No

It is well established that childhood adversity is associated with long lasting effects on development including both negative physical and mental health outcomes. Research demonstrates that adverse childhood experiences influence neurodevelopment and propose that this may be a mechanism linking adversity and psychopathology. However, little is known how the timing and type of maltreatment experiences may differentially impact longitudinal changes in neural processes of risk-related decision making. Using conditional growth curve modeling, we examined how abuse and neglect across three developmental periods (early childhood, school age, and adolescence) are associated with longitudinal changes in task-based functional connectivity during risk-processing and cognitive control. The current sample included 167 adolescents (13-14 years old at Time 1; 53% male), assessed annually for six years. At each of the six time points, adolescents completed a lottery choice task and a cognitive control task while blood-oxygen-level-dependent (BOLD) responses were monitored with functional magnetic resonance imaging (fMRI). Adolescents reported on maltreatment experiences occurring during ages 1 to 18. Generalized psychophysiological interactions (gPPI) was used to examine task- based functional connectivity in the insula and dACC (dorsal anterior cingulate cortex) during both risk processing and cognitive control, respectively. Although no sensitive periods emerged for the effects of abuse or neglect on functional connectivity during risk processing, chronic abuse (abuse occurring in more than one developmental period) significantly predicted weaker insula-dACC connectivity in late adolescence. For functional connectivity during cognitive control, adolescence emerged as a potential sensitive period for neglect, such that those with neglect experiences occurring during ages 13 to 18 showed slower improvements in dACC- insula connectivity across adolescence. Chronic neglect was also associated with slower improvements in dACC-insula connectivity. Additionally, chronic abuse was significantly associated with stronger improvements in dACC-insula connectivity across adolescence. Collectively, these results suggest that abuse may be linked to a delayed maturation in neural connectivity associated with valuation, but an accelerated maturation in neural connectivity associated with cognitive control. Furthermore, neglect may be linked to a delayed maturation in neural connectivity associated with cognitive control. Both sets of findings involved functional connectivity in both the dACC and insula, important regions involved in salience processing. These findings elucidate the distinct effects of abuse and neglect on connectivity in regions involved in risk-related decision making, including valuation and cognitive control. Future work will benefit from examining how these different pathways may lead to outcomes such as health risk behaviors and psychopathology. / M.S. / Childhood adversity is associated with long lasting effects on development including both negative physical and mental health outcomes. Research shows that adverse childhood experiences influence brain development. However, little is known how the timing and type of maltreatment experiences may differentially impact changes in brain processes of risky decision making across adolescence. We examined how abuse and neglect across three developmental periods (early childhood, school age, and adolescence) are associated with changes in functional connectivity during risk-processing and cognitive control. The current sample included 167 adolescents (13-14 years old at Time 1; 53% male), assessed annually for six years. At each of the six time points, adolescents completed a lottery choice task and a cognitive control task while blood-oxygen-level-dependent (BOLD) responses were monitored with functional magnetic resonance imaging (fMRI). Adolescents reported on maltreatment experiences occurring during ages 1 to 18. Generalized psychophysiological interactions (gPPI) was used to examine task- based functional connectivity in the insula and dACC (dorsal anterior cingulate cortex) during both risk processing and cognitive control, respectively. Results showed that chronic abuse (abuse occurring in more than one developmental period) significantly predicted weaker insula- dACC connectivity in late adolescence. For functional connectivity during cognitive control, those with neglect experiences occurring during ages 13 to 18 showed slower improvements in dACC-insula connectivity across adolescence. Chronic neglect was also associated with slower improvements in dACC-insula connectivity. Additionally, chronic abuse was significantly associated with stronger improvements in dACC-insula connectivity across adolescence. Both sets of findings involved functional connectivity in both the dACC and insula, important regions involved in salience processing. These findings elucidate the distinct effects of abuse and neglect on connectivity in regions involved in risk-related decision making, including valuation and cognitive control. Future work will benefit from examining how these different pathways may lead to outcomes such as health risk behaviors and psychopathology.

