Distinct cellular processes drive motor skill learning in the human brain
Bibliographic record
Abstract
Despite decades of research, the biological mechanisms by which motor skills consolidate in the human brain remain poorly understood. Diffusion MRI provides a unique opportunity to probe biological processes non-invasively, as water displacements occur on the micrometer scale. Using diffusion tensor imaging (DTI), our team showed that motor sequence learning (MSL) induces microstructural changes in the hippocampus and key motor regions, suggesting that declarative and procedural systems may operate as part of the same network. Yet DTI cannot identify the cellular source of these changes, leaving open whether they reflect structural plasticity -remodeling of dendritic and astrocytic processes described in rodents- or transient homeostatic responses that accompany learning -neuronal and astrocytic swelling. Here, we combined ultra-high-gradient diffusion MRI with the compartment-based Soma and Neurite Density Imaging (SANDI) model to disentangle the cellular basis of motor skill memory consolidation. DTI showed that MSL induced rapid microstructural changes in the hippocampus, precuneus, and motor regions, but only those in the precuneus and posterior parietal cortex (PPC) persisted overnight. SANDI revealed that DTI changes were driven by two distinct cellular processes: a transient enlargement of the cell soma across all regions consistent with a short-lived homeostatic response, and a sustained rise in cell-process density restricted to the precuneus and PPC, compatible with structural plasticity. By decomposing diffusion signals into their cellular sources, our work disambiguates transient and enduring processes, providing the first non-invasive evidence for the cellular basis of human motor memory consolidation and a framework for studying neuroplasticity in vivo.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".