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.

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.

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 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.

This course takes you on a journey into the exploration of how the brain shapes and enables our social and affective behaviors. We will examine key questions, such as how we learn from each other, when and in what ways social norms influence us, and how our communication and social decision-making unfold. 

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.

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

Why doesn't the mammalian central nervous system (CNS) regenerate while many other tissues do? Which cutting-edge technologies and models are most effective for studying CNS repair? What regenerative strategies can be designed to rebuild such a complex tissue? Led by renowned experts and featuring distinguished international speakers, this course will delve into the intricacies of how the CNS responds to injury at a cellular and molecular level, as well as the most advanced research into regenerative therapies ranging from stem cell-based to gene therapies. The curriculum spans from fundamental research to preclinical development, with a particular focus on state-of-the-art approaches for studying CNS injury, degeneration, and repair.

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)

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.

Contents of the course: The course will cover the main steps of development from neural stem cells to mature circuits, including the patterning of the neural plate and thus the origin of cell types, the interplay between intrinsic and extrinsic factors, gene regulation including epigenetics, neuro-glia interactions and the role of network activity in shaping the final circuits. Different molecular and tracing technologies, and model organisms will be covered. An important aspect of the course regards molecular technologies for labeling, transcriptional analysis, and genetic manipulation of defined neural populations. Connections between aberrant developmental processes and neurodevelopmental and neurological disorders will be discussed.

Course director

The course is given by four course-leaders: Gonçalo Castelo-Branco, Jens Hjerling-Leffler and Ulrika Marklund all at MBB and François Lallemend at Dept of Neuroscience.