The course is designed to provide students and researchers with a solid understanding of functional Near-Infrared Spectroscopy (fNIRS) as a relatively new tool to measure brain activity and will emphasize both theoretical knowledge and practical skills of fNIRS. The students will gain expertise in the underlying principles of fNIRS, its instrumentation, and various analytical approaches. The primary goal is to empower students with the knowledge of this additional neuroimaging tool to design and execute advanced experiments, interpret fNIRS data effectively, and contribute to cutting-edge research in neuroscience and related fields.

The main purpose of the course is to provide the students with a solid understanding of the tools available to analyze brain structural data measured with structural magnetic resonance imaging (sMRI). The students will develop the ability to critically review results provided by different methods, to select the most adequate tools and experimental designs to answer different questions and to compare their relative advantages.

The course covers the theoretical background to the brain imaging methods sMRI, fMRI, PET, EEG and MEG, such as what aspects of the human brain's structure and function they register, and the operation principles of the imaging instruments. The coursed gives the student a good understanding in how the different methods are used within in academic research as well as within health care. The course also addresses how the imaging methods can be combined in multimodal analyses, and discusses the interplay between development of theory, instrumentation, method, and applications.
The course begins with an introduction to brain imaging methods within neuroscience. In separate course modules, the course then offers the student a deeper understanding of the different methods sMRI, fMRI, PET, EEG and MEG, as well as combining them in multimodal brain imaging. Finally, the students will deepen their knowledge on a topic of their choice in an individual study project.

This course aims to introduce students to health care organization and management, and how this affects public health. There is a special focus on the opportunities offered by digitization and how these can be utilized in quality and improvement work.

The purpose of the course is to introduce the topic of artificial intelligence (AI) in mental healthcare focused on theoretical development, ethics and practical application informed by a scientific approach.

This course will elevate your AI proficiency, preparing you to actively engage in the digital evolution of healthcare. It offers a comprehensive perspective on the healthcare shift, steered by medical necessities, and bolstered by innovative artificial intelligence solutions.

This course provides students with in-depth knowledge in the field of digital health from an entrepreneurship perspective. Domains of digital health, needs-based innovation including prototyping, usability and testing as well as data management, intellectual property, reimbursement, business models, ethics and future trends will be discussed and analyzed.

Topics covered include:

• Coding: basic concepts, practical training, testing
• Foundations of sensor technologies
• Foundations of Bluetooth communication
• Usage of advanced programming interfaces (APIs)
• Analysis of time series data
• Introduction to machine learning techniques

This course covers acquisition and advanced analysis of MRI-data in clinical research including scanning
routines, tractography, tract-based spatial statistics, voxel-based morphometry and
support machine vector programming.

Topics covered include:

• Electrophysiological recording techniques
• Design of cognitive paradigms
• Spike detection and spike sorting
• Peri-stimulus time histograms
• Data analysis and statistical evaluation

Topics covered include:

• Basics of MRI and functional MRI
• Design of psychological experiments
• Analysis of functional MRI data
• Functional Neuroanatomy

The Statistics module covers:

Basic test theory; 2-tests for contingency tables; t-Test; non-parametric tests; analysis of variance (ANOVA); multiple testing; power calculations; calculation rules for probabilities and neurobiological applications; guidelines for choice of analysis strategy; software implementations; effect size based hypothesis testing

The Scientific writing module:

• Introduction into general guidelines and rules for scientific writing
• Introduction into the elements of style
• Analysis and discussion of scientific texts
• How to improve and correct a text
• Practices in writing. Students will write their own texts and correct and make suggestions for improvements of the texts of others

Research ethics module covers:

• Main approaches and methods in current research ethics
• Ethical standards of good scientific practice
• Ethical issues related to research with humans
• Ethical issues related to animals
• Ethical issues related to research with biological material

This course is a basic course on advanced fluorescence microscopy imaging and correlation spectroscopy techniques for quantitative characterization of molecular transport and interactions in live cells. The purpose of the course is to give an introduction of the underlying physicochemical principles, hands-on training and an overview of applications of these specialized techniques in biomedical research. At the end of the course, the student will have hands-on experience with live-cell imaging and specialized fluorescence microscopy and correlation spectroscopy techniques. The course is suitable for doctoral students lacking training in mathematics, physics, or optical engineering who want to apply these techniques in their research.

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 is given online. The teaching and learning activities used are web lectures, written examination, individual writing exercises, an individual written assignment, and reading of course literature and other distributed materials.

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.

Selection will be based on:
1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation),
2) start date of doctoral studies (priority given to earlier start date)