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Huang Q, Liu H, Chien CW. Intra-limb joint coordination measures of upper limb and hand movements: A systematic review. Gait Posture 2024; 108:289-300. [PMID: 38176149 DOI: 10.1016/j.gaitpost.2023.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 12/01/2023] [Accepted: 12/24/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND People with central nervous system disorders typically have difficulties in coordination of the upper limb and hand movements, which significantly impairs their activities of daily living. Laboratory-based measures can provide quantitative and objective information about intra-limb coordination to aid the rehabilitation process of this population. However, there is currently no comprehensive review of laboratory-based measures. RESEARCH QUESTIONS The aim of this review was to identify and summarize laboratory-based intra-limb coordination measures for different upper limb and hand movements. METHODS Searches were performed in the CINAHL, Embase, IEEE Xplore, MEDLINE, PubMed and Web of Science databases to identify studies published between 2013 and 2022. Two authors independently performed paper selection, data extraction and quality assessment. RESULTS 21 papers were identified, and six types of coordination measures were classified. These included principal component analysis, continuous relative phase analysis, correlation analysis, regression analysis, uncontrolled manifold analysis, and uncorrelated surrogate data analysis, in descending order of occurrence. Regarding psychometric properties, all measures demonstrated good discriminative validity. However, only the principal component analysis approach and the continuous relative phase analysis approach were found to have good convergent validity and responsiveness, respectively. In terms of their practicality, these measures were primarily utilized for quantifying coordination in individuals with neurological disorders, with a greater emphasis on the coordination of upper limb movements rather than hand movements. SIGNIFICANCE This review summarized and critiqued the characteristics of six types of joint coordination measures. Researchers and clinicians should therefore select appropriate measures based on individual needs. Future research should continue on analysing coordination in individuals with pathological conditions and exploring the application of these measures in quantifying hand movement coordination, to advance current knowledge and inform rehabilitation practices.
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Affiliation(s)
- Quting Huang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China.
| | - Haiyun Liu
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China
| | - Chi-Wen Chien
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong Special Administrative Region of China
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Torres FDF, Ramalho BL, Rodrigues MR, Schmaedeke AC, Moraes VH, Reilly KT, Carvalho RDP, Vargas CD. Plasticity of face-hand sensorimotor circuits after a traumatic brachial plexus injury. Front Neurosci 2023; 17:1221777. [PMID: 37609451 PMCID: PMC10440702 DOI: 10.3389/fnins.2023.1221777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/17/2023] [Indexed: 08/24/2023] Open
Abstract
Background Interactions between the somatosensory and motor cortices are of fundamental importance for motor control. Although physically distant, face and hand representations are side by side in the sensorimotor cortex and interact functionally. Traumatic brachial plexus injury (TBPI) interferes with upper limb sensorimotor function, causes bilateral cortical reorganization, and is associated with chronic pain. Thus, TBPI may affect sensorimotor interactions between face and hand representations. Objective The aim of this study was to investigate changes in hand-hand and face-hand sensorimotor integration in TBPI patients using an afferent inhibition (AI) paradigm. Method The experimental design consisted of electrical stimulation (ES) applied to the hand or face followed by transcranial magnetic stimulation (TMS) to the primary motor cortex to activate a hand muscle representation. In the AI paradigm, the motor evoked potential (MEP) in a target muscle is significantly reduced when preceded by an ES at short-latency (SAI) or long-latency (LAI) interstimulus intervals. We tested 18 healthy adults (control group, CG), evaluated on the dominant upper limb, and nine TBPI patients, evaluated on the injured or the uninjured limb. A detailed clinical evaluation complemented the physiological investigation. Results Although hand-hand SAI was present in both the CG and the TBPI groups, hand-hand LAI was present in the CG only. Moreover, less AI was observed in TBPI patients than the CG both for face-hand SAI and LAI. Conclusion Our results indicate that sensorimotor integration involving both hand and face sensorimotor representations is affected by TBPI.
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Affiliation(s)
- Fernanda de Figueiredo Torres
- Laboratory of Neurobiology of Movement, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory of Neuroscience and Rehabilitation, Institute of Neurology Deolindo Couto, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Bia Lima Ramalho
- Laboratory of Neurobiology of Movement, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory of Neuroscience and Rehabilitation, Institute of Neurology Deolindo Couto, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Research, Innovation and Dissemination Center for Neuromathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | - Marcelle Ribeiro Rodrigues
- Laboratory of Neurobiology of Movement, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory of Neuroscience and Rehabilitation, Institute of Neurology Deolindo Couto, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ana Carolina Schmaedeke
- Laboratory of Neurobiology of Movement, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory of Neuroscience and Rehabilitation, Institute of Neurology Deolindo Couto, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Victor Hugo Moraes
- Laboratory of Neurobiology of Movement, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory of Neuroscience and Rehabilitation, Institute of Neurology Deolindo Couto, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Karen T. Reilly
- Trajectoires Team, Lyon Neuroscience Research Center, Lyon, France
- University UCBL Lyon 1, University of Lyon, Lyon, France
| | - Raquel de Paula Carvalho
- Laboratory of Neuroscience and Rehabilitation, Institute of Neurology Deolindo Couto, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Research, Innovation and Dissemination Center for Neuromathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
- Laboratory of Child Development and Motricity, Department of Human Movement Science, Institute of Health and Society, Universidade Federal de São Paulo, Santos, Brazil
| | - Claudia D. Vargas
- Laboratory of Neurobiology of Movement, Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Laboratory of Neuroscience and Rehabilitation, Institute of Neurology Deolindo Couto, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Research, Innovation and Dissemination Center for Neuromathematics, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
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