Browsing by Subject "Connectivity"
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Item Access Across America: Auto 2018 Data(2020-01-31) Murphy, Brendan; Owen, Andrew; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by auto in the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by auto, and it allows for a direct comparison of the auto accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. The data available describe access to jobs by auto in the states of Arkansas, California, District of Columbia, Florida, Illinois, Iowa, Maryland, Massachusetts, Minnesota, North Carolina, Tennessee, Washington, and Virginia, and the metropolitan areas within these states.Item Access Across America: Auto 2021 Data(2023-09-21) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by auto in the 50 largest (by population) metropolitan areas in the United States. The data include access at realistic observed driving speeds by time of day and road segment. The underlying speed data inputs restrict data sharing to participating sponsor states. The data available describe access to jobs by auto in the states/districts of California, Connecticut, District of Columbia, Florida, Illinois, Maryland, Massachusetts, Michigan, Minnesota, North Carolina, Texas; and the metropolitan areas within these states. These data are part of a longitudinal study. Auto data for additional years can be found in the Accessibility Observatory Data collection: http://hdl.handle.net/11299/200592Item Access Across America: Bike 2017 Data(2020-02-03) Murphy, Brendan; Owen, Andrew; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by biking in the 50 largest (by population) metropolitan areas in the United States, on low-stress and higher-stress streets via a Level of Traffic Stress analysis process. It is the most detailed evaluation to date of access to jobs by bike nationally, and it allows for a direct comparison of the bicycle accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study.Item Access Across America: Bike 2019 Data(2021-01-29) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by bicycling in the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by biking, and incorporates a Level of Traffic Stress analysis to allow calculation of access to jobs on bike networks of different traffic stress tolerances. This dataset allows for a direct comparison of the biking accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Access Across America: Bike 2017 data are available at https://conservancy.umn.edu/handle/11299/211418, however the 2017 version of this dataset was produced without implementation of Level of Traffic Stress analysis, and the methodologies differ substantially.Item Access Across America: Bike 2021 Data(2023-08-28) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by bicycling across the United States. It is the most detailed evaluation to date of access to jobs by biking, and incorporates a Level of Traffic Stress analysis to allow calculation of access to jobs on bike networks of different traffic stress tolerances. This dataset allows for a direct comparison of the biking accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Previous datasets (Access Across America: Bike 2019) are available at https://conservancy.umn.edu/handle/11299/218194.Item Access Across America: Transit 2014 Data(2014-12-05) Owen, Andrew; Levinson, David M; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThis data was created as part of a study that examined the accessibility to jobs by transit in 46 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas.Item Access Across America: Transit 2015 Data(2017-02-02) Owen, Andrew; Levinson, David M; Murphy, Brendan; aowen@umn.edu; Owen, AndrewThese data were created as part of a study that examined the accessibility to jobs by transit in 49 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study; Access Across America: Transit 2014 data are available at http://hdl.handle.net/11299/168064.Item Access Across America: Transit 2016 Data(2018-03-28) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit in 49 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study; Access Across America: Transit 2015 data are available at https://conservancy.umn.edu/handle/11299/183801. Access Across America: Transit 2014 data are available at http://hdl.handle.net/11299/168064.Item Access Across America: Transit 2017 Data(2018-10-08) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit in 49 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Access Across America: Transit 2016 data are available at https://conservancy.umn.edu/handle/11299/195065. Access Across America: Transit 2015 data are available at https://conservancy.umn.edu/handle/11299/183801. Access Across America: Transit 2014 data are available at http://hdl.handle.net/11299/168064.Item Access Across America: Transit 2018 Data(2020-01-31) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit in the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Transit data for additional years can be found in the Accessibility Observatory Data collection: http://hdl.handle.net/11299/200592Item Access Across America: Transit 2019 Data(2021-01-26) Murphy, Brendan; Owen, Andrew; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit in the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Transit data for