Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography

Copyright © 2011 Society for Neuroscience and the authors. The The Journal of Neuroscience uses a Creative Commons Attribution-NonCommercial-ShareAlike licence: http://creativecommons.org/licenses/by-nc-sa/4.0/.Despite a homogenous macroscopic appearance on magnetic resonance images, subregions of the amygdala express distinct functional profiles as well as corresponding differences in connectivity. In particular, histological analysis shows stronger connections for superficial (i.e., centromedial and cortical), compared with deep (i.e., basolateral and other), amygdala nuclei to lateral orbitofrontal cortex and stronger connections of deep compared with superficial, nuclei to polymodal areas in the temporal pole. Here, we use diffusion weighted imaging with probabilistic tractography to investigate these connections in humans. We use a data-driven approach to segment the amygdala into two subregions using k-means clustering. The identified subregions are spatially contiguous and their location corresponds to deep and superficial nuclear groups. Quantification of the connection strength between these amygdala clusters and individual target regions corresponds to qualitative histological findings in non-human primates, indicating such findings can be extrapolated to humans. We propose that connectivity profiles provide a potentially powerful approach for in vivo amygdala parcellation and can serve as a guide in studies that exploit functional and anatomical neuroimaging.The Wellcome Trust, a Max Planck Research Award and Swiss National Science Foundation

Uncertainty-Aware Principal Component Analysis

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection. We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA. In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data. For this, we propose factor traces as a novel visualization that enables to better understand the influence of uncertainty on the chosen principal components. We provide multiple examples of our technique using real-world datasets. As a special case, we show how to propagate multivariate normal distributions through PCA in closed form. Furthermore, we discuss extensions and limitations of our approach

PerfVis: Pervasive Visualization in Immersive AugmentedReality for Performance Awareness

Developers are usually unaware of the impact of code changes to the performance of software systems. Although developers can analyze the performance of a system by executing, for instance, a performance test to compare the performance of two consecutive versions of the system, changing from a programming task to a testing task would disrupt the development flow. In this paper, we propose the use of a city visualization that dynamically provides developers with a pervasive view of the continuous performance of a system. We use an immersive augmented reality device (Microsoft HoloLens) to display our visualization and extend the integrated development environment on a computer screen to use the physical space. We report on technical details of the design and implementation of our visualization tool, and discuss early feedback that we collected of its usability. Our investigation explores a new visual metaphor to support the exploration and analysis of possibly very large and multidimensional performance data. Our initial result indicates that the city metaphor can be adequate to analyze dynamic performance data on a large and non-trivial software system.Comment: ICPE'19 vision, 4 pages, 2 figure, conferenc

Incorporating Biodiversity-Ecosystem Function Relationships into Models and Conservation Planning

