🧠 Brain Dynamics Workshop:
Theory, Analysis and Interpretation

@Neuro_Syntax

🖥 Session01

* Introduction to brain signals and their biological substrates
* Signals, Time Series, and intro to Fourier Transform


🖥 Session02

* Discrete Time Fourier Transform
* Welch Power Spectrum
* Sampling Frequency Theorem, Aliasing, Nyquist Frequency
* Spectral Leakage
* Window Functions
+ Matlab Implementations


🖥 Session03

* Accurate scaling of fourier coefficients
* The effect of varying window length and overlap on power spectrum
* What are oscillators?
* How can oscillations be generated in the brain? Mechanisms of oscillations in the brain
* Neural oscillations: sustained activity or transient bursts?
* Stationarity and Non-stationarity
* Short-time fourier transform
* Morlet Wavelet representation
+ Matlab Implementations

🖥 Session04

* What are timestamps and what are they used for?
* Multitaper Method for Spectral Estimation: Variability and the problem with averages, Time-locked and Non-time locked, Phase locked and non-phase locked, Slepian Tapers
* Digital Filters - Design and Implementation: What are filters? What are different types of filteres? What are they used for? How do they affect the signal?
+ Matlab Implementations

🖥 Session05

* Hilbert Transform
* Envelope magnitude estimation
* Oscillatory Bout Detection using filter-Hilbert Method
* Oscillatory Bout Detection using BOSC
+ Matlab Implementations


🖥 Session06

* Periodic and aperiodic components in brain signal
* How aperiodic component can affect power analysis
* Parameterizing neural power spectra into periodic and aperiodic components
* Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations


🖥 Session07

* Methodological considerations for studying neural oscillations


🖥 Session08

* Introducing Phase Time Series
* The conditions for a meaningful Phase (Bedrosian Theorem)
* Uses of Phase Time Series
- Inter-trial Phase Clustering
- Cross-frequency phase-amplitude coupling
- Spik-field coherence
- Travelling Waves


🖥 Session09

* Cross-frequency Coupling
————Introduction
————Network Architecture and Mechanisms
+ Phase-phase coupling
+ Phase-frequency coupling
+ Phase-amplitude coupling
+ Amlitude-amplitude coupling


🖥 Session10

* Cross-frequency Coupling - Implementation
— Cross-frequency amplitude amplitude coupling
— Cross-frequency phase-amplitude coupling (PAC)
— Modualtion Index
— Dissociating spurious from valid PACs
— Harmonics
— Harmonic vs Non-Harmonic PAC


🖥 Session11

* Cross-frequency Coupling - Implementation
— Filter Settings for Phase and Amplitude Estimation in phase-amplitude coupling measures
— Detecting n:m phase-phase coupling
— Surrogate statistical tests for cross-freq coupling and cautions
* Cross-frequency Coupling - Function
— Integration Problem
— CFC for long-distance communication
— Multi-item/sequence representation
— Sensory Parsing


🖥 Session12

Brain Oscillations and the Importance of Waveform Shape

* Introduction and Examples of Nonsinusoidal signals in the neural recordings

* Physiological Relevance of Oscillation Waveform
— Features of Nonsinusoidal Waveforms Relate to Physiology
— Features of Oscillatory Waveforms Relate to Behavior and Disease

* Methods
— Zero-crossing/Quadrant Point Method of Phase Estimation
— Empirical mode decompositions
——— Computations
——— Drawbacks
——— Ensemble Empirical Mode Decomposition
——— Noise-aided Multivariate Empirical Mode Decomposition
— Cycle-by-cycle analysis of Neural Oscillations
— Instantaneous Frequency



#BrainDynamicsWorkshop #neurosyntax