P4‐112: Network efficiency changes in Alzheimer's diease and mild cognitive impairment
Bibliographic record
Abstract
Since Alzheimer's disease (AD) disrupts the structure and function of the brain many years prior to the emergence of outward symptoms, one of the current priorities in AD research is to discover new ways with which to identify the disease at an early stage, with the aim of modifying its progression. Based upon theAlzheimer's Disease Neuroimaging Initiative (ADNI), a large multi-center, longitudinal study of AD, we have utilized morphological and graph theoretical techniques to identify patterns in structural cortical connectivity unique to AD and mild cognitive impairment (MCI). Structural T1 images from the ADNI database were processed using the Civet pipeline to obtain an estimate of cortical thickness for each subject. The resulting cortical surface was parcellated using the AAL atlas, and a correlation matrix was obtained for each of the AD, MCI, and NC groups, based upon the mean thickness for each cortical region. To investigate the network properties of the resulting cortical networks, we computed global and local efficiency, two graph measures which estimate the information processing capacity of a network. We evaluated intergroup differences by generating a large bootstrap sample of random permutations of the correlative networks, across a range of graph sparsity constraints. Global efficiency decreases across all sparsity values, from NC to MCI to AD. The integrated areas under these curves are significantly different (p < 0.001; see Figure, right). The trend for local efficiency is less clear (see Figure, left), with NC > MCI > AD for low scarcity values, and the reverse relationship higher values. Integration of these curves shows a reverse pattern to that of global efficiency, with NC < AD < MCI (p < 0.001).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".