Large-Scale Brain Simulation to Characterize Neural Circuits of Schizophrenia
Bibliographic record
Abstract
Background: Schizophrenia is a complex disorder with multifactorial etiology involving genetic, environmental, and biological factors.These intertwined factors contribute to the exceptionally complex and challenging pathogenesis of the disease.Despite some advancements in schizophrenia research, the lack of standardized therapeutic approaches undoubtedly adds to the challenges posed by the disorder.Therefore, deeper understanding of this disorder is warranted to identify more effective treatment strategies.The brain, composed of intricate networks of neurons forming complex synaptic connections, represents the most sophisticated structure governing cognitive processes and behaviors within the nervous system.Recent technological advancements such as magnetic resonance imaging, large-scale brain models, and transcriptomic data offer the potential to reveal aberrant connectivity patterns within the brains of schizophrenia patients, providing new perspectives for research and intervention.Objective: This research primarily focuses on differences in brain activity patterns between individuals with schizophrenia and healthy controls.Methods: A mesoscopic-scale brain model is constructed employing a standard MRI dataset containing details of structural and functional connections.Using a field model methodology, this study attempts to simulate brain neural behaviors in different groups and individuals, approximating the empirical functional and structural connectivity data.The interconnected neural network model utilizes coupled Wilson-Cowan models with each representing a given brain region, traversing high-dimensional parameter spaces, thereby enhancing our understanding of brain operations under different conditions.Furthermore, in exploring the role of receptor expression in schizophrenia, this study integrates receptor gene expression maps from the Allen Brain Institute.This integration aims to reveal potential variations in receptor expression among schizophrenic patients, establishing a connection between the computational model and the biological impacts triggered by receptor expressions.Results: Through whole-brain simulations using the original Wilson-Cowan model, we observed some differences between the two groups at the aggregate level.However, it is worth noting that iv Conclusion: This study elucidates brain characteristics through large-scale simulations, revealing SC-FC correlations and optimal structure-function relationships.Focused on schizophrenia, it constructs a mesoscopic brain model, integrating receptor expression data to highlight differences in patients, particularly in 5-HT1A receptor expression these differences are not captured by significant differences in the model parameters, which may imply that the differences between the groups stem from other factors not directly captured by the model.Preliminary findings highlight differences among the participant groups, particularly concerning 5-HT1A receptor expression.For a more refined exploration, specifically regarding variations in receptor expression in distinct brain regions, this study applies some adjustments to the original receptor expression data.The adjusted fitting results align with previous literature reports..
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".