Shorter and inflexible intrinsic neural timescales of the self in schizophrenia
Bibliographic record
Abstract
Background: Schizophrenia is hypothesized to involve a disturbance in the temporal dynamics of self-processing, specifically within the interoceptive, exteroceptive, and cognitive layers of the self. This study aimed to investigate the intrinsic neural timescales (INTs) within these self-processing layers among people with schizophrenia. Methods: We conducted a functional magnetic resonance imaging (fMRI) study to investigate INTs, as measured by the autocorrelation window, among people with schizophrenia and healthy controls during both resting-state and task (memory encoding and retrieval) conditions. We obtained data from the UCLA Consortium for Neuropsychiatric Phenomics data set and preprocessed using fMRIPrep. Results: We included 45 people with schizophrenia and 65 healthy controls. Compared with controls, participants with schizophrenia exhibited significantly shorter INTs across all 3 self-processing layers during rest ( p < 0.05). In addition, those with schizophrenia showed less INT shortening during task states, leading to reduced rest–task differences in INT across all self-processing layers ( p < 0.05). We observed similar patterns of shortened INTs in primary sensory and motor regions. Limitations We included people with schizophrenia taking medication, which may influence INTs; our study was also limited by the relatively slow temporal resolution of the fMRI data and the higher variability of the autocorrelation function in the schizophrenia group, compared with the control group. Conclusion: Our findings suggest that schizophrenia is characterized by a global temporal disturbance of the self, manifesting as shorter and inflexible INTs across self-processing and sensorimotor regions. These results support the hypothesis that schizophrenia involves a fundamental disruption in the temporal integration of neural signals, contributing to the core self-disturbance observed in the disorder.
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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) | 0.001 | 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".