Dysconnectivity of multiple resting-state networks in patients with schizophrenia who have persistent auditory verbal hallucinations
Bibliographic record
Abstract
BACKGROUND: Functional neuroimaging studies on schizophrenia have suggested abnormal task-related functional connectivity in patients with schizophrenia who have auditory verbal hallucinations (AVHs). However, little is known about intrinsic functional connectivity in these patients. METHODS: Between January 2009 and February 2010, we studied patients with schizophrenia who had persistent and treatment-refractory AVHs in comparison with healthy controls. Using functional magnetic resonance imaging, we studied the functional connectivity of multiple resting state networks (RSNs) and their relation to symptom severity. We analyzed the data using a spatial group independent component analysis, and we used random-effects t tests to compare spatial components between groups. RESULTS: There were 10 patients and 14 controls enrolled in this study. In total, 16 RSNs were identified, from which we selected 4 networks of interest for further analyses. Within a speech-related network, patients showed increased connectivity in bilateral temporal regions and decreased connectivity in the cingulate cortex. Within 2 additional RSNs associated with attention and executive control, respectively, patients exhibited abnormal connectivity in the precuneus and right lateral prefrontal areas. We found correlations between measures of AVH severity and functional connectivity of the left anterior cingulate, left superior temporal gyrus and right lateral prefrontal cortex. LIMITATIONS: The relatively small sample size, the patients' use of antipsychotic medication and the lack of a clinical control group have to be considered as potential limitations. CONCLUSION: Our findings indicate that disrupted intrinsic connectivity of a speech-related network could underlie persistent AVHs in patients with schizophrenia. In addition, the occurrence of hallucinatory symptoms seems to modulate RSNs associated with attention and executive control.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".