Posts Tagged Cognitive Rehabilitation

[Abstract] The Effect of Noninvasive Brain Stimulation on Poststroke Cognitive Function: A Systematic Review

Abstract

Introduction. Cognitive impairment after stroke has been associated with lower quality of life and independence in the long run, stressing the need for methods that target impairment for cognitive rehabilitation. The use of noninvasive brain stimulation (NIBS) on recovery of language functions is well documented, yet the effects of NIBS on other cognitive domains remain largely unknown. Therefore, we conducted a systematic review that evaluates the effects of different stimulation techniques on domain-specific (long-term) cognitive recovery after stroke. 

Methods. Three databases (PubMed, EMBASE, and PsycINFO) were searched for articles (in English) on the effects of NIBS on cognitive domains, published up to January 2018. 

Results. A total of 40 articles were included: randomized controlled trials (n = 21), studies with a crossover design (n = 9), case studies (n = 6), and studies with a mixed design (n = 4). Most studies tested effects on neglect (n = 25). The majority of the studies revealed treatment effects on at least 1 time point poststroke, in at least 1 cognitive domain. Studies varied highly on the factors time poststroke, number of treatment sessions, and stimulation protocols. Outcome measures were generally limited to a few cognitive tests. 

Conclusion. Our review suggests that NIBS is able to alleviate neglect after stroke. However, the results are still inconclusive and preliminary for the effect of NIBS on other cognitive domains. A standardized core set of outcome measures of cognition, also at the level of daily life activities and participation, and international agreement on treatment protocols, could lead to better evaluation of the efficacy of NIBS and comparisons between studies.

https://journals.sagepub.com/doi/abs/10.1177/1545968319834900

, , , , , ,

Leave a comment

[ARTICLE] Technology-based cognitive training and rehabilitation interventions for individuals with mild cognitive impairment: a systematic review

Abstract

Background

Individuals with mild cognitive impairment (MCI) are at heightened risk of developing dementia. Rapid advances in computing technology have enabled researchers to conduct cognitive training and rehabilitation interventions with the assistance of technology. This systematic review aims to evaluate the effects of technology-based cognitive training or rehabilitation interventions to improve cognitive function among individuals with MCI.

Methods

We conducted a systematic review using the following criteria: individuals with MCI, empirical studies, and evaluated a technology-based cognitive training or rehabilitation intervention. Twenty-six articles met the criteria.

Results

Studies were characterized by considerable variation in study design, intervention content, and technologies applied. The major types of technologies applied included computerized software, tablets, gaming consoles, and virtual reality. Use of technology to adjust the difficulties of tasks based on participants’ performance was an important feature. Technology-based cognitive training and rehabilitation interventions had significant effect on global cognitive function in 8 out of 22 studies; 8 out of 18 studies found positive effects on attention, 9 out of 16 studies on executive function, and 16 out of 19 studies on memory. Some cognitive interventions improved non-cognitive symptoms such as anxiety, depression, and ADLs.

Conclusion

Technology-based cognitive training and rehabilitation interventions show promise, but the findings were inconsistent due to the variations in study design. Future studies should consider using more consistent methodologies. Appropriate control groups should be designed to understand the additional benefits of cognitive training and rehabilitation delivered with the assistance of technology.

Background

Due to the aging of the world’s population, the number of people who live with dementia is projected to triple to 131 million by the year 2050 []. Development of preventative strategies for individuals at higher risk of developing dementia is an international priority []. Mild cognitive impairment (MCI) is regarded as an intermediate stage between normal cognition and dementia []. Individuals with MCI usually suffer with significant cognitive complaints, yet do not exhibit the functional impairments required for a diagnosis of dementia. These people typically have a faster rate of progression to dementia than those without MCI [], but the cognitive decline among MCI subjects has the potential of being improved []. Previous systematic reviews of cognitive intervention studies, both cognitive training and cognitive rehabilitation, have demonstrated promising effects on improving cognitive function among subjects with MCI [].

Recently, rapid advances in computing technology have enabled researchers to conduct cognitive training and rehabilitation interventions with the assistance of technology. A variety of technologies, including virtual reality (VR), interactive video gaming, and mobile technology, have been used to implement cognitive training and rehabilitation programs. Potential advantages to using technology-based interventions include enhanced accessibility and cost-effectiveness, providing a user experience that is immersive and comprehensive, as well as providing adaptive responses based on individual performance. Many computerized cognitive intervention programs are easily accessed through a computer or tablet, and the technology can objectively collect data during the intervention to provide real-time feedback to participants or therapists. Importantly, interventions delivered using technology have shown better effects compared to traditional cognitive training and rehabilitation programs in improving cognitive function and quality of life []. The reasons for this superiority are not well-understood but could be related to the usability and motivational factors related to the real-time interaction and feedback received from the training system [].

