Evidence map›Paper›PMID 41040166›Full record

ArticlebioRxiv : the preprint server for biology2025

Distinct cellular processes drive motor skill learning in the human brain.

Guillermina Griffa, Marco Palombo, Abraham Yeffal, Hong-Hsi Lee, Susie Y Huang, Valeria Della-Maggiore

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Guillermina GriffaIFIBIO Houssay, School of Medicine, Department of Physiology, University of Buenos Aires, Argentina.ORCID 0000-0002-1648-7621
Marco PalomboCardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology and School of Computer Science and Informatics, Cardiff University, Cardiff, UK.ORCID 0000-0003-4892-7967
Abraham YeffalIFIBIO Houssay, School of Medicine, Department of Physiology, University of Buenos Aires, Argentina.ORCID 0000-0002-9645-6664
Hong-Hsi LeeAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-3663-6559
Susie Y HuangAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-2950-7254
Valeria Della-MaggioreIFIBIO Houssay, School of Medicine, Department of Physiology, University of Buenos Aires, Argentina.ORCID 0000-0001-9586-4255

Funding

CONNECTOME 2.0: DEVELOPING THE NEXT GENERATION HUMAN MRI SCANNER FOR BRIDGING STUDIES OF THE MICRO-, MESO- AND MACRO-CONNECTOMEU01EB026996 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI BASSER, PETER J., HUANG, SUSIE YI · 2018 to 2022
$14.2M
Training and Dissemination CoreP41EB030006 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Susie Yi Huang · 2020 to 2026
$10.9M
Project 4P41EB015896 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ROSEN, BRUCE R · 2012 to 2018
$9.8M
Toward a Validated in Vivo Imaging Marker of Axonal Damage Predictive of Progressive Disability in Multiple SclerosisR01NS118187 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI HUANG, SUSIE YI · 2021 to 2025
$2.8M
Revealing tissue microstructure in the brain gray matter in Alzheimer's disease using in vivo high-gradient diffusion MRIDP5OD031854 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI LEE, HONG HSI · 2021 to 2025
$2.1M
Developing a validated biomarker of cortical neurodegeneration in Alzheimer's disease using high-gradient diffusion MRIR21AG085795 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI HUANG, SUSIE YI · 2024 to 2025
$459k
NIA NIH HHS R21 AG085795NIBIB NIH HHS P41 EB015896NIBIB NIH HHS P41 EB030006NIBIB NIH HHS U01 EB026996NIH HHS DP5 OD031854NINDS NIH HHS R01 NS118187
6 · The paper itself

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.

Identifiers

PMID41040166
PMCPMC12485752

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.