Functional Connectome Alterations Across the Spectrum of Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) pathology is marked by the deposition of amyloid-β plaques and hyperphosphorylated tau neurofibrillary tangles. This pathology begins years before the first clinical symptoms emerge and progresses through several stages before clinical diagnosis. AD’s pathology alters the brain’s functional connectivity (FC) patterns and these altered FC patterns may serve as imaging markers to diagnose and assess the progression of AD. In this review, we summarize the recent literature investigating connectome alterations across the AD spectrum, spanning preclinical, prodromal, and clinical stages. We identify specific regions and functional connections that are altered across different stages of AD and discuss their relevance to cognition. We also highlight the potential of connectome-based predictive modeling as an individual-specific method in the quest for early diagnosis of AD. The default mode network (DMN) shows significant changes across stages, and its core hubs consistently exhibit reduced connectivity with the medial temporal lobe in association with disease pathology. From a dynamic FC point of view, the flexibility of different networks, especially DMN, was reduced as a result of AD onset and persisted across the stages. These disruptions were also linked to reduced cognitive performance, particularly in domains such as memory and executive function. By bringing together evidence on both disease-specific and stage-specific alterations in FC, this review aims to identify patterns that are most informative for understanding AD progression and their potential for advancing early diagnosis.
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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 | 0.001 | 0.001 |
| 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".