Off-Equilibrium Fluctuation-Dissipation Theorem Paves the Way in Alzheimer’s Disease Research
Bibliographic record
Abstract
ABSTRACT INTRODUCTION Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. Although traditional methods have provided insights into brain dynamics in AD, they have limitations in capturing non-equilibrium dynamics across disease stages. Recent studies suggest that dynamic functional connectivity in resting-state networks (RSNs) may serve as a biomarker for AD, but the role of deviations from dynamical equilibrium remains underexplored. OBJECTIVE This study applies the off-equilibrium fluctuation-dissipation theorem (FDT) 1 to analyze brain dynamics in AD, aiming to compare deviations from equilibrium in healthy controls, patients with mild cognitive impairment (MCI), and those with AD. The goal is to identify potential biomarkers for early AD detection and understand disease progression’s mechanisms. METHODS We employed a model-free approach based on FDT to analyze functional magnetic resonance imaging (fMRI) data, including healthy controls, MCI patients, and AD patients. Deviations from equilibrium in resting-state brain activity were quantified using fMRI scans. In addition, we performed model-based simulations incorporating Amyloid-Beta (A β ), tau burdens, and Generative Effective Connectivity (GEC) for each subject. RESULTS Our findings show that deviations from equilibrium increase during the MCI stage, indicating hyperexcitability, followed by a significant decline in later stages of AD, reflecting neuronal damage. Model-based simulations incorporating A β and tau burdens closely replicated these dynamics, especially in AD patients, highlighting their role in disease progression. Healthy controls exhibited lower deviations, while AD patients showed the most significant disruptions in brain dynamics. DISCUSSION The study demonstrates that the off-equilibrium FDT framework can accurately characterize brain dynamics in AD, providing a potential biomarker for early detection. The increase in non-equilibrium deviations during the MCI stage followed by their decline in AD offers a mechanistic explanation for disease progression. Future research should explore how combining this framework with other dynamic brain measures could further refine diagnostic tools and therapeutic strategies for AD and other neurodegenerative diseases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".