Neuroimaging Findings in the At-Risk Mental State: A Review of Recent Literature
Bibliographic record
Abstract
The at-risk mental state (ARMS) has been the subject of much interest during the past 15 years. A great deal of effort has been expended to identify neuroimaging markers that can inform our understanding of the risk state and to help predict who will transition to frank psychotic illness. Recently, there has been an explosion of neuroimaging literature from people with an ARMS, which has meant that reviews and meta-analyses lack currency. Here we review papers published in the past 2 years, and contrast their findings with previous reports. While it is clear that people in the ARMS do show brain alterations when compared with healthy control subjects, there is an overall lack of consistency as to which of these alterations predict the development of psychosis. This problem arises because of variations in methodology (in patient recruitment, region of interest, method of analysis, and functional task employed), but there has also been too little effort put into replicating previous research. Nonetheless, there are areas of promise, notably that activation of the stress system and increased striatal dopamine synthesis seem to mark out patients in the ARMS most at risk for later transition. Future studies should focus on these areas, and on network-level analysis, incorporating graph theoretical approaches and intrinsic connectivity networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".