Structure‐function decoupling in genetic frontotemporal dementia
Bibliographic record
Abstract
BACKGROUND: Functional network integrity is important for maintaining cognitive performance during the 10-20 year presymptomatic period of frontotemporal dementia (FTD), conferring resilience to advancing neuropathology and atrophy. The extent to which functional integrity relies on preserved structural connectivity is unclear. Here, we test the relationship between functional connectivity and structural connectivity, termed structure-function coupling, against genetic risk for FTD and disease progression. METHOD: We studied 56 symptomatic and 165 pre-symptomatic FTD-mutation carriers, and 141 family members without mutations, from the GENFI cohort. Diffusion weighted imaging and functional magnetic resonance imaging (Siemens MR platforms) were acquired and analysed using established approaches to quantify participant-level structural and functional connectomes (Figure 1-(1)). Connectomes were defined in the Brainnetome Atlas and re-mapped onto a subcortical network and seven resting-state networks based on the Yeo Networks (Figure 1-(2)). An inter-subject regularized canonical correlation analysis (CCA) with permutation-based cross-validation was used to jointly analyse the structural and functional connectomes (Figure 1-(3-4)). Second-level analysis with robust multiple linear regression models tested for differences between non-carriers, pre-symptomatic carriers and symptomatic carriers in the strength of association between structural and functional CCA subject scores. Age, sex, head motion and scanner site were included as covariates. RESULT: Canonical correlation analysis identified significant components linking structural and functional connectivity. The first component (r=0.656, p <0.001) reflected a structural connectivity pattern with high within- and between-networks loadings (Figure 1-(5)) with strong within-networks functional connectivity and weak-to-negative between-network functional connectivity (Figure 1-(6)). This component associated structural integrity with function segregation, whereby individuals with high structural connectivity within and between networks exhibit greater functional network segregation as shown by strong within-network functional connectivity and weak between network connectivity. The strength of this structure-function coupling was greater for non-carriers compared to pre-symptomatic carriers (Figure 1-(7)). Symptomatic carriers showed minimal relationship between structural and functional scores, indicating structure-function decoupling, consistent with the hypothesis that cognitive decline is triggered by critical decoupling of previously synergistic neural systems. CONCLUSION: Our findings demonstrate progressive de-coupling between structural connectivity and functional segregation over the course of genetic frontotemporal dementia. These results have implications for designing pre-symptomatic disease-modifying 'preventative' trials, supported by imaging-based surrogate markers of neural system dynamics.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".