Thalamocortical functional connectivity in youth with attention-deficit/hyperactivity disorder
Bibliographic record
Abstract
BACKGROUND: Few studies have empirically tested the relationships between anatomically defined thalamic nuclei and functionally defined cortical networks, and little is known about their implications in attention-deficit/hyperactivity disorder (ADHD). This study aimed to investigate the functional connectivity of the thalamus in youth with ADHD, using both anatomically and functionally defined thalamic seed regions. METHODS: Resting-state functional MRIs obtained from the publicly available ADHD-200 database were analyzed. Thalamic seed regions were defined functionally and anatomically based on Yeo's 7 resting-state-network parcellation atlas and the AAL3 atlas, respectively. Functional connectivity maps of the thalamus were extracted, and thalamocortical functional connectivity was compared between youth with and without ADHD. RESULTS: Using the functionally defined seeds, significant group differences in thalamocortical functional connectivity and significant negative correlations between thalamocortical connectivity and ADHD symptom severity were observed within the boundaries of corresponding large-scale networks. However, in the analysis using the anatomically defined thalamic seeds, significant group differences in connectivity and significant positive correlations were observed outside the expected boundaries of major anatomic projections. The thalamocortical connectivity originating from the lateral geniculate nuclei of the thalamus was significantly correlated with age in youth with ADHD. LIMITATIONS: The small sample size and smaller proportion of girls were limiting factors. CONCLUSION: Thalamocortical functional connectivity based on the intrinsic network architecture of the brain appears to be clinically relevant in ADHD. The positive association between thalamocortical functional connectivity and ADHD symptom severity may represent a compensatory process recruiting an alternative neural network.
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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 | 0.002 |
| 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".