Functional connectivity associations with markers of disease progression in <i>GRN</i> mutation carriers
Bibliographic record
Abstract
BACKGROUND: Autosomal dominant progranulin (GRN) mutations are a common genetic cause of frontotemporal lobar degeneration. Though clinical trials for GRN-related therapies are underway, there is an unmet need for biomarkers that can predict symptom onset and track disease progression. We previously showed that presymptomatic GRN carriers exhibit thalamocortical hyperconnectivity that increases with age when they are presumably closer to symptom onset. However, whether hyperconnectivity arises concomitantly with markers of neurodegeneration remains unclear. METHOD: Utilizing T1 and task-free functional magnetic resonance imaging (tf-fMRI) from 49 presymptomatic and 26 symptomatic GRN mutation carriers, we determined the relationships between functional connectivity as measured by voxel-wise whole brain degree and GRN-relevant markers of disease progression, which included plasma neurofilament light chain (NfL) concentrations, CSF complement C1q and C3b protein levels, grey matter atrophy, and OCD symptom severity. RESULT: NfL concentrations were associated with frontotemporoparietal and thalamic hyperconnectivity in presymptomatic GRN carriers and extensive regions of atrophy in symptomatic carriers. Complement levels were associated with regions of hyperconnectivity, but not gray matter, in symptomatic carriers. Presymptomatic carriers with thalamic hyperconnectivity tended to have lower grey matter volume in bilateral insula and left lateral parietal cortex, which are among regions that deteriorate in GRN-FTD. OCD symptom severity was associated with hypoconnectivity across all GRN carriers. CONCLUSION: In presymptomatic carriers, the co-occurrence of hyperconnectivity, high NfL, and low gray matter suggests that tf-fMRI hyperconnectivity may portend the onset of the neurodegenerative phase. These findings point toward hyperconnectivity as an indicator of approaching symptomatic onset.
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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) | 0.003 | 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".