Contributions of Human Prefrontal Cortex to the Recogitation of Thought

Human beings have a unique ability to not only verbally articulate past and present experiences, as well as potential future ones, but also evaluate the mental representations of such things. Some evaluations do little good, in that they poorly reflect facts, create needless emotional distress, and contribute to the obstruction of personal goals, whereas some evaluations are the converse: They are grounded in logic, empiricism, and pragmatism and, therefore, are functional rather than dysfunctional. The aim of non-pharmacological mental health interventions is to revise dysfunctional thoughts into more adaptive, healthier ones; however, the neurocognitive mechanisms driving cognitive change have hitherto remained unclear. Therefore, this thesis examines the role of the prefrontal cortex (PFC) in this aspect of human higher cognition using the relatively new method of functional near-infrared spectroscopy (fNIRS). Chapter 1 advances recogitation as the mental ability on which cognitive restructuring largely depends, concluding that, as a cognitive task, it is a form of open-ended human problem-solving that uses metacognitive and reasoning faculties. Because these faculties share similar executive resources, Chapter 2 discusses the systems in the brain involved in controlled information processing, specifically the nature of executive functions and their neural bases. Chapter 3 builds on these ideas to propose an information-processing model of recogitation, which predicts the roles of different subsystems localized within the PFC and elsewhere in the context of emotion regulation. This chapter also highlights several theoretical and empirical challenges to investigating this neurocognitive theory and proposes some solutions, such as to use experimental designs that are more ecologically valid. Chapter 4 focuses on a neuroimaging method that is best suited to investigating questions of spatial localization in ecological experiments, namely functional near-infrared spectroscopy (fNIRS). Chapter 5 then demonstrates a novel approach to investigating the neural bases of interpersonal interactions in clinical settings using fNIRS. Chapter 6 explores physical activity as a ‘bottom-up’ approach to upregulating the PFC, in that it might help clinical populations with executive deficits to regulate their mental health from the ‘top-down’. Chapter 7 addresses some of the methodological issues of investigating clinical interactions and physical activity in more naturalistic settings by assessing an approach to recovering functional events from observed brain data. Chapter 8 draws several conclusions about the role of the PFC in improving psychological as well as physiological well-being, particularly that rostral PFC is inextricably involved in the cognitive effort to modulate dysfunctional thoughts, and proposes some important future directions for ecological research in cognitive neuroscience; for example, psychotherapy is perhaps too physically stagnant, so integrating exercise into treatment environments might boost the effectiveness of intervention strategies

Understanding Mental Health and Cognitive Restructuring With Ecological Neuroscience

Neuroimaging and neuropsychological methods have contributed much toward an understanding of the information processing systems of the human brain in the last few decades, but to what extent do cognitive neuroscientific findings represent and generalize to the inter- and intra-brain dynamics engaged in adapting to naturalistic situations? If it is not marked, and experimental designs lack ecological validity, then this stands to potentially impact the practical applications of a paradigm. In no other domain is this more important to acknowledge than in human clinical neuroimaging research, wherein reduced ecological validity could mean a loss in clinical utility. One way to improve the generalizability and representativeness of findings is to adopt a more “real-world” approach to the development and selection of experimental designs and neuroimaging techniques to investigate the clinically-relevant phenomena of interest. For example, some relatively recent developments to neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) make it possible to create experimental designs using naturalistic tasks that would otherwise not be possible within the confines of a conventional laboratory. Mental health, cognitive interventions, and the present challenges to investigating the brain during treatment are discussed, as well as how the ecological use of fNIRS might be helpful in bridging the explanatory gaps to understanding the cultivation of mental health

Big Software for SmallSats: Adapting cFS to CubeSat Missions

Expanding capabilities and mission objectives for SmallSats and CubeSats is driving the need for reliable, reusable, and robust flight software. While missions are becoming more complicated and the scientific goals more ambitious, the level of acceptable risk has decreased. Design challenges are further compounded by budget and schedule constraints that have not kept pace. NASA's Core Flight Software System (cFS) is an open source solution which enables teams to build flagship satellite level flight software within a CubeSat schedule and budget. NASA originally developed cFS to reduce mission and schedule risk for flagship satellite missions by increasing code reuse and reliability. The Lunar Reconnaissance Orbiter, which launched in 2009, was the first of a growing list of Class B rated missions to use cFS. Large parts of cFS are now open source, which has spurred adoption outside of NASA. This paper reports on the experiences of two teams using cFS for current CubeSat missions. The performance overheads of cFS are quantified, and the reusability of code between missions is discussed. The analysis shows that cFS is well suited to use on CubeSats and demonstrates the portability and modularity of cFS code