Thesis advisor: Stefano Anzellotti / Autism Spectrum Disorder (ASD) displays uniquely in every individual, creating disparities in symptom severity, genetics, and functional connectivity. Examining the relationship between genetic and functional connectivity variability could help to better understand individual differences in ASD. From this, improved diagnosis, treatment, and understanding of ASD can be developed. To resolve individual differences in symptom severity and presentation, I generated matrices of subject functional connectivity data and compared this to gene expression maps. Multivariate regression analysis was performed on the data to anticipate ASD symptoms from these correlation matrices and to establish which genes have the largest impact on these predictions. The ANOVAs ran on the data were not significant, but there were several genes implicated in specific aspects of ASD. STX1A, MVP, CDKL5, and RABEP2 were the only genes correlated across more than one subtype of ASD. These results pave the way for future research to investigate the roles of these genes in a larger size of ASD subjects. / Thesis (BS) — Boston College, 2023. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Psychology and Neuroscience.

In recent years, there has been a growing recognition regarding the importance of walkability in urban design. Walking, as the most common form of physical activity, has gained recognition for its numerous benefits. While walkability has been extensively studied by urban designers, there is a gap in understanding how to promote and enhance walkability in suburban areas. The United States has historically prioritized car-centric transportation systems, resulting in less developed infrastructure for walking and cycling. Although efforts have been made in recent years to improve conditions for pedestrians and cyclists, there is still much progress needed to elevate the country's standing. The City of Alexandria, Virginia, boasts a diverse population and is known for its unique neighborhood called Old Town. Old Town is widely recognized for its high level of walkability, characterized by streets that are designed to prioritize pedestrians, creating a welcoming environment that encourages social interaction and a strong sense of community. However, in the suburban areas surrounding Old Town, there is a notable lack of walkability. This study look at challenges and opportunities in promoting walkability in a suburban area of Alexandria. By examining the specific context of Alexandria, This thesis aims to create a more walkable environment in an area that currently focuses on cars. The goal is to create more livable and pedestrian-friendly suburban environments that encourage walking and bicycling, and support the well-being of residents. / Master of Science / In recent times, there has been a growing acknowledgment of the value in designing cities that prioritize walkability and placing pedestrians' needs. Walking, which is the most popular way to stay active, has been recognized for its numerous benefits for our health and well-being. However, when it comes to making suburban areas more walkable, there is still a lot we don't fully understand. In the past, many urban designs in the United States have focused on cars, making it challenging for people to walk or cycle comfortably. While efforts have been made to improve conditions for pedestrians and cyclists, European countries are still ahead in terms of walking and cycling rates. The City of Alexandria, located in Virginia, is characterized by its diverse population and renowned for its distinctive neighborhood known as Old Town. Old Town is celebrated for its walkability, with streets that prioritize people over vehicles. IN contrast, the surrounding suburban areas don't enjoy the same level of walkability. This study aims to explore the obstacles and opportunities in making suburban areas more walkable, focusing on Alexandria's context. By implementing design solutions, aim to transform suburban areas into vibrant, pedestrian-friendly communities that promote walking and biking, contributing to the overall well-being of residents.

No description available.

No description available.

No description available.

<p>This thesis is concerned with a structure called the Reeb graph. There are three main problems considered. The first is devising an efficient algorithm for comnstructing the Reeb graph of a simplicial complex with respect to a generic simplex-wise linear real-valued function. We present an algorithm that builds the Reeb graph in almost optimal worst-case deterministic time. This was the first deterministic algorithm with the time bound which is linear up to a logarithmic factor. Without loss of generality, the complex is assumed to be 2-dimensional. The algorithm works by sweeping the function values and maintaining a spanning forest for the preimage, or the level-set, of the value. Using the observation that the operations that change the level-sets are off-line, we was able to achieve the above bound.</p><p>The second topic is the dynamic Reeb graphs. As the function changes its values, the Reeb graph also changes. We are interested in updating the Reeb graph. We reduce this problem to a graph problem that we call retroactive graph connectivity. We obtain algorithms for dynamic Reeb graphs, by using data structures that solve the graph problem. </p><p>The third topic is an argument regarding the complexity of computing Betti numbers. This problem is also related to the Reeb graph by means of the vertical Homology classes. The problem considered here is whether the realization of a simplicial complex in the Euclidean 4-space can result in an algorithm for computing its Homology groups faster than the usual matrix reduction methods. Using the observation that the vertical Betti numbers can always be computed more efficiently using the Reeb graph, we show that the answer to this question is in general negative. For instance, given a square matrix over the field with two elements, we construct a simplicial complex in linear time, realized in euclidean 4-space and a function on it, such that its Horizontal homology group, over the same field, is isomorphic with the null-space of the matrix. It follows that the Betti number computation for complexes realized in the 4-space is as hard as computing the rank for a sparse matrix.</p> / Dissertation

