Methodological evaluation of brainasymmetry based on T1w MRI with application in Parkinson's disease neuroimaging
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) techniques enable us to study the neurobiology of the cerebral asymmetry, which is important in functional domains, e.g., language, motor skills, etc., and may interact with endogenous structural organization.One of the main steps in MRI workflow is image registration, the process of spatially aligning the participants' MRI to a reference image.The accuracy of the registration can be impacted by the choice of registration parameters and steps with downstream impact on the accurate estimation of regional and global brain asymmetry.Currently, a systematic analysis of the methodological choices for registration protocols on reliable detection of brain asymmetry is lacking.We compared common image registration methodologies with different parametric settings to establish their sensitivity for detecting brain asymmetry and its relationship with symptoms in Parkinson's disease.We leveraged a large sample (N=438) of de novo Parkinson patients and matched controls to explore the relationship of brain asymmetry with neurodegenerative pathology at baseline and over the course of the disease progression.Parametric comparison revealed that multilevel parametric settings result in larger effect sizes in detecting asymmetry but at higher computational cost.Subject-specific registration to an unbiased template with resampling to ICBM stereotaxic space is the most sensitive to the local characteristics (i.e.asymmetry), while direct registration approaches are more sensitive to global effects of group measures (i.e., age).Demarcation of longitudinal trajectories based on the baseline asymmetry patterns found small effect size but nevertheless significant relationship between brain asymmetry at baseline (regardless of directionality or magnitude) and longitudinal symptom changes for MoCA, and its directionally for putamen.
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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 | 0.033 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".