BACKGROUND: Obesity is a serious health condition that also a risk factor for socio-emotional challenges and medical problems. Preliminary evidence suggests obesity may also be associated with difficulty in accurately identifying emotions, particularly negative emotions. In addition, poor emotion recognition has been linked to weaker executive functioning skills, which is a common challenge in obesity. The direct relationship between body mass index (BMI) and emotion recognition is poorly understood in young adults and warrants further exploration. HYPOTHESES: We predicted that 1) after controlling for sociodemographic, clinical, and executive functioning variables and that 2) BMI would be negatively associated with emotion recognition accuracy for negative emotions (i.e., anger, sadness, and fear) but not positive emotions. METHODS: Using a subset of the Human Connectome Project dataset (N=799), we conducted a hierarchal linear regression (HLR) to test the relationship between overall emotion recognition and the following predictors, adding in steps: 1) sociodemographic and clinical variables, 2) executive functioning variables, and 3) BMI. RESULTS: Contrary to our hypotheses, BMI was not significantly associated with overall emotion recognition accuracy. Instead, Hispanic ethnicity, greater cognitive flexibility (Dimensional Change Card Sort task), and larger working memory (List Sorting Working Memory Test) was associated with better overall emotion recognition accuracy. Similarly, these same dimensions, as well as being female, was associated with better negative emotion recognition accuracy. / Psychology

My doctoral work has addressed the anatomy-function relationship by illustrating 1) the unique topology of brain anatomy for biologically plausible functional connectome to exist, 2) a higher vulnerability of neuroanatomy against the genetic control for a brain hub region, whereas lower genetic influence on its functional fingerprints, and 3) a global functional plasticity following a local injury beyond its anatomical boundaries. All together, the work here has demonstrated the interplay between brains structure and function, as well as the impact of familial relatedness (heritability) on these measures.

付記する学位プログラム名: 京都大学卓越大学院プログラム「メディカルイノベーション大学院プログラム」 / 京都大学 / 新制・課程博士 / 博士(医学) / 甲第25191号 / 医博第5077号 / 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 林 康紀, 教授 高橋 淳 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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.

Multi-site MRI studies collect large amounts of data in a short time frame. Large sample sizes are desirable to address power and replicability issues that have been problematic for scientists in the past. Although multi-site MRI solves the sample size problem, it brings with it a new set of challenges. Scanning the same person at different sites might result in differences in MRI derived measurements. In this thesis we compared three approaches to facilitate the analysis of multi-site MRI data: quantitative R1 mapping, adding site as a covariate in a linear model, and using the ComBat method. We also investigated the relationship between two common MRI measurements: signal and volume. We collected data from 64 healthy participants across 3 GE scanners and 1 Siemens scanner at 3T. We found that signal intensity was different between vendors whereas volume was not. Our R1 method resulted in values that were different across vendor and significantly lower than those reported in the literature. B1+ maps used to calculate R1 were different across sites. Using a scale factor, we were able to compensate for mistakes in R1 mapping. We also found that adding site as a covariate corrected mean differences in signal intensity across sites, but not differences in variance. The ComBat method gave best similarity between sites. However, since different people were scanned at each site, we couldn’t evaluate the effectiveness of each method as variation in the data could have been due to site effects or heterogeneity in participants. White matter volume and signal intensity in the white matter were correlated in males but not in females. We found that this low correlation was caused by outliers in our female sample. The correlation between white matter volume and signal in males suggests that both metrics are measuring myelin and can be used as converging evidence to detect changes in brain myelination. / Thesis / Master of Science (MSc)

This theoretical thesis covers the issues of Autism Spectrum Disorders (ASD) in relation to the brain connectome research. ASD belong to the group of neurodevelopmental syndromes and are characterized by deficits in communication skills, social interaction and stereotypic behaviors. The prevalence of ASD increases, its etiopathogenesis is very likely multifactorial. Within the ASD syndrome, precise differential diagnostic algorithms are difficult to implement in the absence of objective biomarkers. Extensive neuroscientific research, including the connectome projects, might improve the diagnostic and therapeutic procedures in the field of Psychiatry, including ASD. The individual's connectome profile might well serve as a new biomarker in psychiatric diagnostic and therapeutic approaches. The brain connectome represents the net of all neuronal connections in the brain. Mapping of the connectome across all ages, in health and in disease, is the main goal of the Human Connectome Project (HCP). The first HCP data show great interindividual variability with the environmental factors playing a crucial role. Extensive neurobiology research data on mechanisms of memory support the vital role of environmental stimulation in compensating for behavioral symptoms in ASD. Applied behavior analysis (ABA) is an...