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 introduces a modular, function-based framework for interpreting QEEG findings and guiding neurofeedback training. Instead of normalizing EEG activity without context, the authors emphasize evaluating specific cortical modules and the coherence (connectivity) between them to understand functional deficits and optimize training.
Core Concepts #
Cortical Modules #
Modules are functionally specialized cortical regions aligned with 10–20 electrode sites (e.g., FP1 for logical attention, O1 for right-field visual processing). Each module contributes to cognitive, emotional, motor, or perceptual functioning.
Coherence as Functional Connectivity #
Coherence reflects the degree of cooperation between brain regions:
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Hypocoherence → under-connected modules, inefficient processing, longer reaction times.
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Hypercoherence → overly rigid cross-talk, reduced flexibility, stereotyped responses.
QEEG Patterns Identified #
The paper categorizes abnormalities into six groups:
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Modular insufficiencies – excessive slow activity or reduced fast activity.
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Diffuse insufficiencies – global slowing associated with toxic/metabolic issues.
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Modular excesses – elevated beta indicating anxiety, hyperfocus, or tics.
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Diffuse amplitude excesses – global beta elevations typical in alcoholism/anxiety.
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Disconnections – hypocoherence between functional modules (e.g., reading networks).
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Hyperconnections – excessive coherence leading to inflexible processing.
Assessment and Training Approach #
The authors advocate:
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Using QEEG power + coherence metrics to map modular dysfunction.
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Training up or down specific frequencies or coherence levels depending on deficits.
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Employing auto-thresholding neurofeedback to normalize activation and connectivity.
Case Examples #
Three cases (ages 7–15) illustrate the model:
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Case 1: Visual–motor disconnection misinterpreted as ADD, resolved via coherence training.
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Case 2: Anxiety-related beta excess and motor-planning disconnection improved with targeted protocols.
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Case 3: Complex dyslexia with multiple insufficiencies, disconnections, and hyperconnections—improved from 1st-grade to 5th-grade reading level within 3 months after combined amplitude and coherence training.
Clinical Implications #
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The modular model provides a functionally grounded method for interpreting QEEG data.
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It supports targeted neurofeedback tailored to specific cortical functions and network interactions.
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The authors note strongest utility for static cortical dysfunctions (e.g., learning issues, residual head injury) rather than subcortical disorders.
Key Takeaways #
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QEEG offers a real-time window into cortical activation and connectivity patterns.
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Neurofeedback can be more effective when guided by functional modular analysis, not just statistical deviation.
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Understanding module-level deficits helps clarify complex presentations often mislabeled as ADHD or general learning disorders.
