Autistic children demonstrate a constellation of traits with varying degrees of severity in areas including language differences, restrictive and repetitive behaviors, and sensory processing differences. However, the relationship between sensory processing and these other behaviors are not well understood especially their neurobiological underpinnings. Therefore, this research examined behavioral measures of semantic language, sensory traits, and associated brain networks in 20 autistic children (ages 6-11) and 22 typically developing (TD) age matched peers. Mann-Whitney U tests revealed a strong correlation between sensory traits and general composite and semantic language in both groups of participants. Sensory seeking traits were most significantly correlated with overall and semantic language scores in our autistic participants. Resting state functional network connectivity was also examined and correlated with behavioral measures. The autistic participants demonstrated three networks of interest that were correlated with semantic language scores. These networks demonstrated both over and underconnectivity, and the brain regions involved provided functions in multisensory integration, language, somatosensory processing, and prediction (among other functions). These findings point to an association between sensory integration and language, especially semantics in both the neurotypical population and autistic individuals. Furthermore, for the autistic population it presents novel information about brain regions and connectivity patterns that may contribute to the relationships between semantic language and sensory differences in the autism.

Clinical care for major depressive disorder (MDD) would be greatly improved if we had reliable clinical predictors of individual antidepressant treatment outcome. While, at the present time, no biomarkers have sufficiently proven utility to be ready for clinical application, several neuroimaging modalities have shown promise for such development. Attempts to combine the recently developed modality of resting-state functional Magnetic Resonance Imaging (rs-fMRI) with pharmacological challenges to explore the impact of antidepressants on resting-state brain connectivity have just begun (McCabe et al., 2011a, McCabe et al., 2011b). The aim of the current study was to investigate the effects of a single dose of the SSRI (selective serotonin reuptake inhibitor) escitalopram on resting-state functional connectivity in health.

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The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions such as autism spectrum disorder, as well as for predicting psychosocial characteristics such as age. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) than temporal features of connectivity changes (connectome variability). The primary goal of the current study was to investigate the effectiveness of using the connectome variability in classifying an individual’s pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the connectome variability are reliable across various analysis procedures. To this end, three open public large rs-fMRI datasets including ABIDE, COBRE, and NKI were used. The static-FC and the connectome variability metrics were calculated with various brain parcellations and parameters and then utilized for subsequent machine learning (ML) classification and prediction. The results demonstrated that including the connectome variability increased the ML performances significantly in most cases of analytical variations. In addition, including the connectome variability prevented ML performance deterioration when excessive components were used. In conclusion, the current finding proved the usefulness of the connectome variability and its reliability. / M.S. / Functional magnetic resonance imaging (fMRI) with functional connectivity (FC) analysis has been widely used to understand the human brain’s system and cognitive processes. Especially, the resting-state fMRI (rs-fMRI) has been regarded as a comprehensive map of the brain’s large-scale functional architecture. Previous seminal findings demonstrated that brain regions show synchronized patterns even without any external stimulus or task (Biswal et al., 1995; Power et al., 2011), and recent studies also demonstrated that functional network architecture during tasks can be formed based on resting-state network architecture primarily suggesting that the resting-state is an intrinsic and fundamental of brain organization functionally. At the early stage of fMRI FC studies, researchers commonly adopted static measure of connectivity (static-FC) such as Pearson correlation. However, the brain has a dynamic nature, thus the static approach does not capture temporal information of the brain. In this context, time-varying or dynamic-FC has been suggested as a promising substitute. The derived dynamic-FC usually has been used to distinguish several dynamic states by identifying repeated spatial dynamic-FC profiles. Another utilization is quantifying moment-to-moment changes of dynamic-FC (connectome variability) which can represent how much dynamic-FC is stable. Interestingly, although its importance of dynamic-FC temporal features, few studies have utilized connectome variability. In addition, only a few studies compared static-FC and connectome variability (Fong et al., 2019; Wang et al., 2018). Therefore, it is necessary to demonstrate the benefits of connectome variability and its reliability across various cognitive domains and analytic procedures. To this aim, this study used three large open fMRI datasets: ABIDE comprised of autism spectrum disorder and typical development, COBRE comprised of schizophrenia and control group, and NKI which is a developmental dataset across the lifespan. In individuals’ resting-state fMRI, brain signal time series was extracted using various parcellation methods including AAL2 atlas (Rolls et al., 2015), bilateralized AAL2 atlas, and LAIRD network atlas (Laird et al., 2011). To calculate static-FC, pairwise Pearson correlation was used. For the dynamic-FC, sliding-window correlation was used with 60 second window size. Additional 90 second and 120 second sliding window sizes were also used to test the reliability of the current study. The additional sliding window sizes showed almost identical results to that of the main sliding window size (60s). The derived dynamic-FC was used to calculate ‘connectome variability’ using mean square successive difference (MSSD). The calculated static-FC and the connectome variability were inputted to support vector machine (SVM) for group classifications or support vector regression (SVR) for predicting individuals’ characteristics. Before machine learning analysis (SVM, SVR), lasso regression was adopted as a feature selection method. The SVM results showed that including connectome variability increased group classification performances in ABIDE and COBRE datasets. Interestingly, including connectome variability improved the robustness of SVM classification when the number of components was controlled. Similarly, the SVR results also demonstrated that including connectome variability increased prediction performances for autism symptom severity score (ADOS), social responsiveness score (SRS), and individuals’ age. These benefits were consistent across three parcellation schemes. In conclusion, the current study demonstrated that the connectome variability is useful to classify different groups and to predict individuals’ characteristics. Such benefits were reliable across multiple cognitive domains and robust to several analytic procedures. These results emphasized that the connectome variability which has been usually overlooked reflects some aspects of functional brain architecture, and future fMRI studies should more attend connectome variability between brain regions.

