Neuroscience and Neuroimaging Specialization Course Syllabus

Overview: This specialization offers a comprehensive, hands-on introduction to neuroscience and neuroimaging, blending foundational neuroanatomy with advanced imaging techniques and computational analysis. Structured across five core modules and a final project, the course series spans approximately 30–40 hours of content. Learners will progress from brain structure and function to fMRI principles, advanced imaging analysis, and practical neuroimaging coding in R. With a strong emphasis on real-world applications, this program prepares students for roles in neuroengineering, data science, and brain-computer interface research.

Module 1: Fundamental Neuroscience for Neuroimaging

Estimated time: 5 hours

• Introduction to neuroanatomy
• Basics of brain structure and function
• Overview of major brain regions and neural systems
• Preparing for neuroimaging techniques

Module 2: Principles of fMRI 1

Estimated time: 7 hours

• How fMRI works: blood-oxygen-level-dependent (BOLD) signal
• Experimental design in neuroimaging
• Basics of functional connectivity
• Interpreting fMRI data outputs

Module 3: Principles of fMRI 2

Estimated time: 7 hours

• Advanced fMRI analysis methods
• Diffusion tensor imaging (DTI) fundamentals
• Magnetic resonance spectroscopy (MRS) basics
• Modeling approaches in fMRI data interpretation

Module 4: Introduction to Neurohacking in R

Estimated time: 7 hours

• Using R for neuroimaging analysis
• Data manipulation and preprocessing of MRI data
• Visualization techniques for structural MRI
• Statistical analysis of neuroimaging datasets in R

Module 5: Neural Coding and Network Dynamics

Estimated time: 6 hours

• Neural coding: how neurons represent information
• Signal variability and neural decoding methods
• Biophysical modeling of neurons: Hodgkin-Huxley model
• Computational models of dendrites and spiking behavior

Module 6: Final Project

Estimated time: 8 hours

• Analyze a real fMRI dataset using R
• Apply neural decoding techniques to infer brain states
• Present findings with visualizations and interpretation

Prerequisites

• Basic understanding of biology or neuroscience
• Familiarity with programming (MATLAB, Python, or R)
• Introductory knowledge of statistics and linear algebra

What You'll Be Able to Do After

• Explain how the brain encodes and processes information
• Design and interpret fMRI experiments
• Analyze neuroimaging data using R
• Apply computational models to simulate neural activity
• Build foundational skills for research in neuroengineering or data science