Evaluation of functional connectivity in subdivisions of the thalamus in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Previous studies in schizophrenia revealed abnormalities in the cortico-cerebellar-thalamo-cortical circuit (CCTCC) pathway, suggesting the necessity for defining thalamic subdivisions in understanding alterations of brain connectivity.AimsTo parcellate the thalamus into several subdivisions using a data-driven method, and to evaluate the role of each subdivision in the alterations of CCTCC functional connectivity in patients with schizophrenia. METHOD: There were 54 patients with schizophrenia and 42 healthy controls included in this study. First, the thalamic structural and functional connections computed, based on diffusion magnetic resonance imaging (MRI, white matter tractography) and resting-state functional MRI, were clustered to parcellate thalamus. Next, functional connectivity of each thalamus subdivision was investigated, and the alterations in thalamic functional connectivity for patients with schizophrenia were inspected. RESULTS: Based on the data-driven parcellation method, six thalamic subdivisions were defined. Loss of connectivity was observed between several thalamic subdivisions (superior-anterior, ventromedial and dorsolateral part of the thalamus) and the sensorimotor system, anterior cingulate cortex and cerebellum in patients with schizophrenia. A gradual pattern of dysconnectivity was observed across the thalamic subdivisions. Additionally, the altered connectivity negatively correlated with symptom scores and duration of illness in individuals with schizophrenia. CONCLUSIONS: The findings of the study revealed a wide range of thalamic functional dysconnectivity in the CCTCC pathway, increasing our understanding of the relationship between the CCTCC pathway and symptoms associated with schizophrenia, and further indicating a potential alteration pattern in the thalamic nuclei in people with schizophrenia.Declaration of interestNone.
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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".