Coordinate Network Mapping of Focal Brain Volume Differences in ADHD Reveals Common Patterns That Lack Specificity: A Systematic Review
Bibliographic record
Abstract
Objective: Attention-deficit/hyperactivity disorder (ADHD) has been associated with decreased regional brain volume, yet no consistent localization has emerged across studies. This discrepancy has been attributed to ADHD's diagnostic heterogeneity; however, one alternative is that ADHD is associated with alterations of brain networks, not individual regions. To test this hypothesis, we compared a traditional anatomic likelihood estimate (ALE) with a "coordinate network mapping" (CNM) approach using data from 38 studies comparing regional brain volumes in ADHD versus healthy controls. Methods: We performed an ALE analysis, determining above-chance convergence between experiments. Next, we calculated the overlap with the putamen and default mode network, defined a priori. We then applied CNM, generating connectivity maps for each study and statistically comparing these maps to identify common areas of connectivity across studies. Finally, we compared the network map of ADHD with several control groups of neuropsychiatric disorders and with randomly generated coordinates. Results: ALE identified no significant spatial convergence between experiments. We also found only limited spatial overlap with the default mode network and weak functional connectivity with the putamen. Conversely, CNM revealed that the heterogenous coordinates fell within a consistent brain network characterized by connectivity with the reward and cingulo-opercular "action mode" networks. However, we could not differentiate this network from the CNM-derived networks in control groups. Interpretation: Although this network is biologically plausible and consistent with ADHD symptoms, the findings suggest that this network is not specific to ADHD and may reflect large-scale brain networks. Although this meta-analysis adds to the literature on the neurobiology of ADHD, the nonspecific findings convey the importance of studying ADHD at the symptom level.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".