High resolution diffusion tensor imaging of the human cortex reveals non-linear trajectories over the healthy lifespan
Bibliographic record
Abstract
The human cortex undergoes significant macrostructural and microstructural changes across the lifespan, which can be assessed using high-resolution diffusion tensor imaging (DTI). In healthy individuals, diffusion is typically greater perpendicular to the cortical surface, aligning with neuronal bodies and apical dendrites. This study examined DTI metrics in 190 healthy individuals (ages 5-74 years) to characterize normative cortical changes across neurodevelopment and aging. Whole-brain DTI data were acquired with 1.5 mm isotropic resolution and a b-value of 1000 s/mm² acquired in only 3:36 minutes at 3T. Cortical segmentation was performed exclusively on diffusion images to yield thickness, radiality, fractional anisotropy (FA), mean (MD), axial (AD), and radial diffusivity (RD) in total cortex as well as five lobes and were compared versus age. Cortical thickness decreased exponentially which differed from the diffusion metric cross-sectional age trajectories. FA, MD, AD, and RD exhibited u-shaped trajectories reaching minimum values in adulthood (~20-40 years). In contrast, radiality showed a cubic pattern, declining in childhood, stabilizing from 20-55 years, then decreasing again after 55, with the largest early-life changes in the temporal and occipital lobes and later-life declines in the frontal and parietal lobes. Steeper childhood DTI changes may reflect increased myelination of tangential fibers, as well as the growth of neuronal axons, somata, and dendrites, while elderly changes likely indicate reduced cell body density and radius. This study provides a baseline for future research into neurodevelopment and neurodegenerative diseases across the lifespan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".