Ventricle shape features as a reliable differentiator between the behavioral variant frontotemporal dementia and other dementias
Bibliographic record
Abstract
Abstract Background In the absence of a pathologic genetic mutation, diagnostic certainty of the behavioral variant frontotemporal dementia (bvFTD) still relies on convergence of clinical criteria and imaging findings. Using deformation‐based morphometry (DBM), we recently showed that ventricular volume can discriminate bvFTD from cognitively normal controls (CN)1. Here, we investigate the performance of shape‐based ventricular features to differentiate bvFTD from Alzheimer’s Dementia (AD), mild cognitive impairment (MCI), semantic and progressive non‐fluent aphasia variants of FTD (SV and PNFA), and CN. Method Data included 825 participants: 59 bvFTD, 28 SV, 30 PNFA, and 105 CN from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) and 322 age‐matched amyloid β+ MCI, 127 age‐matched amyloid β+ AD and 164 age‐matched CN from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A previously validated patch‐based label fusion technique was employed to segment the lateral ventricles 2. Linearly registering the images to ICBM2009c template, the ventricles were divided into anterior‐posterior (using coronal slice y=120) and left‐right halves (separately for frontal and temporal lobes). Total ventricle volume (TVV), anterior‐to‐posterior ratio (APR) and frontal and temporal parenchymal left‐right ratios (LRFR and LRTR) were used alone and in combination with each other, age, and sex, to differentiate bvFTD from all other cohorts. A support vector machine classifier (fitcsvm from MATLAB with a linear kernel) was trained on each feature set to perform the classification task using 10‐fold cross validation, repeated 100 times. Result Using APR to identify bvFTD from a mixed age‐matched cohort (Control, MCI, AD, SV and PNFA) yielded an accuracy of 92%. Adding additional features did not improve global classification performances. The top accuracies against each individual cohort were 89% for bvFTD vs Control, 91% for bvFTD vs MCI using APR+TVV, and 83% for bvFTD vs AD using APR+LRTR. The best accuracies discriminating bvFTD from SV and PNFA were 71% and 76% respectively, using APR+TVV+LRTR+LRFR. Conclusion The APR is an easy to obtain and generalizable ventricle‐based feature from T1‐weighted MRIs (routinely acquired and available in the clinic) that can be used to differentiate bvFTD from normal subjects, other FTD variants, MCI, and AD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".