Lectures

NEW IN 2020!

Fall 2020: The course was rearranged to begin with the core concepts of data and analysis. New lectures will be added as the course progresses. Prerecorded lecture videos using Zoom can be provided on request. At some point, I’ll add more polished prerecorded lectures.

These new slides are designed to work well with new tutorials on NEWBI4fMRI.com

You can still access the old slides in the old organization.

Here is the syllabus I use for my graduate course on Neuroimaging of Cognition.

All the slides can be downloaded as a single .zip file

Lecture 01: INTRODUCTION

• Lecture 01a: Course introduction

• Lecture 01b: Why fMRI?

Lecture 02: MRI, fMRI AND fNIRS DATA

• Lecture 02a: MRI and fMRI basics

• Lecture 02b: fNIRS

• Lecture 02c: Class experiment design

• Lecture 02d: Introducing jargon using class experiment design

• Lecture 02e: Review: Key slides for Review

Lecture 03: fMRI STATISTICS

• Lecture 03a: fMRI statistics: Correlation

• Lecture 03b: fMRI statistics: Using the General Linear Model (GLM) to do a correlation

• Lecture 03c: fMRI statistics: Extending the GLM to more conditions and multiple runs

• Lecture 03d: fMRI statistics: Contrasts

• Lecture 03e: fMRI statistics: Corrections

• Lecture 03f: Review: Key slides for Review

Lecture 04: PREPROCESSING AND MODELLING CONFOUNDS

• Lecture 04a: Data quality

• Lecture 04b: Head motion correction

• Lecture 04c: Improving GLM statistics

• Lecture 04d: Preprocessing

• Lecture 04e: Review: Key slides for Review

Lecture 05: BASICS OF EXPERIMENTAL DESIGN

• Lecture 05a: Participants

• Lecture 05b: Subtraction Logic

• Lecture 05c: Confounds

• Lecture 05d: Conditions and Baselines

• Lecture 05e: Block-Design Timing

• Lecture 05f: Optional: Strategies for successful scanning

• Lecture 05g: Review: Key slides for Review

Lecture 06: ESTIMATION OF EVENT RESPONSES

• Lecture 06a: Event-related averages

• Lecture 06b: The imperfections of the hemodynamic response function

• Lecture 06c: The problem of event history

• Lecture 06d: Design types (Block, Event-related, Mixed)

  Lecture 06e: Deconvolution of event responses

• Lecture 06f: Review: Key slides for Review

Lecture 07: GROUP DATA

• Lecture 07a: Standardization

• Lecture 07b: Group GLM

• Lecture 07c: Group GLM example

• Lecture 07d: Regions of Interest

• Lecture 07e: Non-independence errors

• Lecture 07f: Revisiting sample size recommendations

• Lecture 07g: Review: Key slides for Review

Lecture 08: MULTIVOXEL PATTERN ANALYSIS (MVPA)

• Lecture 08a: Intro to MVPA

• Lecture 08b: Pattern Classifiers

• Lecture 08c: Optional: Example of Classifiers

• Lecture 08d: Representational Similarity Analysis (RSA)

• Lecture 08e: Optional: Example of RSA

• Lecture 08f: Review of RSA

• Lecture 08g: MVPA wrap-up

• Lecture 08h: Review: Key slides for Review

Lecture 09: ADVANCED METHODS

• Lecture 09a: fMRI Adaptation

• Lecture 09b: Parametric Designs

• Lecture 09c: Factorial Designs and ANOVA

• Lecture 09d: Group Analyses (ANOVA & ANCOVA)

• Lecture 09e: Optional: Dealing with Motion Confounds Between Groups

• Lecture 09f: Intersubject Correlations

• Lecture 09g: Independent Component Analysis

• Lecture 09h: Review: Key slides for Review

Lecture 10: BRAIN CONNECTIVITY

• Lecture 10a: Introduction to Connectivity

• Lecture 10b: Functional and Effective Connectivity

• Lecture 10c: Structural Connectivity: Diffusion-Weighted Imaging

• Lecture 10d: Connectomes and Graph Theory

• Lecture 10e: Key Slides for Review

Lecture 11: MRI PHYSICS AND THE BOLD SIGNAL

• Lecture 11a: MRI Hardware

• Lecture 11b: MRI Physics

• Lecture 11c: fMRI Physics

• Lecture 11d: MRI Safety

• Lecture 11e: BOLD Signal

• Lecture 11f: Key Slides for Review