The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Short-Term Learning Induces White Matter Plasticity in the Fornix
Open accessAbstract
Magnetic resonance imaging (MRI) has greatly extended the exploration of neuroplasticity in behaving animals and humans. Imaging studies recently uncovered structural changes that occur in gray and white matter, mainly after long-term training. A recent diffusion tensor imaging (DTI) study showed that training in a car racing game for 2 h induces changes in the hippocampus and parahippocampal gyri. However, the effect of short-term training on the white matter microstructure is unknown. Here we investigated the influence of short learning tasks on structural plasticity in the white matter, and specifically in the fornix, in humans and rats. Human subjects performed a 2 h spatial learning task, and rats underwent training for 1 d in a Morris water maze. Between tasks, subjects were scanned with DTI, a diffusion MRI framework sensitive to tissue microstructure. Using tract-based spatial statistics, we found changes in diffusivity indices in both humans and rats. In both species, changes in diffusion in the fornix were correlated with diffusion changes in the hippocampus, as well as with behavioral measures of improvement in the learning tasks. These results, which provide the first indication of short-term white matter plasticity in the human brain, suggest that the adult brain white matter preserves dynamic characteristics and can be modified by short-term learning experiences. The extent of change in white matter was correlated with their extent in gray matter, suggesting that all components of the neural network are capable of rapid remodeling in response to cognitive experiences.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“It is now known that task learning can alter dMRI attributes (Hofstetter et al., 2013; McKenzie et al., 2014; Sampaio-Baptista et al., 2013; Xiao et al., 2016)”
From The cingulum bundle: Anatomy, function, and dysfunction (2018) · cites this paper as Hofstetter et al., 2013
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The rat brain in stereotaxic coordinates
Palkovits, M. (1983) · Neuropeptides · doi:10.1016/0143-4179(83)90049-5 · Cited by 28,959
The Rat Brain in Stereotaxic Coordinates
Paxinos, G., & Watson, C. (1983) · Elsevier eBooks · doi:10.1016/c2009-0-63235-9 · Cited by 28,585
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Smith, S. M., Jenkinson, M., Johansen‐Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.024 · Cited by 6,598
SMITH, S., & NICHOLS, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.061 · Cited by 5,250
Navigation-related structural change in the hippocampi of taxi drivers
Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S. J., & Frith, C. D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.070039597 · Cited by 3,225
Changes in grey matter induced by training
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004) · Nature · doi:10.1038/427311a · Cited by 2,373
The plastic human brain cortex
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144216 · Cited by 1,929
ADULT NEUROGENESIS IN THE MAMMALIAN CENTRAL NERVOUS SYSTEM
Ming, G., & Song, H. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.051804.101459 · Cited by 1,891
Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review
Assaf, Y., & Pasternak, O. (2008) · Journal of Molecular Neuroscience · doi:10.1007/s12031-007-0029-0 · Cited by 1,537
Self-citation
Cited by
Sorted by citations
A new mechanism of nervous system plasticity: activity-dependent myelination
Fields, R. D. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn4023 · Cited by 794
The cingulum bundle: Anatomy, function, and dysfunction
Bubb, E. J., Metzler-Baddeley, C., & Aggleton, J. P. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.05.008 · Cited by 740
“It is now known that task learning can alter dMRI attributes (Hofstetter et al., 2013; McKenzie et al., 2014; Sampaio-Baptista et al., 2013; Xiao et al., 2016), suggesting a more dynamic role for white matter.”
White matter and cognition: making the connection
Filley, C. M., & Fields, R. D. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00221.2016 · Cited by 479
White Matter Plasticity in the Adult Brain
Sampaio‐Baptista, C., & Johansen‐Berg, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.11.026 · Cited by 432
Needle, A. R., Lepley, A. S., & Grooms, D. R. (2016) · Sports Medicine · doi:10.1007/s40279-016-0666-y · Cited by 332
Metadata
- Authors
- Hofstetter, S., Tavor, I., Tzur Moryosef, S., & Assaf, Y.
- Affiliations
- Tel Aviv University (IL)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2013-07-31
- Volume
- 33
- Issue
- 31
- Pages
- 12844-12850
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6618548
- OpenAlex
- W2052969270
- Cited by
- 190
- References
- 44
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 13 · 2023: 14 · 2022: 12 · 2021: 16 · 2020: 15 · 2019: 19 · 2018: 15 · 2017: 24 · 2016: 17 · 2015: 16 · 2014: 9 · 2013: 4
- FWCI
- 11.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- European Commission (238292)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- white matter plasticity, fornix, short-term learning, neuroplasticity, diffusion tensor imaging, hippocampus, spatial learning, Morris water maze, diffusion anisotropy, human-rat comparison
- MeSH terms
- Adult, Animals, Brain Mapping, Female, Hippocampus, Humans, Learning, Male, Nerve Fibers, Myelinated, Statistics as Topic, Time Factors, Anisotropy, Rats, Wistar, Maze Learning, Fornix, Brain, Rats, Young Adult, Diffusion Tensor Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
