognitive and executive functions such as the working memory. The implications that TBI has on working memory is that it relatively decreases the activation and connectivity capacity among the main areas of the working memory network which may result in difficulties of attention and concentration. This review summarises five studies about TBI and working memory that uses different working memory task while examiningwith brain imaging techniques. The studies conclude that TBI has a negative impact on working memory since the ability becomes weak.