Towards a Coherent View of Brain Connectivity Thomas F. Collura, PhD, PE

Disclaimer: The content below was generated with the assistance of AI and then reviewed and edited by BrainMaster Technologies, Inc. It is provided for educational and informational purposes only and does not constitute medical advice.

Summary #

This article provides a comprehensive explanation of how the electroencephalogram (EEG) reflects both local and long-distance neuronal synchrony. It outlines how various EEG connectivity measures—including coherence, phase, synchrony, correlation, comodulation, and asymmetry—can be used to assess and train brain network function.

Understanding EEG-Based Connectivity #

Local vs. Non-Local Activation #

Tom explains that measurable EEG arises when cortical pyramidal cells fire in synchrony. Even a small synchronized population (~1%) can account for most of the observed electrical field. Extending this concept, connectivity reflects coordinated activity between separate brain regions.

Postprocessed vs. Real-Time Connectivity Analysis #

The article contrasts:

• FFT-based postprocessing, which offers precision but introduces delays unsuitable for neurofeedback.

• Real-time digital filters/complex demodulation, which allow immediate connectivity estimation during training.

Major Connectivity Metrics #

Coherence #

• Measures phase stability between two EEG signals.

• Independent of absolute amplitudes.

• Useful for identifying consistent interregional coupling.

Phase Difference #

• Indicates the exact phase separation between signals.

• Prone to challenges such as wraparound and instability, but important for understanding timing relationships.

Similarity (Synchrony) #

• High when signals are in phase and of similar amplitude.

• Useful for assessing synchronous neural activation.

Spectral Correlation Coefficient (SCC) #

• Compares spectral shape across frequencies.

• Sensitive to amplitude morphology, independent of phase.

Comodulation #

• Measures how component amplitudes wax and wane together over time.

• Reflects time-linked neural activation patterns.

Asymmetry #

• Captures relative amplitude differences between hemispheres.

• Often used for frontal asymmetry research.

Summed & Differenced Channels #

• Simple waveform operations revealing shared vs. unique activity.

• Useful in determining monopolar vs. bipolar training strategies.

Applications in Assessment and Neurofeedback Training #

Connectivity measures help clinicians and researchers:

• Characterize whole-brain functional integration.

• Guide neurofeedback protocols.

• Avoid undesirable outcomes such as hypercoherence or hypocoherence.

• Leverage normative databases to inform connectivity-based training targets.

Real-time z-score systems are highlighted as an emerging method for adaptive training, enabling practitioners to guide clients toward normative connectivity ranges.

Key Takeaways #

• Connectivity measurement is concept-driven and depends heavily on methodological choices.

• No single measure is sufficient; each offers distinct insights.

• Normative references and controlled training frameworks are essential to avoid adverse results.

• Connectivity-based neurofeedback remains an evolving field requiring further research.