Utility of Harmonisation for Fixel‐Based Metrics in Travelling Subjects and Alzheimer's Disease Data
Bibliographic record
Abstract
Fixel-based analysis (FBA) is an advanced diffusion MRI analysis technique that facilitates the evaluation of white matter microstructure within 'fixels' (specific fibre populations within a voxel). In recent years, FBA has gained prominence for its ability to better characterise fibre tract-specific changes than the more conventional diffusion MRI approaches and has shown promise in elucidating the pathophysiology of psychiatric and neurological diseases. However, FBA has been predominantly limited to single-centre studies, minimising the generalisability of the technique. In this study, the popular ComBat harmonisation technique was adapted for whole-brain FBA of diffusion MRI data. The study evaluates the effectiveness of ComBat in harmonising FBA metrics of fibre density, fibre cross-section and the combined metric of fibre density and cross-section in a large travelling subject dataset (n = 49, scan = 162). Participants were scanned across multiple centres, using different scanner models and imaging protocols, and FBA metrics were compared under these varying conditions before and after harmonisation. In addition, the impact of ComBat harmonisation on disease-related findings was evaluated in an independent multi-centre Alzheimer's disease (AD) dataset, by comparing the same fixel-based measures in patients with AD (n = 27) to those in cognitively normal control participants (n = 29) before and after ComBat harmonisation. We demonstrated that ComBat harmonisation effectively mitigated variability across scanner sites, scanner models, and protocols, in the travelling subject dataset, thus enhancing the comparability of FBA metrics. Notably, ComBat harmonisation improved the detection of AD-related changes in the fornix, a critical white matter tract associated with cognitive function, and strengthened the correlations between FBA metrics and cognitive scores. These results underscore the potential of ComBat harmonisation in enhancing the reliability of multi-centre neuroimaging studies, supporting the use of harmonisation techniques for accurate detection of disease-specific changes in neurodegenerative conditions. The ability to perform ComBat harmonisation within the whole-brain FBA pipeline may help further this fibre-specific technique into large-scale multi-centre studies.
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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".