Posts Tagged Sensory function

[Abstract+References] Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke

Objective. Somatosensory function is critical to normal motor control. After stroke, dysfunction of the sensory systems prevents normal motor function and degrades quality of life. Structural neuroplasticity underpinnings of sensory recovery after stroke are not fully understood. The objective of this study was to identify changes in bilateral cortical thickness (CT) that may drive recovery of sensory acuity. Methods. Chronic stroke survivors (n = 20) were treated with 12 weeks of rehabilitation. Measures were sensory acuity (monofilament), Fugl-Meyer upper limb and CT change. Permutation-based general linear regression modeling identified cortical regions in which change in CT was associated with change in sensory acuity. Results. For the ipsilesional hemisphere in response to treatment, CT increase was significantly associated with sensory improvement in the area encompassing the occipital pole, lateral occipital cortex (inferior and superior divisions), intracalcarine cortex, cuneal cortex, precuneus cortex, inferior temporal gyrus, occipital fusiform gyrus, supracalcarine cortex, and temporal occipital fusiform cortex. For the contralesional hemisphere, increased CT was associated with improved sensory acuity within the posterior parietal cortex that included supramarginal and angular gyri. Following upper limb therapy, monofilament test score changed from 45.0 ± 13.3 to 42.6 ± 12.9 mm (P = .063) and Fugl-Meyer score changed from 22.1 ± 7.8 to 32.3 ± 10.1 (P < .001). Conclusions. Rehabilitation in the chronic stage after stroke produced structural brain changes that were strongly associated with enhanced sensory acuity. Improved sensory perception was associated with increased CT in bilateral high-order association sensory cortices reflecting the complex nature of sensory function and recovery in response to rehabilitation.

Keywords 

1. Wolf, SL, Winstein, CJ, Miller, JP; EXCITE Investigators. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA. 2006;296:20952104. doi:10.1001/jama.296.17.2095. Google ScholarCrossrefMedlineISI
2. Lo, AC, Guarino, PD, Richards, LG. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362:17721783. doi:10.1056/NEJMoa0911341.Google ScholarCrossrefMedlineISI
3. McCabe, J, Monkiewicz, M, Holcomb, J, Pundik, S, Daly, JJ. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96:981990. doi:10.1016/j.apmr.2014.10.022. Google ScholarCrossrefMedlineISI
4. Johansen-Berg, H, Dawes, H, Guy, C, Smith, SM, Wade, DT, Matthews, PM. Correlation between motor improvements and altered fMRI activity after rehabilitative therapy. Brain. 2002;125(pt 12):27312742Google ScholarCrossrefMedline
5. Luft, AR, McCombe-Waller, S, Whitall, J. Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial. JAMA. 2004;292:18531861. doi:10.1001/jama.292.15.1853. Google ScholarCrossrefMedlineISI
6. Pundik, S, McCabe, JP, Hrovat, K. Recovery of post stroke proximal arm function, driven by complex neuroplastic bilateral brain activation patterns and predicted by baseline motor dysfunction severity. Front Hum Neurosci. 2015;9:394. doi:10.3389/fnhum.2015.00394. Google ScholarCrossrefMedline
7. Desrosiers, J, Noreau, L, Rochette, A, Bourbonnais, D, Bravo, G, Bourget, A. Predictors of long-term participation after stroke. Disabil Rehabil. 2006;28:221230. doi:10.1080/09638280500158372. Google ScholarCrossrefMedlineISI
8. Carey, L, Macdonell, R, Matyas, TA. SENSe: Study of the Effectiveness of Neurorehabilitation on Sensation: a randomized controlled trial. Neurorehabil Neural Repair. 2011;25:304313. doi:10.1177/1545968310397705. Google ScholarSAGE JournalsISI
9. Cramer, SC, Nelles, G, Benson, RR. A functional MRI study of subjects recovered from hemiparetic stroke. Stroke. 1997;28:25182527Google ScholarCrossrefMedlineISI
10. Carey, JR, Kimberley, TJ, Lewis, SM. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain. 2002;125(pt 4):773788Google ScholarCrossrefMedline
11. Carey, LM, Abbott, DF, Lamp, G, Puce, A, Seitz, RJ, Donnan, GA. Same intervention-different reorganization: the impact of lesion location on training-facilitated somatosensory recovery after stroke. Neurorehabil Neural Repair. 2016;30:9881000. doi:10.1177/1545968316653836.Google ScholarSAGE JournalsISI
12. Carey, LM, Matyas, TA. Frequency of discriminative sensory loss in the hand after stroke in a rehabilitation setting. J Rehabil Med. 2011;43:257263. doi:10.2340/16501977-0662. Google ScholarCrossrefMedlineISI
13. Borstad, AL, Bird, T, Choi, S, Goodman, L, Schmalbrock, P, Nichols-Larsen, DS. Sensorimotor training and neural reorganization after stroke: a case series. J Neurol Phys Ther. 2013;37:2736. doi:10.1097/NPT.0b013e318283de0d. Google ScholarCrossrefMedlineISI
14. Gauthier, LV, Taub, E, Perkins, C, Ortmann, M, Mark, VW, Uswatte, G. Remodeling the brain: plastic structural brain changes produced by different motor therapies after stroke. Stroke. 2008;39:15201525. doi:10.1161/STROKEAHA.107.502229. Google ScholarCrossrefMedlineISI
15. Zheng, X, Schlaug, G. Structural white matter changes in descending motor tracts correlate with improvements in motor impairment after undergoing a treatment course of tDCS and physical therapy. Front Hum Neurosci. 2015;9:229. doi:10.3389/fnhum.2015.00229. Google ScholarCrossrefMedlineISI
16. Bailey, CH, Kandel, ER. Structural changes accompanying memory storage. Annu Rev Physiol. 1993;55:397426. doi:10.1146/annurev.ph.55.030193.002145. Google ScholarCrossrefMedlineISI
17. Jones, TA, Chu, CJ, Grande, LA, Gregory, AD. Motor skills training enhances lesion-induced structural plasticity in the motor cortex of adult rats. J Neurosci. 1999;19:1015310163Google ScholarCrossrefMedlineISI
18. Wang, L, Conner, JM, Rickert, J, Tuszynski, MH. Structural plasticity within highly specific neuronal populations identifies a unique parcellation of motor learning in the adult brain. Proc Natl Acad Sci U S A. 2011;108:25452550. doi:10.1073/pnas.1014335108. Google ScholarCrossrefMedline
