Romina Mizrahi: The crucial role of positron emission tomography (PET) in precision medicine in psychiatry
Bibliographic record
Abstract
Mental health and substance use disorders represent a major public health burden worldwide, and current diagnostic methods rarely include objective, quantifiable metrics. In this Genomic Press Interview, Dr. Romina Mizrahi, Professor at McGill University Department of Psychiatry and Principal Investigator, Clinical & Translational Sciences (CaTS) lab at the Douglas Research Center, discusses how positron emission tomography (PET) imaging provides transformative opportunities to study the molecular mechanisms underlying psychiatric disorders. Dr. Mizrahi uses PET to study the pathophysiology of schizophrenia, clinical high risk (CHR) for psychosis, and addiction with a focus on cannabis use. She was the first to investigate in-vivo dopamine response to stress in CHR, schizophrenia, and cannabis users. Importantly, Dr. Mizrahi pioneered PET studies with novel radiotracers, including [ 11 C]-(+)-PHNO, [ 18 F]-FEPPA, [ 11 C]-CURB, [ 11 C]-NOP, [ 11 C]SL25.1188 and [ 18 F]SynVesT-1 to image dopamine, neuroinflammation, endocannabinoid, nociceptin expression, monoaminoxidase B (MAO-B) and synaptic density (respectively) in psychosis spectrum, cannabis use, and more recently in suicide phenotypes. These molecular imaging techniques allow for identifying biomarkers related to specific disorders, discovering new treatment targets, early behavioral intervention, and assessing real-time treatment responses. Dr. Mizrahi's research aims to improve individualized treatment decisions and predictions of treatment response in psychiatry by integrating PET data with genetic, clinical, and environmental data. Dr. Mizrahi is a champion for interdisciplinary collaborations aimed at improving the science of mental health, and she has published over 160 papers in high-impact journals. She is highly involved with public health, including extensive media engagement and testimony as a witness at the Canadian House of Commons standing committee on youth marijuana use, an important global priority in the context of cannabis legalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".