Posts Tagged Neuroplasticity

[TED] 3 Clues to undertand your Brain – TED Talks

Vilayanur Ramachandran tells us what brain damage can reveal about the connection between celebral tissue and the mind, using three startling delusions as examples.

Neurologist V.S. Ramachandran looks deep into the brain’s most basic mechanisms. By working with those who have very specific mental disabilities caused by brain injury or stroke, he can map functions of the mind to physical structures of the brain.



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[ARTICLE] Neuroplasticity of cognitive control networks following cognitive training for chronic traumatic brain injury – Full Text


Cognitive control is the ability to coordinate thoughts and actions to achieve goals. Cognitive control impairments are one of the most persistent and devastating sequalae of traumatic brain injuries (TBI). There have been efforts to improve cognitive control in individuals with post-acute TBI. Several studies have reported changes in neuropsychological measures suggesting the efficacy of cognitive training in improving cognitive control. Yet, the neural substrates of improved cognitive control after training remains poorly understood. In the current study, we identified neural plasticity induced by cognitive control training for TBI using resting-state functional connectivity (rsFC). Fifty-six individuals with chronic mild TBI (9 years post-injury on average) were randomized into either a strategy-based cognitive training group (N = 26) or a knowledge-based training group (active control condition; N = 30) for 8 weeks. We acquired a total of 109 resting-state functional magnetic resonance imaging from 45 individuals before training, immediately post-training, and 3 months post-training. Relative to the controls, the strategy-based cognitive training group showed monotonic increases in connectivity in two cognitive control networks (i.e., cingulo-opercular and fronto-parietal networks) across time points in multiple brain regions (pvoxel < 0.001, pcluster < 0.05). Analyses of brain-behavior relationships revealed that fronto-parietal network connectivity over three time points within the strategy-based cognitive training group was positively associated with the trail making scores (pvoxel < 0.001, pcluster < 0.05). These findings suggest that training-induced neuroplasticity continues through chronic phases of TBI and that rsFC can serve as a neuroimaging biomarker of evaluating the efficacy of cognitive training for TBI.

1. Introduction

A traumatic brain injury (TBI) occurs when external force is applied to the head leading to disruptions of brain structure and function (Faul et al., 2010). Though an insult to the brain occurs instantaneously, a TBI incident can be the beginning of a chronic disease process rather than an isolated event or final outcome across all levels of initial injury severity: moderate or severe TBI (Corrigan et al., 2014Masel and DeWitt, 2010Whitnall et al., 2006) and mild-to-severe TBI (Masel and DeWitt, 2010Whitnall et al., 2006). For example, TBI can be a risk factor for cognitive impairments (Arciniegas et al., 2002Rabinowitz and Levin, 2014), psychiatric disorders (Hesdorffer et al., 2009), reduced social functioning (Temkin et al., 2009), and neurodegenerative diseases such as chronic traumatic encephalopathy (McKee et al., 2013). A substantial number of individuals with TBI sustain TBI-related disabilities. For example, 57% of individuals 16 years or older with moderate or severe TBI were moderately or severely disabled, and 39% had a worse global outcome at 5 years post-injury compared to their outcome level at 1 or 2 years post-injury (Corrigan et al., 2014). Currently, as many as 5.3 million people in the U.S. are facing challenges of TBI-related disability (Frieden et al., 2015). The actual number of individuals continuing to suffer from chronic TBI (>6 months post-injury time) effects may be greater than the estimates given the lack of public awareness of TBI in the past and the limited sensitivity of conventional neuropsychological measures (Katz and Alexander, 1994). Additionally, conventional clinical imaging (e.g., CT scanning) may be insensitive to identifying brain abnormalities especially in individuals with mild TBI (Tellier et al., 2009). Substantial numbers of individuals with sustained TBI necessitates further rehabilitation research in chronic TBI (Katz and Alexander, 1994).

Resting-state functional connectivity (rsFC) is a technique measuring the temporal coherence of blood oxygenation level dependent (BOLD) signal from anatomically separated brain regions acquired at rest. Since its inception (Biswal et al., 1995), rsFC in resting-state functional magnetic imaging (rsfMRI) has provided new insights about brain networks that can better explain the underlying mechanisms of human behavior or function (van den Heuvel and Hulshoff Pol, 2010). RsFC studies in clinical populations are increasingly popular because they do not require that subjects perform a specific task. RsFC is well-positioned to identify both the patterns of injury and the associations between injury and behavioral impairments in TBI (Sharp et al., 2014). This is especially important as diffuse axonal injury (DAI) is one of the primary injury mechanisms of TBI (Smith et al., 2003). DAI induces multi-focal injuries to axons which provide the structural basis of spatially distributed brain networks. Thus, DAI leads to a breakdown of brain network connectivity. In the context of rehabilitation, rsFC is also a promising technique to measure neuroplasticity within the injured brain, as rsFC has been successfully utilized to provide evidence for experience-induced neuroplasticity of the adult human brain in vivo ( Guerra-Carrillo et al., 2014Kelly and Castellanos, 2014). For example, in healthy subjects, previous studies reported changes in rsFC after motor training (Lewis et al., 2009Taubert et al., 2011), cognitive training (Jolles et al., 2013Mackey et al., 2013Takeuchi et al., 2013), and physical activity in older adults (Voss et al., 2010). In clinical populations, changes in rsFC after cognitive rehabilitation for cognitive symptoms associated with multiple sclerosis has been reported (de Giglio et al., 2016Keshavan et al., 2017). This technique is well-suited to investigating neuroplasticity induced by rehabilitation for TBI.