19. Maguire, EA, Gadian, DG, Johnsrude, IS. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci U S A. 2000;97:43984403. doi:10.1073/pnas.070039597. Google ScholarCrossrefMedlineISI
20. Draganski, B, Gaser, C, Busch, V, Schuierer, G, Bogdahn, U, May, A. Neuroplasticity: changes in grey matter induced by training. Nature. 2004;427:311312. doi:10.1038/427311a. Google ScholarCrossrefMedlineISI
21. Sampaio-Baptista, C, Scholz, J, Jenkinson, M. Gray matter volume is associated with rate of subsequent skill learning after a long term training intervention. Neuroimage. 2014;96:158166. doi:10.1016/j.neuroimage.2014.03.056. Google ScholarCrossrefMedline
22. Nouri, S, Cramer, SC. Anatomy and physiology predict response to motor cortex stimulation after stroke. Neurology. 2011;77:10761083. doi:10.1212/WNL.0b013e31822e1482. Google ScholarCrossrefMedlineISI
23. Gauthier, LV, Taub, E, Mark, VW, Barghi, A, Uswatte, G. Atrophy of spared gray matter tissue predicts poorer motor recovery and rehabilitation response in chronic stroke. Stroke. 2012;43:453457. doi:10.1161/STROKEAHA.111.633255. Google ScholarCrossrefMedlineISI
24. Sterr, A, Dean, PJ, Vieira, G, Conforto, AB, Shen, S, Sato, JR. Cortical thickness changes in the non-lesioned hemisphere associated with non-paretic arm immobilization in modified CI therapy. Neuroimage Clin. 2013;2:797803. doi:10.1016/j.nicl.2013.05.005. Google ScholarCrossrefMedline
25. Schaechter, JD, Moore, CI, Connell, BD, Rosen, BR, Dijkhuizen, RM. Structural and functional plasticity in the somatosensory cortex of chronic stroke patients. Brain. 2006;129(pt 10):27222733. doi:10.1093/brain/awl214. Google ScholarCrossrefMedline
26. Kopp, B, Kunkel, A, Flor, H. The Arm Motor Ability Test: reliability, validity, and sensitivity to change of an instrument for assessing disabilities in activities of daily living. Arch Phys Med Rehabil. 1997;78:615620Google ScholarCrossrefMedlineISI
27. Duncan, PW, Lai, SM, Keighley, J. Defining post-stroke recovery: implications for design and interpretation of drug trials. Neuropharmacology. 2000;39:835841Google ScholarCrossrefMedlineISI
28. Fischl, B. FreeSurfer. Neuroimage. 2012;62:774781. doi:10.1016/j.neuroimage.2012.01.021.Google ScholarCrossrefMedlineISI
29. Reuter, M, Rosas, HD, Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage. 2010;53:11811196. doi:10.1016/j.neuroimage.2010.07.020. Google ScholarCrossrefMedlineISI
30. Greve, DN, Van der Haegen, L, Cai, Q. A surface-based analysis of language lateralization and cortical asymmetry. J Cogn Neurosci. 2013;25:14771492. doi:10.1162/jocn_a_00405. Google ScholarCrossrefMedlineISI
31. Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM, Nichols, TE. Permutation inference for the general linear model. Neuro-image. 2014;92:381397. doi:10.1016/j.neuroimage.2014.01.060. Google ScholarCrossrefMedlineISI
32. Smith, SM, Nichols, TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:8398. doi:10.1016/j.neuroimage.2008.03.061. Google ScholarCrossrefMedlineISI
33. Nichols, T, Holmes, A. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:125. doi:10.1016/B978-012264841-0/50048-2.Google ScholarCrossrefMedlineISI
34. Winkler, AM, Ridgway, GR, Douaud, G, Nichols, TE, Smith, SM. Faster permutation inference in brain imaging. Neuroimage. 2016;141:502516. doi:10.1016/j.neuroimage.2016.05.068.Google ScholarCrossrefMedline
35. Desikan, RS, Ségonne, F, Fischl, B. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968980. doi:10.1016/j.neuroimage.2006.01.021. Google ScholarCrossrefMedlineISI
36. Thut, G, Théoret, H, Pfennig, A. Differential effects of low-frequency rTMS at the occipital pole on visual-induced alpha desynchronization and visual-evoked potentials. Neuroimage. 2003;18:334347Google ScholarCrossrefMedline
37. Amedi, A, Floel, A, Knecht, S, Zohary, E, Cohen, LG. Transcranial magnetic stimulation of the occipital pole interferes with verbal processing in blind subjects. Nat Neurosci. 2004;7:12661270. doi:10.1038/nn1328. Google ScholarCrossrefMedlineISI