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.

The default mode network (DMN) is active in the absence of task demands and during self-referential thought. Considerable evidence suggests that the DMN is involved in normative aspects of social cognition, and as such, disruptions in the function of DMN would be expected in disorders characterized by alterations in social function. Consistent with this notion, work in autism spectrum disorder (ASD) and social anxiety disorder (SAD) has demonstrated altered activation of several core regions of the DMN relative to neurotypical controls. Despite emergent evidence for alterations within the same brain systems in SAD and ASD, as well as a behavioral continuum of social impairments, no study to date has examined what is unique and what is common to the brain systems within these disorders. Therefore, the primary aim of the current study is to precisely characterize the topology of neural connectivity within the DMN in SAD and ASD and neurotypical controls in order to test the following hypotheses through functional and structural connectivity analyses of the DMN. Our analyses demonstrate increased coavtivation of the dorsomedial prefrontal cortex in ASD and SAD compared to controls, as well as over and under connectivity in structural brain connectivity in ASD. These results may reflect general deficits in social function at rest, and disorder specific alterations in structural connectivity in ASD. / Master of Science

Each year, about 800,000 individuals die by suicide globally, affecting millions more. Mitigating suicide risks by targeting modifiable factors such as the sleep disturbances of insomnia and nightmares, which are prevalent and linked to suicidality is important. This study investigated the structural and functional brain differences related to sleep disturbances and suicidality, with the anterior cingulate (caudal and rostral), insula, middle frontal gyrus, posterior cingulate, thalamus, amygdala, and orbitofrontal cortex as seed regions. Participants had no history of suicidal ideation (NSI; n=43) or suicidal ideation within the past two weeks (SI; n=25). Measures for analyses included the Insomnia Severity Index (ISI), Disturbing Dream and Nightmare Severity Index (DDNSI), and Frequency of Suicidal Ideation Inventory (FSII). The relationships between group (control vs suicidal ideation), structural measurements (cortical surface area, cortical thickness, gray matter volume), insomnia and nightmares across the eight regions in each hemisphere were examined. Functional connectivity-change differences were measured across wake and sleep with the eight regions as seeds. The SI group had smaller cortical surface area and gray matter volumes in the left insula (t= 2.58, p = 0.012; t = 2.44, p = 0.017); however, not after adjusting for multiple comparisons. ISI and FSII total scores correlated with each other and the surface area and gray matter volume of the left insula. In a mediation model, ISI total score was significantly related to insula surface area and FSII total score (p = 0.023; p =0.027), but the insula surface area was not significantly associated with FSII total score (p = 0.075). The indirect effect of ISI on FSII through the left insula surface area was not significant (p =0.161). The SI group had smaller changes from wake to sleep than the NSI group in the functional connectivity of the right thalamus to the left and right superior/middle temporal regions. Other neurological mechanisms could be at play as only the cortical surface area and gray matter volume in the left insula had implied differences between groups and the structural differences did not mediate the relationship between insomnia and suicidality. Smaller functional connectivity-changes differences across wake and sleep for SI compared to NSI, potentially indicate deficits in auditory inhibition.

