Global brain connectivity alterations in patients with schizophrenia and bipolar spectrum disorders
Bibliographic record
Abstract
BACKGROUND: The human brain is organized into functionally distinct modules of which interactions constitute the human functional connectome. Accumulating evidence has implicated perturbations in the patterns of brain connectivity across a range of neurologic and neuropsychiatric disorders, but little is known about diagnostic specificity. Schizophrenia and bipolar disorders are severe mental disorders with partly overlapping symptomatology. Neuroimaging has demonstrated brain network disintegration in the pathophysiologies; however, to which degree the 2 diagnoses present with overlapping abnormalities remains unclear. METHODS: We collected resting-state fMRI data from patients with schizophrenia or bipolar disorder and from healthy controls. Aiming to characterize connectivity differences across 2 severe mental disorders, we derived global functional connectivity using eigenvector centrality mapping, which allows for regional inference of centrality or importance in the brain network. RESULTS: Seventy-one patients with schizophrenia, 43 with bipolar disorder and 196 healthy controls participated in our study. We found significant effects of diagnosis in 12 clusters, where pairwise comparisons showed decreased global connectivity in high-centrality clusters: sensory regions in patients with schizophrenia and subcortical regions in both patient groups. Increased connectivity occurred in frontal and parietal clusters in patients with schizophrenia, with intermediate effects in those with bipolar disorder. Patient groups differed in most cortical clusters, with the strongest effects in sensory regions. LIMITATIONS: Methodological concerns of in-scanner motion and the use of full correlation measures may make analyses more vulnerable to noise. CONCLUSION: Our results show decreased eigenvector centrality of limbic structures in both patient groups and in sensory regions in patients with schizophrenia as well as increased centrality in frontal and parietal regions in both groups, with stronger effects in patients with schizophrenia.
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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 | 0.002 |
| 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".