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International Graduate School in Interdisciplinary Neuroscience
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IGSIN presents several courses within neuroscience and statistics.

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PhD-Course in functional neuroimaging (fMRI) IGSIN911

 

Date: Week 37-38, September 15., 16., 18., 19., and 20. 

BB-building 9th floor 


1. Course content

The course is intended to PhD students holding a Master degree in neuroscience, psychology, natural sciences or equivalent, as well as medical students, taking part in a research-training programme in medicine. The course will give an introduction to the field of neuroimaging. The course is divided into three parts.

The first part is a short theoretical introduction into neuroscience and neuroimaging, covering all relevant aspects on physiology, neuroanatomy, some of the most relevant functional networks, as well as the technical aspects behind structural and functional magnetic resonance imaging and related methods, such as DTI and perfusion measurements.

In the second part, the course will introduce the most relevant experimental techniques, used in functional neuroimaging, as well as the methods, used for analysing functional as well as structural MRI data.

The third part is the practical part, where an experiment will be developed, performed on the scanner, analysed, and the results will be discussed.

2. Learning Outcomes

After finishing the course, the students will have basic knowledge of neuroanatomy, and functional neuroimaging approaches. This includes that they learned the technical and physical principles behind magnetic resonance imaging and its different applications, they now what the BOLD effect is and how it could be used for displaying neuronal activations. The students are familiar with the typical experimental fMRI designs, and know the limitations of the different method.

The students got an understanding of the parameters, which are most relevant for designing and fMRI experiment, acquiring and analysing the fMRI data. They will be able to perform the processing of the data and to specify a general linear model, based on the experimental design, they are able to analyse the data within standard fMRI analysis software, and they are able to describe the results in an appropriate way.

3. Course description

Part 1: Introduction to neuroscience

This first part of the course introduces to fundamental knowledge in physiology and neuroanatomy. The course is mostly aimed to repeat these issues rather than introducing them as new knowledge. The content of this part is restricted to those aspects, which are relevant for neuroimaging. These are in particular aspects of configuration of neurons, neuronal signal transmission, physiological basis of the BOLD effect, configuration of the cortex, the different lobes, Brodmann areas, functional division of different brain areas, which are relevant for visual, auditory and sensomotoric processing, as well as some important cognitive networks.  The first part continues by introducing the technical aspects of various measurement techniques, commonly used in the field of neuroscience. The main focus is thereby on MR based techniques, such as ordinary structural imaging, functional imaging, diffusion tensor imaging, perfusion imaging (ASL) and spectroscopy.

Part 2: Planning, performing, and analysing a fMRI study (Theory)

This part of the course introduced to the various experimental techniques, using in functional imaging. These are not only block- and event-related designs, but also parametric designs, resting state studies, longitudinal studies, and clinical applications of fMRI. The selection of the experimental design has also consequences on how the study could be performed on the scanner. The various aspects, which have to be considered, will be explained.

Further, different analysis strategies are introduced. These are mainly based on the general linear model (GLM) or on independent component analyses (ICA). The mathematical backgrounds and how they are implemented will be explained. The reference software for this course will be the matlab-based software ‘statistical parametric mapping’ (SPM) and the ‘group ICA fMRI toolbox’ (GIFT).

Other software packages, such as FSL and BrainVoyager will be mentioned, as well, but all practical tasks will be demonstrated in SPM.

This part of the course concludes with a discussion of the advantages and disadvantages of the methods explained, especially with respect to possible clinical applications.

Part 3: Planning, performing, and analysing a fMRI study (Praxis)

In the practical part of the course, students are encouraged to develop a fMRI study. This study will be performed on the scanner, located at the Haukeland university hospital and the data will be analysed with the methods, introduced in the second part. The practical session is also aimed to be used for discussing actual project. Students may have the possibility to present their own studies and to discuss issues such as experimental design and analysis strategies.

This part of the course concludes with some guidelines on how results should be reported, which tools are available for identifying brain areas, etc.

4. Evaluation

The course is concluded by a written home exam (essay).

5. Course credits

3 ECTS

Note that in-person presence is required at least for 80% of the course to obtain any ECTS points. We discourage from registering for the course if this criterion cannot be met

6. Pre-requirements

The course is intended to PhD students that completed a Master degree in neuroscience, psychology, natural science or equivalent, as well as medical students that take part in a research training programme in medicine (forskerlinjen) or equivalent. 

Each student should have access to MATLAB®, preferably on their own laptop.

7. Prior knowledge:        

There is no prior knowledge necessary, but a basic introduction into Neuroscience is recommended.

Those students, more interested in technical aspects of MR imaging are encouraged to visit first a course in MR physics, like course MEDT8011, hold at NTNU.

8. Teaching methods

Lectures, group discussions, group work and practical session

9. Teaching language

The lectures will be held in English and the practical session will be held in Norwegian and English

10. Week 37-38, September 15., 16., 18., 19., and 20. 

 

Schedule
Photo:
Schedule 2023

 

11. Course arrangement & Participants

 

The course will be part of the course program of the International Graduate School in Interdisciplinary Neuroscience (IGSIN; https://www.uib.no/rs/igsin). The lectures are open for everyone; the practical session is limited to 15 students.

