In this thesis we were interested in combining functional connectivity (from functional Magnetic Resonance Imaging) and structural connectivity (from Diffusion Tensor Imaging) with a data fusion approach. While data fusion approaches provide an abundance of information they are underutilized due to their complexity. To solve this problem, we integrated the ease of a neuroimaging toolbox, known as the Functional And Tractographic Analysis Toolbox (FATCAT) with a data fusion approach known as the anatomically weighted functional connectivity (awFC) approach - to produce a practical and more efficient pipeline. We studied the connectivity within resting-state networks of different populations using this novel pipeline. We performed separate analyses with traditional structural and functional connectivity for comparison with the awFC findings - across all three projects. In the first study we evaluated the awFC of participants with major depressive disorder compared to controls. We observed significant connectivity differences in the default mode network (DMN) and the ventral attention network (VAN). In the second study we studied the awFC of MDD remitters compared to non-remitters at baseline and week-8 (post antidepressant), and evaluated awFC in remitters longitudinally from baseline to to week-8. We found significant group differences in the DMN, VAN, and frontoparietal network (FPN) for remitters and non-remitters at week-8. We also found significant awFC longitudinally from baseline to week-8 in the dorsal attention network (DAN) and FPN. We also tested the associations between connectivity strength and cognition. In the third study we studied the awFC in children exposed to pre- and postnatal adversity compared to controls. We observed significant differences in the DMN, FPN, VAN, DAN, and limbic network (LIM). We also assessed the association between connectivity strength in middle childhood and motor and behavioural scores at age 3. Therefore, the FATCAT-awFC pipeline, we designed was capable of identifying group differences in RSN in a practical and more efficient manner. / Thesis / Doctor of Philosophy (PhD)

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Bipolar disorder (BD) and unipolar depression (UD) are two severe mood disorders, with BD often misdiagnosed as UD. Given their severity and high rates of misdiagnosis, it is of paramount importance to understand the psychological and neurobiological mechanisms underlying these disorders to enhance our ability to diagnose, treat, and prevent them effectively. Many neuroimaging studies have shown that mood disorders are associated with abnormal reward-related responses, particularly in the ventral striatum (VS). Yet, the link between mood disorders and reward-related responses in other regions remains inconclusive, thus limiting our understanding of the pathophysiology of mood disorders. To provide insights into the neurobiological underpinnings of reward processing dysfunction in mood disorders, two studies were conducted. Study 1 (Chapter 2) is a coordinate-based meta-analysis of 41 whole-brain neuroimaging studies encompassing reward-related responses from a total of 794 patients with major depressive disorder (MDD), and 803 healthy controls (HC). It aims to address inconsistencies in the literature by synthesizing the literature quantitatively. The findings of Study 1 indicate that MDD is associated with opposing abnormalities in the reward circuit: hypo-responses in the VS and hyper-responses in the orbitofrontal cortex (OFC). These findings provide a foundation for Study 2 (Chapter 3) and help to reconceptualize our understanding of reward processing abnormalities in UD by suggesting a role for dysregulated corticostriatal connectivity. Study 2 is the first fMRI study to employ region-of-interest (VS and OFC), whole-brain, activation, connectivity, and network analyses to examine the similarities and differences in reward-related brain activation patterns between 46 children with remitted bipolar I disorder, 48 children with remitted MDD, and 46 HC. The results of Study 2 revealed differential connectivity in corticostriatal circuitry during reward processing among BD, UD, and HC in pre-adolescence. Specifically, BD exhibited increases in OFC-VS connectivity during anticipation of larger reward, whereas UD and HC showed no changes in OFC-VS connectivity across anticipation conditions ranging from large loss to large reward. Furthermore, BD and UD generally showed more abnormal whole-brain responses to reward anticipation in accordance with the valence of the stimuli than HC. These findings suggest that pre-adolescents with BD and UD exhibit reward processing dysfunction during reward anticipation relative to HC even outside of acute periods of illness. Taken together, the dissertation provides novel insight into the nature of reward processing abnormalities in mood disorders in pre-adolescence. As early onset BD or UD often is associated with long treatment delays and a persistently pernicious illness course, this dissertation may aid efforts to ensure early accurate diagnosis, which may improve our ability to intervene with appropriate treatments and result in a more benign prognosis and course of illness over the lifespan. / Psychology

