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Displaying 45 results
The goal of the course is to teach evidence-based therapies and evidence-based methods for neurodevelopmental disorders in children, and cognitive rehabilitation for children with brain injuries and/or brain function changes. Emphasis is on practical application by using case studies to give students insight into which treatment/training is appropriate in different situations.
The historical background of clinical neuropsychology and its development, the current status of the discipline, and possible future development will be reviewed. The status of clinical neuropsychology in Iceland, in the Nordic countries, and elsewhere in Europe will be discussed. Methodology and working practices in clinical neuropsychology will be introduced, as well as examples of referral questions, how they are answered, and what kinds of questions cannot be answered. Anatomy and physiology of the brain will be discussed, along with the various cognitive domains (e.g., memory, language, attention, and visual processing), how they relate to brain function, and how they are categorized (e.g., aphasia, apraxia, neglect). The main research methods used within neurology (e.g., EEG, EP, MRI) will be introduced.
This course aims to equip students with a broad understanding of digital health, emphasizing not only technical skills but also ethical considerations and critical thinking when designing, developing, and implementing digital tools in healthcare settings. The main objective is to set the stage for digital health, in general, and to understand the impact of design, development, and use of digital tools within healthcare settings for optimization purposes, in particular. By the end of the course, the students will be able to illustrate introductory knowledge of digital health, encompassing design, development, and utilization of digital tools in healthcare settings, as demonstrated by their assignment, where the focus is to design a mobile application for a specific case as well as reflect on the ethical implications of working with artificial intelligence as an embedded part of healthcare.
The course instructor is Dr. Anna Sigridur Islind, a professor at the department of computer science at Reykjavik University.
To register, contact: islind@ru.is
This course offers a comprehensive introduction to the field of biomedical instrumentation, focusing on the design, application, and measurement systems used in healthcare. Students will gain an in-depth understanding of biomedical sensors, instrumentation amplifiers, and the associated systems that monitor vital physiological parameters such as heart, brain, and muscle activity. Key areas of study include:
- Medical Device, definition, classification and basics on regulatory requirements: in-depth definition on medical device, medical instrumentation and classifications. An overview of CE requirements for medical devices, ensuring students are well-versed in the regulations governing medical device manufacturing.
- Common Sensors and Transducers: An introduction to the principles of functioning of sensors, transducers and biosensors with special focus on resistive sensors applications and principle of functioning
- Origin of Electrical Signals in the Human Body: The course will cover the physiological basis of electrical signals originating from the human body, exploring the mechanisms behind signals generated by the heart, brain, muscles, and other vital organs.
- Biomedical Sensors and Instrumentation Amplifiers: A detailed exploration of the various types of sensors and amplifiers used to capture, amplify, and process biological signals. Emphasis will be placed on their use in healthcare applications, such as patient monitoring and diagnostic systems.
- Pacemaker and Ultrasound Technologies: A thorough overview of pacemaker and ultrasound technologies, including their principles of operation, applications in healthcare, and current advancements in these life-saving systems.
- Clinical Environments and Electrical Safety in Medical Devices: A focus on the design and functions of the technological park of operating theatre and intensive care unit including their electrical safety standards and design specifications.
- Practical Training in Instrumentation Amplifier Design: Students will receive training in building and testing a life-signs instrumentation amplifier, gaining hands-on experience in the creation and application of biomedical devices.
- Measurement of Biomedical Signals: Practical exercises and Hands-on experience will include the measurement of typical biomedical signals, such as EEG, EMG, and ECG, from the human body, allowing students to apply theoretical concepts in real-world settings.
The fundamentals of biomechanics are introduced and how movement and muscle forces influence loading on the various joints of the body. Calculations of joints moments and joint forces will be carried out in both statically determined and statically undetermined systems. During the course, measurements on human movement in 3D space will be discussed. Gait analysis will be covered as well as a biomechanical analysis of various joints of the body such as the knee, hip, spine, shoulder and wrist. Finally topics regarding how diseases such as osteoporosis, arthritis and other degenerative conditions affect human biomechanics will be discussed.