In a previous study, we reported the efficacy of strategy-based cognitive training for chronic TBI, utilizing neuropsychological measures (Vas et al., 2016). This training is an integrative program to improve cognitive control by exerting more efficient thinking strategies for selective attention and abstract reasoning (see the Materials and methods section for the details of training protocols). Cognitive control (also called executive function) is the ability to coordinate thoughts and actions to achieve goals while adjusting these goals according to changing environments (Nomura et al., 2010). Cognitive control is critical to successfully perform daily life tasks (Botvinick et al., 2001Diamond, 2013). Thus, impairment in cognitive control is one of the most persistent and devastating sequalae of TBI (Cicerone et al., 2000Rabinowitz and Levin, 2014), and empirical studies demonstrating the efficacy of cognitive rehabilitation for improving cognitive control of individuals with post-acute TBI are valuable in the literature on TBI rehabilitation (Cicerone et al., 2006McDonald et al., 2002). In the current study, we describe rehabilitation-induced changes in brain connectivity.

Cognitive control has been extensively investigated in the field of cognitive neuroscience (Power and Petersen, 2013). Of note, Dosenbach and colleagues (Dosenbach et al., 2006) identified a set of regions that are active across multiple cognitive control tasks. A follow-up study (Dosenbach et al., 2007) revealed two distinct resting-state networks related to cognitive control: the cingulo-opercular network and fronto-parietal network. The cingulo-opercular network consists of bilateral anterior insula/frontal opercula (aI/fO), bilateral anterior prefrontal cortices (aPFC), dorsal anterior cingulate cortex (dACC), and thalamus, and it is thought to support stable maintenance of task mode and strategy during cognitive processes (Dosenbach et al., 2007 ;  Dosenbach et al., 2008). The fronto-parietal network comprises of bilateral dorsolateral prefrontal cortices (dlPFC), bilateral dorsal frontal cortices (dFC), bilateral inferior parietal lobules (IPL), bilateral intraparietal sulci (IPS), middle cingulate cortex (mCC), and bilateral precunei (PCUN), supporting active, adaptive online control during cognitive control processes (Dosenbach et al., 2007 ;  Dosenbach et al., 2008). The cingulo-opercular network and fronto-parietal network are also referred to as the salience network and central executive network, respectively (Seeley et al., 2007). The salience and central executive networks are often referred to in the context of interactions among these networks and the default mode network (Menon and Uddin, 2010). However, in this report, we will refer to them as the cingulo-opercular and fronto-parietal networks, as we conducted current study in the context of cognitive control. TBI-induced disruptions to the cingulo-opercular in mild-to-severe TBI (Bonnelle et al., 2012Jilka et al., 2014Stevens et al., 2012) and fronto-parietal networks in mild TBI (Mayer et al., 2011Stevens et al., 2012) have been previously reported. Specifically, TBI decreases the white matter integrity of the cingulo-opercular network (Bonnelle et al., 2012) and functional connectivity between the cingulo-opercular and default networks during a cognitive control task (Jilka et al., 2014). Additionally, individuals with mild TBI showed increases and decreases in rsFC with the cingulo-opercular (Stevens et al., 2012) and fronto-parietal networks (Mayer et al., 2011Stevens et al., 2012) across brain regions, relative to healthy individuals.

We utilized rsfMRI to identify the effects of a strategy-based cognitive training for chronic TBI on the cognitive control networks (i.e., cingulo-opercular and fronto-parietal networks) compared to a knowledge-based comparison condition. We focused on the cingulo-opercular and fronto-parietal networks as our training protocols were aimed at improving cognitive control processes (See the Materials and methods section for the details of training protocols). We randomized individuals with chronic mild TBI into two eight-week training groups (strategy- versus knowledge-based), and we acquired their MRI scans over three time points (prior to training, after training, and at three-months follow-up after training completed). We then investigated the spatial and temporal patterns of training-induced changes in cingulo-opercular and fronto-parietal networks connectivity of these individuals. We hypothesized that strategy-based cognitive training would induce changes in the cingulo-opercular and fronto-parietal networks connectivity relative to the knowledge-based training program. This prediction is based on findings from previous rsfMRI studies demonstrating neuroplasticity in healthy adults and other clinical populations (de Giglio et al., 2016Jolles et al., 2013Keshavan et al., 2017Lewis et al., 2009Mackey et al., 2013Takeuchi et al., 2013Taubert et al., 2011Voss et al., 2010) and the efficacy of strategy-based cognitive training for chronic TBI (Vas et al., 2016).[…]


Continue —> Neuroplasticity of cognitive control networks following cognitive training for chronic traumatic brain injury


Fig. 1

Fig. 1. Seed locations. Black and yellow circles represent seeds for the cingulo-opercular network and fronto-parietal network, respectively. aI/fO, anterior insula/frontal operculum; aPFC, anterior prefrontal cortex; dACC, dorsal anterior cingulate cortex; dFC, dorsal frontal cortex; dlPFC, dorsolateral prefrontal cortex; IPS; intraparietal sulcus; mCC, middle cingulate cortex; PCUN, precuneus; L, left; R, right.


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[Abstract+References] Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review

Acquired brain injury (ABI) is associated with a range of cognitive and motor deficits, and poses a significant personal, societal, and economic burden. Rehabilitation programs are available that target motor skills or cognitive functioning. In this review, we summarize the existing evidence that training may enhance structural neuroplasticity in patients with ABI, as assessed using structural magnetic resonance imaging (MRI)–based techniques that probe microstructure or morphology. Twenty-five research articles met key inclusion criteria. Most trials measured relevant outcomes and had treatment benefits that would justify the risk of potential harm. The rehabilitation program included a variety of task-oriented movement exercises (such as facilitation therapy, postural control training), neurorehabilitation techniques (such as constraint-induced movement therapy) or computer-assisted training programs (eg, Cogmed program). The reviewed studies describe regional alterations in white matter architecture and/or gray matter volume with training. Only weak-to-moderate correlations were observed between improved behavioral function and structural changes. While structural MRI is a powerful tool for detection of longitudinal structural changes, specific measures about the underlying biological mechanisms are lacking. Continued work in this field may potentially see structural MRI metrics used as biomarkers to help guide treatment at the individual patient level.

