PSL712 - ADVANCED APPLICATIONS IN NEUROIMAGING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| ADVANCED APPLICATIONS IN NEUROIMAGING | PSL712 | 1st Semester | 2 | 2 | 3 | 10 |
| Prequisites | PSL618 Selected Topics in Neuroimaging | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Question and Answer Preparing and/or Presenting Reports Demonstration Drill and Practice Problem Solving Brain Storming | |||||
| Instructor (s) | Department Staff | |||||
| Course objective | The course aims to develop the student?s knowledge and data processing skill in the neuroimaging area via advanced theoretical and practical information. | |||||
| Learning outcomes |
| |||||
| Course Content | The covered topics by the course are introduction to neuroimaging techniques, properties of structural MR and voxel based morphometry (VBM), diffusion tensor imaging (DTI) and processing of DTI data, introduction to functional near-infrared spectroscopy (fNIRS) technique and processing of fNIRS data, principles of fMRI and experimental designs, preprocessing of fMRI data using different programs and advanced statistical signal analyses (factorial designs), and clinical applications of fMRI. Course Level: Graduate Course Coordinator: Prof. Dr. Sait ULUÇ Course Supervisor: Prof. Dr. Sait ULUÇ Course Assistants: It will be given by the course instructor by the department. Internship Status: None | |||||
| References | Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E. & Penny, W.D. (2007). Statistical parametric mapping: The analysis of functional brain images. London: Elsevier. Mori, S. (2007). Introduction to diffusion tensor Imaging. Amsterdam: Elsevier. Faro, S.H. & Mohamed, F.B. (2010). BOLD fMRI: A guide to functional imaging for neuroscientists. New York: Springer. Hüsing, B., Jancke, L. & Tag, B. (2006). Impact assessment of neuroimaging. Zürich: vdf Hochschulverlag AG an der ETH Zürich. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Neuroimaging techniques |
| Week 2 | Structural MR, Voxel Based Morphometry (VBM) and data processing |
| Week 3 | Structural MR, Voxel Based Morphometry (VBM) and data processing |
| Week 4 | Diffusion Tensor Imaging (DTI) and data processing |
| Week 5 | Diffusion Tensor Imaging (DTI) and data processing |
| Week 6 | Midterm exam |
| Week 7 | Introduction to functional Near-Infrared Spectroscopy (fNIRS) technique and data processing |
| Week 8 | Introduction to functional Near-Infrared Spectroscopy (fNIRS) technique and data processing |
| Week 9 | Principles of fMRI and experimental designs |
| Week 10 | Preprocessing of fMRI data using different programs and advanced statistical signal analyses |
| Week 11 | Midterm exam |
| Week 12 | Preprocessing of fMRI data using different programs and advanced statistical signal analyses |
| Week 13 | Preprocessing of fMRI data using different programs and advanced statistical signal analyses |
| Week 14 | Preprocessing of fMRI data using different programs and advanced statistical signal analyses |
| Week 15 | Clinical applications of fMRI |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 8 | 10 |
| Application | 8 | 20 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 1 | 10 |
| Seminar | 0 | 0 |
| Midterms | 1 | 20 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 18 | 60 |
| Percentage of final exam contributing grade succes | 1 | 40 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 16 | 2 | 32 |
| Laboratory | 16 | 3 | 48 |
| Application | 8 | 5 | 40 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 25 | 25 |
| Homework assignment | 14 | 3 | 42 |
| Midterms (Study duration) | 1 | 20 | 20 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 71 | 93 | 307 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest