Semi-Automated Biomarker Discovery from Pharmacodynamic Effects on EEG in ADHD Rodent Models
Tatsuya Yokota1, Zbigniew R Struzik2, Peter Jurica1
1RIKEN Brain Science Institute, Hirosawa, Wako, Saitama, Japan.
Scientific Reports
|March 28, 2018
Summary
We developed a new method to create biomarkers from electroencephalography (EEG) to study drug effects in attention deficit/hyperactivity disorder (ADHD) rodent models.
Area of Science:
- Neuroscience
- Pharmacology
- Biomarker Discovery
Background:
- Attention deficit/hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder.
- Understanding the pharmacodynamic effects of medications like methylphenidate (MPH) and atomoxetine (ATX) is crucial for ADHD treatment.
- Electroencephalography (EEG) provides valuable insights into brain activity dynamics.
Purpose of the Study:
- To propose a semi-automatic method for designing biomarkers from EEG spectral power.
- To investigate the pharmacodynamic effects of MPH and ATX in rodent models of ADHD.
- To validate the developed biomarkers for genetic stratification and drug effect assessment.
Main Methods:
- Utilized rodent models: spontaneously hypertensive rat (SHR), Wistar-Kyoto rat (WKY), and Wistar rat (WIS).
- Recorded EEG patterns after administering MPH and ATX.
- Quantified EEG spectral power using smoothing filters, outlier truncation, linear regression, and Fisher discriminant analysis (FDA).
Main Results:
- Identified significant benefits in pharmacodynamic parameters, particularly the slope of EEG spectral power.
- Developed composite biomarkers through FDA for enhanced data interpretation.
- Demonstrated the validity of composite biomarkers for stratifying genetic models and assessing drug effects.
Conclusions:
- The proposed semi-automatic methodology effectively captures pharmacodynamic effects on EEG spectral power.
- The novel composite biomarkers are valuable tools for ADHD research and drug development.
- This generic approach can be widely applied to analyze EEG spectral power dynamics.
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