Aspect Medical Systems has developed and commercialized a computational index based on bispectral analysis of the EEG that quantifies the effect of anesthetics on the brain. This technology is now in use at over 14,000 locations worldwide and has been used to monitor more than 5 million patients. The technology has been the subject of over 1000 published articles and abstracts. We propose to expand on this technology, developing algorithms based on bispectral analysis of the EEG that predict response to antidepressant medications in patients with major depressive disorder. The objective is to provide a simple to use device that will optimize objective prediction of medication response at pre-drug administration baseline and before the clinical appearance of response. Preliminary work indicates that in a select group of patients with major depression, the EEG bispectrum is sensitive to frontal lobe disturbances that differ between patients who are responsive to antidepressant medication and those who are not. Our method uses a small set of recording electrodes applied to the forehead and scalp and provides a significant "ease of use" factor over conventional 19-channel EEG recordings, allowing for general implementation in doctor's offices and other non-specialized clinical settings. A. Specific Aims 1) Develop and validate a predictive algorithm based on processed EEG measures to determine whether a patient with unipolar major depressive disorder would respond to a specific class of antidepressant therapy. 2) Compare the predictive value of the new algorithm to current standard of practice. 3) Develop a practical clinical method with sufficient ease of use to allow implementation of predictive algorithms in a broad range of clinical situations.
Thesaurus Terms: antidepressant, artificial intelligence, electroencephalography, electronic recording system, frontal lobe /cortex, major depression, method development, psychopharmacology computer program /software, electrode, neuropharmacology, pharmacokinetics clinical research, human data
