The dissertation discusses various node identification techniques as well as their downstream effects on network characteristics using task-activated fMRI data from two working memory paradigms: a verbal n-back task and a visual n-back task. The three node identification techniques examined within this work include: a group-aggregated approach, a subject-specific approach, and a voxel wise approach. The first chapters highlight crucial differences between group-aggregated and subject-specific methods of isolating nodes prior to undirected functional connectivity analysis. Results show that the two techniques yield significantly different network interactions and local network characteristics, despite having their network nodes restricted to the same anatomical regions. Prior to the introduction of the third technique, a chapter is dedicated to explaining the differences between a priori approaches (like the previously introduced group-aggregated and subject-specific techniques) and no a priori approaches (like the voxel wise approach). The chapter also discusses two ways to aggregate signal for node representation within a network: using the signal from a single voxel or aggregating signal across a group of neighboring voxels. Subsequently, a chapter is dedicated to introducing a novel processing pipeline which uses a data driven voxel wise approach to identify network nodes. The novel pipeline defines nodes using spatial temporal features generated by a deep learning algorithm and is validated by an analysis showing that the isolated nodes are condition and subject specific. The dissertation concludes by summarizing the main takeaways from each of the three analyses as well as highlighting the advantages and disadvantages of each of the three node identification techniques. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20967号 / 医博第4313号 / 新制||医||1026(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 村井 俊哉, 教授 松田 秀一, 教授 安達 泰治 / 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(医学) / 甲第21671号 / 医博第4477号 / 新制||医||1035(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 溝脇 尚志, 教授 黒田 知宏 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

The ability to regulate emotions is essential for human well-being. Among posttraumatic stress disorder (PTSD) patients, the capability to control emotions is impaired. Trauma-focused cognitive behavioral therapy is a recommended treatment for patients diagnosed with PTSD. Usually, cognitive reappraisal is considered the primary regulation technique in cognitive behavioral therapy treatment. The strategy aims to decrease negative or increase positive emotions by changing the interpretation of an event to alter the meaning of the situation. The aim of this thesis was to conduct a systematic review of the neural correlates of cognitive reappraisal among post-traumatic stress disorder patients. Through a systematic search, screening, and selection process out of initial 545 articles, six studies were included for data extraction and discussion. Using functional magnetic resonance imaging, participants utilized the strategy of cognitive reappraisal during an emotion regulation task in the scanner. Using functional magnetic resonance imaging, the neural activity of the participants in the included studies was compared during a cognitive reappraisal task. The result revealed a tendency of decreased activity in prefrontal cortices in PTSD patients during reappraisal compared to controls, indicating deficient recruitment of prefrontal cortices in PTSD patients during reappraisal.

Bipolar spectrum disorders (BSDs) and substance use disorders (SUDs) are highly co-occurring and both are associated with dysfunction in neural networks that mediate reward processing and motivated behavior. Furthermore, despite their high comorbidity rate, limited research into their shared neural mechanisms or potential prospective risk factors exists. This study attempted to elucidate common neural pathways for these disorders, and adds to the small but growing literature on possible prospective predictors of these disorders. We employed a task-based functional magnetic resonance imaging (fMRI) study to examine regions-of-interest (ventral striatum [VS], orbitofrontal cortex [OFC], ventromedial prefrontal cortex [vmPFC], dorsolateral prefrontal cortex [dlPFC]) and connectivity (VS-OFC, VS-vmPFC, vmPFC-dlPFC) analyses to examine neural reward processing as potential predictors of future substance and mood symptoms, and to explore differences among groups of participants with and without BSDs and SUDs. Results from this study provided evidence that blunted activation in the VS and dlPFC and greater negative connectivity between the vmPFC and dlPFC, key reward and control circuits, is implicated in prospective substance use. However, we did not find evidence to support our hypothesis that reward-related neural responses predict BSD symptoms or could differentiate individuals with co-occurring BSDs and SUDs from healthy volunteers. The study highlights the importance of larger, longitudinal studies to more fully probe neurodevelopmental trajectories in mood, substance, and related disorders. We also conducted an extensive review of the neural reward literature in BSDs and SUDs to understand possible pre-existent mechanisms. Results of the review provided support for an equifinality/multifinality perspective in that similar neural reward processing dysfunctions can lead to both BSDs and SUDs and different neural reward processing abnormalities can lead to a single outcome (e.g., SUDs). Taken together, results from the dissertation address an important gap in the literature on BSD-SUD comorbidity, suggest possible shared mechanisms that predispose to both disorders, and provide a backdrop for future work in this area to inform more theoretically-targeted interventions and prevention. / Psychology

