S78. EXAMINING AN EVENT-RELATED BRAIN POTENTIAL INDEX OF SEMANTIC PRIMING IN CANNABIS-USING INDIVIDUALS AT CLINICAL HIGH-RISK FOR PSYCHOSIS
Bibliographic record
Abstract
Individuals at clinical high-risk (CHR) for schizophrenia experience subthreshold symptoms of this disorder and are at elevated risk for developing it. Among CHR patients as well as the general population, cannabis use has been associated with increased risk of developing psychosis. Psychosis and psychosis-like symptoms in schizophrenia patients, healthy individuals and regular cannabis users have been linked to deficits in processing relationships between meaningful (semantic) stimuli. To seek neurophysiological evidence that semantic processing deficits mediate the relationship between cannabis use and psychosis-like symptoms in the CHR state, we used the electroencephalographic N400 event-related potential (ERP) as a measure of semantic processing. The N400 is seen in response to meaningful stimuli and is smaller when the target stimulus is more related to a preceding prime. We hypothesized that there would be a smaller N400 amplitude difference between target stimuli that are related versus unrelated to a preceding prime stimulus (i.e., smaller semantic priming effects) in cannabis-using compared to non-cannabis-using CHR patients. We recorded ERPs in 12 antipsychotic-naïve CHR patients with history of present or past cannabis dependence disorder, 11 CHR patients with no history of cannabis use and 13 healthy control participants (HCPs). Participants viewed prime words each followed by a target which was either a related or unrelated word, or a nonword, in a lexical-decision task. Equal numbers of each target type were presented at prime-target stimulus-onset asynchronies (SOAs) of 300 and 750 ms. Across SOAs, we saw a trend toward N400 semantic priming effects being smaller for both CHR/C+ and CHR/C- compared to controls (p = 0.078). The results suggest that cannabis use does not modulate semantic priming deficits in CHR patients. A limitation of the study was that some of the cannabis-dependent CHR group were in remission. Future studies of CHR patients comparing those who have current cannabis dependence with non-users might detect differences between these groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".