Evidence map›Paper›PMID 41751310›Full record

ArticleBiomedicines2026

Neurorehabilitation as Network Perturbation: Shaping Neuroplasticity with Robotics, Virtual Reality, and Neuromodulation.

Rocco Salvatore Calabrò, Angelo Quartarone

Abstract readComment
In one paragraph

Article in Biomedicines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Network-oriented neurorehabilitation after stroke: a paradigmatic approach.Journal of neuroengineering and rehabilitation · 2026
    Review
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

2 authors.

Rocco Salvatore CalabròIRCCS Centro Neurolesi "Bonino-Pulejo", 98123 Messina, Italy.ORCID 0000-0002-8566-3166
Angelo QuartaroneIRCCS Centro Neurolesi "Bonino-Pulejo", 98123 Messina, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurological injury induces widespread neuroplastic changes that extend well beyond focal structural damage, altering synaptic function, circuit dynamics, and large-scale network organization. While these processes provide the biological substrate for recovery, they can also drive the stabilization of maladaptive network states that constrain long-term functional improvement. Traditional neurorehabilitation has largely emphasized compensation and task practice, often without explicitly targeting the neural dynamics that underlie persistent disability. In this Opinion, we propose that contemporary rehabilitation technologies, including robotics, virtual reality, and neuromodulation, should be conceptualized as mechanistically grounded interventions that actively perturb neural networks and interact with the pathobiology of post-injury reorganization. Drawing on advances in systems and network neuroscience, we examine key molecular, synaptic, and network-level mechanisms that govern adaptive and maladaptive plasticity, and discuss how these technologies modulate error processing, sensory context, and excitability landscapes to reshape recovery trajectories. We argue that when interventions are appropriately structured, timed, and combined within adaptive and closed-loop frameworks, technology-assisted rehabilitation can move beyond compensation and toward principled modulation of neuroplasticity, aligning therapeutic innovation with the biological rules that govern recovery. This perspective highlights the need for network-informed biomarkers and longitudinal approaches to translate technological advances into durable functional gains.

Indexed as

closed-loopfunctional connectivitynetwork neuroscienceneuromodulationneuroplasticityneurorehabilitationroboticsvirtual reality

Identifiers

PMID41751310
PMCPMC12937845

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.