additional years can be found in the Accessibility Observatory Data collection: http://hdl.handle.net/11299/200592Item Access Across America: Transit 2021 Data(2023-08-31) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit across the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's metropolitan areas. These data are part of a longitudinal study. Transit data for additional years can be found in the Accessibility Observatory Data collection: http://hdl.handle.net/11299/200592Item Community design and how much we drive(Journal of Transport and Land Use, 2012) Marshall, Wesley; Garrick, NormanThe preponderance of evidence suggests that denser and more connected communities with a higher degree of mixed land uses results in fewer vehicle kilometers traveled (VKT). However, there is less agreement as the size of the effect. Also, there is no clear understanding as to the aspects of community design that are most important in contributing to lower VKT. One reason why there is some confusion on this point is that past studies have not always made a clear distinction between different community and street network design characteristics such as density, connectivity, and configuration. In this research, care was taken to fully characterize the different features of the street network including a street pattern classification system that works at the neighborhood level but also focuses on the citywide street network as a separate entity. We employ a spatial kriging analysis of NHTS data in combination with a generalized linear regression model in order to examine the extent to which community design and land use influence VKT in 24 California cities of populations from 30,000 to just over 100,000. Our results suggest that people living in denser street network designs tended to drive less. Connectivity, however, played an adverse role in performance.Item Comparing methods for assessing habitat connectivity: a case study of guinas (Leopardus guigna) in a fragmented Chilean landscape(2014-09) Castro Bustamante, Rodrigo AlfredoFragmentation creates a matrix which can facilitate or impede connections among patches of habitat. Least-cost path (LCP) and circuit theory (CT) are two methods commonly used to evaluate landscape-level connectivity among patches. Both methods use resistance surfaces that can be generated from Resource Selection Functions (RSF) or the Analytical Hierarchy Process (AHP). Despite the potential conservation implications of connectivity analyses, the methods are rarely compared. I quantified how RSF and AHP resistance surfaces affect estimates of connectivity among protected areas using a South American wild cat, güiña (Leopardus guigna), as a case study. I found that 1) path rankings and predicted locations of pinch points depended on the metric and resistance surface used, and 2) LCP is more sensitive to resistance surfaces than CT. These results confirm that connectivity analysis methods should be carefully considered and compared before they are used for conservation decisions.Item Distribution, range connectivity, and trends of bear populations in Southeast Asia(2017-06) Scotson, LorraineSun bears and Asiatic black bears co-occur in Southeast Asia with wide areas of overlapping range. Both species are in decline, and are vulnerable to extinction due mainly to habitat loss and illegal hunting. Efforts to conserve bears in Southeast Asia are hampered by a lack of basic knowledge of distribution, population trends and habitat configuration. To advance the scientific understanding of sun bears and Asiatic back bears in this region I investigated fine and broad scale patterns of distribution. In Lao PDR, I gathered data on bear occurrence using bear sign transects walked in multiple forest blocks throughout the country. To model the country-wide relative abundance of bears and habitat quality, I related bear sign to environmental factors associated with bear occurrence. Within global sun bear range, I gathered camera trap records of sun bear detections from seven sun bear range countries. To generate quantitative measures of sun bear population trends, I related sun bear detection rates to tree cover and estimated related changes in country and global-level sun bear populations based on tree cover loss. To evaluate the global extent of sun bear range connectivity, I used the modelled relationship between sun bears and tree cover to create a habitat suitability index, and I identified areas of fractured range that have created unnatural subpopulations that are at risk from isolation. In Lao PDR, bears selected for areas of high elevation, rugged terrain, and areas of high tree density far from roads. My model-based estimates of sun bear global population trends predicted that over a 30-year period, sun bear populations in mainland southeast Asia have potentially declined by close to 20%, and insular sun bear populations have declined by ~50%. I identified seven potential sun bear subpopulations; two that are fully isolated with no potential for inter-subpopulation movement, and in the other five, inter and intra-subpopulation habitat fragmentation occurs in a continuum of severity. My findings advance the understanding of patterns in bear distribution and trends in southeast Asia, identify research priorities, and lay a framework for future monitoring efforts at