Mapping the effects of functional and structural network reorganization on the tau‐cognition relationship in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Tau pathology can spread through connectivity‐based networks, with certain regions (or epicenters) accumulating more tau than others. Such spatial vulnerabilities may be due to their unique apical position in the cortical hierarchy, which can be elucidated through ‘gradients of connectivity’ (Margulies 2016 PNAS). Prior work showed that the primary gradient of functional connectivity unveils a uni‐to‐transmodal topography of the healthy neocortex which highly correlates with a cognitive gradient of perception‐to‐abstraction. Here, we hypothesized that the gradients of functional/structural connectivity interact with tau to affect cognitive functions in Alzheimer’s disease (AD). Method We included 213 participants from TRIAD (103 CN Aß‐, 103 CN Aß+, and 75 CI Aß+) with diffusion‐weighted MRI, resting‐state functional MRI, 18F‐MK6240 tau‐PET, and an extensive cognitive battery. We performed non‐linear dimensionality reduction on the individual functional and structural connectomes, and extracted the first components (‘gradients’) ‐explaining most variance (G1FC and G1SC). First, we compared G1FC_or_SC between diagnostic groups. Second, we investigated the interaction effect of G1FC_or_SC*tauSUVR on cognition (across 9 cognitive domains). Last, we investigated whether the tau‐cognition relationship changed in a topography‐specific manner along the cortical hierarchy, within (equally‐sized) gradient‐derived meta‐ROIs along G1FC_or_SC. Results were compared to Braak‐derived regional associations. Analyses were adjusted for age, sex, APOE, education, and multiple comparisons. Result We observed reduced segregation of functional networks in AD compared to controls, with unimodal (lower‐order cognitive) and transmodal (higher‐order cognitive) regions moving closer on G1FC. This may indicate loss of network specialization in AD. Participants who had both higher tau and G1FC alterations had more cognitive impairment (Fig.1A; shown for MMSE/language). This interaction‐effect was less pronounced with G1SC (Fig.1B). Last, tau correlated with cognition in a topography‐specific progressive manner (i.e., along the transmodal‐unimodal G1FC axis and anterior‐posterior G1SC axis) (Fig.1C). Notably, tau correlated with delayed memory progressively along the posterior‐anterior G1SC axis (R2 = 0.93 in all and R2 = 0.95 in A+) and BraakI‐VI axis (R2 = 0.77 in all and R2 = 0.28 in A+). Conclusion Our work supports the contribution of connectome‐driven tau distribution on cognitive impairment in AD. Connectome gradients may provide a spatial framework to study tau spreading along the major axes of brain organization underlying specific cognitive domains.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".