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to data centers with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, low-power AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Edge Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of TensorFlow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration

Machine-Learning Space Applications on SmallSat Platforms with TensorFlow

Due to their attractive benefits, which include affordability, comparatively low development costs, shorter development cycles, and availability of launch opportunities, SmallSats have secured a growing commercial and educational interest for space development. However, despite these advantages, SmallSats, and especially CubeSats, suffer from high failure rates and (with few exceptions to date) have had low impact in providing entirely novel, market-redefining capabilities. To enable these more complex science and defense opportunities in the future, small-spacecraft computing capabilities must be flexible, robust, and intelligent. To provide more intelligent computing, we propose employing machine intelligence on space development platforms, which can contribute to more efficient communications, improve spacecraft reliability, and assist in coordination and management of single or multiple spacecraft autonomously. Using TensorFlow, a popular, open-source, machine-learning framework developed by Google, modern SmallSat computers can run TensorFlow graphs (principal component of TensorFlow applications) with both TensorFlow and TensorFlow Lite. The research showcased in this paper provides a flight-demonstration example, using terrestrial-scene image products collected in flight by our STP-H5/CSP system, currently deployed on the International Space Station, of various Convolutional Neural Networks (CNNs) to identify and characterize newly captured images. This paper compares CNN architectures including MobileNetV1, MobileNetV2, Inception-ResNetV2, and NASNet Mobile

Cavitation in shock wave lithotripsy: the critical role of bubble activity in stone breakage and kidney trauma

Objective: Shock Wave Lithotripsy (SWL) is the use of shock waves to fragment kidney stones. We have undertaken a study of the physical mechanisms responsible for stone comminution and tissue injury in SWL. SWL was originally developed on the premise that stone fragmentation could be induced by a short duration, high amplitude positive pressure pulse. Even though the SWL waveform carries a prominent tensile component, it has long been thought that SW damage to stones could be explained entirely on the basis of mechanisms such as spallation, pressure gradients, and compressive fracture. We contend that not only is cavitation also involved in SWL, bubble activity plays a critical role in stone breakage and is a key mechanism in tissue damage. Methods: Our evidence is based upon a series of experiments in which we have suppressed or minimized cavitation, and discovered that both stone comminution and tissue injury is similarly suppressed or minimized. Some examples of these experiments are (1) application of overpressure, (2) time reversal of acoustic waveform, (3) acoustically-transparent, cavitation-absorbing films, and (4) dual pulses. In addition, using passive and active ultrasound, we have observed the existence of cavitation, in vivo, and at the site of tissue injury. Results: Numerical and experimental results showed mitigation of bubble collapse intensity by time-reversing the lithotripsy pulse and in vivo treatment showed a corresponding drop from 6.1% ± 1.7% to 0.0% in the hemorrhagic lesion. The time-reversed wave did not break stones. Stone comminution and hemolysis were reduced to levels very near sham levels with the application of hydrostatic pressure greater than the near 10-MPa amplitude of the negative pressure of the lithotripter shock wave. A Mylar sheet 3-mm from the stone surface did not inhibit erosion and internal cracking, but a sheet in contact with the stone did. In water, mass lost from stones in a dual pulse lithotripter is 8 times greater than with a single lithotripter, but in glycerol, which reduces the pressures generated in bubble implosion, the enhancement is lost. Conclusion: This cavitation-inclusive mechanistic understanding of SWL is gaining acceptance and has had clinical impact. Treatment at slower SW rate gives cavitation bubble clusters time to dissolve between pulses and increases comminution. Some SWL centers now treat patients at slower SW rate to take advantage of this effect. An elegant cavitation-aware strategy to reduce renal trauma in SWL is being tested in experimental animals. Starting treatment at low amplitude causes vessels to constrict and this interferes with cavitation-mediated vascular injury. Acceptance of the role of cavitation in SWL is beginning to be embraced by the lithotripter industry, as new dual-pulse lithotripters—based on the concept of cavitation control— have now been introduced