The course Applications of Digital Health aims to teach students about the intersection between healthcare and technology in two parts. In the first part the students learn about different ways technology can support existing processes in healthcare or create new ways of prevention, diagnosis, treatment or monitoring of health conditions. In the second part of the course the students acquire fundamental knowledge on machine learning and how machine learning is used in healthcare. The students will learn to implement their own predictive models using different types of health data in Python.
The course consists of:
- Lectures: Build a strong theoretical base on digital health and machine learning.
- Applied Lectures: Learn how to apply this knowledge in Python, visit two research labs at the university, learn soft skills such as reading scientific papers and presenting.
- Lab Sessions: No frontal teaching, only interactive problem solving in Pythons assignments, Q&A sessions, presentation workshop and project coaching.
Grading
12 weeks of course. 6 hours of direct lecture per week plus independent work of the students. • Small Assignments (20%): The students have to hand in 4 Jupyter Notebooks with code exercises and questions about the code. Two of these small assignments are about basic Python skills and two small assignments are on Machine Learning in. The students are encouraged to work in groups, but everyone needs to hand in their own file.
o Grading: Pass or Fail.
o Goal: Learn Python.
• Paper Presentation (10%): The students are provided with scientific papers about applications of digital health (including e.g. VR technology for Anxiety treatment, AI-based dermatology screening, health monitoring with smartwatches,…). Each student has to pick a paper, read it carefully and teach the content to their classmates in a 10 minute presentation. The students additionally have to read one of their classmates papers and prepare questions to ask them after their presentation.
o Grading: Grade between 0-10 based on the presentation and answers to the questions.
o Goal: Practice paper reading and presenting
• Written Exam (30%): This classical assessment form aims to test the students knowledge on Digital Health and Machine Learning. The exam is 60 minutes and includes 5 questions on digital health and 5 questions on machine learning. The students receive a mock exam with examples of questions and there will be a Q & A session before the exam to answer open questions about the mock exam.
o Grading: Grade between 0-10 based on answers in the exam.
o Goal: Strengthen your theoretical knowledge
Finall Project (40%): In this project, the students will apply their machine learning knowledge to a medical dataset. They will work in groups of 3-4 students, where every group receives a different data set.
o Grading: Grade between 0-10 based on final presentation, a Jupyter notebook with code, and a final report.
o Goal: Combine everything the students have learned.
This course will focus on behavioural, psychological, neurobiological, and neuropsychological processes underlying the acquisition of new knowledge and its subsequent consolidation and retrieval in human animals. Where possible, attempts will be made to integrate these levels in a multidisciplinary framework. Additionally, the application of learning and memory paradigms in clinical and cognitive research will be discussed.
This course will provide students with a thorough background in the newly emergent field of social cognitive neuroscience. A broad range of social phenomena will be examined at multiple levels (1) the social level including experience and behaviors (2) the cognitive level which deals with information processing systems and (3) the neural level which deals with brain/neuronal bases of the first two levels.
This intensive blended learning course equips PhD researchers with the entrepreneurial mindset, skills, and tools needed to transform scientific discoveries into viable innovations and ventures. The course combines online pre-work with a hands-on, 5-day lecture and workshop series (10-14th of November) and aims to give students and researchers an overview about entrepreneurship as well as common business tools and strategies, to be able to assess the commercialization potential of a scientific idea and to develop solutions towards market needs. Topics include:
- Bringing Scientific Inventions and Research Ideas to Market
- Entrepreneurship in all its Facets
- Designing for Demand / Lean Canvas
- Business Strategies and Models
- Effectuation and Design Thinking
- Intellectual Property
- Financing and Funding Strategies
- Ethics and Regulatory Frameworks
- Risk Management and Mitigation
- Networking, Collaboration and Pitching
Through lectures, interactive workshops, peer collaboration, expert mentoring, and real-world case studies, researchers will learn how to identify opportunities, mobilize resources, and create value (financial, societal and/or cultural). Students will work in groups on three assignments: Lean Canvas, a report detailing Lean Canvas findings and assumptions and a slide deck for final presentations.
