Бакалавриат
2025/2026
Основы нейровизуализации и интерфейса мозг-компьютер с использованием МРТ
Статус:
Курс по выбору (Когнитивная нейробиология)
Кто читает:
Базовая кафедра Института биоорганической химии им. академиков М.М. Шемякина и Ю.А. Овчинникова РАН
Где читается:
Факультет биологии и биотехнологии
Когда читается:
4-й курс, 1, 2 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Course Syllabus
Abstract
The course "MRI and Brain-Computer Interface, Neuroimaging Data Analysis" is one of the elective educational elements in the educational program "Cognitive Neuroscience". The course combines two main areas devoted to the basics of functional MRI and MRS methods, as well as real-time data processing and a brain-computer interface based on functional MRI. The course is conducted on the basis of fundamental theoretical knowledge and the latest scientific publications. It includes lectures, exercises and practical classes that allow students to apply and implement the knowledge gained. In particular, during the course on functional MRI and MRS methods, students will study the neurobiological foundations of a signal dependent on the level of oxygen in the blood, current prerequisites for the physics of MRI, types of contrasts and images possible on modern MRI scanners, learn to determine the quality of data, master standard pre-processing of functional data, computer modeling and statistical parametric mapping. Students will then learn how to build models for single-session analysis (Level 1), how to analyze data across a group of subjects (Level 2), and how to compare groups of subjects and repeated measures data. Students will also learn relevant aspects of cognitive neuroscience, assessing brain activity and connectivity outcomes, block- and event-related fMRI experiments, and examining behavioral measures using traditional statistical methods. Students will also learn how to perform preprocessing and spectral fitting. Hands-on sessions will include exercises in elements of traditional (pre)processing. At the end of the course, students will be expected to complete an independent data analysis exercise based on exemplary fMRI and fMRI datasets.
Learning Objectives
- The goal of the course is to provide a comprehensive understanding of the principles of MR physics and fMRI, statistical parametric mapping and connectivity analysis methods, as well as to demonstrate their application in cognitive/social neuroscience and clinical practice (using the example of emotion and depression regulation, including real-time fMRI and neurofeedback)
Expected Learning Outcomes
- Demonstrate an understanding of the basics of MR physics and BOLD contrast: T1/T2 relaxation, EPI imaging, sources of artifacts and ways to minimize them.
- Understand the principles of fMRI and statistical parametric mapping: GLM, design matrix, contrasts, multiple comparison corrections (FWE/FDR).
- Be able to plan the design of an fMRI study (block/event-related, task vs. resting-state), justify power and take into account confounders (movement, physiology).
- Have basic fMRI preprocessing skills: QC, motion correction, normalization to standard space, smoothing, temporal filtering.
- Be able to construct and interpret activation maps; perform ROI and whole-brain analysis; extract BOLD time series and assess functional/effective connectivity (correlations, PPI at a basic level).
- Demonstrate an understanding of real-time fMRI and neurofeedback: target selection, feedback metrics, training protocols, evaluation of effectiveness and limitations.
- Understand the basics of EEG processing: filtering, abstracting, artifact removal (including ICA), basic spectral and epochal analysis.
- Apply elements of machine learning to EEG/MRI data: feature extraction, correct cross-validation, leakage prevention, basic interpretability of models.
- Be able to compare neuroimaging results with clinical tasks (emotion regulation, depression), formulate conclusions and limitations of translation.
- Know the basics of reproducibility and data culture: ethics, documentation and data organization (e.g. BIDS), open protocols and pre-registration.
- Have experience in analyzing a small set of fMRI/EEG data in modern software and prepare a short written report and oral presentation of the results.
Interim Assessment
- 2025/2026 2nd module0.075 * Activity + 0.075 * Activity + 0.3 * Case + 0.25 * Homework + 0.3 * Project
Bibliography
Recommended Core Bibliography
- Zhang, Y. (2007). Fundamentals of Biostatistics (6th ed.). Bernard Rosner. The American Statistician, 183. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.bes.amstat.v61y2007mmayp183.183
- Кельберт, М. Я. Вероятность и статистика в примерах и задачах : учебное пособие / М. Я. Кельберт, Ю. М. Сухов. — Москва : МЦНМО, [б. г.]. — Том I : Основные понятия теории вероятностей и математической статистики — 2007. — 456 с. — ISBN 978-5-94057-253-4. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/9353 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
Recommended Additional Bibliography
- Байесовская статистика: Star Wars®, LEGO®, резиновые уточки и многое другое. - 978-5-4461-1655-3 - Курт Уилл - 2021 - Санкт-Петербург: Питер - https://ibooks.ru/bookshelf/377035 - 377035 - iBOOKS
- Зенков, А. В. Математическая статистика в задачах и упражнениях : учебное пособие / А. В. Зенков. - Москва ; Вологда : Инфра-Инженерия, 2022. - 108 с. - ISBN 978-5-9729-0866-0. - Текст : электронный. - URL: https://znanium.com/catalog/product/1902586
- Наглядная статистика. Используем R! : учебное пособие / А. Б. Шипунов, Е. М. Балдин, П. А. Волкова, А. И. Коробейников. — Москва : ДМК Пресс, 2014. — 298 с. — ISBN 978-5-94074-828-1. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/50572 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.