1. O’Rance, L, Fortune, N. Disability in Australia: Acquired Brain Injury. Canberra, AustraliaAustralian Institute of Health and Welfare2007:124Google Scholar
2. Rabinowitz, A, Levin, HS. Cognitive sequale of traumatic brain injury. Psychiatr Clin North Am. 2014;37:111Google ScholarCrossrefMedline
3. Kuhtz-Buschbeck, JP, Hoppe, B, Gölge, M, Dreesmann, M, Damm-Stünitz, U, Ritz, A. Sensorimotor recovery in children after traumatic brain injury: analyses of gait, gross motor, and fine motor skills. Dev Med Child Neurol. 2003;45:821828Google ScholarCrossrefMedline
4. Hayes, JP, Bigler, ED, Verfaellie, M. Traumatic brain injury as a disorder of brain connectivity. J Int Neuropsychol Soc. 2016;22:120137. doi:10.1017/S1355617715000740. Google ScholarCrossrefMedline
5. Drijkoningen, D, Caeyenberghs, K, Vander Linden, C, Van Herpe, K, Duysens, J, Swinnen, SP. Associations between muscle strength asymmetry and impairments in gait and posture in young brain-injured patients. J Neurotrauma. 2015;32:13241332. doi:10.1089/neu.2014.3787. Google ScholarCrossrefMedline
6. Nocentini, U, Bozzali, M, Spanò, B. Exploration of the relationships between regional grey matter atrophy and cognition in multiple sclerosis. Brain Imaging Behav. 2014;8:378386. doi:10.1007/s11682-012-9170-7. Google ScholarCrossrefMedline
7. Hulkower, MB, Poliak, DB, Rosenbaum, SB, Zimmerman, ME, Lipton, ML. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol. 2013;34:20642074. doi:10.3174/ajnr.A3395. Google ScholarCrossrefMedline
8. Caeyenberghs, K, Wenderoth, N, Smits-Engelsman, BC, Sunaert, S, Swinnen, SP. Neural correlates of motor dysfunction in children with traumatic brain injury: exploration of compensatory recruitment patterns. Brain. 2009;132(pt 3):684694Google ScholarCrossrefMedline
9. Chen, H, Epstein, J, Stern, E. Neural plasticity after acquired brain injury: evidence from functional neuroimaging. PM R. 2010;2(12 suppl 2):S306S312. doi:10.1016/j.pmrj.2010.10.006. Google ScholarCrossrefMedline
10. Choo, PL, Gallagher, HL, Morris, J, Pomeroy, VM, van Wijck, F. Correlations between arm motor behavior and brain function following bilateral arm training after stroke: a systematic review. Brain Behav. 2015;5:e00411. doi: 10.1002/brb3.411. Google ScholarCrossrefMedline
11. Matthews, PM, Johansen-Berg, H, Reddy, H. Non-invasive mapping of brain functions and brain recovery: applying lessons from cognitive neuroscience to neurorehabilitation. Restor Neurol Neurosci. 2004;22:245260Google ScholarMedline
12. Prosperini, L, Piattella, MC, Giannì, C, Pantano, P. Functional and structural brain plasticity enhanced by motor and cognitive rehabilitation in multiple sclerosis. Neural Plast. 2015;2015:481574. doi:10.1155/2015/481574. Google ScholarCrossrefMedline
13. Reid, LB, Boyd, RN, Cunnington, R, Rose, SE. Interpreting intervention induced neuroplasticity with fMRI: the case for multimodal imaging strategies. Neural Plast. 2016;2016:2643491. doi:10.1155/2016/2643491.Google ScholarCrossrefMedline
14. Richards, LG, Stewart, KC, Woodbury, ML, Senesac, C, Cauraugh, JH. Movement-dependent stroke recovery: a systematic review and meta-analysis of TMS and fMRI evidence. Neuropsychologia. 2008;46:311Google ScholarCrossrefMedline
15. Mechelli, A, Crinion, JT, Noppeney, U. Neurolinguistics: structural plasticity in the bilingual brain. Nature. 2004;431:757. doi:10.1038/431757a. Google ScholarCrossrefMedline
16. Takeuchi, H, Sekiguchi, A, Taki, Y. Training of working memory impacts structural connectivity. J Neurosci. 2010;30:32973303. doi:10.1523/JNEUROSCI.4611-09.2010. Google ScholarCrossrefMedline
17. van Tulder, M, Furlan, A, Bombardier, C, Bouter, L; Editorial Board of the Cochrane Collaboration Back Review Group. Updated method guidelines for systematic reviews in the cochrane collaboration back review group. Spine (Phila Pa 1976). 2003;28:12901299Google ScholarCrossrefMedline
18. Fritz, NE, Cheek, FM, Nichols-Larsen, DS. Motor-cognitive dual-task training in persons with neurologic disorders: a systematic review. J Neurol Phys Ther. 2015;39:142153. doi:10.1097/NPT.0000000000000090. Google ScholarCrossrefMedline
19. Guzmán, J, Esmail, R, Karjalainen, K, Malmivaara, A, Irvin, E, Bombardier, C. Multidisciplinary rehabilitation for chronic low back pain: systematic review. BMJ. 2001;322:15111516Google ScholarCrossrefMedline
20. Karjalainen, K, Malmivaara, A, van Tulder, M. Multidisciplinary biopsychosocial rehabilitation for subacute low back pain in working-age adults: a systematic review within the framework of the Cochrane Collaboration Back Review Group. Spine (Phila Pa 1976). 2001;26:262269Google ScholarCrossrefMedline
21. 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 ScholarCrossrefMedline
22. Schlaug, G, Marchina, S, Norton, A. Evidence for plasticity in white-matter tracts of patients with chronic Broca’s aphasia undergoing intense intonation-based speech therapy. Ann N Y Acad Sci. 2009;1169:385394. doi:10.1111/j.1749-6632.2009.04587.x. Google ScholarCrossrefMedline
