Clinical subtypes of frontotemporal dementia show different patterns of cortical atrophy
Bibliographic record
Abstract
Abstract Background Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder associated with atrophy of the frontal and/or anterior temporal lobes, it is highly heterogeneous and represents about 5% of all cases of dementia and it is one of the most common causes of early‐onset dementia (Onyike and Diehl‐Schmid, 2013). FTD syndromes can be divided into three major groups: the behavioral variant (bvFTD) characterised by prominent early behavioral and personality changes, and the two language variants: the semantic variant (svFTD) and the non‐fluent primary progressive aphasia (pnfaFTD). Method In our study we used MRI scans from a frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) database: 133 age and sex matched controls, 70 bvFTD, 36 svFTD and 30 pnfaFTD. Each scan was pre‐processed (Intensity normalization, N3, linear registration, brain masking); tissue classification was performed using BISON (Dadar 2020). All scans were then processed using FALCON (Fonov 2020) to extract its cortical surface anc calculate thickness. Statistical analysis was performed on vertex level. Result Figure 1 shows relative cortical difference between groups relative to controls, Figure 2 shows areas of significant differences (FDR 1%): the decrease of cortical thickness in the bvFTD group was located in frontal lobes, bilaterally as well as the temporal poles (specially in medial frontal and dorsolateral prefrontal areas). In svFTD, however, cortical thinning is limited to the anterior and lateral temporal lobe, predominantly on the left side. Finally, pnfaFTD shows left sided cortical thinning in prefrontal and Broca’s areas. Conclusion The identification of predicted patterns of atrophy in each FTD subtype supports the validity of FALCON as an accurate tool to measure cortical changes in neurodegenerative disease. FALCON could be a useful tool for the development of the biomarkers associated with disease and improve understanding of disease progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".