38. Merabet, LB, Swisher, JD, McMains, SA. Combined activation and deactivation of visual cortex during tactile sensory processing. J Neurophysiol. 2006;97:16331641. doi:10.1152/jn.00806.2006. Google ScholarCrossrefMedline
39. Amedi, A, Malach, R, Hendler, T, Peled, S, Zohary, E. Visuo-haptic object-related activation in the ventral visual pathway. Nat Neurosci. 2001;4:324330. doi:10.1038/85201. Google ScholarCrossrefMedlineISI
40. Stoesz, MR, Zhang, M, Weisser, VD, Prather, SC, Mao, H, Sathian, K. Neural networks active during tactile form perception: common and differential activity during macrospatial and microspatial tasks. Int J Psychophysiol. 2003;50:4149Google ScholarCrossrefMedline
41. Lacey, S, Tal, N, Amedi, A, Sathian, K. A putative model of multisensory object representation. Brain Topogr. 2009;21:269274. doi:10.1007/s10548-009-0087-4. Google ScholarCrossrefMedline
42. Kim, JK, Zatorre, RJ. Tactile-auditory shape learning engages the lateral occipital complex. J Neurosci. 2011;31:78487856. doi:10.1523/JNEUROSCI.3399-10.2011. Google ScholarCrossrefMedline
43. Botvinick, M, Cohen, J. Rubber hands “feel” touch that eyes see. Nature. 1998;391:756. doi:10.1038/35784. Google ScholarCrossrefMedlineISI
44. Zangaladze, A, Epstein, CM, Grafton, ST, Sathian, K. Involvement of visual cortex in tactile discrimination of orientation. Nature. 1999;401:587590. doi:10.1038/44139. Google ScholarCrossrefMedlineISI
45. Sathian, K. Analysis of haptic information in the cerebral cortex. J Neurophysiol. 2016;116:17951806. doi:10.1152/jn.00546.2015. Google ScholarCrossrefMedline
46. Hsiao, S, Gomez-Ramirez, M. Touch. In: Gottfried, JA ed. Neurobiology of Sensation and Reward. Frontiers in Neuroscience. Boca Raton, FLCRC Press/Taylor & Francis2011Google ScholarCrossref
47. Vincent, JL, Snyder, AZ, Fox, MD. Coherent spontaneous activity identifies a hippocampal-parietal memory network. J Neurophysiol. 2006;96:35173531. doi:10.1152/jn.00048.2006.Google ScholarCrossrefMedlineISI
48. Roland, PE, O’Sullivan, B, Kawashima, R. Shape and roughness activate different somatosensory areas in the human brain. Proc Natl Acad Sci U S A. 1998;95:32953300Google ScholarCrossrefMedlineISI
49. Bodegård, A, Geyer, S, Grefkes, C, Zilles, K, Roland, PE. Hierarchical processing of tactile shape in the human brain. Neuron. 2001;31:317328Google ScholarCrossrefMedline
50. Karhu, J, Tesche, CD. Simultaneous early processing of sensory input in human primary (SI) and secondary (SII) somatosensory cortices. J Neurophysiol. 1999;81:20172025Google ScholarCrossrefMedline
51. Fabri, M, Polonara, G, Pesce, MD, Quattrini, A, Salvolini, U, Manzoni, T. Posterior corpus callosum and interhemispheric transfer of somatosensory information: an fMRI and neuropsychological study of a partially callosotomized patient. J Cogn Neurosci. 2001;13:10711079. doi:10.1162/089892901753294365. Google ScholarCrossrefMedlineISI
52. Chung, Y, Han, S, Kim, HS. Intra- and inter-hemispheric effective connectivity in the human somatosensory cortex during pressure stimulation. BMC Neurosci. 2014;15:43. doi:10.1186/1471-2202-15-43. Google ScholarCrossrefMedline
53. Mohajerani, MH, Aminoltejari, K, Murphy, TH. Targeted mini-strokes produce changes in interhemispheric sensory signal processing that are indicative of disinhibition within minutes. Proc Natl Acad Sci U S A. 2011;108:E183E191. doi:10.1073/pnas.1101914108. Google ScholarCrossrefMedlineISI
54. Blankenburg, F, Ruff, CC, Bestmann, S. Interhemispheric effect of parietal TMS on somatosensory response confirmed directly with concurrent TMS-fMRI. J Neurosci. 2008;28:1320213208. doi:10.1523/JNEUROSCI.3043-08.2008. Google ScholarCrossrefMedline
55. Inoue, K, Kawashima, R, Sugiura, M. Activation in the ipsilateral posterior parietal cortex during tool use: a PET study. Neuroimage. 2001;14:14691475. doi:10.1006/nimg.2001.0942. Google ScholarCrossrefMedlineISI
56. Takatsuru, Y, Fukumoto, D, Yoshitomo, M, Nemoto, T, Tsukada, H, Nabekura, J. Neuronal circuit remodeling in the contralateral cortical hemisphere during functional recovery from cerebral infarction. J Neurosci. 2009;29:1008110086. doi:10.1523/JNEUROSCI.1638-09.2009.Google ScholarCrossrefMedlineISI
