Evidence map›Paper›PMID 28928708›Full record

ArticleFrontiers in neurology2017

Neural Patterns of Reorganization after Intensive Robot-Assisted Virtual Reality Therapy and Repetitive Task Practice in Patients with Chronic Stroke.

Soha Saleh, Gerard Fluet, Qinyin Qiu, Alma Merians, Sergei V Adamovich, Eugene Tunik

Abstract read
In one paragraph

Article in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 pooled it
–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

27 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  13. Virtual Reality Aided Therapy towards Health 4.0: A Two-Decade Bibliometric Analysis.International journal of environmental research and public health · 2022
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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.

Soha SalehHuman Performance and Engineering Research, Kessler Foundation, West Orange, NJ, United States.
Gerard FluetDepartment of Rehabilitation and Movement Science, Rutgers University, Newark, NJ, United States.
Qinyin QiuDepartment of Rehabilitation and Movement Science, Rutgers University, Newark, NJ, United States.
Alma MeriansDepartment of Rehabilitation and Movement Science, Rutgers University, Newark, NJ, United States.
Sergei V AdamovichDepartment of Rehabilitation and Movement Science, Rutgers University, Newark, NJ, United States.
Eugene TunikDepartment of Physical Therapy, Movement, and Rehabilitation Science, Northeastern University, Boston, MA, United States.

Funding

Optimizing Hand Rehabilitation Post-Stroke Using Interactive Virtual EnvironmentsR01HD058301 · NICHD · NEW JERSEY INSTITUTE OF TECHNOLOGY · PI ADAMOVICH, SERGEI V, MERIANS, ALMA S · 2009 to 2021
$4.7M
Planning and Updating in Frontoparietal Networks for GraspingR01NS085122 · NINDS · RBHS-SCHOOL/ HEALTH RELATED PROFESSIONS · PI ADAMOVICH, SERGEI V, TUNIK, EUGENE · 2014 to 2025
$3.2M
Visual Augmentation Through Virtual Reality to Rehabiliate the Hand after StrokeK01HD059983 · NICHD · UNIV OF MED/ DENT OF NJ-SCH/HLTH REL PRO · PI TUNIK, EUGENE · 2009 to 2013
$673k
NICHD NIH HHS K01 HD059983NICHD NIH HHS R01 HD058301NINDS NIH HHS R01 NS085122
6 · The paper itself

Abstract

Several approaches to rehabilitation of the hand following a stroke have emerged over the last two decades. These treatments, including repetitive task practice (RTP), robotically assisted rehabilitation and virtual rehabilitation activities, produce improvements in hand function but have yet to reinstate function to pre-stroke levels-which likely depends on developing the therapies to impact cortical reorganization in a manner that favors or supports recovery. Understanding cortical reorganization that underlies the above interventions is therefore critical to inform how such therapies can be utilized and improved and is the focus of the current investigation. Specifically, we compare neural reorganization elicited in stroke patients participating in two interventions: a hybrid of robot-assisted virtual reality (RAVR) rehabilitation training and a program of RTP training. Ten chronic stroke subjects participated in eight 3-h sessions of RAVR therapy. Another group of nine stroke subjects participated in eight sessions of matched RTP therapy. Functional magnetic resonance imaging (fMRI) data were acquired during paretic hand movement, before and after training. We compared the difference between groups and sessions (before and after training) in terms of BOLD intensity, laterality index of activation in sensorimotor areas, and the effective connectivity between ipsilesional motor cortex (iMC), contralesional motor cortex, ipsilesional primary somatosensory cortex (iS1), ipsilesional ventral premotor area (iPMv), and ipsilesional supplementary motor area. Last, we analyzed the relationship between changes in fMRI data and functional improvement measured by the Jebsen Taylor Hand Function Test (JTHFT), in an attempt to identify how neurophysiological changes are related to motor improvement. Subjects in both groups demonstrated motor recovery after training, but fMRI data revealed RAVR-specific changes in neural reorganization patterns. First, BOLD signal in multiple regions of interest was reduced and re-lateralized to the ipsilesional side. Second, these changes correlated with improvement in JTHFT scores. Our findings suggest that RAVR training may lead to different neurophysiological changes when compared with traditional therapy. This effect may be attributed to the influence that augmented visual and haptic feedback during RAVR training exerts over higher-order somatosensory and visuomotor areas.

Indexed as

connectivity analysisfunctional magnetic resonance imaging neuroimagingmotor control and learning/plasticityrehabilitationstrokevirtual reality

Identifiers

PMID28928708
PMCPMC5591400

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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.