country and region-level scales. I conclude with recommendations on how to better manage camera trap data for secondary research and sharing.Item Effective Disconnection of Intrinsic Networks in the Prefrontal Cortex: Convergence across Primate and Mouse Models of Schizophrenia(2018-09) Zick, JenniferIndividuals who are afflicted with schizophrenia experience a disorienting array of symptoms that include sensations of nonexistent stimuli (hallucinations), fixed beliefs not grounded in reality (delusions), emotional disturbances, and a generalized disorganization of thought. Some of the most fundamental aspects of consciousness can be disrupted in schizophrenia, such as the capacity to maintain a continuous thought process, plan and predict future actions and consequences, discern threatening from beneficial stimuli, and consciously inhibit impulsive or harmful behavior. Descriptions of the subjective experience of schizophrenia often revolve around the idea that the executive “self” of an individual is disconnected or no longer whole. Executive functions are thought to be distributed throughout cortical and subcortical networks, but to the extent that they can be localized they tend to depend on proper functioning of regions within the prefrontal cortex. In particular, the dorsolateral prefrontal cortex (DLPFC) of primates is considered to be vital in the process of organizing thought, and likewise the disorganization of thought in schizophrenia is linked to dysfunction in this region. For example, the DLPFC contains a densely interconnected circuit of pyramidal neurons that can sustain neural activity in the absence of sensory input, which is thought to underlie our ability to maintain a concept “in mind” after it has disappeared. What happens when these fundamental processes are disrupted? The manifestations can range from subtle disturbances in the integration of sensory input to a failure to distinguish reality from imagination. In this dissertation, I describe the contributions I have made to the understanding of schizophrenia during the course of my graduate school training. I was given the opportunity to begin my work on this project by analyzing preexisting neural data obtained from the DLPFC in a pharmacological primate model of schizophrenia . From there, I developed a surgical and recording protocol that allowed me to generate comparable in vivo data from the prefrontal cortex of awake Dgcr8+/- mice, an established genetic model of schizophrenia. Despite the disparities between these two animal models, I report convergent patterns indicating a disruption of neuronal correlations in the prefrontal regions of both monkeys given dissociative drugs and mice carrying a schizophrenia-associated mutation. In both studies, I found evidence that neurons in the disease state were not synchronizing their activity with each other as effectively as in the control state. Furthermore, the effective transfer of information between pairs of neighboring neurons was reduced. These results suggest that the intrinsic circuitry of the prefrontal cortex may be disconnected in schizophrenia, and that this disconnection relates to a reduction in coincident spiking activity of neighboring neurons. It is plausible that such a dissolution of local prefrontal connectivity could result in a failure to achieve the cognitively demanding task of thought organization. While much is yet to be learned about the nature of schizophrenia, my findings have the potential to motivate the development of novel approaches to the restoration of function in this devastating disease.Item A Network-Based Framework for Hydro-Geomorphic Modeling and Decision Support with Application to Space-Time Sediment Dynamics, Identifying Vulnerabilities, and Hotspots of Change(2016-05) Czuba, JonathanIncreasing pressure to meet the food, water, and energy demands of our growing society in a changing climate has strained the physical, chemical, and biological functioning of watersheds to maintain ecosystem services, such as providing clean water, and to sustain a productive and diverse ecosystem. Confronted with multifaceted environmental issues, watershed managers could use a simple first-order approach for understanding how physical, chemical, and biological processes operate within a watershed to guide watershed-management decisions. This research advances a network-based modeling framework for guiding effective landscape management decisions towards sustainability focusing on understanding large-scale system functioning and predicting the emergence of vulnerabilities, “hotspots” of change, and unexpected system behavior. Based on a combination of mathematical theory, field-data analysis, and numerical simulations applied to the dynamics of bed-material sediment (i.e., the sediment composing the riverbed) on river networks, we (1) identify a resonant frequency of sediment supply from network topology and sediment-transport dynamics that could lead to an unexpected downstream amplification of sedimentological response in the Minnesota River Basin; (2) identify hotspots of likely sediment-driven fluvial geomorphic change where sediment has a tendency to persist and exacerbate channel migration on the Greater Blue Earth River Network; and (3) elucidate the hierarchical role of river-network structure on bed-material sediment