34.1 SEPARABLE AND REPLICABLE NEURAL STRATEGIES DURING SOCIAL BRAIN FUNCTION IN PEOPLE WITH AND WITHOUT SEVERE MENTAL ILLNESS
Bibliographic record
Abstract
Case-control study design and disease heterogeneity may be major limiting factors impeding biomarker discovery in brain disorders, including serious mental illnesses. In order to identify biologically/behaviorally driven as opposed to diagnostically driven sub-groups of individuals, we used hierarchical clustering to identify participants with similar patterns of brain activity during a facial Imitate/Observe functional MRI task. Participants (N=179; 109 with a schizophrenia spectrum disorder and 70 healthy controls) were scanned at three sites during the performance of the imitate/observe task. Hierarchical clustering was performed to identify data-driven groups of participants who shared similar patterns of neural circuit activation. The number of groups was determined using cluster stability analysis, defined as local minimums for instability across a range from 2 to 10 clusters. The new data-driven groups were compared on social and neurocognitive test performance completed out of the scanner. Three clusters with distinct patterns of neural activity were found. Participants showed greater similarity to their cluster than to their diagnostic category (t(178)=14.0, p=1.3x10-30) or site (p > 0.40). The largest cluster represented ‘typical activators’, with activity in the canonical ‘simulation’ circuit. The other clusters represented a ‘diffuse/inefficient’ activating group, and an ‘efficient/deactivating’ group. The efficient/deactivating group had the highest social cognitive and neurocognitive test scores (F(2, 170)=5.32, p=0.006; post-hoc t tests p<0.05). The hierarchical clustering analysis was repeated on a replication sample (N=108; SSD, euthymic bipolar disorder, or HC), which identified the same three cluster patterns. Our findings demonstrate replicable different patterns of neural activity among individuals during a socio-emotional task independent of DSM-diagnosis or scan site. Our findings may provide objective neuroimaging endpoints (or biomarkers) for subgroups of individuals in target engagement research aimed at enhancing cognitive performance independent of diagnostic category.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".