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Neuroimaging genetics
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Neuroimaging genetics
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Academic year 2023/2024
- Course ID
- NEU0274B
- Teacher
- Fabrizio Pizzagalli (Lecturer)
- Year
- 2nd year
- Teaching period
- First semester
- Type
- Basic, Related or integrative
- Credits/Recognition
- 2
- Course disciplinary sector (SSD)
- FIS/07 - applied physics (a beni culturali, ambientali, biologia e medicina)
- Delivery
- Formal authority
- Language
- English
- Attendance
- Optional
- Type of examination
- Quiz
- Type of learning unit
- modulo
- Modular course
- Neuroimaging II (NEU0274)
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Sommario del corso
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Course objectives
The course aims to introduce the principles of imaging genetics studies.
Students will learn about neuroimage processing, design workflows to process brain images and basic usage of popular NeuroImaging softwares (e.g. SPM, FSL, Freesurfer). The second part of the course will focus on genetic association and statistical topics like meta-analyses for Big Data harmonization, including practical hands-on examples.
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Results of learning outcomes
At the end of the course the student will be able to:
- Define optimal strategy for NeuroImaging data processing.
- Understand the main methodological aspects for Imaging Genetics studies.
- Interpret the Imaging Genetics findings reported in literature.
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Program
- Introduction to Imaging Genetics: Why and how is imaging genetics going to make a difference to our understanding of the brain?
- Basic genetics
- Complex Diseases studies (Disease prediction)
- Anatomical MRI Analysis
- Image Registration
- Segmentation & Morphometry (VBM, SBM, sulcal-based morphometry)
- Diffusion MRI Image Processing (Tractography and other techniques)
- Network Analysis (Applications of Graph Theory)
- NeuroImaging Genetics
- Heritability and genetic correlations
- Pleiotropy
- Imaging Complex Diseases - Brain Structure
- Imaging Complex Diseases - Connectivity
- Hands-on:
- Run real experiments
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Course delivery
Frontal lessons presenting material (slides) that will be available to the students and hands-on running experiments.
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Learning assessment methods
The exam aims to verify the achievement of the expected learning outcomes by presenting related works as 'journal club' and answering questions during the discussion of the papers.
We intend to foster the achievement of broad knowledge rather than notional knowledge.
Suggested readings and bibliography
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- https://lecerveau.mcgill.ca/
- https://www.sciencedirect.com/topics/computer-science/imaging-genetics
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Notes
Bibliographic references of articles updated to the most recent knowledge of neuroradiology will be suggested.
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