{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:37:40Z","timestamp":1760233060943,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["CIHR-FDN-148384"],"award-info":[{"award-number":["CIHR-FDN-148384"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pragmatic, objective, and accurate motor assessment tools could facilitate more frequent appraisal of longitudinal change in motor function and subsequent development of personalized therapeutic strategies. Brain functional connectivity (FC) has shown promise as an objective neurophysiological measure for this purpose. The involvement of different brain networks, along with differences across subjects due to age or existing capabilities, motivates an individualized approach towards the evaluation of FC. We advocate the use of EEG-based resting-state FC (rsFC) measures to address the pragmatic requirements. Pertaining to appraisal of accuracy, we suggest using the acquisition of motor skill by healthy individuals that could be quantified at small incremental change. Computer-based tracing tasks are a good candidate in this regard when using spatial error in tracing as an objective measure of skill. This work investigates the application of an individualized method that utilizes Partial Least Squares analysis to estimate the longitudinal change in tracing error from changes in rsFC. Longitudinal data from participants yielded an average accuracy of 98% (standard deviation of 1.2%) in estimating tracing error. The results show potential for an accurate individualized motor assessment tool that reduces the dependence on the expertise and availability of trained examiners, thereby facilitating more frequent appraisal of function and development of personalized training programs.<\/jats:p>","DOI":"10.3390\/s22249857","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T03:43:49Z","timestamp":1671075829000},"page":"9857","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Method for Estimating Longitudinal Change in Motor Skill from Individualized Functional-Connectivity Measures"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0925-6147","authenticated-orcid":false,"given":"Nader","family":"Riahi","sequence":"first","affiliation":[{"name":"Schools of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan","family":"D\u2019Arcy","sequence":"additional","affiliation":[{"name":"Schools of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada"},{"name":"DM Centre for Brain Health, Department of Radiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"},{"name":"HealthTech Connex, Surrey, BC V3V 0E8, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-9977","authenticated-orcid":false,"given":"Carlo","family":"Menon","sequence":"additional","affiliation":[{"name":"Schools of Engineering Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada"},{"name":"Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1177\/1747493017714176","article-title":"Biomarkers of stroke recovery: Consensus-based core recommendations from the stroke recovery and rehabilitation roundtable","volume":"12","author":"Boyd","year":"2017","journal-title":"Int. J. Stroke"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1186\/s12984-017-0277-3","article-title":"Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery","volume":"14","author":"Philips","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/S1474-4422(17)30283-1","article-title":"Prediction of motor recovery after stroke: Advances in biomarkers","volume":"16","author":"Stinear","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1097\/WCO.0b013e3283186f96","article-title":"Biomarkers of recovery after stroke","volume":"21","author":"Milot","year":"2008","journal-title":"Curr. Opin. Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"036013","DOI":"10.1088\/1741-2552\/ab0b82","article-title":"Scoring upper-extremity motor function from EEG with artificial neural networks: A preliminary study","volume":"16","author":"Zhang","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Krakauer, J.W., Hadjiosif, A., Xu, J., and Wong, A.L. (2019). Motor learning. Comprehensive Physiology, Wiley.","DOI":"10.1002\/cphy.c170043"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1016\/j.neuroimage.2012.02.070","article-title":"Why use a connectivity-based approach to study stroke and recovery of function?","volume":"62","author":"Carter","year":"2012","journal-title":"Neuroimage"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1089\/brain.2011.0008","article-title":"Functional and effective connectivity: A review","volume":"1","author":"Friston","year":"2011","journal-title":"Brain Connect."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1002\/hbm.24458","article-title":"Neuronal dynamics enable the functional differentiation of resting state networks in the human brain","volume":"40","author":"Marino","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.tics.2005.08.011","article-title":"A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence","volume":"9","author":"Fries","year":"2005","journal-title":"Trends Cogn. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3389\/fnsys.2015.00175","article-title":"A Tutorial review of functional connectivity analysis methods and their interpretational pitfalls","volume":"9","author":"Bastos","year":"2016","journal-title":"Front. Syst. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/j.cub.2009.04.028","article-title":"The resting human brain and motor learning","volume":"19","author":"Albert","year":"2009","journal-title":"Curr. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"862207","DOI":"10.3389\/fphys.2022.862207","article-title":"Resting state EEG directed functional connectivity unveils changes in motor network organization in subacute stroke patients after rehabilitation","volume":"13","author":"Pirovano","year":"2022","journal-title":"Front. Physiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1111\/ejn.13797","article-title":"An EEG index of sensorimotor interhemispheric coupling after unilateral stroke: Clinical and neurophysiological study","volume":"47","author":"Pichiorri","year":"2018","journal-title":"Eur. J. Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"669915","DOI":"10.3389\/fnhum.2021.669915","article-title":"Connectivity measures differentiate cortical and subcortical sub-acute ischemic stroke patients","volume":"15","author":"Fanciullacci","year":"2021","journal-title":"Front. Hum. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9663","DOI":"10.1523\/JNEUROSCI.1166-20.2020","article-title":"Spontaneous Network coupling enables efficient task performance without local task-induced activations","volume":"40","author":"Allaman","year":"2020","journal-title":"J. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1093\/brain\/awv156","article-title":"Connectivity measures are robust biomarkers of cortical function and plasticity after stroke","volume":"138","author":"Wu","year":"2015","journal-title":"Brain"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1109\/TNSRE.2020.2978381","article-title":"Estimating Fugl-Meyer upper extremity motor score from functional-connectivity measures","volume":"28","author":"Riahi","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"S250","DOI":"10.1016\/j.neuroimage.2004.07.020","article-title":"Partial least squares analysis of neuroimaging data: Applications and advances","volume":"23","author":"McIntosh","year":"2004","journal-title":"Neuroimage"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/wics.51","article-title":"Partial least squares regression and projection on latent structure regression (PLS Regression)","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1177\/1545968313491000","article-title":"A standardized approach to the Fugl-Meyer assessment and its implications for clinical trials","volume":"27","author":"See","year":"2013","journal-title":"Neurorehabil. Neural Repair"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13","DOI":"10.2340\/1650197771331","article-title":"The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance","volume":"7","author":"Leyman","year":"1975","journal-title":"Scand. J. Rehabil. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.neuroimage.2011.06.014","article-title":"Changes occur in resting state network of motor system during 4 weeks of motor skill learning","volume":"58","author":"Ma","year":"2011","journal-title":"NeuroImage"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e00801","DOI":"10.7554\/eLife.00801","article-title":"Skill learning strengthens cortical representations of motor sequences","volume":"2","author":"Wiestler","year":"2013","journal-title":"eLife"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"509258","DOI":"10.3389\/fnhum.2020.509258","article-title":"White matter neuroplasticity: Motor learning activates the internal capsule and reduces hemodynamic response variability","volume":"14","author":"Frizzell","year":"2020","journal-title":"Front. Hum. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1007\/s10548-014-0394-2","article-title":"Two intrinsic coupling types for resting-state integration in the human brain","volume":"28","author":"Guggisberg","year":"2014","journal-title":"Brain Topogr."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neuroimage.2014.01.026","article-title":"Resting-state cortical connectivity predicts motor skill acquisition","volume":"91","author":"Wu","year":"2014","journal-title":"NeuroImage"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3389\/fncom.2014.00036","article-title":"The role of alpha-rhythm states in perceptual learning: Insights from experiments and computational models","volume":"8","author":"Sigala","year":"2014","journal-title":"Front. Comput. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"046027","DOI":"10.1088\/1741-2552\/aa6abd","article-title":"Electroencephalographic identifiers of motor adaptation learning","volume":"14","author":"Patoglu","year":"2017","journal-title":"J. Neural Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/j.neuroimage.2018.03.054","article-title":"Resting-state functional connectivity predicts the ability to adapt arm reaching in a robot-mediated force field","volume":"174","author":"Faiman","year":"2018","journal-title":"NeuroImage"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.neuroimage.2018.05.003","article-title":"Resting-state connectivity predicts visuo-motor skill learning","volume":"176","author":"Manuel","year":"2018","journal-title":"NeuroImage"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"\u00d6zdenizci, O., Meyer, T., Wichmann, F., Peters, J., Sch\u00f6lkopf, B., \u00c7etin, M., and Grosse-Wentrup, M. (2019, January 6\u20139). Neural signatures of motor skill in the resting brain. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.","DOI":"10.1109\/SMC.2019.8914252"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Riahi, N., Ruth, W., D\u2019Arcy, R.C.N., and Menon, C. (IEEE Trans. Neural Syst. Rehabil. Eng., 2022). A method for using neurofeedback to guide mental imagery for improving motor skill, IEEE Trans. Neural Syst. Rehabil. Eng., Early Access.","DOI":"10.1109\/TNSRE.2022.3218514"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cohen, M.X. (2014). Analyzing Neural Time Series Data: Theory and Practice, MIT Press. Chapter 26.","DOI":"10.7551\/mitpress\/9609.001.0001"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.neuroimage.2010.07.034","article-title":"Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review","volume":"56","author":"Krishnan","year":"2011","journal-title":"Neuroimage"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.neuron.2015.09.034","article-title":"Rhythms for cognition: Communication through coherence","volume":"88","author":"Fries","year":"2015","journal-title":"Neuron"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"25605","DOI":"10.3390\/ijms161025605","article-title":"Perturbation of brain oscillations after ischemic stroke: A potential biomarker for post-stroke function and therapy","volume":"16","author":"Rabiller","year":"2015","journal-title":"Int. J. Mol. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1038\/35067550","article-title":"The brainweb: Phase synchronization and large-scale integration","volume":"2","author":"Varela","year":"2001","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/MSP.2017.2777518","article-title":"Electroencephalography source connectivity: Aiming for high resolution of brain networks in time and space","volume":"35","author":"Hassan","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/S0028-3932(02)00158-6","article-title":"Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning","volume":"41","author":"Doyon","year":"2002","journal-title":"Neuropsychologia"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9867196","DOI":"10.1155\/2018\/9867196","article-title":"Biomarkers of Rehabilitation Therapy Vary according to Stroke Severity","volume":"2018","author":"Quinlan","year":"2018","journal-title":"Neural Plast."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9857\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:41:40Z","timestamp":1760146900000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9857"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":41,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249857"],"URL":"https:\/\/doi.org\/10.3390\/s22249857","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}