 

Contact information

Professor Marco Hirnstein

Registration and administrative questions to Vivian Fosse

Registation deadline: August 15.

Registration/application here

 

Criteria:
PhD ved IGSIN
PhD ved Psyk fak
PhD ved UiB ellers
PhD ved andre institusjoner 
Supervisors IGSIN
Veiledere ved Psyk fak 
Veiledere ved UiB ellers

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Course in the software package R

November 8., 9., and 10. - 2023

 

BB-building 9th floor 

 

We have a limit of 15 participants

 

General content

The course will give a beginner / low-level introduction to the R programming language, a powerful tool for performing various types of statistical analyses and visualisation of data. In contrast to other software packages for statistical analyses, R is a programming language, which has to be learned like any other programming language.

You will learn 1) Basic concepts of R-programming syntax and logic 2) how to import and work with data and 3) how to visualize these data – and statistics – in very simple ways.

The statistical methods used in the course will be limited in the complexity, opting rather to demonstrate how R can be used. Methods will include descriptive statistics, simple hypothesis-testing (p-values, t-tests), correlation and regression concepts – with hands on exercises. 

Finally, the course will also introduce the various options for creating high-quality and illustrative figures and other outputs within R, through the package ggplot2, with examples of basic plots (scatter, line, bars) and how to customize these plots (colours, labels, themes).

R is ideal to probe and investigate one’s own data in meaningful ways – and this course will get you started.

 

 

Type of course

Methods

 

General learning objectives

After completion of the course, the candidate will be able to prepare datasets and to perform (simple) statistical analyses within the software package R.

 

Knowledge

After completion of the course, the candidate…..

  • Has knowledge of the logical structure of the programming language R
  • Has knowledge of the fundamental commands and data handling
  • Has knowledge of how to organise data and to define appropriate data tables and types
  • Has knowledge of how to program an appropriate statistical analysis
  • Has knowledge of how to visualise the data and statistics in meaningful ways.

Skills

After completion of the course, the candidate…..

  • Can perform simple standard statistical test within R, like descriptive statistics, t-tests/p-values, and various types of correlation and regression analyses.
  • Can read and evaluate the output of statistical analyses performed in R and is able to interpret possible error messages
  • Can create various types of high-quality figures and result tables

General competence

  • After completion of the course, the candidate…..

- Can master how to best perform statistical analyses within R and how to create suitable outputs for publications.

 

Required previous knowledge: Master Degree in disciplines relevant to educational sciences, psychology and public health.

Recommended previous knowledge: Prior knowledge of any programming language is beneficial but not at all mandatory, since this course aim at introducing R (and programming-logic) from scratch.

Course credits  The course has been approved as an elective 1 ECTS course

Forms of assessment: Pass with 80 % attendance of the lectures and hands-on training activities

Registration/application deadline: October 20

Register here

 

Contact information

Associate professor: Kjetil.vikene@uib.no

Registration and administrative questions to Vivian Fosse, vivian.fosse@uib.no

______________________________________________________________________________________________

 

Introduction to Practical Machine Learning

Dates:  Spring 2024

January 25: 0930-1430 (introduction to R)

February 1:  0930-1430

February 8:  0930-1430

February 15: 0930-1430

February 22: 0930-1430

March 12: 0930-1430, Group work presentation

 

Teaching language: English

Lecturer: Senior Researcher Mohammad Khalil, SLATE

Course in cooperation with Center for the Science of Learning and Technology (SLATE)

General content

This course gives an intuitive introduction to machine learning both in theory and practice. The course has three parts. The first is a brief, introductory lecture to the software “R”, which will be used to han-dle the practical examples of data management and machine learning. The second part will cover “Machine learning: What is it?” and describe how it allows the discovery of knowledge from data. Since data is the fuel that drives machine learning, we will dive into data management where descrip-tion of organization, storage, cleansing, filtration, and preparation of data collected and used in re-search projects will be explained. In machine learning, multiple topics including data as a source for future decision-making, supervised and unsupervised learning techniques will be covered.

 

The third part includes a group project where PhD candidates will work together on datasets, either of their choice (i.e., PhD-related) or open datasets, and apply machine learning techniques to distill in-teresting patterns and knowledge. Candidates will be given 3 weeks to work on the project and pre-sent their results in the class.

 

The course will have a maximum capacity of 20 students.

Type of course

Methods

General learning objectives

After completion of the course, the PhD candidates will be able to conceptualise the different types of machine learning. Candidates will be able to begin writing and executing R scripts, filter, manage, and clean data as well as apply machine learning techniques on datasets.

Reading list: Nwanganga, F., & Chapple, M. (2020). Practical machine learning in R. John Wiley & Sons. Other literature will be provided before the class on Mitt UiB

 

Course credits

In order to obtain 2 ECTS, every participant needs to…

1) attend - in person - at least 80 % of the lectures

2) participate in the group work

In order to obtain 2.5 ECTS, every participant has to additionally attend the introduction to R lecture (5h) as well as reading and preparing for the introductory lecture (8h).

In case one of the mandatory tasks are not carried out/delivered in time, no ECTS point can be obtained.

 

Registration latest  January 5 : Registration form

Contact information:

Course administrator: vivian.fosse@uib.no

Questions regarding course content etc. mohammad.khalil@uib.no