Technologies in Translational Neuroscience

• Describe the neurobiological, physiological and molecular mechanisms that are relevant when approaching engineering problems related to the nervous system.
• Identify key physiological parameters and biological processes that must be understood in order to define a measurable neuroscience problem.
• Explain how electrophysiological systems and biochemical assays are used to quantify neuronal and neurovascular activity in experimental models.
• Describe how controlled experimental paradigms in patients are structured to probe disease relevant mechanisms.
• Explain how RNA based tools and rational peptide design strategies are applied to modulate and study biological responses in experimental systems.
• Analyse how genomic data, and registry infrastructures generate large scale datasets that can inform mechanistic questions in neuroscience.
• Evaluate scientific studies with focus on how appropriate technologies are selected, applied, and integrated to address specific biological questions.
• Formulate an experimental approach in which relevant physiological knowledge, measurement technologies, and data analysis strategies are combined to address a defined problem in translational neuroscience.
• Use generative artificial intelligence tools to obtain background knowledge in a neuroscience topic, critically evaluate the accuracy and limitations of the generated information against scientific literature and assess how such tools can support the rapid acquisition of prerequisite knowledge when approaching a new problem in neuroscience.

The course introduces physiological and pathophysiological principles that motivate technological development in translational neuroscience. Headache and migraine are used as examples of research themes to illustrate how neurovascular and sensory mechanisms can be investigated with advanced experimental methods. Students work with research articles and case-based material to examine how neuroscience research defines mechanistic questions and how experimental approaches are selected to investigate them. Through these cases, students learn how to identify what is known about a given biological problem, which neurobiological mechanisms are relevant, and which physiological parameters must be measured in order to study them.

Students are introduced to experimental methods currently used in translational neuroscience research, including electrophysiological recordings, approaches for measuring neuronal and neurovascular activity, and quantitative biochemical assays for neuropeptides and proteins. These methods are discussed in relation to the biological information they generate and the mechanistic questions they address. Clinical research paradigms conducted in hospital-based settings are included to illustrate how human experimental models are structured and how physiological and biochemical outputs are obtained. RNA-based modulation tools and rational peptide design strategies are introduced as examples of molecular approaches used to manipulate gene expression and receptor systems relevant to neuronal and neurovascular signalling. Genomic and registry based data systems are examined to illustrate how large scale datasets capturing genetic variation, gene regulation, and molecular profiles contribute to mechanistic understanding of disease related processes.

During the course, students visit at least one research environment where experimental methods used in translational neuroscience are demonstrated in practice, allowing them to observe how measurement technologies and experimental workflows are implemented in active research.

Basic knowledge corresponding to course 22208 Human Physiology is recommended.

Problem based activities, lectures, class discussions, and exercises. Research articles and case analyses support methodological understanding. Minimum one course session includes a visit to a research environment where experimental technologies are demonstrated in practice.

Minimum: 10.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.