Abnormal Asymmetry of Brain Connectivity in Schizophrenia
Bibliographic record
Abstract
Recently, a growing body of data has revealed that beyond a dysfunction of connectivity among different brain areas in schizophrenia patients (SCZ), there is also an abnormal asymmetry of functional connectivity compared with healthy subjects. The loss of the cerebral torque and the abnormalities of gyrification, with an increased or more complex cortical folding in the right hemisphere may provide an anatomical basis for such aberrant connectivity in SCZ. Furthermore, diffusion tensor imaging studies have shown a significant reduction of leftward asymmetry in some key white-matter tracts in SCZ. In this paper, we review the studies that investigated both structural brain asymmetry and asymmetry of functional connectivity in healthy subjects and SCZ. From an analysis of the existing literature on this topic, we can hypothesize an overall generally attenuated asymmetry of functional connectivity in SCZ compared to healthy controls. Such attenuated asymmetry increases with the duration of the disease and correlates with psychotic symptoms. Finally, we hypothesize that structural deficits across the corpus callosum may contribute to the abnormal asymmetry of intra-hemispheric connectivity in schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".