Toward a structural plasticity paradigm: New approaches to overcoming plateaus in neuro-rehabilitation outcomes

Keywords

Structural plasticity / Neurorehabilitation / Therapeutic plateau / Closed-loop Intervention / Brain-computer interface (BCI)

CRediT authorship contribution statement

Sen Yin: Writing - review & editing. Danyang Li: Methodology, Writing - original draft, Writing-review & editing. Shouwei Yue: Writing - review & editing, Conceptualization, Funding acquisition. All the authors have read and approved the final version of this manuscript.

Ethics approval

All studies involving human participants cited herein were con- ducted in accordance with the Declaration of Helsinki, and those in- volving animals followed the ARRIVE guidelines. Approval from in- stitutional review boards was obtained as per original publications.

Funding

This study was supported by the National Natural Science Foundation of China [Grant no. 82572920].

Data availability

Not applicable.

Declaration of Competing Interest

The authors declare no conflict of interest. Shouwei Yue, Editor-in- Chief of Healthcare and Rehabilitation confirms no involvement in any stage of this article's peer-review process, ensuring unbiased editorial decision-making.

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors declare that no Generative AI or AI-assisted technolo- gies were used in the writing process.

Acknowledgments

We would like to express our sincere gratitude to all our colleagues and collaborators who contributed to the completion of this study and the preparation of this manuscript.

Special thanks are extended to Danyang Li for his diligent work in conducting the literature research and drafting the article, Sen Yin for his valuable suggestions throughout the research and writing process, and Shouwei Yue for his key role in determining the overall framework and academic positioning of this work.

The authors are solely responsible for the content of this article.

References

[1] Grefkes C, Fink GR. Connectivity-based approaches in stroke and recovery of function. Lancet Neurol. 2014; 13(2):206-216. https://doi.org/10.1016/S1474-4422(13)70264-3

[2] Krakauer JW, Carmichael ST, Corbett D, Wittenberg GF. Getting neurorehabilitation right: what can be learned from animal models? Neurorehabil Neural Repair. 2012; 26(8):923-931. https://doi.org/10.1177/1545968312440745

[3] Nudo RJ. Functional and structural plasticity in motor cortex: implications for stroke recovery. Phys Med Rehabil Clin N Am. 2003; 14(1 Suppl):S57-S76. https://doi.org/10.1016/s1047-9651(02)00054-2

[4] Sluyter R, McEwan TB, Sophocleous RA, Stokes L. Methods for studying P2X4 receptor ion channels in immune cells. J Immunol Methods. 2024; 526:113626. https://doi.org/10.1016/j.jim.2024.113626

[5] Masamizu Y, Tanaka YR, Tanaka YH, et al. Two distinct layer-specific dynamics of cortical ensembles during learning of a motor task. Nat Neurosci. 2014; 17(7):987-994. https://doi.org/10.1038/nn.3739

[6] Tremblay S, Rogasch NC, Premoli I, et al. Clinical utility and prospective of TMS-EEG. Clin Neurophysiol. 2019; 130(5):802-844. https://doi.org/10.1016/j.clinph.2019.01.001

[7] Riquelme PF, Mery I, Aguilera S, et al. Association of fractional anisotropy in white matter bundles with plasma biomarkers of neurodegeneration and glial reactivity in individuals with cognitive complaints without dementia. Res Sq [Preprint]. 2026. https://doi.org/10.21203/rs.3.rs-8621977/v1, Posted on line Jan 182026.

[8] Boyd LA, Hayward KS, Ward NS, et al. Biomarkers of stroke recovery: consensus- based core recommendations from the stroke recovery and rehabilitation roundtable. Int J Stroke. 2017; 12(5):480-493. https://doi.org/10.1177/1747493017714176

[9] Wolpert DM, Flanagan JR. Computations underlying sensorimotor learning. Curr Opin Neurobiol. 2016; 37:7-11. https://doi.org/10.1016/j.conb.2015.12.003

[10] Hu J, Gao PY, Di R, Chen O, Tang Y. Chronic pain and cognitive dysfunction: clinical implement, mechanism, and therapeutic strategy. J Neurosci. 2026; 46(9):e1251252026. https://doi.org/10.1523/JNEUROSCI.1251-25.2026

[11] Lloyd DG, Saxby DJ, Pizzolato C, et al. Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. J Sci Med Sport. 2023; 26(Suppl 1):S30-S39. https://doi.org/10.1016/j.jsams.2023.04.001

[12] Demers M, Winstein CJ. Neurorehabilitation needs a qualitative perspective: a case exemplar from stroke recovery and rehabilitation. Front Hum Neurosci. 2026; 20:1707789. https://doi.org/10.3389/fnhum.2026.1707789

[13] Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng. 2020; 17(4):041001. https://doi.org/10.1088/1741-2552/aba162

[14] Cantillo-Negrete J, Carino-Escobar RI, Ortega-Robles E, Arias-Carrión O. A comprehensive guide to BCI-based stroke neurorehabilitation interventions. MethodsX. 2023; 11:102452. https://doi.org/10.1016/j.mex.2023.102452

[15] Calabrò RS, Calderone A, Simoncini L, et al. The potential of robotics: a systematic review of neuroplastic changes following advanced lower limb rehabilitation in neurological disorders. Neurosci Biobehav Rev. 2026; 180:106459. https://doi.org/10.1016/j.neubiorev.2025.106459

[16] Ramanathan DS, Guo L, Gulati T, et al. Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke. Nat Med. 2018; 24(8):1257-1267. https://doi.org/10.1038/s41591-018-0058-y

[17] Luo Y, Liu X, Yang M. Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation. Front Rehabil Sci. 2026; 7:1666530. https://doi.org/10.3389/fresc.2026.1666530

[18] Niazi IK, Navid MS, Rashid U, et al. Associative cued asynchronous BCI induces cortical plasticity in stroke patients. Ann Clin Transl Neurol. 2022; 9(5):722-733. https://doi.org/10.1002/acn3.51551

[19] Pan X, Zhang R, Xia X, Cui H, Liu L, Chen X. Feasibility of a hybrid SSVEP-motor imagery BCI with robotic feedback for upper limb motor rehabilitation in stroke patients. J Neurosci Methods. 2026; 432:110780. https://doi.org/10.1016/j.jneumeth.2026.110780

[20] Gouret A, Delaux A, Le Bars S, Chokron S. Rewiring attention: virtual reality and brain-computer interfaces in the rehabilitation of unilateral spatial neglect. J Clin Med. 2026; 15(3):1036. https://doi.org/10.3390/jcm15031036

[21] Oliveira I, Russo M, Almeida AI, Vourvopoulos A, Mendes Pereira C. Recommendations for combining brain-computer interface, motor imagery, and virtual reality in upper limb stroke rehabilitation: qualitative participatory design study. JMIR Rehabil Assist Technol. 2025; 12:e71789. https://doi.org/10.2196/71789

[22] Dimyan MA, Cohen L. Neuroplasticity in the context of motor rehabilitation after stroke. Nat Rev Neurol. 2011; 17(2):76-85. https://doi.org/10.1038/nrneurol.2010.200