Quantitative methods in electroencephalography to access therapeutic response

Area of Science:

• Pharmacometrics and Quantitative Pharmacology
• Neuroscience and Computational Biology
• Clinical Pharmacy and Drug Development

Background:

• Pharmacometrics quantitatively analyzes drug-patient interactions, encompassing pharmacokinetics, pharmacodynamics, and disease monitoring to understand drug response variability.
• Electroencephalography (EEG) signals are crucial for evaluating the central nervous system's response to centrally acting drugs.
• Understanding drug effects on brain activity is vital for optimizing therapeutic outcomes and patient safety.

Purpose of the Study:

• To promote quantitative methods for assessing the therapeutic response of centrally acting drugs.
• To discuss and evaluate various quantitative signal processing techniques applied to EEG data.
• To demonstrate the clinical relevance of these quantitative analyses in pharmaceutical practice.

Main Methods:

• Utilized quantitative methods including Fast Fourier Transform, Magnitude Square Coherence, Conditional Entropy, Generalised Linear semi-canonical Correlation Analysis, Statistical Parametric Network, and Mutual Information Function.
• Analyzed electroencephalography (EEG) signals acquired after drug administration.
• Employed PhysioNet online clinical database for EEG data acquisition and Matlab for simulations and analysis.

Main Results:

• Demonstrated changes in EEG power spectrum, such as increased theta frequency power in specific brain regions after buspirone administration.
• Showcased how drug-induced alterations in functional connectivity and cognitive functions can be quantified via EEG coherence values (e.g., with bromazepam).
• Indicated the potential of these quantitative EEG measures to assess adverse effects of drugs like opioids and inform treatment guidelines.

Conclusions:

• Quantitative analysis of EEG signals provides valuable insights into drug effects on the central nervous system.
• These methods contribute to a deeper understanding of drug-patient interactions, aiding in the development of pharmaceutical practice.
• The application of computational and mathematical approaches to clinical data, particularly EEG, is essential for advancing drug efficacy and safety evaluations.