Novel Transfer Function Based Approaches for Analysis of Resting-State Connectivity in Patients with Optic Neuritis
Bibliographic record
Abstract
Generating reliable imaging markers to study connectivity changes of brain regions associated with optic neuritis (ON) allows ON to be used as a system model for advanced treatment and pathology of multiple sclerosis (MS). Markers that can differentiate ON subject groups are obtained using new transfer function based approaches to characterize the connectivity paths of visual signal propagation. We suggest three thresholding methods to alleviate the effect of possible noisy peaks in the transfer function spectrum. Artificial neural networks (ANN) are used to provide an improved classifier via the integration of the proposed metrics. We propose a new simulated annealing approach to improve the sensitivity of ANN trained with small datasets such as obtained from ON data. We evaluate the ability of the metrics to differentiate between normal subjects and ON patients, with and without MS, using two-way and three-way receiver operator characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".