Unsustainable use of nature and climate change are leading to unprecedented biodiversity declines. These declines have cascading impacts on ecosystem function and ecosystem services, and ultimately on human well-being. International agreements have been adopted that aim to address both crises. The Paris Agreement, adopted in 2015, set global emission reductions targets. In 2022, most countries agreed to the Kunming-Montreal Global Biodiversity Framework (GBF). The GBF sets 23 ambitious targets for 2030 ranging from reducing threats to biodiversity, meeting people’s needs through sustainable use and benefit sharing, and solutions for implementation. Although adopting global goals and targets is an important first step, additional work is required for on-the-ground implementation. Important knowledge gaps include understanding how biodiversity, ecosystem functions, and ecosystem services are linked, modeling how policy scenarios could lead to different outcomes for biodiversity and ecosystem services, and guidance for where and how to prioritize conservation actions. This dissertation aims to fill some of these gaps. Chapters 1 and 2 explore how biodiversity conservation can affect important ecosystem functions and services. Chapter 3 moves from improving our baseline knowledge to thinking about how we can achieve our conservation goals through prioritizing restoration actions. In chapter 1, I focus on the importance of biodiversity-ecosystem function relationships for urban systems. The proportion of people living in urban areas is growing globally. Thus, understanding how to manage urban biodiversity, ecosystem functions, and ecosystem services is important. Biodiversity can increase ecosystem functioning in natural systems. However, few studies have assessed the relationship between biodiversity and ecosystem functioning in urban areas, which differ in abiotic factors, species compositions, food webs, and turnover rates. I systematically reviewed documented evidence of biodiversity-ecosystem function relationships in urban environments and assessed factors that influenced the direction of the relationships. I show that increasing biodiversity, even in small areas, can increase local ecosystem functioning in urban areas. Therefore, local management that increases biodiversity can have positive benefits for ecosystems and people. I also identify research gaps and opportunities to improve biodiversity-ecosystem function research in the urban realm moving forward and discuss how to improve urban green space management. In chapter 2, I explored how biodiversity-ecosystem functioning relationships can be incorporated into modeling. Models of how changes in drivers, including land use change and climate change, lead to changes in biodiversity and ecosystem services are useful tools for policymakers as they consider how to sustainably manage natural resources. Despite known interactions between biodiversity, ecosystem functioning, and ecosystem services, models projecting changes in these domains typically operate independently and do not account for interactions or feedbacks, which may lead to inaccurate estimates in ecosystem functioning and ecosystem service projections. In this chapter, I focused on how plant species diversity affects biomass production and carbon storage. I used the Biogeographic Infrastructure for Large‐scaled Biodiversity Indicators (BILBI) model, a macroecological community-level model, to estimate plant species persistence under different climate and land use change scenarios in 2050. I linked this with empirical data on biodiversity-biomass production relationships to assess how biodiversity loss will affect carbon storage globally. I found that biodiversity has the potential to cause as much carbon loss as emissions from other sources (i.e., they are within the range of uncertainty from biodiversity-mediated carbon loss), so achieving Sustainable Development Goal 15 (Life on Land) is essential to achieving Goal 13 (Climate Action). Because the Paris Agreement does not account for emissions from biodiversity loss, science on its carbon impacts, and action as a result, could be underestimated. This analysis points to the important role that maintaining and/or enhancing the diversity of plant species within areas of natural vegetation, rather than simply maximizing the extent of these areas, can play in addressing the climate change crisis. Alongside increasing the global extent of protected areas to prevent rapid carbon loss from ecosystem degradation, increasing plant species diversity in degraded ecosystems can increase carbon storage potential. However, existing international initiatives like the Bonn Challenge and the Paris Agreement focus on forest extent rather than forest quality for protection, afforestation, and reforestation, and thus are missing a key opportunity for action. In chapter 3, I looked at how we can achieve proposed biodiversity conservation goals. Reversing trends in biodiversity loss and achieving the Convention on Biological Diversity (CBD) 2050 vision of “Living in harmony with nature” will require not only conserving remaining biodiversity, but also restoring degraded areas. Recent legislative and executive actions in the U.S. have recognized the importance of restoration. Given limited budgets, deciding where to restore habitat will be an important need in the coming decade. In this chapter, I developed a modeling approach to maximize conservation benefit/restoration cost ratios that can be used to map restoration priorities. I illustrated this approach using a case study for highly threatened grassland ecosystems in the Great Plains region of Kansas. I found that for the indicator species that we chose, shortgrass and mixed-grass prairies had the highest conservation benefit to cost ratio. Setting a minimum restoration threshold for each habitat type allowed me to identify high priority tallgrass prairie sites. The modeling approach is flexible and can be updated for different ecosystems, species, and conservation priorities. I outlined potential alterations that can be made in future analyses, depending on desired restoration goals. Biodiversity conservation can increase ecosystem functioning and services. In this dissertation, I show that conserving biodiversity is important for urban ecosystem functioning and global carbon sequestration. Restoring biodiversity will have positive outcomes for ecosystem functions, ecosystem services, and people. My restoration prioritization model can therefore be used to implement conservation actions to achieve global and national biodiversity conservation goals and targets