Deficit in parietal memory network underlies auditory hallucination: a longitudinal study
Bibliographic record
Abstract
Abstract Auditory hallucination is a prominent and common symptom in schizophrenia. Previous neuroimaging studies have yielded mixed results of its brain network deficits. We proposed a novel hypothesis that parietal memory network, centered at the precuneus, plays a critical role in auditory hallucination. This network is adjacent and partially overlaps with the default mode network, and has been associated with brain function of familiarity labelling in memory processing. Using a longitudinal design and a large cohort of first-episode, drug-naïve schizophrenia patients, we examined this hypothesis and further investigated whether the functional connectivity patterns of the parietal memory network can serve as a neuroimaging marker for auditory hallucination and help to predict future treatment effects. Resting-state scans from 59 first-episode drug-naïve schizophrenic patients (27 with and 32 without hallucination) and 53 healthy control subjects were acquired at the baseline test, and 56 of them were scanned again after two months. Functional connectivity strength within the parietal memory network and between this network and memory hubs was across the three groups at baseline and follow-up scans. Results showed that decreased functional connectivity strength within the parietal memory network was specific to the auditory hallucination group (p = 0.009, compare to the healthy subjects; p = 0.029, compare to the patients without hallucination), with the precuneus representing the largest group difference. The intra-network connectivity strength of the precuneus negatively correlated with the severity of hallucination at the baseline scan (r = −0.437, p = 0.029), and it was significantly increased after two-month medication (p = 0.039). Logistic regression analysis and crossvalidation test demonstrated that the functional connectivity strength of the precuneus and precuneus-hippocampus connectivity could differentiate patients with or without auditory hallucination with a sensitivity of 0.750 and a specificity of 0.708. Moreover, crossvalidation test showed that these imaging features at the baseline scan well predicted the extents of positive symptom improvement in the hallucination group after the two-month medication (R 2 = 0.433, p = 0.022). Our results provide evidence for a critical role of the parietal memory network underlying auditory hallucination, and further propose a novel neuroimaging marker for identifying patients, accessing severity, and prognosis of treatment effect for auditory hallucination. Abbreviations AH auditory hallucination AHRS Auditory Hallucination Rating Scale AUC Area-under-curve BPRS Brief Psychiatric Rating Scale DMN default mode network DSM-IV Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition DUP duration of untreated psychosis FCS functional connectivity strength HC healthy control IC independent component PMN parietal memory network NMDA N-methyl-D-aspartate RSN resting state network rs-fMRI resting-state functional MRI rTMS repetitive transcranial magnetic stimulation SANS Expanded Version and the Scale for Assessment of Negative Symptoms
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".