Using CForest to Analyze Diffusion Tensor Imaging Data: A Study of White Matter Integrity in Healthy Aging
Bibliographic record
Abstract
Healthy aging has been associated with a global reduction in white matter integrity, which is thought to reflect cognitive decline. The present study aimed to investigate this reduction over a broad range of the life span, using diffusion tensor imaging analyzed with conditional inference random forest modeling (CForest). This approach is sensitive to subtle and potentially nonlinear effects over the age continuum and was used to characterize the progression of decline in greater detail than has been possible in the past. Data were collected from 45 healthy individuals ranging in age from 19 to 67 years. Fractional anisotropy (FA) was estimated using probabilistic tractography for a number of major tracts across the brain. Age coincided with a nonlinear decrease in FA, with onset beginning at ∼30 years of age and the steepest declines occurring later in life. However, several tracts showed a transient increase before this decline. The progression of decline varied by tract, with steeper but later decline occurring in more anterior tracts. Finally, strongly right-handed individuals demonstrated relatively preserved FA until more than a decade following the onset of decline of others. These results demonstrate that using a novel, nonparametric analysis approach, previously reported reductions in FA with healthy aging were confirmed, while at the same time, new insight was provided into the onset and progression of decline, with evidence suggesting increases in integrity continuing into adulthood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".