Robust hierarchically organized whole‐brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography
Bibliographic record
Abstract
Abstract Background Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface‐based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico‐subcortical networks from multi‐cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles. Methods We utilized resting‐state and anatomical MRI data from n = 406 participants ( n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume‐based approaches, (2) a surface‐based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT). Results The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface‐based approach (Cohen's D volume vs. surface 0.27–1.00, all p < 10 −6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18–0.96, all p < 10 −4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface‐based approach and PINT (Number of differing pairwise‐correlations: volume: 404, surface: 570, PINT: 628, FDR corrected). Conclusion Surface‐based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".