The autism spectrum among transgender youth: default mode functional connectivity
Bibliographic record
Abstract
The common intersection of autism and transgender identities has been described in clinical and community contexts. This study investigates autism-related neurophenotypes among transgender youth. Forty-five transgender youth, evenly balanced across non-autistic, slightly subclinically autistic, and full-criteria autistic subgroupings, completed resting-state functional magnetic resonance imaging to examine functional connectivity. Results confirmed hypothesized default mode network (DMN) hub hyperconnectivity with visual and motor networks in autism, partially replicating previous studies comparing cisgender autistic and non-autistic adolescents. The slightly subclinically autistic group differed from both non-autistic and full-criteria autistic groups in DMN hub connectivity to ventral attention and sensorimotor networks, falling between non-autistic and full-criteria autistic groups. Autism traits showed a similar pattern to autism-related group analytics, and also related to hyperconnectivity between DMN hub and dorsal attention network. Internalizing, gender dysphoria, and gender minority-related stigma did not show connectivity differences. Connectivity differences within DMN followed previously reported patterns by designated sex at birth (i.e. female birth designation showing greater within-DMN connectivity). Overall, findings suggest behavioral diagnostics and autism traits in transgender youth correspond to observable differences in DMN hub connectivity. Further, this study reveals novel neurophenotypic characteristics associated with slightly subthreshold autism, highlighting the importance of research attention to this group.
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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 | 0.000 |
| 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".