Prolonged latent 'baseline' state of large-scale resting state networks in Alzheimer's disease as revealed by hidden Markov modelling
Bibliographic record
Abstract
Abstract Alzheimer's disease (AD) is a progressive neurodegenerative disorder. While resting state fMRI holds great promise in identification of diagnostic markers, how spatio-temporal dynamics of functional networks are reconfigured in AD remains elusive. We employed hidden Markov model to examine the time-resolved information of resting state fMRI data from Alzheimer's Disease Neuroimaging Initiative dataset. Two hundred and ninety-four participants well selected (23 with AD, 54 with mild cognitive impairment and 217 normal controls). We focused on the mean activation map which allows reliable measurement for statistical characteristics of spatial distribution of the latent states. At the time scale of seconds, we detected a 'baseline' state at which all the resting state networks had low activation levels. Moreover, AD patients tended to spend more time on this 'baseline' state and less time on the default mode network states than healthy elderly subjects. The prolonged latent 'baseline' state in AD probably reflects departure of the brain from criticality. Our findings provide important clues that help understand mechanisms underlying the reorganization of large-scale functional networks for AD.
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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 | 0.003 |
| 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 | 0.001 |
| 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".