Executive functioning-related brain abnormalities associated with the genetic liability for schizophrenia: an activation likelihood estimation meta-analysis
Bibliographic record
Abstract
BACKGROUND: Relatives of schizophrenia patients demonstrate abnormalities in prefrontal cortical activation during executive processing as measured by functional neuroimaging, albeit not consistently. A meta-analysis was conducted to determine whether reliable patterns of brain hypo- and hyperactivity, especially in the middle frontal region, were present in the relatives of patients. METHOD: Seventeen studies, containing 18 samples of relatives and controls, were included in this meta-analysis. Studies were included if relatives of schizophrenia patients were compared to controls, an executive processing task was used, and standard space coordinates were reported for the functional activations. Activation likelihood estimation (ALE) was implemented to find convergence across functional neuroimaging experiment coordinates. A separate analysis was conducted to assess the potential impact of a priori hypothesis testing used in region-of-interest (ROI) approaches on the meta-analysis results. RESULTS: Relatives demonstrated hypo- and hyperactivity in statistically overlapping right middle frontal regions [Brodmann area (BA) 9/10]. Use of an ROI analysis that a priori focused on prefrontal regions resulted in more findings of reduced activity in the middle frontal region. CONCLUSIONS: The cortical regions identified by this meta-analysis could potentially serve as intermediate biological markers in the search for candidate genes for schizophrenia. As neurocognitive deficits are related to functional impairments in patients, a better understanding of neural and genetic vulnerabilities would be beneficial in our efforts to remediate these important deficits.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".