Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience

Cognitive neuroscience has considerable untapped potential to translate our understanding of brain function into applications that maintain, restore, or enhance human cognition. Complex, real-world phenomena encountered in daily life, professional contexts, and in the arts, can also be a rich source of information for better understanding cognition, which in turn can lead to advances in knowledge and health outcomes. Interdisciplinary work is needed for these bi-directional benefits to be realized. Our cognitive neuroscience team has been collaborating on several interdisciplinary projects: hardware and software development for brain stimulation, measuring human operator state in safety-critical robotics environments, and exploring emotional regulation in actors who perform traumatic narratives. Our approach is to study research questions of mutual interest in the contexts of domain-specific applications, using (and sometimes improving) the experimental tools and techniques of cognitive neuroscience. These interdisciplinary attempts are described as case studies in the present work to illustrate non-trivial challenges that come from working across traditional disciplinary boundaries. We reflect on how obstacles to interdisciplinary work can be overcome, with the goals of enriching our understanding of human cognition and amplifying the positive effects cognitive neuroscientists have on society and innovation.

A Self-Calibrated Single Wavelength Biosensor for Measuring Oxygen Saturation

Traditional methods for measuring blood oxygen use multiple wavelengths, which produce an intrinsic error due to ratiometric measurements. These methods assume that the absorption changes with the wavelength, but in fact the scattering changes as well and cannot be neglected. We found that if one measures in a specific angle around a cylindrical tissue, called the iso-pathlength (IPL) point, the reemitted light intensity is unaffected by the tissue's scattering. Therefore, the absorption can be isolated from the scattering, which allows the extraction of the subject's oxygen saturation. In this work, we designed an optical biosensor for reading the light intensity reemitted from the tissue, using a single light source and multiple photodetectors (PDs), with one of them in the IPL point's location. Using this bio-device, we developed a methodology to extract the arterial oxygen saturation using a single wavelength light source. We proved this method is not dependent on the light source and is applicable to different measurement locations on the body, with an error of 0.5%. Moreover, we tested thirty-eight males and females with the biosensor under normal conditions. Finally, we show the results of measuring subjects in a hypoxic chamber that simulates extreme conditions with low oxygen.

Convergence and transdisciplinary teaching in quantitative biology

The United States National Science and Technology Council has made a call for improving STEM (Science, Technology, Engineering, and Mathematics) education at the convergence of science, technology, engineering, and mathematics. The National Science Foundation (NSF) views convergence as the merging of ideas, approaches, and technologies from widely diverse fields of knowledge to stimulate innovation and discovery. Teaching convergency requires moving to the transdisciplinary level of integration where there is deep integration of skills, disciplines, and knowledge to solve a challenging real-world problem. Here we present a summary on convergence and transdisciplinary teaching. We then provide examples of convergence and transdisciplinary teaching in plant biology, and conclude by discussing limitations to contemporary conceptions of convergency and transdisciplinary STEM.

Imaging of Sarcopenia

Sarcopenia is currently underdiagnosed and undertreated, but this is expected to change because sarcopenia is now recognized with a specific diagnosis code that can be used for billing in some countries, as well as an expanding body of research on prevention, diagnosis, and management. This article focuses on practical issues of increasing interest by highlighting 3 hot topics fundamental to understanding sarcopenia in older adults: definitions and terminology, current diagnostic imaging techniques, and the emerging role of opportunistic computed tomography.

Experimental Nuclear Medicine Meets Tumor Biology

Personalized treatment of cancer patients demands specific and validated biomarkers for tumor diagnosis and therapy. The development and validation of such require translational preclinical models that recapitulate human diseases as accurately as possible. Moreover, there is a need for convergence of different (pre)clinical disciplines that openly share their knowledge and methodologies. This review sheds light on the differential perception of biomarkers and gives an overview of currently used models in tracer development and approaches for biomarker discovery.

