Italian and English 

Students will be provided with teaching material in the form of slides and updated scientific literature on the topics of the course that will be made available on the Teams channel and on the e-learning platform of the course.
The following textbooks will be used during the course:
Neuroimaging. Per lo studio del cervello umano. Katiuscia Sacco
Analisi e modelli di segnali biomedici. Luigi Landini, Nicola Vanello
Analyzing Neural Time Series Data: Theory and Practice, Mike X Cohen
MATLAB for Brain and Cognitive Scientists, Mike X Cohen
The details of the parts of the reference textbooks that will be addressed during the course will be made available on the Teams channel and on the e-learning platform of the course.
 

The course introduces the student to the analysis of data deriving from neuroimaging experiments in humans. Emphasis is placed on creating a deep understanding of the signals acquired through the various neuroimaging techniques and the analytical and computational approaches for their treatment. At the same time, addressing typical examples of data and analysis methods will allow students to acquire specific skills in the topics of the course. An additional objective of the course is also to understand the consequences on the interpretation of brain function of the various choices made in the data analysis process.
 

Students are required to know the basics of mathematics for cognitive science, and elements of signal analysis.
 

The module consists of 48 hours of lessons, held on the days defined as per the teaching calendar.
The frontal teaching includes theoretical lessons supplemented by application examples. During teaching, the solution of some problems is carried out collectively in the classroom.
Attendance is optional, recommended, and the test final will be the same for attending and not attending students.
 

The assessment of students' preparation will involve a series of in-class tests based on two presentations of articles suggested by the students and agreed upon in advance with the teacher. For the assessments, the total available points (30) will be distributed based on indicators of formal correctness, richness of content, and rigor in presentation, including the appropriateness of technical language. The final score will be the sum of the partial scores.

The topics of the presentations, which will constitute the exam, will reflect those covered during the course and present in the curriculum. They will be elaborated to encourage students to reflect on the connections between various topics. To pass the exam, the student must demonstrate knowledge of all the topics in the course curriculum.
Specifically, a grade will be assigned between:
18 and 21 if the student demonstrates sufficient knowledge and skills in the course topics, with particular reference to the spatiotemporal characteristics of data measured through different techniques;
22 and 25 if the student demonstrates good knowledge and skills in all course topics;
26-29 if the student demonstrates very good knowledge and skills in all course topics and a more than good level of scientific rigor;
30 if the student demonstrates excellent knowledge and skills in all course topics and a high level of scientific rigor;
30 cum laude if the student demonstrates outstanding knowledge and skills in all course topics and a high level of scientific rigor, as well as the ability to make connections and expand beyond the topics covered.
 

 

Meetings with the students are scheduled on Mondays from 2.30 pm to 4.30 pm at ITAB, Chieti campus. Students are suggested to confirm the meeting by email: laura.marzetti@unich.it