Neuroengineering and Brain Plasticity — with Faranak Farzan
Bibliographic record
Abstract
Faranak Farzan works at a fascinating intersection of engineering and neuroscience — innovating technological solutions to mental health issues like depression and addiction. Using the concept of brain plasticity, Faranak speaks to host Am Johal about how technological interventions can help the brain to rewire itself. She delves into the exciting opportunities neuroengineering presents for streamlining diagnosis and treatment, reducing the burden on patients who often go through years of trial-and-error before being matched with the right treatment. They also discuss Faranak’s research specific to youth mental health, the ethical implications of neurotechnology, and the importance of community perspectives in co-creating brain health solutions.\nDr. Faranak Farzan is the Chair in Technology Innovations for Youth Addiction Recovery and Mental Health at the School of Mechatronic Systems Engineering at SFU. Dr. Farzan is the founder and Scientific Director of Centre for Engineering-Led Brain Research at SFU. She has obtained her Bachelor in Electrical and Biomedical Engineering from McMaster University, her PhD in Biomedical Engineering and Medical Science from University of Toronto, and her Postdoctoral training in Cognitive Neurology from Harvard Medical School. Prior to joining SFU, she was Assistant Professor of Psychiatry at The University of Toronto, and Independent Scientist at the Centre for Addiction and Mental Health. Dr. Farzan leads a unique research program at the cross section of Engineering and Neuroscience. The program is aimed at development and practical implementation of neurotechnology and computational approaches for studying human brain health and function, and for diagnosis and treatment of neuropsychiatric disorders. Dr. Farzan has authored over 70 peer-reviewed journal articles, and her work has received funding from prestigious agencies such as CIHR, NSERC, CFI, CIHR, NIMH, NARSAD, Brain Canada, Kids Brain Health Network, and Michael Smith Foundation for Health Research
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".