Platform Leadership

Faranak Farzan, PhD

Active Sites

The EEG Platform operates at the following research sites:

  • Centre for Addiction and Mental Health (Toronto, Ontario)
  • Queen’s University (Kingston, Ontario)
  • Rotman Research Institute, Baycrest (Toronto, Ontario) *analysis site only
  • Simon Fraser University (Surrey, British Columbia)
  • University of British Columbia (Vancouver, British Columbia)
  • University Health Network (Toronto, Ontario)

Platform Overview

The Electroencephalography (EEG) Platform is responsible for the collection, standardization and analysis of brain electrical activity data, also known as electrophysiological or EEG data. This platform studies patterns of electrical activity in the brain during rest, while performing functional tasks, and in response to non-invasive brain stimulation such as Transcranial Magnetic Stimulation (TMS). The platform has an overarching aim of identifying EEG biomarkers, and subsequently developing EEG-based healthcare technologies that could be used in both research centres and clinics to diagnose depression, predict response to various depression treatments, and guide design of novel targeted therapies. The development of such healthcare technologies requires standardized data collection and integration across multiple participating EEG sites. The EEG Platform provides leadership in this pursuit and interacts with other platforms to identify an optimal integrated technology for transforming lives of individuals with depression.


Resources and Publications

Pilot study on EEG predictors of response

Baskaran, A., Farzan, F., Milev, R., Brenner, C. A., Alturi, S., McAndrews, M. P., Blier, P., Evans, K., Foster, J.A., Frey, B.N., Giacobbe, P., Lam, R.W., Leri, F., MacQueen, G.M.,  Müller, D.J., Parikh, S.V., Rotzinger, S., Soares, C.N., Strother, S.C., Turecki, G. Kennedy, S. H. (2017). The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study. Journal of Affective Disorders, 227, 542-549. doi:10.1016/j.jad.2017.10.028

Methodology paper and guidelines for multi-site EEG

The EEG Platform has developed and implemented a standardized guideline for conducting multi-site EEG studies. The team identified potential sources of data acquisition variance and factors in study design that need careful consideration to minimize bias in research results in multi-site studies. The paper also introduces a streamlined EEG preprocessing toolbox (open source MATLAB app) to standardize, assess, and track the impact of EEG data pre-processing decisions and subjectivity in EEG data cleaning on study outcomes.

Farzan, F., Atluri, S., Frehlich, M., Dhami, P., Kleffner, K., Price, R., Lam, R.W., Frey, B.N., Milev, R., Ravindran, A., McAndrews, M.P., Wong, W., Blumberger, D., Daskalakis, Z.J., Vila-Rodriguez, F., Alonso, E., Brenner, C.A., Liotti, M., Dharsee, M., Arnott, S.R., Evans, K.R., Rotzinger, S., Kennedy, S. H. (2017). Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the Canadian Biomarker Integration Network in Depression. Scientific Reports, 7(1). doi:10.1038/s41598-017-07613-x


Recent Presentations 

Atluri S., Wong W., Kennedy S.H., Rotzinger S., Blumberger D.M., Daskalakis Z.J., Farzan F., CAN-BIND EEG Team, CAN-BIND Investigators Team. (2018, May). Supervised Machine Learning for Early Prediction of Escitalopram Response in Depression: An EEG Study. Poster session presented at Society of Biological Psychiatry 73rd Annual Meeting. New York, NY. 

Farzan F. (2017, December). Electrophysiological Neuromarkers in Understanding Mechanisms of Action of Different Antidepressant Modalities and Predicting Clinical Response in Major Depressive Disorder. In S. Kennedy (Chair) Multimodal Strategies to Identify Precision Biomarkers of Treatment Response in Depression. American College of Neuropsychopharmacology, Palm Springs, USA.