PET and SPECT Imaging in Psychiatric Disorders
Bibliographic record
Abstract
OBJECTIVES: To review recent findings from positron emission tomography (PET) and single photon emission computed tomography (SPECT) studies that investigate the pathophysiology and treatment of schizophrenia, depression, and dementia. METHODS: We carried out a review of the literature. RESULTS: PET and SPECT studies have provided evidence of dopamine system dysregulation in patients with schizophrenia and variable loss of monoamines in patients with depression. Antipsychotic response has been demonstrated to be associated with blockade of dopamine D2 receptors, and antidepressant response has now been linked to blockade of serotonin transporter receptors. PET and SPECT have been extensively evaluated as diagnostic procedures for dementia. Substantial progress has been made in developing radioligands that bind to amyloid deposits in the brain, which should provide new opportunities for early diagnosis and treatment monitoring in Alzheimer's disease. CONCLUSION: Advances in PET and SPECT imaging have provided new insights into the biology of major psychiatric disorders and their treatment. In the future, we can expect that these imaging techniques will become more central to the management of psychiatric disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".