Personalized virtual brains of Alzheimer’s Disease link dynamical biomarkers of fMRI with increased local excitability
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the accumulation of abnormal beta-amyloid (Aβ) and hyperphosphorylated Tau (pTau). These proteinopathies disrupt neuronal activity, causing, among others, an excessive and hypersynchronous neuronal firing that promotes hyperexcitability and leads to brain network dysfunction and cognitive deficits. In this study, we used computational network modeling to build a causal inference framework to explain AD-related abnormal brain activity. We constructed personalized brain network models with a set of working points to enable maximum dynamical complexity for each brain. Structural brain topographies were combined, either with excitotoxicity, or postsynaptic depression, as two leading mechanisms of the Aβ and pTau on neuronal activity. By applying various levels of these putative mechanisms to the limbic regions that typically present, with the earliest and largest protein burden, we found that the excitotoxicity is sufficient and necessary to reproduce empirical biomarkers two biometrics associated with AD pathology: homotopic dysconnectivity and a decrease in limbic network dynamical fluidity. This observation was shown not only in the clinical groups (aMCI and AD), but also in healthy subjects that were virtually-diseased with excitotoxicity as these abnormal proteins can accumulate before the appearance of any cognitive changes. The same findings were independently confirmed by a mechanistic deep learning inference framework. Taken together, our results show the crucial role of protein burden-induced hyperexcitability in altering macroscopic brain network dynamics, and offer a mechanistic link between structural and functional biomarkers of cognitive dysfunction due to 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 | 0.000 | 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) | 0.001 | 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".