23. Breier, J, Juranek, J, Papanicolaou, A. Changes in maps of language function and the integrity of the arcuate fasciculus after therapy for chronic aphasia. Neurocase. 2011;17:506517. doi:10.1080/13554794.2010.547505. Google ScholarCrossrefMedline
24. Caria, A, Weber, C, Brötz, D. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology. 2011;48:578582. doi:10.1111/j.1469-8986.2010.01117.x. Google ScholarCrossrefMedline
25. Nordvik, JE, Schanke, AK, Walhovd, K, Fjell, A, Grydeland, H, Landrø, NI. Exploring the relationship between white matter microstructure and working memory functioning following stroke: a single case study of computerized cognitive training. Neurocase. 2012;18:139151. doi:10.1080/13554794.2011.568501.Google ScholarCrossrefMedline
26. Borstad, AL, Bird, T, Choi, S, Goodman, L, Schmalbrock, P, Nichols-Larsen, DS. Sensorimotor training induced neural reorganization after stroke: a case series. J Neurol Phys Ther. 2013;37:2736. doi:10.1097/NPT.0b013e318283de0d. Google ScholarCrossrefMedline
27. Lazaridou, A, Astrakas, L, Mintzopoulos, D. Diffusion tensor and volumetric magnetic resonance Imaging using an MR-compatible hand-induced robotic device suggests training-induced neuroplasticity in patients with chronic stroke. Int J Mol Med. 2013;32:9951000. doi:10.3892/ijmm.2013.1476. Google ScholarCrossrefMedline
28. Särkämö, T, Ripollés, P, Vepsäläinen, H. Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study. Front Hum Neurosci. 2014;8:245. doi:10.3389/fnhum.2014.00245. Google ScholarCrossrefMedline
29. Wan, CY, Zheng, X, Marchina, S, Norton, A, Schlaug, G. Intensive therapy induces contralateral white matter changes in chronic stroke patients with Broca’s aphasia. Brain Lang. 2014;136:17. doi:10.1016/j.bandl.2014.03.011. Google ScholarCrossrefMedline
30. Fan, YT, Lin, KC, Liu, HL, Chen, YL, Wu, CY. Changes in structural integrity are correlated with motor and functional recovery after post-stroke rehabilitation. Restor Neurol Neurosci. 2015;33:835844. doi:10.3233/RNN-150523. Google ScholarCrossrefMedline
31. Young, BM, Stamm, JM, Song, J. Brain-computer interface training and stroke affects patterns of brain-behavior relationships in corticospinal motor fibers. Front Hum Neurosci. 2016;10:457Google ScholarCrossrefMedline
32. Wilkins, KB, Owen, M, Ingo, C, Carmona, C, Dewald, J, Yao, J. Neural plasticity in moderate to severe chronic stroke following a device-assisted task-specific arm/hand intervention Front Neurol. 2017;8:284. doi:10.33389/fneur.2017.00284. Google ScholarCrossrefMedline
33. Yang, HE, Kyeong, S, Lee, SH. Structural and functional improvements due to robot-assisted gait training in the stroke-injured brain. Neurosci Lett. 2017;637:114119Google ScholarCrossrefMedline
34. Ibrahim, I, Tintera, J, Skoch, A. Fractional anisotropy and mean diffusivity in the corpus callosum of patients with multiple sclerosis: the effect of physiotherapy. Neuroradiology. 2011;53:917926. doi:10.1007/s00234-011-0879-6. Google ScholarCrossrefMedline
35. Filippi, M, Riccitelli, G, Mattioli, F. Multiple sclerosis: effects of cognitive rehabilitation on structural and functional MR imaging measures—an explorative study. Radiology. 2012;262:932940. doi:10.1148/radiol.11111299. Google ScholarCrossrefMedline
36. Bonzano, L, Tacchino, A, Brichetto, G. Upper limb motor rehabilitation impacts white matter microstructure in multiple sclerosis. Neuroimage. 2014;90:107116. doi:10.1016/j.neuroimage.2013.12.025. Google ScholarCrossrefMedline
37. Prosperini, L, Fanelli, F, Petsas, N. Multiple sclerosis: changes in microarchitecture of white matter tracts after training with a video game balance board. Radiology. 2014;273:529538. doi:10.1148/radiol.14140168. Google ScholarCrossrefMedline
38. Rasova, K, Prochazkova, M, Tintera, J, Ibrahim, I, Zimova, D, Stetkarova, I. Motor programme activating therapy influences adaptive brain functions in multiple sclerosis: clinical and MRI study. Int J Rehabil Res. 2015;38:4954. doi:10.1097/MRR.0000000000000090. Google ScholarCrossrefMedline
39. Ernst, A, Sourty, M, Roquet, D. Functional and structural cerebral changes in key brain regions after facilitation programme for episodic future thought in relapsing-remitting multiple sclerosis patients. Brain Cogn. 2016;105:3445Google ScholarCrossrefMedline
40. Cruickshank, TM, Thompson, JA, Domínguez, D. The effect of multidisciplinary rehabilitation on brain structure and cognition in Huntington’s disease: an exploratory study. Brain Behav. 2015;5:e00312. doi:10.1002/brb3.312. Google ScholarCrossrefMedline
41. Metzler-Baddeley, C, Cantera, J, Coulthard, E, Rosser, A, Jones, DK, Baddeley, RJ. Improved executive function and callosal white matter microstructure after rhythm exercise in Huntington’s disease. J Huntingtons Dis. 2014;3:273283. doi:10.3233/JHD-140113. Google ScholarCrossrefMedline
42. Sehm, B, Taubert, M, Conde, V. Structural brain plasticity in Parkinson’s disease induced by balance training. Neurobiol Aging. 2014;35:232239. doi:10.1016/j.neurobiolaging.2013.06.021. Google ScholarCrossrefMedline
