Free‐water Diffusion and Quantitative T1 mapping in FTLD
Bibliographic record
Abstract
Abstract Background Neuroinflammation has been proposed as a common feature of neurodegenerative diseases (NDs). Differences in inflammatory profiles have been observed between NDs suggesting disease specific inflammatory profiles. In frontotemporal lobar degeneration (FTLD), neuroinflammation and proteinopathy are expected in frontal and temporal regions, depending on subtype. Free‐water diffusion (FWD) and T1 maps are non‐specific biomarkers of neuroinflammation, where increased tissue water caused by cytokine release is expected to increase both FWD and T1 values. Here we investigate T1 mapping and FWD as candidate neuroinflammatory biomarker in FTLD. Method Data was acquired in 25 subjects: 8 corticobasal syndrome (CBS, 4M 4F, mean age 65), 3 semantic variant primary progressive aphasia (svPPA, 3F, mean age 80), 4 progressive supranuclear palsy (PSP, 3F, 1M mean age 77), 7 behavioural variant frontotemporal dementia (bvFTD) (6M,1F mean age 68), and 3 healthy control subjects with a family history of FTLD but gene negative (1M,2F, mean age 52). T1‐weighted MPRAGE, diffusion‐weighted EPI (two shells) for free‐water mapping, and MP2RAGE for qT1 mapping were acquired on a Siemens MAGNETOM Prisma 3T scanner. Skull stripping and binary mask creation were done using ICVmapp3r on T1‐weighted MPRAGE scans. MP2RAGE T1 maps were corrected for B1+ using a separately acquired B1+ map. FWD maps were processed with Synb0, FSL's topup, FSL's eddy, and in‐house MATLAB to generate final FWD maps. FWD and T1 maps were co‐registered using FSL flirt. FWD and T1 values were extracted in the caudate, as well as frontal and temporal regions where FTLD pathology is expected. Result FWD maps show good contrast between tissue and CSF, whereas T1 maps show good contrast between grey matter, white matter and CSF. We observe trends in the expected directions with increased T1 and FW in frontal vs occipital regions in PSP, bvFTD, CBS and svPPA patients, and increased FW and T1 in the frontal cortex in bvFTD, CBS, and PSP patients compared to clinically normal individuals. Conclusion Our preliminary results suggest that both FWD and qT1 are increased in areas where pathology is expected in FTLD syndromes. Further studies will as examine the relationship between FWD, qT1 and biofluid based neuroinflammatory biomarkers.
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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 | 0.002 |
| 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) | 0.002 | 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".