Checking date: 15/07/2019


Course: 2019/2020

Neuroimaging
(18063)
Study: Master in Information Health Engineering (359)
EPI


Coordinating teacher: DESCO MENENDEZ, MANUEL

Department assigned to the subject: Department of Bioengineering and Aerospace Engineering

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Students are expected to have completed
- Deep learning - Biomedical image processing (in case the student had not taken a similar subject in the bachelor degree)
Competences and skills that will be acquired and learning results.
Basic competences CB6 Having and understanding the knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context CB7 Students know how to apply their acquired knowledge and problem-solving skills in new or unfamiliar settings within broader (or multidisciplinary) contexts related to their field of study. CB8 Students are able to integrate knowledge and to face the complexity of making judgments based on information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of their knowledge and judgments. CB9 Students know how to communicate their conclusions and the knowledge and ultimate reasons behind them to specialised and non-specialised audiences in a clear and unambiguous way. CB10 Students have the learning skills that will enable them to continue studying in a way that will be largely self-directed or autonomous. General competences CG2 Ability to apply the knowledge of skills and research methods related to engineering. CG3 Ability to apply the knowledge of research skills and methods related to Life Sciences. CG4 Ability to contribute to the widening of the frontiers of knowledge through an original research, part of which merits publication referenced at an international level. CG5 Ability to perform a critical analysis and an evaluation and synthesis of new and complex ideas. CG6 Ability to communicate with the academic and scientific community and with society in general about their fields of knowledge in the modes and languages commonly used in their international scientific community. Specific competences CE6 Ability to understand the basis of the main technologies involved in biomedical imaging systems. CE7 Ability to solve a biomedical problem from an engineering perspective based on the acquisition and processing of biomedical images
Description of contents: programme
Introduction: Course presentation; Neuroimaging methods General concepts: data formats, processing tools (parallel, cloud), reliability (multiple comparison corrections ) Structural neuroimaging and spectroscopy Practical session 1 (structural) Diffusion Practical session 2 (diffusion) Functional MRI (Tasks, BOLD, Design and processing) MRI Resting state Connectivity and graph theory Practical session 3 (connectivity) Machine Learning, example 1 Machine Learning, example 2 Practical session 4 ML Closure: Can you believe all these results?
Learning activities and methodology
AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total hours number presencial hours number % Student Presence AF3 134 134 100% AF4 42 42 100% AF5 24 0 0% AF6 120 0 0% AF7 248 0 0% AF8 16 16 100% SUBJECT TOTAL 600 184 30,66%
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40

The course syllabus and the academic weekly planning may change due academic events or other reasons.