57. Nelles, G, Spiekermann, G, Jueptner, M. Reorganization of sensory and motor systems in hemiplegic stroke patients. A positron emission tomography study. Stroke. 1999;30:15101516Google ScholarCrossrefMedlineISI
58. Jang, SH, Lee, MY. Correlation between somatosensory function and cortical activation induced by touch stimulation in patients with intracerebral hemorrhage. Int J Neurosci. 2013;123:248252. doi:10.3109/00207454.2012.755968. Google ScholarCrossrefMedlineISI
59. Bannister, LC, Crewther, SG, Gavrilescu, M, Carey, LM. Improvement in touch sensation after stroke is associated with resting functional connectivity changes. Front Neurol. 2015;6:165. doi:10.3389/fneur.2015.00165. Google ScholarCrossrefMedline
60. Kang, X, Herron, TJ, Cate, AD, Yund, EW, Woods, DL. Hemispherically-unified surface maps of human cerebral cortex: reliability and hemispheric asymmetries. PloS One. 2012;7:e45582. doi:10.1371/journal.pone.0045582. Google ScholarCrossrefMedline
61. Maingault, S, Tzourio-Mazoyer, N, Mazoyer, B, Crivello, F. Regional correlations between cortical thickness and surface area asymmetries: a surface-based morphometry study of 250 adults. Neuropsychologia. 2016;93(pt B):350364. doi:10.1016/j.neuropsychologia.2016.03.025. Google ScholarCrossrefMedline
62. Van de Winckel, A, Wenderoth, N, De Weerdt, W. Frontoparietal involvement in passively guided shape and length discrimination: a comparison between subcortical stroke patients and healthy controls. Exp Brain Res. 2012;220:179189. doi:10.1007/s00221-012-3128-2. Google ScholarCrossrefMedline
63. Borstad, A, Schmalbrock, P, Choi, S, Nichols-Larsen, DS. Neural correlates supporting sensory discrimination after left hemisphere stroke. Brain Res. 2012;1460:7887. doi:10.1016/j.brainres.2012.03.060. Google ScholarCrossrefMedlineISI
64. Lindberg, PG, Schmitz, C, Engardt, M, Forssberg, H, Borg, J. Use-dependent up- and down-regulation of sensorimotor brain circuits in stroke patients. Neurorehabil Neural Repair. 2007;21:315326. doi:10.1177/1545968306296965. Google ScholarSAGE JournalsISI
65. Dechaumont-Palacin, S, Marque, P, De Boissezon, X. Neural correlates of proprioceptive integration in the contralesional hemisphere of very impaired patients shortly after a subcortical stroke: an FMRI study. Neurorehabil Neural Repair. 2008;22:154165. doi:10.1177/1545968307307118. Google ScholarSAGE JournalsISI
66. Zatorre, RJ, Fields, RD, Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15:528536. doi:10.1038/nn.3045.Google ScholarCrossrefMedlineISI
67. Fields, RD. Changes in brain structure during learning: fact or artifact? Reply to Thomas and Baker. Neuroimage. 2013;73:260267. doi:10.1016/j.neuroimage.2012.08.085. Google ScholarCrossrefMedline
68. Xu, T, Yu, X, Perlik, AJ. Rapid formation and selective stabilization of synapses for enduring motor memories. Nature. 2009;462:915919. doi:10.1038/nature08389. Google ScholarCrossrefMedlineISI
69. Kleim, JA, Hogg, TM, VandenBerg, PM, Cooper, NR, Bruneau, R, Remple, M. Cortical synaptogenesis and motor map reorganization occur during late, but not early, phase of motor skill learning. J Neurosci. 2004;24:628633. doi:10.1523/JNEUROSCI.3440-03.2004. Google ScholarCrossrefMedlineISI
70. Sampaio-Baptista, C, Khrapitchev, AA, Foxley, S. Motor skill learning induces changes in white matter microstructure and myelination. J Neurosci. 2013;33:1949919503. doi:10.1523/JNEUROSCI.3048-13.2013. Google ScholarCrossrefMedlineISI
71. Erickson, KI. Evidence for structural plasticity in humans: comment on Thomas and Baker (2012). Neuroimage. 2013;73:237238. doi:10.1016/j.neuroimage.2012.07.003. Google ScholarCrossrefMedline
72. Feydy, A, Carlier, R, Roby-Brami, A. Longitudinal study of motor recovery after stroke: recruitment and focusing of brain activation. Stroke. 2002;33:16101617Google ScholarCrossrefMedlineISI
73. Hamzei, F, Liepert, J, Dettmers, C, Weiller, C, Rijntjes, M. Two different reorganization patterns after rehabilitative therapy: an exploratory study with fMRI and TMS. Neuroimage. 2006;31:710720. doi:10.1016/j.neuroimage.2005.12.035. Google ScholarCrossrefMedlineISI

via Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke – Svetlana Pundik, Aleka Scoco, Margaret Skelly, Jessica P. McCabe, Janis J. Daly, 2018

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[WEB SITE] Improve your function. Improve your life.

Impaired function from a neurological injury such as stroke may result in both sensory and motor deficits.

Limited use of the hand or arm can typically lead to impaired sensory communication to the brain (touch, feel, aware of joint movement). Research shows that sensory electrical stimulation (SES) can be an effective treatment strategy for improving sensory and motor function.

With SES, the main goal is to maximize input by providing stimulation at very low-level (i.e., without producing a muscle contraction). Studies show that providing SES to an impaired nervous system can prime the cortex ultimately leading to improve neuroplasticity, motor recovery and function. 

Which means you’re one step closer to improving your function, independence, life

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[ARTICLE] Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot

Abstract (provisional)

Background

Selecting and maintaining an engaging and challenging training difficulty level in robot-assisted stroke rehabilitation remains an open challenge. Despite the ability of robotic systems to provide objective and accurate measures of function and performance, the selection and adaptation of exercise difficulty levels is typically left to the experience of the supervising therapist.

Methods

We introduce a patient-tailored and adaptive robot-assisted therapy concept to optimally challenge patients from the very first session and throughout therapy progress. The concept is evaluated within a four-week pilot study in six subacute stroke patients performing robot-assisted rehabilitation of hand function. Robotic assessments of both motor and sensory impairments of hand function conducted prior to the therapy are used to adjust exercise parameters and customize difficulty levels. During therapy progression, an automated routine adapts difficulty levels from session to session to maintain patients? performance around a target level of 70%, to optimally balance motivation and challenge.

Results

Robotic assessments suggested large differences in patients? sensorimotor abilities that are not captured by clinical assessments. Exercise customization based on these assessments resulted in an average initial exercise performance around 70% (62%?20%, mean?std), which was maintained throughout the course of the therapy (64%?21%). Patients showed reduction in both motor and sensory impairments compared to baseline as measured by clinical and robotic assessments. The progress in difficulty levels correlated with improvements in a clinical impairment scale (Fugl-Meyer Assessment) (rs = 0.70), suggesting that the proposed therapy was effective at reducing sensorimotor impairment.

Conclusions

Initial robotic assessments combined with progressive difficulty adaptation have the potential to automatically tailor robot-assisted rehabilitation to the individual patient. This results in optimal challenge and engagement of the patient, may facilitate sensorimotor recovery after neurological injury, and has implications for unsupervised robot-assisted therapy in the clinic and home environment.

The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.

via JNER | Abstract | Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot.

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[ARTICLE] Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot

Abstract (provisional)

Background

Selecting and maintaining an engaging and challenging training difficulty level in robot-assisted stroke rehabilitation remains an open challenge. Despite the ability of robotic systems to provide objective and accurate measures of function and performance, the selection and adaptation of exercise difficulty levels is typically left to the experience of the supervising therapist.

Methods

We introduce a patient-tailored and adaptive robot-assisted therapy concept to optimally challenge patients from the very first session and throughout therapy progress. The concept is evaluated within a four-week pilot study in six subacute stroke patients performing robot-assisted rehabilitation of hand function. Robotic assessments of both motor and sensory impairments of hand function conducted prior to the therapy are used to adjust exercise parameters and customize difficulty levels. During therapy progression, an automated routine adapts difficulty levels from session to session to maintain patients? performance around a target level of 70%, to optimally balance motivation and challenge.

Results

Robotic assessments suggested large differences in patients? sensorimotor abilities that are not captured by clinical assessments. Exercise customization based on these assessments resulted in an average initial exercise performance around 70% (62%?20%, mean?std), which was maintained throughout the course of the therapy (64%?21%). Patients showed reduction in both motor and sensory impairments compared to baseline as measured by clinical and robotic assessments. The progress in difficulty levels correlated with improvements in a clinical impairment scale (Fugl-Meyer Assessment) (rs = 0.70), suggesting that the proposed therapy was effective at reducing sensorimotor impairment.

Conclusions

Initial robotic assessments combined with progressive difficulty adaptation have the potential to automatically tailor robot-assisted rehabilitation to the individual patient. This results in optimal challenge and engagement of the patient, may facilitate sensorimotor recovery after neurological injury, and has implications for unsupervised robot-assisted therapy in the clinic and home environment.

The complete article is available as aprovisional PDF . The fully formatted PDF and HTML versions are in production.

via JNER | Abstract | Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot.

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