Frontoparietal network integrity supports cognitive function in pre‐symptomatic frontotemporal dementia: Multimodal analysis of brain function, structure, and perfusion
Bibliographic record
Abstract
INTRODUCTION: Genetic mutation carriers of frontotemporal dementia can remain cognitively well despite neurodegeneration. A better understanding of brain structural, perfusion, and functional patterns in the pre-symptomatic stage could inform accurate staging and potential mechanisms. METHODS: We included 207 pre-symptomatic genetic mutation carriers and 188 relatives without mutations. The gray matter volume, cerebral perfusion, and resting-state functional network maps were co-analyzed using linked independent component analysis (LICA). Multiple regression analysis was used to investigate the relationship of LICA components to genetic status and cognition. RESULTS: Pre-symptomatic mutation carriers showed an age-related decrease in the left frontoparietal network integrity, while non-carriers did not. Executive functions of mutation carriers became dependent on the left frontoparietal network integrity in older age. DISCUSSION: The frontoparietal network integrity of pre-symptomatic mutation carriers showed a distinctive relationship to age and cognition compared to non-carriers, suggesting a contribution of the network integrity to brain resilience. HIGHLIGHTS: A multimodal analysis of structure, perfusion, and functional networks. The frontoparietal network integrity decreases with age in pre-symptomatic carriers only. Executive functions of pre-symptomatic carriers dissociated from non-carriers.
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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 | 0.000 |
| 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".