Improving Spatial Normalization of Brain Diffusion MRI to Measure Longitudinal Changes of Tissue Microstructure in the Cortex and White Matter
Bibliographic record
Abstract
Background Fractional anisotropy (FA) and mean diffusivity (MD) are frequently used to evaluate longitudinal changes in white matter (WM) microstructure. Recently, there has been a growing interest in identifying experience‐dependent plasticity in gray matter using MD. Improving registration has thus become a major goal to enhance the detection of subtle longitudinal changes in cortical microstructure. Purpose To optimize normalization of diffusion tensor images (DTI) to improve registration in gray matter and reduce variability associated with multisession registrations. Study Type Prospective longitudinal study. Subjects Twenty‐one healthy subjects (18–31 years old) underwent nine MRI scanning sessions each. Field Strength/Sequence 3.0T, diffusion‐weighted multiband‐accelerated sequence, MP2RAGE sequence. Assessment Diffusion‐weighted images were registered to standard space using different pipelines that varied in the features used for normalization, namely, the nonlinear registration algorithm (FSL vs. ANTs), the registration target (FA‐based vs. T 1 ‐based templates), and the use of intermediate individual (FA‐based or T 1 ‐based) targets. We compared the across‐session test–retest reproducibility error of these normalization approaches for FA and MD in white and gray matter. Statistical Tests Reproducibility errors were compared using a repeated‐measures analysis of variance with pipeline as the within‐subject factor. Results The registration of FA data to the FMRIB58 FA atlas using ANTs yielded lower reproducibility errors in white matter ( P < 0.0001) with respect to FSL. Moreover, using the MNI152 T 1 template as the target of registration resulted in lower reproducibility errors for MD ( P < 0.0001), whereas the FMRIB58 FA template performed better for FA ( P < 0.0001). Finally, the use of an intermediate individual template improved reproducibility when registration of the FA images to the MNI152 T 1 was carried out within modality (FA–FA) ( P < 0.05), but not via a T 1 ‐based individual template. Data Conclusion A normalization approach using ANTs to register FA images to the MNI152 T 1 template via an individual FA template minimized test–retest reproducibility errors both for gray and white matter. Level of Evidence 1 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2020;52:766–775.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".