The brain has the capacity to modify itself structurally and functionally, to adapt to novel circumstances. Adaptive changes in neural circuitry that become intransigent, such as continued hypervigilance after resolution of a threat situation, become maladaptive and may facilitate development of psychiatric disorders such as Major Depressive Disorder (MDD). Although MDD pathogenesis is unclear, hypothalamic-pituitary-adrenal axis dysregulation may facilitate the neuroplastic changes observed in MDD. Whether these neuroplastic changes facilitate the development of MDD or develop due to MDD remains unclear. The characterization of neuroplastic changes in MDD has resulted in sometimes contradictory findings. There are gaps in understanding the timing of neuroplastic changes in MDD, and how and when they are affected by antidepressant treatment. Characterization of neuroplasticity in MDD may uncover different phenotypes and aid in the discovery of a predictive biomarker of antidepressant treatment response. This dissertation presents the results of a series of neuroimaging studies. Chapter 1 provides an introduction to neuroplasticity and MDD. In Chapter 2 results of a study examining hippocampal memory function in treatment naïve patients with MDD are presented. Chapter 3 exhibits findings from a study examining effects of an acute tryptophan depletion paradigm in midlife women receiving estrogen-based treatment on an emotional conflict task. Chapter 4 discusses results from an examination of unmedicated patients with MDD and healthy control participants on an emotional conflict task. Chapter 5 presents longitudinal data of the sample from Chapter 4, and the effect of 8 weeks of treatment with antidepressant escitalopram on performance on an emotional conflict task. In Chapter 6 a case study is presented of a patient with long-standing overt ventriculomegaly, whose chief complaint was of mood and cognitive impairments. Chapter 7 summarizes the findings and contributions of this body of research and discusses clinical implications and future directions. / Dissertation / Doctor of Philosophy (PhD) / The characterization of brain changes in Major Depressive Disorder (MDD) has resulted in contradictory findings, and gaps in understanding how the brain changes in response to antidepressant treatment. This dissertation aims to characterize brain changes in MDD through a series of neuroimaging studies. Chapter 1 provides an introduction to MDD and brain changes in MDD. Chapter 2 presents an examination of memory in treatment naïve patients with MDD. Chapter 3 presents a study of acute tryptophan depletion in midlife women receiving estrogen-based treatment on an emotional conflict task. Chapter 4 examines unmedicated patients with MDD and healthy control participants on an emotional conflict task. Chapter 5 examines the effects of antidepressant treatment on performance on an emotional conflict task. Chapter 6 presents a case study of a patient with ventriculomegaly with mood and cognitive impairments. Chapter 7 summarizes the contributions of this research and discusses implications and future directions.

Risky decision-making and social influence are associated with many health-risk behaviors. However, more work is necessary to understand risky decision-making and social influence. Additionally, to begin identifying ways to change individuals' engagement in health-risk behaviors, more work is necessary to understand whether and how risky decision-making and social influence can be modulated. Using computational modeling in conjunction with other techniques, this dissertation 1) explores mechanisms underlying risky decision-making under social influence (Study 1) and 2) examines how individuals could modulate risky decision-making and social influence (Studies 2 and 3). Study 1 identifies a novel social heuristic decision-making process whereby individuals who are more uncertain about risky decisions follow others and proposes dorsolateral prefrontal cortex (dlPFC) as a 'controller' of this heuristic. Study 2 finds that giving individuals agency in viewing social information increases the utility of that information. Study 3 finds that some individuals can modulate brain patterns associated with risky decision-making using a real-time fMRI (rt-fMRI) neurofeedback paradigm, and preliminarily shows that this leads to behavior change in risky decision-making. In sum, these studies expand on previous work elucidating mechanisms of risky decision-making under social influence and suggest two possible avenues (agency and real-time fMRI neurofeedback) by which individuals can be taught to change their behavior when making risky decisions under social influence. / Doctor of Philosophy / Risky decision-making and social influence are associated with many health-risk behaviors such as smoking and alcohol use. However, more work is necessary to understand risky decision-making and social influence. Additionally, to identify ways to change individuals' engagement in health-risk behaviors, more work is necessary to understand how risky decision-making and social influence can be changed. Here, computational modeling, a way to quantify individual's behavior, is used in a series of studies to 1) understand how individuals make risky decisions under social influence (Study 1) and 2) test ways in which individuals can be guided to change the way they respond to social influence (Study 2) and make risky decisions (Study 3). Study 1 shows that individuals who do not have strong preferences respond to social information in a different way than those who do and utilizes neuroimaging to identify a particular brain region which may be responsible for this process. Study 2 shows that individuals are more influenced by others when they ask to see their choices, as compared to passively viewing others' choices. Study 3 shows that a brain–computer interface can be used to guide individuals to change their brain activity related to risky decision-making and preliminarily demonstrates that following this training individuals change their risky decisions. Together, these studies further the field's understanding of how individuals make risky decisions under social influence and suggest avenues for behavior change in risky decision-making under social influence.