dynamics in propagating, altering, and amalgamating the emergent large temporal fluctuations and periodicities of bed-sediment thickness. By embedding small-scale bed-material sediment dynamics on a river network, this research shows that it is possible to gain a better understanding of the large-scale system functioning whereby management actions that target the identified critical times, places, and processes in the landscape will be most effective at improving water quality and the health of the aquatic ecosystem.Item Neural Impact of Cognitive Remediation for Schizophrenia in a Randomized Controlled Trial(2016-06) Ramsay, IanCognitive remediation training for schizophrenia has been shown to have modest influence on both cognitive and psychosocial functioning, but much is not understood about the neurobiology associated with these interventions. The current randomized placebo-controlled trial and review sought to replicate and expand on previous findings demonstrating that improvements from cognitive remediation are associated with changes in prefrontal brain activation and functional connectivity. Results suggest that cognitive remediation influences both prefrontal and thalamic brain areas, and that changes within the connections between these regions may reflect improvements in overall cognition. The implications of these findings as well as how neuroplastic changes might influence cognition, psychosocial functioning, or symptom profile in schizophrenia will be discussed.Item Neurometric Encoding and Decoding: Using Multivariate Functional Connectivity Methods to Describe Cognitive States, Traits and Clinical Endophenotypes(2014-10) Moodie, CraigThis research was undertaken for the purpose of demonstrating the neurometric utility of functional connectivity methods by combining metrics that utilize information derived from independent component analyses (ICAs) with traditional fMRI and graph theory analyses. The combination of these methodologies was used to establish traits and evaluate cognitive states from a behavioral genetics perspective, as well as to posit connectivity endophenotypes related to psychiatric and neurological diseases. The studies described below demonstrate that the metrics used to study intrinsic connectivity networks (ICNs) are useful tools for studying the in vivo brain in states of normalcy and disease. For instance, by examining ICNs across tasks and monozygotic twins, it was possible to establish these brain networks as traits. The ICNs were stable across cognitive states, while still exhibiting sensitivity to specific demands. In addition, the state- dependent modulation of these ICNs, as well as their other characteristics, was shown to be influenced by genetic factors in two separate twin samples. In the second twin sample, and a study of connectivity phenotypes related to schizophrenia, ICNs were useful for establishing the relationships between ICNs and tasks in both cases. The task-related characteristics and resting state profiles of ICNs were also useful for establishing novel endophenotypes of the disease states of schizophrenia and Parkinson's disease. Overall, this research serves to establish the study of the brain's intrinsic connectivity across the domains of both cognitive and clinical neuroscience and this work serves a contribution to the understanding of the dimensions along which normal and abnormal neurobiological functioning lie, and how intrinsic connectivity networks can be examined in both spheres.Item Targeting the Brain in Brain-Computer Interfacing: The Effect of Transcranial Current Stimulation and Control of a Physical Effector on Performance and Electrophysiology Underlying Noninvasive Brain-Computer Interfaces(2017-07) Baxter, BryanBrain-computer interfaces (BCIs) and neuromodulation technologies have recently begun to fulfill their promises of restoring function, improving rehabilitation, and enhancing abilities and learning. However, lengthy user training to achieve acceptable accuracy is a barrier to BCI acceptance and use by patients and the general population. Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technology whereby a low level of electrical current is injected into the brain to alter neural activity and has been found to improve motor learning and task performance. A barrier to optimizing behavioral effects of tDCS is that we do not yet understand how neural networks are affected by stimulation and how stimulation interacts with ongoing endogenous activity. The purpose of this dissertation was to elucidate strategies to improve BCI control by targeting the user through two approaches: 1. Subject control of a robotic arm to enhance user motivation and 2. tDCS application to improve behavioral outcomes and alter networks underlying sensorimotor rhythm-based BCI performance. The primary results illustrate that targeted tDCS of the motor network interacts with task specific neural activity to improve BCI performance and alter neural electrophysiology. This effect on neural activity extended across the task network, beyond the area of direct stimulation, and altered connectivity unilaterally and bilaterally between frontal and parietal cortical regions. These findings suggest targeted neuromodulation interacts with endogenous neural activity and can be used to improve motor-cognitive task performance.