Accumulating and heterogeneous network‐knockout profiles in amnestic mild cognitive impairment and Alzheimer’s disease dementia
Bibliographic record
Abstract
Abstract Background Large‐scale brain networks are disrupted in Alzheimer’s disease (AD). We posit that network disruption may be explained by additive and multifactor knockout of multiple networks (functional and structural) as opposed to a single system or set of subsystems. Here we test this hypothesis using a data‐driven structural covariance network analysis. Methods Demographic, imaging and available biomarker data were downloaded from 77 normal controls (NC) (age= 72±6, M/F=33/44), 106 persons with amnestic mild cognitive impairment (MCI) (age= 73±8, M/F=70/36) and 42 persons with Alzheimer’s dementia (age=75±9, M/F=25/17) that participated in the Alzheimer’s Disease Neuroimaging Initiative 2.0. We used a multivariate covariance technique to isolate large‐scale grey matter networks that relate fiber tract microstructure derived at different stages of injury to vertex‐based cortical thickness maps. Cortical thickness was computed from 3.0 Telsa T1‐weighted MRI and tract‐based white matter microstructural measures were performed using diffusion tensor imaging; these data were submitted to a multivariate data‐driven analysis. Measures of network integrity (NII) were computed for each significant covariance network for all participants. Univariate models assessed the relationship between biomarkers, demographic variables and NIIs for each network. Network knockouts were defined as an NII <1.5 SD below the NC group average for a given network. Knockout combinations were characterized across individuals and average knockout numbers were compared between groups. Results Eight network ensembles related to tract‐based connectivity or recapitulated functional network architecture. Degradation of these systems was additively and differentially linked to increased age, APOE e4 genotype and unique combinations of white matter hyperintensity volume, CSF markers (CSF amyloid and Tau protein levels) and hippocampal volume in MCI. Knockouts exponentially and significantly increased across groups: NC>MCI>AD (P<0.01). There were 64 unique network knockout combinations across all participants. Conclusion Heterogeneous pathological exposures may additively and differentially disrupt large‐scale networks, leading to many possible network knockout sequences that accumulate exponentially from MCI to AD. These findings may partially explain the endophenotypic heterogeneity in AD and should be assessed in other cohort samples.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".