[P1–415]: IN GENETIC FRONTOTEMPORAL DEMENTIA, FUNCTIONAL NETWORK EFFICIENCY IS MAINTAINED UNTIL THE ONSET OF SYMPTOMS: EVIDENCE FOR FUNCTIONAL RESILIENCE TO STRUCTURAL CHANGE
Bibliographic record
Abstract
Structural brain changes occur decades before symptom onset in genetic frontotemporal dementia (Rohrer et al., Lancet Neurol 2015; 4422: 1–10); though cognitive function can be well maintained presymptomatically. We assessed whether the integrity of functional networks would be maintained despite significant atrophy. 99 subjects from the Genetic Frontotemporal Dementia Initiative (GENFI) were recruited. Functional MRI was acquired at 11 sites at a field strength of 1.5T or 3T. 7 subjects were removed because of MRI motion artefacts to leave 24 with genetic FTD (11 C9orf72, 4 GRN and 9 MAPT genes) and 68 asymptomatic related gene carriers (17 C9orf72, 38 GRN, 13 MAPT). Preprocessing was optimised for atrophic brains and included motion correction using wavelet despiking. We assessed topological properties in a binarised network (3% equidense threshold) normalised against 500 random networks with identical degree distribution. Group comparison used mixed effects linear regression and breakpoint analysis used a piecewise linear regression model. FTD subjects demonstrated reduced connection strength (t=-2.56, p=0.01) but not global efficiency (the sum of the inverse path lengths, t=-1.16, p=0.7) compared to asymptomatic gene carriers. However, we found a non-linear pattern of change in global efficiency characterised by a sharp decline at the estimated time of disease onset (significant breakpoint t=2.7, p=0.009), but not connection strength (t=0.18, p=0.9), shown in Figure 1. The changes in the functional network were most marked in highly connected hub regions: 1. hubs were more weakly connected in FTD (df=172.0, t=2.13, p=0.02); 2. the difference in efficiency between hub and non-hub regions was abolished in the FTD group (effect size 0.0025, t=0.73, p=0.5) compared with gene carriers (effect size -0.01, t=-6.12, p<0.00001) and the difference between these effects was significant (df=172.0, t=3.24, p=0.001). We propose that resilience in functional brain networks protects cognition despite significant neuropathology. Our results suggest that a hub-targeted loss of functional connectivity leads to a decompensation in global efficiency that precipitates a sharp decline in cognitive function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".