Major depressive disorder (MDD) will soon represent the most costly and debilitating disorder in the world. Yet, a clear model of the mechanisms underlying MDD remains elusive. This lack of clarity obscures efforts to prevent and treat MDD more effectively. This dissertation seeks to advance an integrated model of the mechanisms underlying MDD across cognitive, neural, and genetic levels of analysis. Building on the empirical foundation of cognitive theories of MDD, the dissertation includes three studies that help address questions about the cognitive mechanisms underlying depression vulnerability and maintenance. Specifically, the three studies focus on identifying 1) how elaborative processing biases, including attentional biases and rumination, give rise to specific symptoms of MDD and 2) elucidating biological mechanisms that may give rise to these biases. Together, these studies help advance an integrated model of MDD that, ultimately, may help facilitate the prevention and treatment of this costly and debilitating disorder. / text

<div><div><div><p>Head injuries in collision sports have been linked to long-term neurological disorders. High school collision sport athletes, a population vulnerable to head injuries, are at a greater risk of chronic damage. Various studies have indicated significant deviations in brain function due to the accumulation of repetitive low-level subconcussive impacts to the head without externally observable cognitive symptoms. The aim of this study was to investigate metabolic changes in asymptomatic collision sport athletes across time within their competition season and as a function of mechanical force to their head. For this purpose, Proton Magnetic Resonance Spectroscopy (MRS) was used as a tool to detect altered brain metabolism in high school collision sport athletes (football and soccer) without diagnosed concussion. Also, sensors were attached to each athletes head to collect the count and magnitude of head impacts during their games and practices. Transient neurometabolic alterations along with prolonged recovery were observed in collision sport athletes.</p><div><div><div><p><br></p><p>Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together activities which are otherwise limited by the availability of patients or funds at a single site. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of obtained functional connectomes. We evaluated individual fingerprints in test- retest visit pairs within and across two sites and present a generalized framework based on principal component analysis (PCA) to improve identifiability. The optimally reconstructed functional connectomes using PCA showed a substantial improvement in individual fingerprinting of the subjects within and across the two sites and test-retest visit pairs relative to the original data. Results demonstrate that the data-driven method presented in the study can improve identifiability in resting-state functional connectomes in multi-site studies.</p></div></div></div></div></div></div>

Functional magnetic resonance imaging (fMRI) based on blood oxygenation level dependent (BOLD) contrast has become a widespread technique in brain research. The central challenge in fMRI is the detection of relatively small activity-induced signal changes in the presence of various other signal fluctuations. Physiological fluctuations due to respiration and cardiac pulsation are dominant sources of confounding variability in BOLD fMRI. This dissertation seeks to characterize and compensate for non-neural physiological fluctuations in fMRI. First, the dissertation presents an improved and generalized technique for correcting T1 effect in cardiac-gated fMRI data incorporating flip angle estimated from fMRI dataset itself. Using an unscented Kalman filter, spatial maps of flip angle and T1 relaxation are estimated simultaneously from the cardiac-gated time series. Accounting for spatial variation in flip angle, the new method is able to remove the T1 effects robustly, in the presence of significant B1 inhomogeneity. The technique is demonstrated with simulations and experimental data. Secondly, this dissertation describes a generalized retrospective technique to precisely model and remove physiological fluctuations from fMRI signal: Physiological Impulse Response Function Estimation and Correction (PIRFECT). It is found that the modeled long-term physiological fluctuations explained significant variance in grey matter, even after removing short-term physiological effects. Finally, application of the proposed technique is observed to substantially increase the intra-session reproducibility of resting-state networks.