43. Díez-Cirarda, M, Ojeda, N, Peña, J. Increased brain connectivity and activation after cognitive rehabilitation in Parkinson’s disease: a randomized controlled trial. Brain Imaging Behav. 2017;11:16401651Google ScholarCrossrefMedline
44. Burciu, RG, Fritsche, N, Granert, O. Brain changes associated with postural training in patients with cerebellar degeneration: a voxel-based morphometry study. J Neurosci. 2013;33:45944604. doi:10.1523/JNEUROSCI.3381-12.2013. Google ScholarCrossrefMedline
45. Han, K, Davis, RA, Chapman, SB, Krawczyk, DC. Strategy-based reasoning training modulates cortical thickness and resting-state functional connectivity in adults with chronic traumatic brain injury. Brain Behav. 2017;7:e00687. doi:10.1002/brb3.687. Google ScholarCrossrefMedline
46. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. Hilsdale, NJLawrence Earlbaum1988Google Scholar
47. Borenstein, M, Hedges, LV, Higgins, JPT, Rothstein, HR. Multiple outcomes or time-points within a study. In: Borenstein, M, ed. Introduction to Meta-Analysis. Chichester, EnglandWiley2009:225238Google ScholarCrossref
48. Thomas, C, Baker, CI. Teaching an adult brain new tricks: a critical review of evidence for training-dependent structural plasticity in humans. Neuroimage. 2013;73:225236. doi:10.1016/j.neuroimage.2012.03.069. Google ScholarCrossrefMedline
49. Smith, S, Rao, A, De Stefano, N. Longitudinal and cross-sectional analysis of atrophy in Alzheimer’s disease: cross-validation of BSI, SIENA and SIENAX. Neuroimage. 2007;36:12001206. doi:10.1016/j.neuroimage.2007.04.035. Google ScholarCrossrefMedline
50. Heiervang, E, Behrens, TE, Mackay, CE, Robson, MD, Johansen-Berg, H. Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage. 2006;33:867877. doi:10.1016/j.neuroimage.2006.07.037. Google ScholarCrossrefMedline
51. Wakana, S, Caprihan, A, Panzenboeck, MM. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage. 2007;36:630644. doi:10.1016/j.neuroimage.2007.02.049. Google ScholarCrossrefMedline
52. Scholz, J, Klein, MC, Behrens, TE, Johansen-Berg, H. Training induces changes in white-matter architecture. Nat Neurosci. 2009;12:13701371. doi:10.1038/nn.2412. Google ScholarCrossrefMedline
53. Taubert, M, Draganski, B, Anwander, A. Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J Neurosci. 2010;30:1167011677. doi:10.1523/JNEUROSCI.2567-10.2010. Google ScholarCrossrefMedline
54. Hofstetter, S, Tavor, I, Tzur Moryosef, S, Assaf, Y. Short-term learning induces white matter plasticity in the fornix. J Neurosci. 2013;33:1284411280. doi:10.1523/JNEUROSCI.4520-12.2013. Google ScholarCrossrefMedline
55. Cercignani, M, Bammer, R, Sormani, MP, Fazekas, F, Filippi, M. Inter-sequence and inter-imaging unit variability of diffusion tensor MR imaging histogram-derived metrics of the brain in healthy volunteers. AJNR Am J Neuroradiol. 2003;24:638643Google ScholarMedline
56. Price, R, Axel, L, Morgan, T. Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM Nuclear Magnetic Resonance Task Group No. 1. Med Phys. 1990;17:287295. doi:10.1118/1.596566. Google ScholarCrossrefMedline
57. Bookstein, FL. “Voxel-based morphometry” should not be used with imperfectly registered images. Neuroimage. 2001;14:14541462Google ScholarCrossrefMedline
58. Thompson, WK, Holland, D; Alzheimer’s Disease Neuroimaging Initiative. Bias in tensor based morphometry Stat-ROI measures may result in unrealistic power estimates. Neuroimage. 2011;57:14. doi:10.1016/j.neuroimage.2010.11.092. Google ScholarCrossrefMedline
59. 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 ScholarCrossrefMedline
60. Eriksson, S, Free, S, Thom, M. Quantitative grey matter histological measures do not correlate with grey matter probability values from in vivo MRI in the temporal lobe. J Neurosci Methods. 2009;181:111118. doi:10.1016/j.jneumeth.2009.05.001. Google ScholarCrossrefMedline
61. Jones, DK, Knösche, TR, Turner, R. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage. 2011;73:239254. doi:10.1016/j.neuroimage.2012.06.081. Google ScholarCrossref
62. Jeurissen, B, Leemans, A, Jones, DK, Tournier, JD, Sijbers, J. Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Hum Brain Mapp. 2011;32:461479. doi:10.1002/hbm.21032. Google ScholarCrossrefMedline
63. Caeyenberghs, K, Metzler-Baddeley, C, Foley, S, Jones, DK. Dynamics of the human structural connectome underlying working memory training. J Neurosci. 2016;36:40564066. doi:10.1523/jneurosci.1973-15.2016. Google ScholarCrossrefMedline
64. Metzler-Baddeley, C, Foley, S, de Santis, S. Dynamics of white matter plasticity underlying working memory training: multimodal evidence from diffusion MRI and relaxometry. J Cogn Neurosci. 2017;29:15091520. doi:10.1162/jocn_a_01127. Google ScholarCrossrefMedline
65. Assaf, Y, Basser, PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27:4858. doi:10.1016/j.neuroimage.2005.03.042. Google ScholarCrossrefMedline
66. Deoni, SC, Rutt, BK, Jones, DK. Investigating exchange and multicomponent relaxation in fully-balanced steady-state free precession imaging. J Magn Reson Imaging. 2008;27:14211429. doi:10.1002/jmri.21079. Google ScholarCrossrefMedline

via Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review – Karen Caeyenberghs, Adam Clemente, Phoebe Imms, Gary Egan, Darren R. Hocking, Alexander Leemans, Claudia Metzler-Baddeley, Derek K. Jones, Peter H. Wilson, 2018

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[Review] Transcranial Electrical Brain Stimulation – Full Text


Transcranial electrical brain stimulation using weak direct current (tDCS) or alternating current (tACS) is being increasingly used in clinical and experimental settings to improve cognitive and motor functions in healthy subjects as well as neurological patients. This review focuses on the therapeutic value of transcranial direct current stimulation for neurorehabilitation and provides an overview of studies addressing motor and non-motor symptoms after stroke, disorders of attention and consciousness as well as Parkinson’s disease.



The past 10 years have seen an increased clinical and experimental focus on noninvasive electrical brain stimulation as an innovative therapeutic approach to support neurorehabilitation. This entails the application of either transcranial direct current stimulation (tDCS), or less commonly, transcranial alternating current stimulation (tACS). Typically, up to 0.8 A/m² is used for up to 40 min per single stimulation session [1]. The electrical current partially penetrates the underlying structures and affects nerve cells, glia and vessels in the stimulated brain area [1] [2]. Early animal experiments during the 1960s and 1970s on the effects of weak DC stimulation demonstrated an excitement-induced change of neurons lasting several hours after the end of the stimulation [3] [4]. Therapeutic studies of the 1970s, at that time mainly concerning the treatment of depression, did not yield any success, which in retrospect could be attributed to the stimulation parameters used. In 2 000 key experiments by Nitsche and Paulus on polarity-related excitability changes in the human motor system after transcranial application of tDCS led to a renewed interest in the approach [5]. The authors documented increased cortical excitability measured by the amplitude of motor-evoked potentials in healthy volunteers after anodal stimulation above the motor cortex lasting at least 9 min [6]. Reversing the direction of stimulation (cathodal tDCS) resulted in a decrease in motor-evoked potential. In addition to the concept of pure excitability modulation, a large number of studies demonstrate modulation of neuroplasticity by tDCS in various ways, including basic scientific and mechanistic findings regarding improvement of synaptic transmission strength [7] [8] [9], long-term influence on learning processes and behavior [10] [11], as well as a therapeutic approach to improve function in neurological and psychiatric disorders associated with altered or disturbed neuroplasticity (overview in [12]). In particular, simultaneous application of tDCS together with different learning paradigms, such as motor or cognitive training, appears to produce favorable effects in healthy subjects and in various patient groups [11] [13].

The following review presents the effects of tDCS on the improvement in the function of some neurological disease patterns which are regularly the focus of neurorehabilitative treatment. This especially includes stroke. In addition, we shall refer to a current database of clinical studies containing a comprehensive list of scientific and clinical studies of tDCS in the treatment of neurological and psychiatric disorders [14].

Post-stroke Motor Impairment

Stroke is one of the primary causes worldwide of permanent limitations of motor function and speech. Despite intensive rehabilitation efforts, approx. 50% of stroke patients remain limited in their motor and speech capabilities [15] [16] [17]. Current understanding of the mechanisms of tDCS is largely based on data documented for the human motor system. The reasons for this include the presence of direct and easily objectifiable measurement criteria (for example, motor-evoked potential, fine motor function), as well as anatomical accessibility of brain motor regions for non-invasive stimulation. Therefore, it is not surprising that the clinical syndrome of stroke with the frequent symptom of hemiparesis as a “lesion model of the pyramidal tract” received significant scientific interest with respect to researching the effects of tDCS, as evidenced by the numerous scientific publications since 2005 ([Fig. 1]). In contrast to earlier largely mechanistic studies, in the past 5 years there has been a trend toward studies addressing clinically-oriented therapeutic issues. […]

Continue —> Thieme E-Journals – Neurology International Open / Full Text

Fig. 2 Illustration of the 3 typical brain stimulation montages exemplified by tDCS above the motor cortex. In example a, the anode (red) is placed above the ipsilesional motor cortex, and the cathode (blue) is located on the contralateral forehead. Example b shows the cathode placed above the motor cortex of the non-lesioned hemisphere, and the anode is placed on the contralateral forehead. Example c illustrates bihemispheric montage, with the anode located above the ipsilesional motor cortex, and the cathode placed above the motor cortex of the non-lesioned hemisphere. The white arrow shows the intracerebral current flow. The goal of these 3 arrangements is to modulate the interaction between both motor cortices by changing the activity of one or both hemispheres c.

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[Editorial] Improving and Predicting Outcomes of Traumatic Brain Injury: Neuroplasticity, Imaging Modalities, and Perspective Therapy – Neural Plasticity

Each year, a new traumatic brain injury (TBI) event occurs in
an estimated 10 million people worldwide, particularly in
young adults. Not only is TBI the leading cause of longterm
disability and mortality worldwide, but it is also
expected to become the third largest cause of global disease
burden by 2020 [1, 2]. TBI is a challenging disease process,
both to treat and to investigate. TBI survivors often experience
substantial and lifelong cognitive, physical, and behavioral
impairments that require long-term access to health
care and disability services.
Over the past three decades, imaging modalities, such as
positron emission tomography (PET),functionalMRI (fMRI),
diffusion tensor imaging (DTI), and transcranial magnetic
stimulation (TMS), have played a pivotal role in predicting
TBI outcome and advancing TBI treatment [3].
Burgeoning evidences for neuroplasticity have shed light
on the potential therapeutic protocols focusing on synaptic
proteins, new network connections, inflammatory reactions,
and the recruitment of immune cells [4]. Future therapies,
including gene therapies or a combination of different pharmacologic
therapies and rehabilitative protocols, which may
benefit victims by targeting multiple mechanisms of recovery,
are of utmost interest and currently under heavy investigation
by devoting neuroscientists.
The articles contained in this special issue include 4
reviews and 2 original research papers: a quantitative study
focusing on predictors of recovery from TBI and a cohort
study on substance related disorder after TBI.

(i) TBI survivors suffer various functional and cognitive
sequelae that may impose serious medical and social
problems. J. Ma et al. reviewed the complicated
pathological mechanisms of diffuse axonal injury
(DAI) in an attempt to facilitate more accurate diagnosis
and hence improve the survival and life quality
of DAI patients.

(ii) Not many studies have discussed the role of synapses
after TBI. Z. Wen et al. provided a comprehensive
review on the role and mechanisms of synapses in
TBI and the correlation between key synaptic proteins
and neuroplasticity. The article also provides
insights on the role of synapses in the treatment
and prognosis of TBI.