The development of functional connectivity is often described as changing from local to distributed connections which give rise to the functional brain networks observed in adulthood. In contrast to the well-explored pattern found in functional connectivity, no research has been published describing effective connectivity development. Also, there is a plethora of literature describing functional connectivity patterns in a variety of neurodevelopmental and internalizing disorders, but there is little consistency in the connectivity patterns discovered for each disorder. Hence, this study aimed to describe functional and effective resting-state connectivity during adolescent development in a typically developing adolescent (TDA) group (n = 128) and to determine how adolescents with comorbid neurodevelopmental disorders (CND) (n = 46) differed. This was accomplished through functional and effective connectivity analysis within and between four networks: the Default Mode Network (DMN), the Salience Network (SN), the Dorsal Attention Network (DAN), and the Frontal Parietal Control Network (FPCN). The results from this study indicate that within-network connectivity decreased across age in the TDA group, which is in opposition to previous work which suggests strengthening within-network connectivity. The CND group displayed hyper-connectivity compared to the TDA group in between-network connectivity with no effect of age. The effective connectivity in the TDA group displayed decreasing connectivity within networks with increasing age, a novel effect not previously reported in the literature. The CND group’s effective connectivity was overall hyper-connected (for within- and between-networks). The functional connectivity patterns in the TDA group suggest that functional connectivity has subtle developmental change during adolescence. Further, the CND group consistently displayed hyper-connectivity in functional and effective connectivity. The CND group, and perhaps similar comorbid groups, may have less efficient networks which could contribute to their disorder(s).

This doctoral thesis outlines several methodological advances in network science aimed towards uncovering rapid, complex interdependencies of electromagnetic brain activity recorded from the Electroencephalogram (EEG). This entails both new analyses and modelling of EEG brain network topologies and a novel approach to analyse rapid dynamics of connectivity. Importantly, we implement these advances to provide novel insights into pathological brain function in Alzheimer's disease. We introduce the concept of hierarchical complexity of network topology, providing both an index to measure it and a model to simulate it. We then show that the topology of functional connectivity estimated from EEG recordings is hierarchically complex, existing in a scale between random and star-like topologies, this is a paradigm shift from the established understanding that complexity arises between random and regular topologies. We go on to consider the density appropriate for binarisation of EEG functional connectivity, a methodological step recommended to produce compact and unbiased networks, in light of its new-found hierarchical complexity. Through simulations and real EEG data, we show the benefit of going beyond often recommended sparse representations to account for a broader range of hierarchy level interactions. After this, we turn our attention to assessing dynamic changes in connectivity. By constructing a unified framework for multivariate signals and graphs, inspired by network science and graph signal processing, we introduce graph-variate signal analysis which allows us to capture rapid fluctuations in connectivity robust to spurious short-term correlations. We define this for three pertinent brain connectivity estimates - Pearson's correlation coefficient, coherence and phase-lag index - and show its benefit over standard dynamic connectivity measures in a range of simulations and real data. Applying these novel methods to EEG datasets of the performance of visual short-term memory binding tasks by familial and sporadic Alzheimer's disease patients, we uncover disorganisation of the topological hierarchy of EEG brain function and abnormalities of transient phase-based activity which paves the way for new interpretations of the disease's affect on brain function. Hierarchical complexity and graph-variate dynamic connectivity are entirely new methods for analysing EEG brain networks. The former provides new interpretations of complexity in static connectivity patterns while the latter enables robust analysis of transient temporal connectivity patterns, both at the frontiers of analysis. Although designed with EEG functional connectivity in mind, we hope these techniques will be picked up in the broader field, having consequences for research into complex networks in general.