LEARNING OUTCOMES
By completing the course, the student gets a good understanding of diffusion MRI acquisition and analysis methods, and an overview of applications of these methods. Student is able to explain the principles of investigating brain microstructure and structural brain connectivity with diffusion MRI and recognize issues in applying these methods in research and clinical work.
Credits: 5
Schedule: 23.04.2024 - 07.06.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Timo Roine
Contact information for the course (applies in this implementation):
Responsible teacher:
Timo Roine, Ph.D., M.Sc.(Tech.)
timo.roine@aalto.fi
+358-50-4752622
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Diffusion MRI acquisition, preprocessing, and analysis methods, including streamlines tractography for structural brain connectivity, graph theoretical analysis of brain networks, and biophysical models for imaging brain microstructure.
applies in this implementation
Session on 23.4. cancelled as the teacher is sick. Instead, a preassignment will need to be done before the Wednesday's session.
Preassignment:
If you are not familiar with magnetic resonance imaging, watch the following video:
https://www.youtube.com/watch?v=jLnuPKhKXVM
Then, start by introducing yourself to the basics of diffusion MRI acquisition with this video:
https://www.youtube.com/watch?v=wWcCKHp09QA
In addition, read Chapter 4 about Physics of diffusion MRI by Paul T. Callaghan from the book Diffusion MRI (2011, ed. D.K. Jones): https://books.google.fi/books?id=dbZCMePD52AC&pg=PA43&hl=fi&source=gbs_toc_r&cad=2#v=onepage&q&f=false
Session 1: Physics of diffusion MRI acquisition and artefacts (24.4.)
Self-study (pre-assignment) and lectures, quiz
Homework: Learning log + self-study (material given at the end of the session)
Session 2: Data preprocessing (29.4.)
Self-study, lecture, practical demonstrations, group discussion, individual reflection, quiz
Homework: Learning log + self-study
Start of the project work
Session 3: Diffusion tensor imaging (14.5.)
Self-study, lecture, practical demonstrations, group discussion, individual reflection, quiz
Homework: Learning log (DL 18.5.) self-study, return first draft of project work report (DL 20.5.)
Session 4: Constrained spherical deconvolution and tractography (21.5.)
Self-study, lecture, practical demonstrations, group discussion, individual reflection, quiz
Homework: Learning log (DL 25.5.). self-study, give peer feedback on the project work report (DL 29.5.)
Session 5: Connectivity networks and microstructural analyses (30.5.)
Self-study, lecture, practical demonstrations, group discussion, individual reflection, quiz
Homework: Learning log + self-study, return project work report (5.6.)
Session 6: Summary of the course, presentations of project works (6.6.)
Seminar presentations, lecture, group discussion, feedback, individual reflection
Assessment Methods and Criteria
valid for whole curriculum period:
The seminar consists of six weekly face-to-face sessions, in which participation is compulsory. Learning in evaluated via small quizzes during the teaching session. Students are expected to read assigned material before each session and engage in discussion. Students will also prepare a project assignment in small groups (for instance, a short research plan or implementation of an analysis algorithm and corresponding documentation). Evaluation 0-5 is based on face-to-face sessions (quiz answers and oral responses) (50%) and the quality of the project work (50%).
Workload
valid for whole curriculum period:
Self-learning (assigned material, approx. 50 pages of professional text per week): 6 x 8 h = 48 h Project work (approx. 6 pages): 60 h, of which reading (30 h) and writing (30 h)
applies in this implementation
Reading: 60 hours, 350 pages of scientific text (pre-assignment 50 pages + 50 pages each week)
Writing: 30 hours, project work, 6 pages (24 hours), learning logs, 0.5 pages per week (6 hours)
Seminars and lectures: 3 hours (+ 3 hours of preparation)
Group work sessions: 3 hours (+ 6 hours of preparation)
Computational exercise sessions: 4.5 hours (+ 18 hours of preparation)
Presentation of group work: 2.5 hours (incl. preparation of slides/poster)
DETAILS
Study Material
valid for whole curriculum period:
Will be announced in the beginning of the course.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2023-2024 Spring V