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.

The default mode network (DMN) is a highly cited neural network whose functional roles are not well understood. Until recently, event related fMRI experiments used to study the DMN could only be conducted in an open-loop format. The purpose of this study was to demonstrate the potential statistical advantages of real-time fMRI studies to conduct closed-loop experiments to directly test putative DMN functions. Using both fMRI simulations and large archival datasets, we demonstrate that open-loop designs are less statistically powerful than closed-loop experiments that can trigger stimuli at controlled levels of brain activity. When simulating event scheduling on resting state data, DMN levels were normally distributed, but the event timing proved to be ineffective in capturing the highest and lowest DMN values on average across subjects. Statistical differences in DMN levels collected by the Human Connectome Project-Aging (HCP-A) during a Go/NoGo task were also reported, along with the network's distributional effects across subjects. When examining DMN levels in 136 subjects more prone to commission errors the mean DMN levels were reported to be higher during and prior to incorrect NoGo responses. Exploring DMN levels in these same individuals reacting to a Go task also revealed differing measurement patterns when compared to all 711 subjects in the study. Additionally, the distribution of total DMN levels across all participants, as well as during a Go or NoGo trial, showed a shift in the mean towards deactivation. Furthermore, the peak at this location was greater and revealed that increased sampling occurred at the mean and under sampling at the tails. Overall, the cumulative findings in this study were successful in providing statistical arguments to support propositions for more powerful closed-loop experimentation in fMRI. / Master of Science / Activity in a neural network is observed through the use of functional MRI (fMRI) by tracking higher levels of oxygenated blood to that region when active and lower quantities when inactive. Neural networks vary in their responsibilities, thus fMRI tasks are designed to trigger a response based on the functional role of the network. This can be exemplified by studying the blood flow to default mode network (DMN), a network responsible for mind wandering, during a task that requires focus. Researchers can then correlate moments of high activity, which indicates a greater degree of mind wandering, or low activity to a correct or incorrect response to the task. Unfortunately, the timing in which a task is presented to the participant is predetermined prior to the subject entering the MRI making it difficult to capture a correct or incorrect response at the precise moment of activation or deactivation. This concept is known as open-loop and often collects data at moments of neutral activity, neither high nor low. In contrast, a closed-loop design allows a researcher to monitor the DMN's activation levels in real time and present the task at a desired time. This provides more useful data to the experimenter as all recorded responses to the task correlate with exact moments of high and low activation. This makes claims about the neural network's role statistically more powerful as there is a greater quantity of data at these moments rather than during a neutral activation state. The purpose of this thesis is to provide statistical arguments that support propositions for more powerful closed-loop experimentation in fMRI.

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