Non-suicidal self-injury (NSSI), defined as the deliberate damaging or destruction of body tissue without intent to die, are common behaviors amongst youth. Although prior work has shown heightened response to negative outcomes and dampened response to positive outcomes across multiple methods, including behavioral and physiological measures, little is known about the neural processes involved in NSSI. This study examined associations between NSSI engagement and responsivity to rewards and losses in youth with and without a lifetime engagement in NSSI. We employed a task-based functional magnetic resonance imaging (fMRI) study to examine differences between regions of interest (ROIs; ventral and dorsal striatum [VS, DS], anterior cingulate cortex [ACC], orbitofrontal cortex [OFC], ventrolateral and ventromedial prefrontal cortex [vlPFC; vmPFC], and insula) and whole-brain connectivity (utilizing bilateral DS, mPFC, and insula seed ROIs) in youth with and without NSSI. We used two reward tasks, in order to examine differences between groups across domains of reward (i.e., monetary and social). Additionally, we examined the specificity of the associations by controlling for dimensional levels of related psychopathology (i.e., aggression and depression). Results from the current study found that NSSI was associated with decreased activation following monetary gains in all ROIs. Further, these differences remained significant when controlling for comorbid psychopathology, including symptoms of aggression and depression. Finally, exploratory connectivity analyses found that NSSI was associated with differential connectivity between regions including the DS, vmPFC, insula, parietal operculum cortex, supramarginal gyrus, cerebellum, and central opercular cortex. Weakened connectivity between these regions could suggest deficits in inhibitory control of emotions in individuals with NSSI, as well as dysfunction in pain processing in individuals with NSSI, whereby these individuals experience pain as more salient or rewarding than individuals without NSSI. Although results did not support our hypotheses, findings suggest disrupted reward processes in youth with NSSI, contributing to our understanding of the role that reward processes may play in NSSI, in the engagement and reinforcement of these behaviors. We also conducted an extensive systematic review of the studies indexing neural structure and function in NSSI, summarizing the literature on the neurobiological correlates of several psychological processes implicated in NSSI engagement, including emotion processes, pain processes, executive processes, social processes, and reward processes. Results of the review highlighted the neural regions most consistently associated with NSSI, including the amygdala, insula, frontal, prefrontal, and orbitofrontal cortices, and the anterior cingulate, dorsal striatum, and ventral striatum. Additionally, data showed that NSSI is associated with greater emotional responses in negative situations, poorer down-regulation of negative emotions, and poorer inhibitory control over impulsive behaviors. Overall, findings suggest that NSSI is associated with maladaptive coping, and that this down-regulation of negative emotion resulting from NSSI may be experienced as rewarding and may serve to reinforce engagement in these behaviors. Finally, this review highlighted the importance of standardizing the methods of indexing neural structure and function in NSSI, specifically in terms of how NSSI is categorized, which comorbid disorders are examined, and how neuroimaging data are collected and analyzed, so that research in this area is comparable and reproducible. / Psychology

Patients with antisocial personality disorder (ASPD) are deceptive, apathetic, and impulsive. Their social behavior is often inappropriate, and they fail to follow social norms, leading to frequent criminal behavior. Understanding the neural correlates of ASPD could alleviate issues for the patients, such as unstable living conditions, as well as financial costs for the justice system and society. Due to previous research and theoretical implications of the prefrontal cortex (PFC) and its role in emotion-regulation and decision-making, it is likely that ASPD patients would show differences in the PFC relative to healthy individuals. Therefore, emphasis is placed on this region. By systematically reviewing articles which used fMRI to examine ASPD patients, this paper aims to understand if the brain activity in the PFC or functional connectivity within these regions differs between ASPD patients and healthy controls. Decreased activity was found in the anterior cingulate cortex (ACC) and dorsolateral PFC (dlPFC) in ASPD patients compared to healthy controls. Further, decreased functional connectivity was found in the frontoparietal control network, default modenetwork, and attentional network. Other prefrontal regions implicated include the medial frontal cortex, orbitofrontal cortex, and medial prefrontal cortex. Most of these regions are important for cognitive control, enabling integration of information regarding, e.g., errors and conflict. Abnormal processing of such information can lead to the impulsive or inappropriate actions often seen in ASPD patients. The PFC seems to play an important functional role in ASPD, mainly the regions responsible for cognitive control, such as the ACC and dlPFC.

Cognitive dissonance is the uncomfortable psychological feeling that arises when something is perceived as contradictory. In 1957, Leon Festinger first developed the theory of cognitive dissonance, which has since continued to be of interest for, among other things, decision-making, moral reasoning, motivation, politics, and science. This systematic review summarises six peer-reviewed studies that use functional magnetic resonance imaging (fMRI) to measure if there is increased activity in the anterior cingulate cortex (ACC) and dorsal anterior cingulate cortex (dACC) during cognitive dissonance in adults. Four studies tested cognitive dissonance during forced choices and two during counter-attitudinal behaviours. The overall fMRI results indicate increased activity in ACC and dACC to dissonance versus control conditions, but with some inconsistency on the exact locations in the brain.

The flow state is a subjective experience that most people can relate to. It represents an optimal balance between skills and difficulty and is the state that people often refer to when performing their best, with phrases like: “I was in the zone” or “I was in the bubble”. The flow state has mainly been studied through its psychological and behavioral components; it is not until lately the neuroscientific aspects have been investigated. This review attempts to go through the existing literature and find potential neural signatures of the flow state. The studies indicate that flow is related to activity in the dorsolateral prefrontal cortex and putamen, but the findings are too divided to reach a conclusion.

The structure of regional correlation graphs built from fMRI-derived data is frequently used in algorithms to automatically classify brain data. Transformation on the data is performed during pre-processing to remove irrelevant or inaccurate information to ensure that an accurate representation of the subject's resting-state connectivity is attained. Our research suggests and confirms that such pre-processed data still exhibits inherent transitivity, which is expected to obscure the true relationships between regions. This obfuscation prevents known solutions from developing an accurate understanding of a subject’s functional connectivity. By removing correlative transitivity, connectivity between regions is made more specific and automated classification is expected to improve. The task of utilizing fMRI to automatically diagnose Attention Deficit/Hyperactivity Disorder was posed by the ADHD-200 Consortium in a competition to draw in researchers and new ideas from outside of the neuroimaging discipline. Researchers have since worked with the competition dataset to produce ever-increasing detection rates. Our approach was empirically tested with a known solution to this problem to compare processing of treated and untreated data, and the detection rates were shown to improve in all cases with a weighted average increase of 5.88%.