Bioimaging and Brain Research
KNOWLEDGE AND UNDERSTANDING:
The course enables students to gain in-depth knowledge about the various existing methods for detecting and mapping human brain function.The first part of the course will provide the necessary background so that the students acquire a clear awareness of the more broader multidisciplinary context of the neuroscience with a clear reference to the aspects of neuroimaging and brain research with particular reference to the magnetic/electrical (EEG/MEG) brain activity and haemodynamic (fMRI) response associated with neuronal activity (EEG-fMRI).CAPACITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
This course is characterizing for the engineering sector (ING-INF / 06).The student will know how to apply correctly the main techniques for the analysis of biomedical images with particular reference to Neuroimaging techniques (MEG-EEG-fMRI) using advanced tools and numerical and computational methods for the analysis of brain data (such EEGLAB, Brainstorm, Statistical Parametric Mapping - SPM and ICA Group Of fMRI Toolbox - GIFT) and be able to critically interpret the results obtained consequently deepening the mechanisms underlying the central nervous system.The skills acquired allow the student to know how to choose the most appropriate analysis tool, the ability to properly interpret the experimental results and to be able to possibly work with the team involved in the study of the problem.TRANSVERSAL SKILLS:
The execution of the project will help improve the ability to use general-purpose computational tools (MATLAB) or specific toolboxes for preprocessing and processing of biomedical images and brain signals. The drafting of the report in the style of a scientific paper and its discussion in the classroom, help improve ability to synthesize, communicative and expose clearly the concepts and results, and will also contribute to improving the critical capacity that also stems from teamwork
The course enables students to gain in-depth knowledge about the various existing methods for detecting and mapping human brain function.The first part of the course will provide the necessary background so that the students acquire a clear awareness of the more broader multidisciplinary context of the neuroscience with a clear reference to the aspects of neuroimaging and brain research with particular reference to the magnetic/electrical (EEG/MEG) brain activity and haemodynamic (fMRI) response associated with neuronal activity (EEG- fMRI).
Program: Content (lectures, 48 hours)
Principles of Human Nervous Systems: Neurons, Neural Circuits, Organization of the Human Central Nervous System and Functional Analysis of Neuronal System. Overview of Complex Brain Functions: The Association Cortices, Memory, Language and Speech. How to detect direct and indirect signals from the brain in a non-invasively way: Electroencephalography (EEG); Magnetoencephalography (MEG); Functional magnetic resonance imaging (fMRI). Multimodal imaging approach: Simultaneous EEGfMRI. Biological and non-biological signal detected by neuroimaging techniques. Neuroimaging signal processing: Event Related Potentials (ERPs), Blind Source Separation (BSS) and Independent Component Analysis (ICA). Brain source localization from EEG/MEG recordings. Functional and Effective Brain connectivity: Granger Causality (GC), Directed Transfer Function (DTF), Partial Directed Coherence (PDC).
Laboratory exercises (24 hours)
- EEGLAB toolbox: an open source framework for EEG/MEG data analysis.
- Brainstorm toolbox: an open source tool for data analysis EEG/MEG particularly oriented in brain source localizations;
- Statistical Parametric Mapping (SPM) toolbox: an open source framework for MEG/fMRI data analysis.
- Group ICA Of fMRI (GIFT) Toolbox: an open source framework for fMRI data analysis.
Development of the examination
LEARNING EVALUATION METHODS
The learning assessment of students consists of three parts:
- Scientific Report (to be performed in groups of three maximum four students): drafting and powerpoint presentation of a scientific report as a deepening of the topics covered during the course, of particular interest to the students.
- Project (to be performed in groups of three maximum four students) is to develop a strategy of pre-processing and processing, using MATLAB and the toolboxes studied during laboratory exercises, on real neuroimaging data.
- An oral, consisting in the discussion of one or more topics covered in the course.
The oral examination must be supported in the same academic year in which the scientific reports and the project occurred. In case of failure of the oral examination, the student will not have to repeat the scientific reports and the project.
LEARNING EVALUATION CRITERIA
To successfully pass the examinatioin, the student must demonstrate, through the three parts test, that he/she has fully understood the concepts presented in the course, is able to apply them using algorithms implemented in Matlab, and has ability of synthesis and communication clarity.
LEARNING MEASUREMENT CRITERIA
For each of the tests specified above, it is assigned a score between zero and thirty. The overall grade, is the average of the scores obtained in all the tests, with rounding to the entire excess.
FINAL MARK ALLOCATION CRITERIA
In order that, the overall outcome of the evaluation is positive, the student must achieve at least the sufficiency, equal to 18/30, in each of the tests described above.
The minimum raiting, equal to eighteen, is assigned to students who demonstrate to be able to solve problems that are placed and sufficient knowledge of the methodologies covered during the course and laboratory. The highest score is obtained by demonstrating in-depth knowledge of the course content in all tests. Laudem is given to students who, having done correctly all tests, have shown a particular brilliance in the exposition and in the preparation of all the tests.
1. Neuroscience, 3rd Edition. Edited by D. Purves, G.J. Augustine, D. Fitzpatrick, W.C. Hall, A.S. LaMantia, J.O. McNamara, and S.M. Williams. Neurology 2005 vol. 64 no. 4 769-769-a. doi: 10.1212/01.WNL.0000154473.43364.47. Chapters 1, 8, 15, 16, 25, 26, 30
2. Magnetoencephalography: From Signals to Dynamic Cortical Networks. Edited by Supek S. and Aine CJ. Springer-Verlag Berlin Heidelberg 2014. doi: 10.1007/978-3-642-33045-2 Part I, II, III.
3. Teaching materials provided by the teacher.
- Biomedical Engineering (Corso di Laurea Magistrale (DM 270/04))