Episodic memories are neurally coded records of personally experienced events across a lifetime. These records are encoded via medial temporal lobe structures in the brain, including the hippocampus, and are commonly called "representations" or "memory traces". Existing studies indicate that information about the neural signal corresponding to a memory representation can be found in functional magnetic resonance imaging (fMRI) data when the pattern across its smallest units (voxels, often 3mm3 sections of the brain) is measured. Many prior studies have measured these voxel patterns in response to stimuli as if they are a spontaneous brain function, regardless of cognitive factors. These studies sometimes find that similarity in the voxel patterns across repetition of a to-be-remembered event predicts later memory retrieval, but the results are inconsistent. The current fMRI study investigated the possibility that cognitive goals during encoding affect the type of neural representation (voxel pattern) that will later support memory retrieval. This seems likely because prior behavioral studies indicate that cognitive variability across repetitions of an event benefits later memory retrieval, which is difficult to reconcile with the common finding that voxel pattern variability across repetitions of an event harms later memory. We tested this hypothesis by comparing voxel patterns that support later memory retrieval to those associated with forgotten items in the medial temporal lobe, including the hippocampus, and lateral occipital cortex. Overall, as previously demonstrated, the behavioral results showed that exposure to variable cognitive goals across repetition of events during encoding benefited subsequent memory retrieval. Voxel patterns in the hippocampus indicated a significant interaction between cognitive goals (variable vs. consistent) and memory (remembered vs. forgotten) such that less voxel pattern similarity for the repeated events with variable cognitive goals, but not consistent cognitive goals, supported later memory success. In other words, variable hippocampal neural activations for the same events under different cognitive goals predicted better later memory performance. However, there was no significant interaction in neural pattern similarity between cognitive goals and memory success in medial temporal cortices or lateral occipital lobe. Instead, higher similarity in voxel patterns in right medial temporal cortices was associated with later memory retrieval, regardless of cognitive goals. In the lateral occipital lobe, the main effects of cognitive goals, hemisphere, and memory success were found but no interactions. In conclusion, we found that the relationship between pattern similarity and memory success in the hippocampus (but not the medial temporal lobe cortex) changes when the cognitive goal during encoding does or does not vary across repetitions of the event. / Master of Science / Episodic memory is a long-term memory of personal experiences which are encoded via the medial temporal lobe in the brain, primarily in the hippocampus. The records of personal experiences in these areas are commonly called "patterns", "representations", or "memory traces". Prior investigations indicate that the way of measuring the neural signals corresponding to personal events is functional magnetic resonance imaging (fMRI). The brain images taken by an fMRI scanner represent the patterns of the smallest unit (voxels, often 3mm3 sections of the brain). Many prior investigations of episodic memory used the voxel patterns but showed mixed results in whether similarity in the voxel patterns across repetition of a repeated event leads to subsequent memory retrieval. One of the possible explanations for mixed results is that the cognitive factors during encoding were neglected. Therefore, the current fMRI study examined how cognitive goals during encoding influence the voxel patterns that later support memory retrieval. During encoding, participants were shown an image repeated with the same or different questions and answered the question on the screen in an fMRI scanner. After 10 days, they were invited to the item memory test on the images that they were given during the encoding phase. The voxel patterns in the medial temporal lobe, including the hippocampus, and the lateral occipital lobe were compared across the repetitions of each image. The behavioral results showed that variable cognitive goals across repeated events during encoding benefited later memory retrieval. Furthermore, less similar voxel patterns in the hippocampus for the images repeated with different questions, but not the same questions, during encoding predicted better later memory success. In the right medial temporal cortices, higher similarity in voxel patterns was significantly associated with later memory retrieval, regardless of cognitive goals. In the lateral occipital lobe, higher voxel pattern similarity was found in the right hemisphere, for images repeated with the same question, and for images successfully retrieved later. In conclusion, we found that the relationship between voxel pattern similarity and memory success in the hippocampus (but not the medial temporal lobe cortex) changes when the cognitive goal during encoding does or does not vary across repetitions of the event.

Post-traumatic stress disorder (PTSD) is a psychiatric disorder that is caused by exposure to traumatic or stressful events in life. 3,4-Methylenedioxymethamphetamine (MDMA) has been shown to be an effective agent in drug-assisted psychotherapy for PTSD. In this systematic review, we aim to evaluate the effect MDMA has on functional connectivity in healthy individuals and individuals with PTSD and investigate the potential mechanisms via which MDMA exerts its effects in MDMA-assisted psychotherapy for PTSD patients. A total of 134 articles from Web of Science and Medline EBSCO were screened and 5 articles relevant for the systematic review were identified. After MDMA administration, an increase and decrease in functional connectivity in multiple brain areas and networks was observed, such as the thalamus, visual cortex, anterior cingulate cortex, hippocampus, amygdala, prefrontal cortex, default mode network, cerebellar network, sensorimotor network, salience network, and executive network. Notably, MDMA increased amygdala-hippocampal functional connectivity which may link to improved emotion regulation and fear extinction in patients receiving MDMA-assisted therapy. The findings evidence the complex effects of MDMA on brain connectivity and highlight the need for further research in this area, although MDMA-assisted therapy does prove to be a promising alternative for treating PTSD.