Electroencephalography (EEG) and Magnetoencephalography (MEG) are powerful non-invasive techniques that measure electrical activity and magnetic fields produced by the brain, respectively. While they excel at capturing the temporal dynamics of neural activity, understanding how different brain regions communicate—their connectivity—requires specialized analytical approaches. This module explores the fundamental concepts and methods used to analyze functional and effective connectivity from EEG/MEG data, crucial for advanced neuroscience research and computational modeling.

Brain connectivity refers to the structural and functional relationships between different brain areas. It's not just about where activity happens, but how different parts of the brain work together to support cognitive functions. We can broadly categorize connectivity into two main types:

Functional connectivity describes statistical dependencies between the time series of neural activity recorded from different brain regions. It indicates that two or more brain areas tend to activate or deactivate together, regardless of whether there's a direct anatomical link. Think of it as observing that two musicians often play in sync, even if they aren't directly facing each other.

Effective connectivity goes a step further by examining the causal influence that one neural system exerts over another. It's about understanding the directionality and strength of these influences. This is akin to understanding which musician is leading the ensemble or how one musician's playing directly impacts another's.

Analyzing connectivity from EEG/MEG data involves various signal processing techniques. These methods aim to quantify the relationships between signals recorded from different sensors or estimated sources.

These methods analyze the raw time series data directly. Common techniques include:

These methods analyze the spectral properties of the signals, looking at how different brain regions synchronize or interact across various frequency bands (e.g., alpha, beta, gamma). Key techniques include:

Phase Locking Value (PLV): Measures the consistency of the phase difference between two signals over time. If the phase difference is consistently similar, it suggests strong functional connectivity. This is like observing that two dancers consistently maintain a specific relative pose throughout a routine.

Granger Causality: A statistical hypothesis test for determining whether one time series is useful in forecasting another. It's a way to infer directional influence, though it doesn't prove direct causation.

Since EEG/MEG sensors are on the scalp, the actual neural sources are distributed. Source localization techniques estimate the location and activity of these underlying neural sources. Connectivity analysis is then often performed on these estimated source activities, providing a more spatially precise understanding of brain communication.

Several challenges exist in EEG/MEG connectivity analysis:

Connectivity analysis using EEG/MEG is vital for:

• Investigating brain states (e.g., sleep, attention).
• Understanding neurological disorders (e.g., epilepsy, Alzheimer's).
• Decoding cognitive processes (e.g., memory, decision-making).
• Validating computational models of brain function.