Neurobiological narratives: experiences of mood disorder through the lens of neuroimaging
Bibliographic record
Abstract
Many scientists, healthcare providers, policymakers and patients are awaiting in anticipation the application of biomedical technologies such as functional neuroimaging for the prediction, diagnosis and treatment of mental disorders. The potential efficacy of such applications is controversial, and functional neuroimaging is not yet routinely used in psychiatric clinics. However, commercial ventures and enthusiastic reporting indicate a pressing need to engage with the social and ethical issues raised by clinical translation. There has been little investigation of how individuals living with mental illness view functional neuroimaging, or of the potential psychological impacts of its clinical use. We conducted 12 semi-structured interviews with adults diagnosed with major depression or bipolar disorder, probing their experiences with mental health care and their perspectives on the prospect of receiving neuroimaging for prediction, diagnosis and planning treatment. The participants discussed the potential role of neuroimages in (i) mitigating stigma; (ii) supporting morally loaded explanations of mental illness due to an imbalance of brain chemistry; (iii) legitimising psychiatric symptoms, which may have previously been de-legitimised since they lacked objective representation, through objective representations of disorder; and (iv) reifying DSM-IV-TR disorder categories and links to identity. We discuss these anticipated outcomes in the context of participant lived experience and attitudes to biologisation of mental illness, and argue for bringing these voices into upstream ethics discussion.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.039 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".