Investigating schizophrenia using local connectivity considerations within the piriform cortex
Bibliographic record
Abstract
One of the two hypotheses which explain the cause of schizophrenia is the aberrant connectivity between neurons. For example, over 250,000 brain cells are generated every minute in a two months old fetus. These cells slither across the brain, seeking out their proper destination, and then send out billions of axons, similarly to new branches of massive trees in a forest. The axons make connections with other brain cells, and a single neuron may have 100,000 connections with other neurons. This connection building phase is followed by a pruning phase. Many of these synapses will die (e.g. only half of the 200 billions generated neurons will survive to adulthood). If the pruning of the synapses is not efficient, then the aberrant connectivity can lead to diseases like schizophrenia. The second hypothetical mechanism underlying schizophrenia can be the low level of local connections between neurons (excessive synaptic pruning). This two hypotheses are investigated experimentally in this paper. In order to do this investigation, we simulate the brain biological system by introducing a neural network model that embeds two sub-systems (zones) within it. In this model, which tries to reproduce the piriform cortex, we perform changes to the number of connections (by increasing or decreasing them). By modifying the connectivity, we attempt to simulate the pruning process which may cause schizophrenia. The analyzed signals which describe these two zones are EEGs. We have investigated the level of chaos and the synchronization between these two zones. Both of these hypotheses have an impact on the level of chaos, but the excessive synaptic pruning hypothesis has a higher impact on the system dynamics regarding the nonlinear interdependence measure, than the insufficient pruning hypothesis
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".