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NSC 672

Applied Magnetic Resonance Imaging for Neuroscience

This is a hands-on graduate course in functional MRI for neuroscience research, built around the full pipeline from experiment design to interpretation: how the hemodynamic signal is generated, how to design a paradigm that isolates it, and how to actually analyze the resulting data. You'll spend the semester running real preprocessing in native and MNI space, fitting GLMs with proper contrasts, doing second-level and ROI analyses, and finishing with multivariate methods (RSA/MVPA) on event-related data you collect yourselves, with BIDS, git, and reproducibility treated as non-negotiable. By the end you should be able to read fMRI papers critically and set up your own imaging study, which is essentially the entry ticket for thesis work in any cognitive or systems neuroscience lab at Bilkent.

Credit3ECTS5BölümNeuroscience

→ STARS müfredatı (resmi syllabus)

Değerlendirme 100% — 3 adım

20%
60%
20%
In-class participation Class participation 20%
Homework Reading, coding and analysis assignments 60%
Project Project or exam 20%

Önerilen kaynaklar 3 kitap

📕
Zorunlu
Functional Magnetic Resonance Imaging
Huettel, Song and McCarthy
Sinauer Associates · Inc.
📕
Zorunlu
Handbook of Functional MRI Data Analysis
Russell A. Poldrack, Jeanette A. Mumford
Thomas E. Nichols · Cambridge University Press
📕
Zorunlu
Statistical Analysis of fMRI Data
Gregory Ashby, The MIT Press Recommended - Web Link: The Basics of MRI Recommended - Web Link: mumfordbrainstats - YouTube Recommended - Web Link: Andrew Jahn - YouTube Recommended - Web Link: MVPA — FMRIF

Haftalık müfredat 14 hafta

Hafta 1
Course introduction and class mechanics. Discussion of course content and structure, with adjustments made as needed to accommodate students’ research interests and needs.
Hafta 2
Tutorial on the principled use of Artificial Intelligence as a research aid, with emphasis on maintaining critical thinking, methodological rigor, and reproducibility while writing code. • Introduction to MRI data, BIDS convention, preprocessing theory • Discussion on good data practices. • Checking open datasets
Hafta 3
Theoretical introduction and hands on to preprocessing, native space and MNI space analyses.
Hafta 4
Theoretical introduction and hands on to preprocessing, native space and MNI space analyses. (cont.)
Hafta 5
Review of fMRI paradigm design basics, blocked designs, event-related designs • Tools for designing experiments (Psychopy, Psychtoolbox) • Discussing good experiment practices. • Data collection for a basic block design experiment • Using git for version control
Hafta 6
Review of fMRI paradigm design basics, blocked designs, event-related designs • Tools for designing experiments (Psychopy, Psychtoolbox) • Discussing good experiment practices. • Data collection for a basic block design experiment • Using git for version control (cont.)
Hafta 7
Generating an onset (event) file for analysis. • Hands on to first level analyses, GLM analyses • Setting up contrasts
Hafta 8
Second level analyses, reporting results
Hafta 9
Region of Interest analyses • Finite impulse response analyses • Parametric modulation
Hafta 10
Data collection for an event-related design paradigm
Hafta 11
Analyses of the event-related design, Reporting results
Hafta 12
Multivariate analyses theory (RSA, MVPA)
Hafta 13
Multivariate analyses on the event-related design data collected.
Hafta 14
Multivariate analyses on the event-related design data collected.

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⚠️ FZ engelleyen şartlar

This course has no final exam

Hocalar 1 bu dönem · 1 geçmiş

Bu dönem (2025-2026 Spring) · 1 section
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