First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
Course unit
INP9087819, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
IN2371, Degree course structure A.Y. 2019/20, A.Y. 2019/20
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Degree course track ICT FOR LIFE AND HEALTH [004PD]
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination IMAGING FOR NEUROSCIENCE
Department of reference Department of Information Engineering
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit


Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines ING-INF/06 Electronic and Information Bioengineering 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 48 102.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2019 course timetable

Prerequisites: The course requires knowledge of the basic methods of the maximum likelihood and Bayesian estimation methods (with particular reference to the Maximum a posteriori estimator) for the identification of the parameters of compartmental and non-compartmental models. It also requires knowledge of the basic principles of nuclear magnetic resonance and positron emission tomography. The student can consider to take the courses of "Modeling and Control of Biological Systems" and of "Biomedical Strumentation" of the Master Degree in Bioengineering (Corso di laurea magistrale in Bioingegneria) to address the above arguments.
The Neuroengineering course also requires a basic knowledge of Matlab programming.
Target skills and knowledge: The course aims to provide knowledge about engineering's own analytical and synthetic methodologies necessary for the study of the central nervous system. In particular, the student will be able to understand the potential and limitations of neuroimaging techniques in the study of pathophysiological brain processes.
Upon successful completion of the course, students will have developed a knowledge of the physical basis of positron emission tomography (PET) and magnetic resonance maging (MRI) and common MRI sequences used in the clinic and for research. They will also understand and explain the post-processing methods commonly used for neuroimging as well as the students will be able to design a MR and/or PET experiment and analyze the resulting data.
Examination methods: Completing one homework assignment is an integral part of this course. Problems are designed to perform processing, and analysis of neuroimaging data and to allow the students to explore implications of the results. The homework is delivered to the professor two weeks before the end of the course. The oral exam takes place a few days after the delivery of the homework.
The final grade is expressed as a combination of the judgments in the two tests (50% homework + 50% oral).
Assessment criteria: At the end of this course the student should have understood the problems related to the quantification of the physiological information from neuroimaging techniques, the theoretical basis of the methods used to process both functional and anatomical neuroimages. He/she should also be able to analyze 4D images of nuclear magnetic resonance imaging and positron emission tomography, to critically elaborate the results to make them accessible to the clinicians and / or researchers. The student should also be able to describe the fundamental morphological and functional characteristics of the human brain.
Course unit contents: The quantification of cerebral hemodynamics: DSC-MRI, DCE-MRI, Arterial Spin Labeling. Generation of brain activation maps and brain connectivity. Generation of parametric maps from PET images. Clustering, PCA and ICA for quantitative imaging. Diffusion Tensor MRI. Anatomical connectivity, functional and effective. Graph theory methods.
Planned learning activities and teaching methods: Frontal lessons and laboratories where MR and PET images are available for the practical application of the methods.
Additional notes about suggested reading: All the material (lessons, Matlab codes, data, videos) is made available on the moodle platform
Textbooks (and optional supplementary readings)
  • Karl J. Friston, John T. Ashburner, Stefan J. Kiebel, Thomas E Nichols, William D. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images. --: Academic Press, 2006. Cerca nel catalogo
  • Paul Tofts, Quantitative MRI of the Brain: Measuring Changes Caused by Disease. --: John Wiley & Sons, Ltd, 2003. Cerca nel catalogo