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Cognitive and neuroimaging findings in developmental coordination disorder: new insights from a systematic review of recent research
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Aim: To better understand the neural and performance factors that may underlie developmental coordination disorder (DCD), and implications for a multi‐component account. Method: A systematic review of the experimental literature published between June 2011 and September 2016 was conducted using a modified PICOS (population, intervention, comparison, outcomes, and study type) framework. A total of 106 studies were included. Results: Behavioural data from 91 studies showed a broad cluster of deficits in the anticipatory control of movement, basic processes of motor learning, and cognitive control. Importantly, however, performance issues in DCD were often shown to be moderated by task type and difficulty. As well, we saw new evidence of compensatory processes and strategies in several studies. Neuroimaging data (15 studies, including electroencephalography) showed reduced cortical thickness in the right medial orbitofrontal cortex and altered brain activation patterns across functional networks involving prefrontal, parietal, and cerebellar regions in children with DCD than those in comparison groups. Data from diffusion‐weighted magnetic resonance imaging suggested reduced white matter organization involving sensorimotor structures and altered structural connectivity across the whole brain network. Interpretation: Taken together, results support the hypothesis that children with DCD show differences in brain structure and function compared with typically developing children. Behaviourally, these differences may affect anticipatory planning and reduce automatization of movement skill, prompting greater reliance on slower feedback‐based control and compensatory strategies. Implications for future research, theory development, and clinical practice are discussed.
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Multisensory integration in children with Developmental Coordination Disorder
Coats, R. O., Britten, L., Utley, A., & Astill, S. L. (2015) · Human Movement Science · doi:10.1016/j.humov.2015.06.011 · Cited by 16
Applications of VR Technologies for Childhood Disability
Green, D., & Wilson, P. H. (2014) · Virtual reality technologies for health and clinical applications · doi:10.1007/978-1-4939-0968-1_10 · Cited by 15
Self-citationChen, F. C., Tsai, C., & Wu, S. K. (2014) · Experimental Brain Research · doi:10.1007/s00221-014-3906-0 · Cited by 15
Chia, L. C., Reid, S. L., Licari, M. K., & Guelfi, K. J. (2013) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2013.03.023 · Cited by 13
Chen, F., & Tsai, C. (2016) · Gait & Posture · doi:10.1016/j.gaitpost.2016.01.029 · Cited by 12
Asmussen, M. J., Przysucha, E., & Zerpa, C. (2014) · Journal of Motor Behavior · doi:10.1080/00222895.2014.945394 · Cited by 11
Tabletop Computer Game Mechanics for Group Rehabilitation of Individuals with Brain Injury
Duckworth, J., Bayliss, J. D., Thomas, P. R., Shum, D., Mumford, N., & Wilson, P. H. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07440-5_46 · Cited by 9
Self-citationJokić, C. S., & Whitebread, D. (2016) · Physical & Occupational Therapy In Pediatrics · doi:10.3109/01942638.2015.1135844 · Cited by 8
Wade, M. G., Tsai, C., Stoffregen, T. A., Chang, C.-H., & Chen, F. (2015) · Journal of Motor Behavior · doi:10.1080/00222895.2015.1025902 · Cited by 7
Technology, rehabilitation and empowerment of people with special needs
Pareto, L., SHARKEY, P. M., & Merrick, J. (2015) · Cited by 7
Chen, F., Tsai, C., Chang, W., Li, Y., Chou, C.-L., & Wu, S.-K. (2015) · Pediatric Physical Therapy · doi:10.1097/pep.0000000000000186 · Cited by 6
Cited by
Blank, R., Barnett, A. L., Cairney, J., Green, D., Kirby, A., Polatajko, H., Rosenblum, S., Smits‐Engelsman, B., Sugden, D., Wilson, P., et al. (2019) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.14132 · Cited by 836
Self-citationResting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know
Lv, H., Wang, Z., Tong, E., Williams, L. M., Zaharchuk, G., Zeineh, M., Goldstein-Piekarski, A. N., Ball, T. M., Liao, C., & Wintermark, M. (2018) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5527 · Cited by 560
Mikami, M., Hirota, T., Takahashi, M., Adachi, M., Saito, M., Koeda, S., Yoshida, K., Sakamoto, Y., Kato, S., Nakamura, K., et al. (2021) · Child Psychiatry & Human Development · doi:10.1007/s10578-020-01013-5 · Cited by 37
Metadata
- Authors
- Wilson, P. H., Smits‐Engelsman, B., Caeyenberghs, K., Steenbergen, B., Sugden, D., Clark, J., Mumford, N., & Blank, R.
- Venue
- Developmental Medicine & Child Neurology
- Publisher
- Wiley
- Published
- 2017-09-05
- Volume
- 59
- Issue
- 11
- Pages
- 1117-1129
- ISSN
- 0012-1622 (ISSN-L)
- 1469-8749
- OpenAlex
- W2751106774
- Cited by
- 308
- References
- 126
- FWCI
- 5.75
- Type
- journal article
- Language
- en
- Open access
- bronze
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 ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
