Amyloid-β and tau pathologies relate to distinctive brain dysconnectomics in preclinical autosomal-dominant Alzheimer’s disease
Bibliographic record
Abstract
The human brain is composed of functional networks that have a modular topology, where brain regions are organized into communities that form internally dense (segregated) and externally sparse (integrated) subnetworks that underlie higher-order cognitive functioning. It is hypothesized that amyloid-β and tau pathology in preclinical Alzheimer’s disease (AD) spread through functional networks, disrupting neural communication that results in cognitive dysfunction. We used high-resolution (voxel-level) graph-based network analyses to test whether in vivo amyloid-β and tau burden was associated with the segregation and integration of brain functional connections, and episodic memory, in cognitively unimpaired Presenilin-1 E280A carriers who are expected to develop early-onset AD dementia in ∼13 y on average. Compared to noncarriers, mutation carriers exhibited less functional segregation and integration in posterior default-mode network (DMN) regions, particularly the precuneus, and in the retrospenial cortex, which has been shown to link medial temporal regions and cortical regions of the DMN. Mutation carriers also showed greater functional segregation and integration in regions connected to the salience network, including the striatum and thalamus. Greater tau burden was associated with lower segregated and integrated functional connectivity of DMN regions, particularly the precuneus and medial prefrontal cortex. In turn, greater tau pathology was related to higher segregated and integrated functional connectivity in the retrospenial cortex and the anterior cingulate cortex, a hub of the salience network. These findings enlighten our understanding of how AD-related pathology distinctly alters the brain’s functional architecture in the preclinical stage, possibly contributing to pathology propagation and ultimately resulting in dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".