Hippocampal alterations in ultra-high risk patients are independent from medication and cannabis use
Bibliographic record
Abstract
In their comment on our article entitled “Hippocampal subdivision and amygdalar volumes in patients in an at-risk mental state for schizophrenia,”1 Borgwardt and colleagues raise some critical questions regarding our finding that hippocampal volume loss is related to an at-risk mental state. Instead, they argue that smaller right hippocampus corpus and tail volumes in ultra-high risk patients (UHR) who later developed schizophrenia compared with those who did not develop schizophrenia may be attributable to cannabis abuse and/or medication effects. In fact, in our sample, one patient who transitioned into psychosis was taking antipsychotic medication and 7 who transitioned had never taken an antipsychotic. In comparison, in the UHR group that did not transition, 10 were taking antipsychotic medication and 11 were not. Notably, there was no significant difference in right hippocampal corpus and tail volume between these 4 groups (F3,27 = 0.668, p = 0.58). Among the UHR patients who transitioned into psychosis, only 1 had previous cannabis abuse (see the 3-month criterion we used1), whereas 7 did not use cannabis. Among the UHR patients who did transition, 8 had comorbid cannabis abuse and 13 were free of cannabis abuse. Again, when we compared the volumes of the right hippocampal corpus and tail, we found no significant difference between the groups (F3,27 = 1.146, p = 0.35). These findings suggest that the differences in the volume of the hippocampus corpus and tail between UHR patients who transitioned into psychosis and those who did not could not be accounted for by the effect of antipsychotic medication or cannabis abuse. Although 2 previous studies did not reveal hippocampal volume differences between converters and nonconverters,2,3 we believe that it would be premature to rule out anatomic abnormalities in UHR states in these brain regions. Our study indicates that hippocampal volume reduction may precede the onset of schizophrenia and may be present in prodromal stages, independent of medication effects or the presence or absence of cannabis abuse.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".