(iii) Molecular studies concerning the microglia-induced
inflammation by M1 phenotype and antiinflammation
by M2 phenotype are a new strategy
for treatment of TBI. In the paper titled “The

Polarization States of Microglia in TBI: A New
Paradigm for Pharmacological Intervention,” H.
Xu et al. examined research on the polarization
of microglia and their roles in the inflammation
response and secondary brain injury after TBI. It is
hoped that decreasing M1 phenotype and increasing
M2 phenotype may shed light on the pharmacotherapy
of TBI.

(iv) Studies to locate the clinical predictors of recovery
from prolonged disorders of consciousness (PDC)
can be arduous. In the original research presented
by H. Abe et al., 14 TBI patients were investigated
using diffusion tensor imaging (DTI) for longterm
follow-ups of 1-2 years. The results disclosed
correlation between initial severity of PDC and
difference in axial diffusivity (AD) and the degree
of recovery from PDC (RPDC). Microstructural
white matter changes in this study implicate their
possible relationship with the degree of RPDC.

(v) Efforts to correct behavioral, cognitive, mood, and
executive impairment of TBI patients are costly.
The article “Rehabilitation Treatment and Progress
of Traumatic Brain Injury Dysfunction” by B. Dang
et al. compiles the current rehabilitation treatment
plans and outcomes of TBI in adults.

(vi) Whether TBI induces substance-related disorder
(SRD) is currently debatable. C.-H. Wu et al. carried
out a cohort study of 19 thousand TBI adults
with no history of mental disorders prior to brain
injury in the original paper titled “Traumatic Brain
Injury and Substance Related Disorder: A 10-Year
Nationwide Cohort Study in Taiwan.” Results show
that the overall incidence of SRD was 3.62-fold
higher in the TBI group and 9.01-fold in the severe
TBI group. The severity of TBI seems to have
strong correlation in the subsequent risks of SRD.

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[BOOK] Virtual Reality for Physical and Motor Rehabilitation – Google Books

Virtual Reality for Physical and Motor Rehabilitation

Front Cover

Patrice L. Tamar WeissEmily A. KeshnerMindy F. Levin
SpringerJul 24, 2014 – Medical – 232 pages

While virtual reality (VR) has influenced fields as varied as gaming, archaeology, and the visual arts, some of its most promising applications come from the health sector. Particularly encouraging are the many uses of VR in supporting the recovery of motor skills following accident or illness.

Virtual Reality for Physical and Motor Rehabilitation reviews two decades of progress and anticipates advances to come. It offers current research on the capacity of VR to evaluate, address, and reduce motor skill limitations, and the use of VR to support motor and sensorimotor function, from the most basic to the most sophisticated skill levels. Expert scientists and clinicians explain how the brain organizes motor behavior, relate therapeutic objectives to client goals, and differentiate among VR platforms in engaging the production of movement and balance. On the practical side, contributors demonstrate that VR complements existing therapies across various conditions such as neurodegenerative diseases, traumatic brain injury, and stroke. Included among the topics:

  • Neuroplasticity and virtual reality.
  • Vision and perception in virtual reality.
  • Sensorimotor recalibration in virtual environments.
  • Rehabilitative applications using VR for residual impairments following stroke.
  • VR reveals mechanisms of balance and locomotor impairments.
  • Applications of VR technologies for childhood disabilities.

A resource of great immediate and future utility, Virtual Reality for Physical and Motor Rehabilitation distills a dynamic field to aid the work of neuropsychologists, rehabilitation specialists (including physical, speech, vocational, and occupational therapists), and neurologists.

Preview this book »

Source: Virtual Reality for Physical and Motor Rehabilitation – Google Books

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[Abstract] EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication


Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottom-up and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.

Source: EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication

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[WEB SITE] Brain Training: Improve Your Neuroplasticity in 9 Easy Steps

Can we improve our capacity for creativity, memory and analysis through brain training exercises? Do online brain training games really work? The simple answer to these questions is yes; we can improve the brain’s ability to function, and we can actually reshape the physical structure of our brains through neuroplasticity training exercises.

Happily in improving your brain’s ability to function, it is not necessary to pay for expensive online games, that ultimately add nothing to the quality of your life. These nine training tips are free to engage in, will improve your brain’s function, and entice you to live life to its fullest!

How We Can Increase Brain Function As We Age

A study of randomly chosen individuals age 57-71 showed improved brain function after just 12 hours of strategic brain training exercises. Using MRIs of the participants brains both before and after, researchers saw upwards of an 8% improvement in blood flow and other indices that indicate improved brain function.

Improved brain function included improved ability to strategize, remember and draw big-picture conclusions from lengthy texts of information.

Remarkably, in a follow up study using MRIs again on the participants, researchers found that the benefits derived from the single training session were still in place one year later. Enhanced synaptic plasticity means that we can think faster, listen better, respond to situations faster and concentrate with greater focus. Creativity is enhanced as well.

MRI of the Brain

By Nevit Dilmen (Own work)(], via Wikimedia Commons
By Nevit Dilmen (Own work)(, via Wikimedia Commons


more —> Brain Training: Improve Your Neuroplasticity in 9 Easy Steps | HealDove

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[Editorial] Motor Priming for Motor Recovery: Neural Mechanisms and Clinical Perspectives – Neurology

Editorial on the Research Topic

Motor Priming for Motor Recovery: Neural Mechanisms and Clinical Perspectives

The Oxford dictionary defines the term priming as “a substance that prepares something for use or action.” In this special issue, we define motor priming as a technique, experience, or activity targeting the motor cortex resulting in subsequent changes in motor behavior. Inadequate functional recovery after neural damage is a persisting burden for many, and this insufficiency highlights the need for new neurorehabilitation paradigms that facilitate the capacity of the brain to learn and recover. The concept of motor priming has gained importance in the last decade. Numerous motor priming paradigms have emerged to demonstrate success to improve functional recovery after injury. Some of the successful priming paradigms that have shown to alter motor behavior and are easily implementable in clinical practice include non-invasive brain stimulation, movement priming, motor imagery, and sensory priming. The full clinical impact of these priming paradigms has not yet been realized due to limited evidence regarding neural mechanisms, safety and effectiveness, dosage, individualization of parameters, identification of the appropriate therapies that need to be provided in combination with the priming technique, and the vital time window to maximize the effectiveness of priming. In this special issue, four manuscripts address critical questions that will enhance our understanding of motor priming paradigms and attempt to bridge the gap between neurophysiology and clinical implementation.

