Functional Connectivity Anomalies in Adolescents with Psychotic Symptoms
Bibliographic record
Abstract
BACKGROUND: Previous magnetic resonance imaging (MRI) research suggests that, prior to the onset of psychosis, high risk youths already exhibit brain abnormalities similar to those present in patients with schizophrenia. OBJECTIVES: The goal of the present study was to describe the functional organization of endogenous activation in young adolescents who report auditory verbal hallucinations (AVH) in view of the "distributed network" hypothesis of psychosis. We recruited 20 young people aged 13-16 years who reported AVHs and 20 healthy controls matched for age, gender and handedness from local schools. METHODS: Each participant underwent a semi-structured clinical interview and a resting state (RS) neuroimaging protocol. We explored functional connectivity (FC) involving three different networks: 1) default mode network (DMN) 2) salience network (SN) and 3) central executive network (CEN). In line with previous findings on the role of the auditory cortex in AVHs as reported by young adolescents, we also investigated FC anomalies involving both the primary and secondary auditory cortices (A1 and A2, respectively). Further, we explored between-group inter-hemispheric FC differences (laterality) for both A1 and A2. Compared to the healthy control group, the AVH group exhibited FC differences in all three networks investigated. Moreover, FC anomalies were found in a neural network including both A1 and A2. The laterality analysis revealed no between-group, inter-hemispheric differences. CONCLUSIONS: The present study suggests that young adolescents with subclinical psychotic symptoms exhibit functional connectivity anomalies directly and indirectly involving the DMN, SN, CEN and also a neural network including both primary and secondary auditory cortical regions.
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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 | 0.000 | 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) | 0.001 | 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".