Interactive Visualization of Multimodal Brain Connectivity: Applications in Clinical and Cognitive Neuroscience

Magnetic resonance imaging (MRI) has become a readily available prognostic and diagnostic method, providing invaluable information for the clinical treatment of neurological diseases. Multimodal neuroimaging allows integration of complementary data from various aspects such as functional and anatomical properties; thus, it has the potential to overcome the limitations of each individual modality. Specifically, functional and diffusion MRI are two non-invasive neuroimaging techniques customized to capture brain activity and microstructural properties, respectively. Data from these two modalities is inherently complex, and interactive visualization can assist with data comprehension. The current thesis presents the design, development, and validation of visualization and computation approaches that address the need for integration of brain connectivity from functional and structural domains. Two contexts were considered to develop these approaches: neuroscience exploration and minimally invasive neurosurgical planning. The goal was to provide novel visualization algorithms and gain new insights into big and complex data (e.g., brain networks) by visual analytics. This goal was achieved through three steps: 3D Graphical Collision Detection: One of the primary challenges was the timely rendering of grey matter (GM) regions and white matter (WM) fibers based on their 3D spatial maps. This challenge necessitated pre-scanning those objects to generate a memory array containing their intersections with memory units. This process helped faster retrieval of GM and WM virtual models during the user interactions. Neuroscience Enquiry (MultiXplore): A software interface was developed to display and react to user inputs by means of a connectivity matrix. This matrix displays connectivity information and is capable to accept selections from users and display the relevant ones in 3D anatomical view (with associated anatomical elements). In addition, this package can load multiple matrices from dynamic connectivity methods and annotate brain fibers. Neurosurgical Planning (NeuroPathPlan): A computational method was provided to map the network measures to GM and WM; thus, subject-specific eloquence metric can be derived from related resting state networks and used in objective assessment of cortical and subcortical tissue. This metric was later compared to apriori knowledge based decisions from neurosurgeons. Preliminary results show that eloquence metric has significant similarities with expert decisions

Distance-Aware Selective Online Query Processing Over Large Distributed Graphs

Performing online selective queries against graphs is a challenging problem due to the unbounded nature of graph queries which leads to poor computation locality. It becomes even difficult when a graph is too large to be fit in the memory. Although there have been emerging efforts on managing large graphs in a distributed and parallel setting, e.g., Pregel, HaLoop and etc, these computing frameworks are designed from the perspective of scalability instead of the query efficiency. In this work, we present our solution methodology for online selective graph queries based on the shortest path distance semantic, which finds various applications in practice. The essential intuition is to build a distance-aware index for online distance-based query processing and to eliminate redundant graph traversal as much as possible. We discuss how the solution can be applied to two types of research problems, distance join and vertex set bonding, which are distance-based graph pattern discovery and finding the structure-wise bonding of vertices, respectively

Probabilistic Human-Robot Information Fusion

This thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability

Artificial intelligence and machine learning in the era of digital transformer monitoring: Exciting developments at Hitachi Energy

The era of digitalization brings new challenges and new paradigms since transformer users and manufacturers alike are moving towards digital solutions. This transition requires new approaches, new architectures, and new ways of looking at data collection, storage, and assessment. Speed and reliability of actionable information become essential at a time when data is ubiquitous, loads are more complex, and energy production moves from traditional plants to distributed generation. This article intends to show some of the ongoing efforts at Hitachi Energy to address these and other demanding technical and economic issues. Our wind power forecast approach deals with the problem of uncertainty in upcoming power demand. We propose a machine learning model to predict power demand to improve the calculation of loadability and cooling / hotspot calculations. Similarly, our Bushing Tan δ and Capacitance Fault Detection solution uses the error of a model to detect problems with Tan δ and capacitance. Our Probabilistic Fault Tree describes an open-source approach that uses Bayesian networks to find the probability of failure of a specific transformer. Finally, we describe two publications made by our team regarding the use of synthetic data created using the Duval Pentagons to generate a model that diagnoses transformer faults; and a patent regarding the creation of an infrastructure that uses blockchain to anonymize users and provide them with information about their transformer fleet using artificial intelligence

Artificial intelligence and machine learning in the era of digital transformer monitoring: Exciting developments at Hitachi Energy

The era of digitalization brings new challenges and new paradigms since transformer users and manufacturers alike are moving towards digital solutions. This transition requires new approaches, new architectures, and new ways of looking at data collection, storage, and assessment. Speed and reliability of actionable information become essential at a time when data is ubiquitous, loads are more complex, and energy production moves from traditional plants to distributed generation. This article intends to show some of the ongoing efforts at Hitachi Energy to address these and other demanding technical and economic issues. Our wind power forecast approach deals with the problem of uncertainty in upcoming power demand. We propose a machine learning model to predict power demand to improve the calculation of loadability and cooling / hotspot calculations. Similarly, our Bushing Tan δ and Capacitance Fault Detection solution uses the error of a model to detect problems with Tan δ and capacitance. Our Probabilistic Fault Tree describes an open-source approach that uses Bayesian networks to find the probability of failure of a specific transformer. Finally, we describe two publications made by our team regarding the use of synthetic data created using the Duval Pentagons to generate a model that diagnoses transformer faults; and a patent regarding the creation of an infrastructure that uses blockchain to anonymize users and provide them with information about their transformer fleet using artificial intelligence