Decreased cortical gyrification and surface area in the left medial parietal cortex in patients with <scp>treatment‐resistant</scp> and <scp>ultratreatment‐resistant</scp> schizophrenia
Bibliographic record
Abstract
Aim Validating the vulnerabilities and pathologies underlying treatment‐resistant schizophrenia (TRS) is an important challenge in optimizing treatment. Gyrification and surface area (SA), reflecting neurodevelopmental features, have been linked to genetic vulnerability to schizophrenia. The aim of this study was to identify gyrification and SA abnormalities specific to TRS. Methods We analyzed 3T magnetic resonance imaging findings of 24 healthy controls (HCs), 20 responders to first‐line antipsychotics (FL‐Resp), and 41 patients with TRS, including 19 clozapine responders (CLZ‐Resp) and 22 FL‐ and clozapine‐resistant patients (patients with ultratreatment‐resistant schizophrenia [URS]). The local gyrification index (LGI) and associated SA were analyzed across groups. Diagnostic accuracy was verified by receiver operating characteristic curve analysis. Results Both CLZ‐Resp and URS had lower LGI values than HCs (P = 0.041, Hedges g [gH] = 0.75; P = 0.013, gH = 0.96) and FL‐Resp (P = 0.007, gH = 1.00; P = 0.002, gH = 1.31) in the left medial parietal cortex (Lt‐MPC). In addition, both CLZ‐Resp and URS had lower SA in the Lt‐MPC than FL‐Resp (P < 0.001, gH = 1.22; P < 0.001, gH = 1.75). LGI and SA were positively correlated in non‐TRS (FL‐Resp) (ρ = 0.64, P = 0.008) and TRS (CLZ‐Resp + URS) (ρ = 0.60, P < 0.001). The areas under the receiver operating characteristic curve for non‐TRS versus TRS with LGI and SA in the Lt‐MPC were 0.79 and 0.85, respectively. SA in the Lt‐MPC was inversely correlated with negative symptoms (ρ = −0.40, P = 0.018) and clozapine plasma levels (ρ = −0.35, P = 0.042) in TRS. Conclusion LGI and SA in the Lt‐MPC, a functional hub in the default‐mode network, were abnormally reduced in TRS compared with non‐TRS. Thus, altered LGI and SA in the Lt‐MPC might be structural features associated with genetic vulnerability to TRS.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".