To register, contact:
Contact your local NTEU project manager
Instructors:
Ásgeir Jónsson – asgeirjo@ru.is
Hallur Þór Sigurðarson – hallursig@ru.is
Susanne Durst – susanned@ru.is
The purpose of this course is to enable doctoral students and other participants to gain an understanding of the major neuroinflammatory diseases and the key players involved, including the interaction between the central nervous and immune systems. An additional purpose is that those who participate in the course learn to understand critical aspects of creating and using experimental systems to model neuroinflammatory diseases.
The course is offered full time, Monday-Friday, 9:00-17:00 at the Center for Molecular Medicine (CMM) on Karolinska University Hospital, campus Solna, building L8, lecture hall and seminar rooms.
This course is given jointly by the doctoral programmes Allergy, immunology and inflammation (Aii) and Neuroscience (Neuro
The purpose of the course is for participants to gain knowledge concerning genetics, molecular mechanisms as well as clinical features and treatment strategies of neurodegenerative disorders.
This course is given in collaboration with the Master's Programme in Biomedicine.
Experimental neuroscience is key to progress in the understanding of how the brain functions. The experimental toolbox for studies in rodents is currently without comparison, allowing detailed investigation of how the brain is built and the function of brain circuits. Technological advances also make it possible to directly connect neurons and circuits to behaviour.
In the Brain Circuits course, students will meet international and KI neuroscientists who have made significant contributions to the study and understanding of neuronal circuits and behaviour. The development and application of novel technologies and analysis (high-density electrophysiology and imaging of single-neuron activity, optogenetics, behavioural tracking, machine learning etc) will be covered, with a focus on advances using transgenic rodents. We have a strong emphasis on engaging junior neuroscientists in the course and on creating a network for future neuroscience leaders.
This course is given in collaboration with the Master's Programme in Biomedicine.
Developmental biology lies at the heart of an effort to understanding complex biological systems. By studying how neural circuits are assembled we can extrapolate key aspects of their function as well as devise strategies for their repair. This course is given to deepen the understanding of how molecular and cellular mechanisms underlie neurobiological function and to widen the horizon of students within the strong Karolinska neuroscience community.
The course is given in collaboration with the Master's Programme in Biomedicine.
This is a full time course given in person at Biomedicum, Campus Solna.
Link to course evaluation
https://survey.ki.se/Report/5biVHpOK5wg
The course consists of theoretical sessions and practical work related to decision-making, memory formation and emotion. It will also include the neuroanatomy related to these functions using both MRI and human brains. The participants will be actively involved in group work dealing with practical and theoretical aspects of cognitive neuroanatomy.
This course is given in collaboration with the Master's Programme in Biomedicine.
Our MATLAB-based comprehensive course is designed to equip you with the essential knowledge and practical skills to delve into biomedical image processing, specifically tailored for biological/medical and neuroimaging applications using MATLAB.
Neuroscience techniques are undergoing a rapid development. These developments open up new possibilities for investigating the brain as a network at various levels. We will introduce a range of advanced techniques which currently are being applied in neuroscience in particular to study brain networks. We aim at covering both the basics of the techniques and how they are applied to address specific research questions.
This course will provide up-to-date insights into the neurobiological basis of language. Students will learn how state-of-the-art methods and approaches are currently being applied, and what are the next big questions for the field. They will also learn to reflect critically about the current questions and answers in the field.
The course is practical and aims at teaching students how to:
- Use the programming environment R and RStudio, which includes installation, how to handle errors, problem solve and access helper documents.
- Use basic concepts of programming, such as data types, logical and arithmetic operators, if else conditions, loops and functions.
