Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach
Bibliographic record
Abstract
INTRODUCTION: Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance. METHODS: We propose a highly-available nodes approach for constructing brain network of patients with MCI and AD. With resting-state functional magnetic resonance imaging (rs-fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier. RESULTS: Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes. CONCLUSIONS: The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs-fMRI data for construction and topology analysis brain network.
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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 | 0.001 |
| 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 | 0.000 |
| 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".