Hallucination‐ and speech‐specific hypercoupling in frontotemporal auditory and language networks in schizophrenia using combined task‐based f<scp>MRI</scp> data: An f<scp>BIRN</scp> study
Bibliographic record
Abstract
Hypercoupling of activity in speech-perception-specific brain networks has been proposed to play a role in the generation of auditory-verbal hallucinations (AVHs) in schizophrenia; however, it is unclear whether this hypercoupling extends to nonverbal auditory perception. We investigated this by comparing schizophrenia patients with and without AVHs, and healthy controls, on task-based functional magnetic resonance imaging (fMRI) data combining verbal speech perception (SP), inner verbal thought generation (VTG), and nonverbal auditory oddball detection (AO). Data from two previously published fMRI studies were simultaneously analyzed using group constrained principal component analysis for fMRI (group fMRI-CPCA), which allowed for comparison of task-related functional brain networks across groups and tasks while holding the brain networks under study constant, leading to determination of the degree to which networks are common to verbal and nonverbal perception conditions, and which show coordinated hyperactivity in hallucinations. Three functional brain networks emerged: (a) auditory-motor, (b) language processing, and (c) default-mode (DMN) networks. Combining the AO and sentence tasks allowed the auditory-motor and language networks to separately emerge, whereas they were aggregated when individual tasks were analyzed. AVH patients showed greater coordinated activity (deactivity for DMN regions) than non-AVH patients during SP in all networks, but this did not extend to VTG or AO. This suggests that the hypercoupling in AVH patients in speech-perception-related brain networks is specific to perceived speech, and does not extend to perceived nonspeech or inner verbal thought generation.
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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 | 0.001 |
| 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) | 0.001 | 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".