Neuroimaging Biomarkers for Friedreich Ataxia: A Cross‐Sectional Analysis of the <scp>TRACK</scp>‐<scp>FA</scp> Study
Bibliographic record
Abstract
OBJECTIVE: We aimed to quantify differences in the brain and spinal cord between Friedreich ataxia and controls, stratified by age and disease stage, including for the first time in young children. METHODS: TRACK-FA is the largest prospective, longitudinal, multi-modal neuroimaging study in Friedreich ataxia to date. We assessed individuals with Friedreich ataxia and controls, 5 to 42 years, at 7 sites across 4 continents. The 17 imaging primary outcome measures (POMs) were selected from metrics that showed a significant longitudinal change in previous small-scale studies. These included brain and spinal cord morphometry (structural magnetic resonance imaging [MRI]) and microstructure (diffusion MRI); brain iron levels (quantitative susceptibility mapping); and spinal cord biochemistry (magnetic resonance spectroscopy). This study is registered with ClinicalTrials.gov (NCT04349514). RESULTS: Between February 2021 and August 2023, we assessed 169 individuals with Friedreich ataxia and 95 controls. Compared to controls, individuals with Friedreich ataxia had lower volume of dentate nucleus and superior cerebellar peduncles; smaller cross-sectional area of spinal cord; lower fractional anisotropy and higher diffusivity in spinal cord and superior cerebellar peduncles; and lower total N-acetyl-aspartate/myo-inositol ratio in spinal cord. Morphometric differences in spinal cord and superior cerebellar peduncles increased dramatically with age during childhood, with rapid development in controls, but not in Friedreich ataxia. Many imaging POMs showed significant associations with clinical severity. INTERPRETATION: Our findings provide strong imaging evidence of impaired development of spinal cord and superior cerebellar peduncles during childhood in Friedreich ataxia and open the way for the use of neuroimaging biomarkers in clinical trials. ANN NEUROL 2025;98:386-397.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".