In their study, “Non-Invasive Brain Stimulation to Enhance Upper Limb Motor Practice Poststroke: A Model for Selection of Cortical Site,” Harris-Love and Harrington elegantly address the extremely important issue of individualizing brain stimulation for upper limb stroke recovery. Many brain stimulation techniques show high interindividual variability and low reliability as the “one-size-for-all” does not fit the vast heterogeneity in recovery observed in stroke survivors. In this article, the authors propose a novel framework that personalizes the application of non-invasive brain stimulation based on understanding of the structural anatomy, neural connectivity, and task attributes. They further provide experimental support for this idea with data from severely impaired stroke survivors that validate the proposed framework.

The issue of heterogeneity poststroke is also addressed by Lefebvre and Liew in “Anatomical Parameters of tDCS to modulate the motor system after stroke: A review.” These authors discuss the variability in research using tDCS for the poststroke population. According to the authors, the most likely sources of variability include the heterogeneity of poststroke populations and the experimental paradigms. Individually based variability of results could be related to various factors including: (1) molecular factors such as baseline measures of GABA, levels of dopamine receptor activity, and propensity of brain-derived neurotropic factor expression; (2) time poststroke, (3) lesion location; (4) type of stroke; and (5) level of poststroke motor impairment. Variability related to experimental paradigms include the timing of the stimulation (pre- or post-training), the experimental task, and whether the protocol emphasizes motor performance (a temporary change in motor ability) or motor learning based (more permanent change in motor ability). Finally, the numerous possibilities of electrode placement, neural targets, and the different setups (monocephalic versus bi-hemispheric) add further complexity. For future work with the poststroke population, the authors suggest that tDCS experimental paradigms explore individualized neural targets determined by neuronavigation.

In another exciting study in this issue, Estes et al. tackle the timely topic of spinal reflex excitability modulated by motor priming in individuals with spinal cord injury. The authors choose to test four non-pharmacological interventions: stretching, continuous passive motion, transcranial direct current stimulation, and transcutaneous spinal cord stimulation to reduce spasticity. Three out of four techniques were associated with reduction in spasticity immediately after treatment, to an extent comparable to pharmacological approaches. These priming approaches provide a low-cost and low-risk alternative to anti-spasticity medications.

In another clinical study in individuals with spinal cord injury, Gomes-Osman et al. examined effects of two different approaches to priming. Participants were randomized to either peripheral nerve stimulation (PNS) plus functional task practice, PNS alone, or conventional exercise therapy. The findings were unexpected. There was no change in somatosensory function or power grip strength in any of the groups. Interestingly, all of the interventions produced changes in precision grip of the weaker hand following training. However, only PNS plus functional task practice improved precision grip in both hands. The authors found that baseline corticospinal excitability were significantly correlated to changes in precision grip strength of the weaker hand. The lack of change in grip strength in any of the groups was surprising. Previous evidence suggests, however, that the corticomotor system is more strongly activated during precision grip as compared to power grip, and the authors suggest that interventions targeting the corticomotor system (i.e., various priming methods) may more strongly effect precision grip.

Overall, this special issue brings together an array of original research articles and reviews that further enhance our understanding of motor priming for motor recovery with an emphasis on neural mechanisms and clinical implementation. We hope that the studies presented encourage future studies on motor priming paradigms to optimize the potential for functional recovery in the neurologically disadvantaged population, and further our understanding of neuroplasticity after injury.

Author Contributions

SM and MS have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


SM is supported by funding from the National Institutes of Health (R01HD075777).

Source: Frontiers | Editorial: Motor Priming for Motor Recovery: Neural Mechanisms and Clinical Perspectives | Neurology

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[Abstract] A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study  


The optimal conditions inducing proper brain activation during performance of rehabilitation robots should be examined to enhance the efficiency of robot rehabilitation based on the concept of brain plasticity. In this study, we attempted to investigate differences in cortical activation according to the speeds of passive wrist movements performed by a rehabilitation robot for stroke patients. 9 stroke patients with right hemiparesis participated in this study. Passive movements of the affected wrist were performed by the rehabilitation robot at three different speeds: 0.25 Hz; slow, 0.5Hz; moderate and 0.75 Hz; fast. We used functional near-infrared spectroscopy to measure the brain activity during the passive movements performed by a robot. Group-average activation map and the relative changes in oxy-hemoglobin (ΔOxyHb) in two regions of interest: the primary sensory-motor cortex (SM1); premotor area (PMA) and region of all channels were measured. In the result of group-averaged activation map, the contralateral SM1, PMA and somatosensory association cortex (SAC) showed the greatest significant activation according to the movements at 0.75 Hz, while there is no significantly activated area at 0.5 Hz. Regarding ΔOxyHb, no significant diiference was observed among three speeds regardless of region. In conclusion, the contralateral SM1, PMA and SAC showed the greatest activation by a fast speed (0.75 Hz) rather than slow (0.25 Hz) and moderate (0. 5 Hz) speed. Our results suggest an optimal speed for execution of the wrist rehabilitation robot. Therefore, we believe that our findings might point to several promising applications for future research regarding useful and empirically-based robot rehabilitation therapy.

Source: A pilot study on the optimal speeds for passive wrist movements by a rehabilitation robot of stroke patients: A functional NIRS study – IEEE Xplore Document

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