The collaborators on this research effort are, from left to right, (standing): Dr. Gary Hasey, MD, Dept. of Psychiatry and Behavioural Neuroscience (PBN) McMaster, Prof. J. Reilly, Dept of ECE, McMaster, Dr. Ahmad Khodayari, Etherton Fellow in the Dept. of PBN, and Dr. Hubert de Bruin, ECE, McMaster. Not shown is Dr. Duncan MacCrimmon, MD, Dept. of PBN.
Major depressive disorder (MDD) is a serious and common mental disorder and is a major cause of workplace disability, with costs very similar to those of diabetes and heart disease. By the year 2020, depression is expected to account for about 15% of total global disease burden, second only to ischemic heart disease. In industrialized countries mental illnesses may account for about 16% of total health care costs and for about 30% of disability claims.
There are approximately 22 available anti-depressant medications for the treatment of MDD that are divided into four classes. Each class functions by altering levels of particular neuro-transmitters in the brain. Despite the severity and extensiveness of MDD, objective procedures for selecting which of these medications is optimal for a specific individual are lacking. Therefore the psychiatrist, when treating MDD, must by necessity resort to a trial-and-error procedure to determine an effective treatment. Typically 60 to 70% of those treated do not remit after the first antidepressant medication trial. Although about 67% will eventually reach remission, up to 4 different antidepressant treatment trials may be required, each taking 6 weeks or longer. The personal and economic cost of delayed or ineffective therapy is substantial, in that in the interval required to reach remission, the patient is likely to be disabled, unable to work, prone to suicide, and suffer intensely.
The objective of this project is to develop machine learning (ML) methods that can predict the response of a particular subject to various therapies for MDD, before treatment begins. The ML methods are based on analysis of the subject's pre-treatment, resting EEG, perhaps complemented by additional biomarkers. The successful development of such a facility will have a major disruptive effect in the practice of psychiatry, since it will significantly improve the chances of an effective treatment being prescribed in the first instance.
Results: So far, we have developed prediction models, based on pilot study data, to predict response to SSRIs (selective serotonin reuptake inhibitors, which are a commonly prescribed form of anti-depressant medication), and to repetitive transcranial magnetic stimulation (rTMS), which is a new, non-invasive form of therapy for the treatment of MDD. We have also developed similar models for prediction of response to the anti-psychotic drug clozapine, which is very effective in treating schizophrenia. Our prediction accuracies in each case are 80% or better.
Current research in this area involves the development of new machine learning methods for this application. Specifically, we are investigating the use of brain source localization methods, improved methods for feature selection and classification, as well as improved kernelization techniques to improve the robustness and accuracy of our predictions.
With financial support from Magstim Ltd., Carmarthenshire, Wales, we are currently in the process of expanding the extent of the available training base, and extending the machine learning prediction approach to a broader range of therapies for MDD.
We are also extending machine learning methodology to perform diagnosis of psychiatric illness, based on the subject's resting EEG.
Related research publications:
This work was featured in a March 2011 IEEE spectrum article: The psychiatrist in the machine.
A powerpoint presentation giving more detail on this work is available here.
The machine learning prediction methods discussed above have been extended to the determination of the age of infants, based on their event-related potentials in response to an audio stimulus. Infant age can be categorized into 6-month old, 12-month old, and adult classes. The method can be used to evaluate the chronological age vs. the “neurological age” of an infant, thereby indicating potential developmental delay.
Proposed research in this area involves the use of brain source localization methods to determine the neurological space-time response to various stimuli. The objective is to provide deeper insight into how the brain processes input stimuli.
Related research publication: A Machine Learning Approach for Distinguishing Age of Infants Using Auditory Evoked Potentials