Improving Fast-Scan Cyclic Voltammetry and Raman Spectroscopy Measurements of Dopamine and Serotonin Concentrations via the Elastic Net

Dopamine and serotonin are two neurotransmitters known to both play a very important role in the human brain. For example, the death of dopamine producing neurons in a region of the brain known as the substantia nigra are known to cause the motor symptoms of Parkinson's disease. Also, many antidepressants are believed to work by increasing the extracellular level of serotonin in the brain. For the first time, it is now possible to measure the release of these two chemicals at sub-second time resolution in a human brain using a technique known as fast-scan cyclic voltammetry, for example from patients undergoing deep brain stimulation (DBS) electrode implantation surgery.

In this work, we aimed to assess the feasibility of obtaining veridical dual measurements of serotonin and dopamine from substrates with mixtures of both chemicals. In the wet lab, data was collected on known concentrations of dopamine and serotonin and then used to make models capable of estimating the concentration of both chemicals from the voltammograms recorded in the patients. A method of linear regression known as the elastic net was used to make models from the wet lab data. The wetlab data was used to compare the performance of univariate and multivariate type models over various concentration ranges from 0-8000nM of dopamine and serotonin. Cross validation revealed that the multivariate model outperformed the univariate model both in terms of the linear correlation between predictions and actual values, and pH induced noise. The pH induced noise for the univariate model was 3.4 times greater for dopamine and 4.1 times greater for serotonin than the multivariate model.

Raman spectroscopy was also investigated as a possible alternative to fast-scan cyclic voltammetry. Raman spectroscopy could have several benefits over fast-scan cyclic voltammetry, including the ability to chronically implant the measurement probe into a patient's brain and make observations over a long period of time. Raman spectroscopy data was collected on known concentrations of dopamine to investigate its potential in making in vivo measurements, however this data collection failed. Therefore, simulations were made which revealed the potential of the elastic net algorithm to determine the Raman spectra of several neurotransmitters simultaneously, even when they are in mixtures and the spectra are obstructed by the noisy background. The multivariate type model outperformed the univariate type model on Raman spectroscopy data and was able to predict dopamine with an error of 805nM RMS and serotonin with an error of 475nM RMS after being trained on concentrations smaller than 5uM of both dopamine and serotonin. In addition, the original Raman spectra of both neurotransmitters was extracted from the noise and reproduced very accurately by this method.