- Use common R packages to perform basic statistical analysis (e.g., t-test, chi2-test, correlation) and visual presentation (e.g., boxplot, histogram and heat-map) of data in R.
The course is structured with the intent to gradually make students more autonomous in writing code. Starting by introducing a concept through a lecture, then providing formative quizzes and tasks relateed to the concept. This all leads up to a project (exam) where the student gets to combine multiple concepts into a project with the intent of solving a certain problem or displaying specific statistical tests of visual components.
The course is aimed for you who want to get an understanding of the principles of PET, the methodology used for neuroreceptor imaging and quantification, as well as to get an insight in important research ongoing in the field and in the clinical applications of PET.
Dive into the cutting-edge world of nuclear medicine with this comprehensive course that blends theory and hands-on practice. In this one-week course, you will gain invaluable knowledge and skills at the forefront of medical imaging and targeted radiopharmaceutical therapies.
This course offers a unique opportunity to:
- Master the fundamentals of radiation physics and biology
- Explore state-of-the-art diagnostic and therapeutic applications in oncology and neurology
- Gain practical experience handling radiopharmaceuticals in a laboratory setting
- Understand the latest developments in personalised medicine using radioactive tracers
Upon completing the course, you will be allowed to handle radiopharmaceuticals and open radioactive sources at Karolinska Institute and Karolinska University Hospital.
The purpose of the course is to give doctoral students a broad knowledge of Alzheimer's disease, covering cellular
mechanisms as well as clinical features and diagnosis. Experts in the field are invited to give the lectures securing
communication of up-to-date knowledge about the disease. Students will also get the opportunity to obtain deeper
knowledge on specific sub-topics during the planned group assignments. The second part will provide pratical
knowledge about brain development, brain anatomy and connectivty and AD and dementia neuropathologies.
The course will introduce neuropsychological assessment in an aging population, focusing on age-related cognitive changes and their neural correlates. An additional purpose is to increase understanding of cognitive aging and how to differentiate between non-pathological cognitive aging and early signs of pathology. After the course, you will be able to define and describe common neuropsychological concepts and measurement techniques and demonstrate an overall understanding of neuropsychological investigation methodology and cognitive diagnostics in aging. The course will give you an increased understanding of cognitive aging and the complexity of differentiating between “normal” and early-stage pathological aging.
Progress in medical innovation stands as a catalyst for worldwide economic growth. Whether within pharmaceuticals, medical devices, biotechnology, information technology, or a fusion of these advancements, the potential advantages extend significantly to private businesses and societal well-being. However, due to long development times and rigorous regulations, these innovative concepts require a lot of financing to establish their presence in the market. Consequently, assessing the value of novel medical innovations remains crucial. The primary objective of this course is to empower researchers with the proficient capability to effectively assess the worth of emerging technologies or processes within the medical industry.
The course aim is that the doctoral student develops a theory of science approach by enabling the doctoral student to understand, employ, reflect upon and critically assess concepts and ideas of theories of science as well as their implications for in particular medical scientific practice. A further aim is to enable the doctoral student to understand, reflect upon and critically assess views on and implications of definitions of health and disease.
The course content lean on Design thinking ideology and methodology that inform research as well as developmental work and form a practice-based foundation for innovation aimed to meet the needs of societal and scientific environments. As a student, you work individually from your own research perspective, and get helpful insights for future research.
The course apply a problem-oriented teaching and learning style, with a pedagogy that enables students to take active responsibility for individual and also group learning in a multi-disciplinary context.
Do you need to turn data into a publication figure? We offer tools and confidence for the student to independently select a statistical method for research questions in their field. The course is practical and includes implementing a basic statistical analysis in R, the leading statistical programming language in bioinformatics and medical science. Furthermore, we give a brief introduction to visualization in R, with a focus on R/ggplot2. Students can bring data from their own research project, or work on data from the course.