On Innovation

Innovation has become an increasingly common topic in healthcare. Private companies, developers, payers, and regulators are devoting attention toward innovative products and processes as a crucial component of their interests in and occupation with healthcare services. Even when there is no consensus as to its definition, "innovation" -as opposed to "invention"- is broadly understood to refer turning a good idea into a practical solution. Adoption and applicability are key components of implementation that are sustained not only on innovation's attributes themselves but also in the characteristics of providers, users, and implementing organizations, as well as the external environment. Regulatory agencies often face the need to make decisions about proposed innovations with obsolete or inadequate normative frameworks and with a high degree of uncertainty about its eventual performance or its risks. Early interaction between developers and dedicated multidisciplinary teams at regulatory agencies may prove instrumental for speeding up the time required for proper evaluation and product registration, as well as the establishment of quality validation mechanisms. Community involvement both in the adoption and vigilance on innovative products and processes is crucial for completing the process of defining their roles and uses.

Environmental health in the biology century: Transitions from population to personalized prevention

IMPACT STATEMENT

There is a rapidly occurring, dynamic change, in the causes of morbidity and mortality in different populations across the globe. More people today are being diagnosed and treated for chronic diseases such as cancer, cardiovascular disease, and diabetes than ever before. Environmental exposures across the lifespan have a profound impact on the outcomes of these chronic diseases. Further, there are more people living today who have survived their therapy from these diagnoses and who are now differentially susceptible to environmental exposures. Collectively, this poses both the challenge and opportunity to the experimental biology and medicine community to build new models that reflect this changing human situation. The extraordinary advances in our understanding of the biology of disease provide extraordinary insights for both therapeutic and prevention strategies. Multidisciplinary teams including biological, physical, engineering and social and behavioral scientists will be needed to address this problem over the next several decades.

Physical stimuli-responsive vesicles in drug delivery: Beyond liposomes and polymersomes

Over the past few decades, a range of vesicle-based drug delivery systems have entered clinical practice and several others are in various stages of clinical translation. While most of these vesicle constructs are lipid-based (liposomes), or polymer-based (polymersomes), recently new classes of vesicles have emerged that defy easy classification. Examples include assemblies with small molecule amphiphiles, biologically derived membranes, hybrid vesicles with two or more classes of amphiphiles, or more complex hierarchical structures such as vesicles incorporating gas bubbles or nanoparticulates in the lumen or membrane. In this review, we explore these recent advances and emerging trends at the edge and just beyond the research fields of conventional liposomes and polymersomes. A focus of this review is the distinct behaviors observed for these classes of vesicles when exposed to physical stimuli - such as ultrasound, heat, light and mechanical triggers - and we discuss the resulting potential for new types of drug delivery, with a special emphasis on current challenges and opportunities.

An overview of deep learning in medical imaging focusing on MRI

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Engineering and physical sciences in oncology: challenges and opportunities

The principles of engineering and physics have been applied to oncology for nearly 50 years. Engineers and physical scientists have made contributions to all aspects of cancer biology, from quantitative understanding of tumour growth and progression to improved detection and treatment of cancer. Many early efforts focused on experimental and computational modelling of drug distribution, cell cycle kinetics and tumour growth dynamics. In the past decade, we have witnessed exponential growth at the interface of engineering, physics and oncology that has been fuelled by advances in fields including materials science, microfabrication, nanomedicine, microfluidics, imaging, and catalysed by new programmes at the National Institutes of Health (NIH), including the National Institute of Biomedical Imaging and Bioengineering (NIBIB), Physical Sciences in Oncology, and the National Cancer Institute (NCI) Alliance for Nanotechnology. Here, we review the advances made at the interface of engineering and physical sciences and oncology in four important areas: the physical microenvironment of the tumour and technological advances in drug delivery; cellular and molecular imaging; and microfluidics and microfabrication. We discussthe research advances, opportunities and challenges for integrating engineering and physical sciences with oncology to develop new methods to study, detect and treat cancer, and we also describe the future outlook for these emerging areas.