Three recent reviews of cognitive training and rehabilitation for use with individuals with MCI and dementia suggest that technology holds promise to improve both cognitive and non-cognitive outcomes []. The reviews conducted by Coyle, et al. [] and Chandler, et al. [] were limited by accessing articles from only two databases, and did not comprehensively cover available technologies. Hill, et al. [] limited their review to papers published until July 2016 and included only older adults aged 60 and above. More technology-based intervention studies have been conducted since then, and only including studies with older adults 60 and above could limit the scope of the review given that adults can develop early-onset MCI in their 40s []. Therefore, the purpose of this review is to 1) capture more studies using technology-based cognitive interventions by conducting a more comprehensive search using additional databases 2) understand the effect of technology-based cognitive interventions on improving abilities among individuals with MCI; and 3) examine the effects of multimodal technology-based interventions and their potential superiority compared to single component interventions.[…]

 

Continue —-> Technology-based cognitive training and rehabilitation interventions for individuals with mild cognitive impairment: a systematic review

, , , , , ,

Leave a comment

[Abstract] Cognitive rehabilitation in patients with traumatic brain injury: A narrative review on the emerging use of virtual reality

Highlights

About 10% of TBI patients have a severe brain damage with severe motor and cognitive dysfunctions.

New cognitive interventions, including VR training, can be useful in TBI.

VR creates a positive, motivating and enjoyable learning experience for the TBI patients.

Abstract

Traumatic brain injury (TBI) is a clinical condition characterized by brain damage due to an external, rapid and violent force. TBI causes attention, memory, affectivity, behaviour, planning, and executive dysfunctions, with a significant impact on the quality of life of the patient and of his/her family. Cognitive and motor rehabilitation programs are essential for clinical recovery of TBI patients, improving functional outcomes and the quality of life. Various researches have underlined the possible effectiveness of innovative techniques, with regard to virtual reality (VR), during the different phases of rehabilitation after TBI. This review aims to evaluate the role of VR tools in cognitive assessment and rehabilitation in individuals affected by TBI. Studies performed between 2010 and 2017 and fulfilling the selected criteria were found on PubMed, Scopus, Cochrane and Web of Sciences databases. The search combined the terms VR, assessment, rehabilitation and TBI. Our review has shown that VR has the potential to provide an effective assessment and rehabilitation tool for the treatment of cognitive and behavioral impairment on TBI patients.

via Cognitive rehabilitation in patients with traumatic brain injury: A narrative review on the emerging use of virtual reality – Journal of Clinical Neuroscience

, , , ,

Leave a comment

[ARTICLE] Combined Cognitive-Motor Rehabilitation in Virtual Reality Improves Motor Outcomes in Chronic Stroke – A Pilot Study – Full Text

Stroke is one of the most common causes of acquired disability, leaving numerous adults with cognitive and motor impairments, and affecting patients’ capability to live independently. Virtual Reality (VR) based methods for stroke rehabilitation have mainly focused on motor rehabilitation but there is increasing interest toward the integration of cognitive training for providing more effective solutions. Here we investigate the feasibility for stroke recovery of a virtual cognitive-motor task, the Reh@Task, which combines adapted arm reaching, and attention and memory training. 24 participants in the chronic stage of stroke, with cognitive and motor deficits, were allocated to one of two groups (VR, Control). Both groups were enrolled in conventional occupational therapy, which mostly involves motor training. Additionally, the VR group underwent training with the Reh@Task and the control group performed time-matched conventional occupational therapy. Motor and cognitive competences were assessed at baseline, end of treatment (1 month) and at a 1-month follow-up through the Montreal Cognitive Assessment, Single Letter Cancelation, Digit Cancelation, Bells Test, Fugl-Meyer Assessment Test, Chedoke Arm and Hand Activity Inventory, Modified Ashworth Scale, and Barthel Index. Our results show that both groups improved in motor function over time, but the Reh@Task group displayed significantly higher between-group outcomes in the arm subpart of the Fugl-Meyer Assessment Test. Improvements in cognitive function were significant and similar in both groups. Overall, these results are supportive of the viability of VR tools that combine motor and cognitive training, such as the Reh@Task. Trial Registration:This trial was not registered because it is a small clinical study that addresses the feasibility of a prototype device.

Introduction

Stroke is one of the most common causes of adult disability and its prevalence is likely to increase with an aging population (WHO, 2015). It is estimated that 33–42% of stroke survivors require assistance for daily living activities 3–6 months post-stroke and 36% continue to be disabled 5 years later (Teasell et al., 2012). Loss of motor control and muscle strength of the upper extremity are the most prevalent deficits and are those that have a greater impact on functional capacity (Saposnik, 2016). Hence, its recovery is fundamental for minimizing long-term disability and improving quality of life. In fact, most rehabilitation interventions focus on facilitating recovery through motor learning principles (Kleim and Jones, 2008). However, learning engages also cognitive processes such as attention, memory and executive functioning, all of which may be affected by stroke (Cumming et al., 2013). Still, conventional rehabilitation methodologies are mostly motor focused, although 70% of patients experience some degree of cognitive decline (Gottesman and Hillis, 2010), which also affects their capability to live independently (Langhorne et al., 2011).[…]

 

Continue —> Frontiers | Combined Cognitive-Motor Rehabilitation in Virtual Reality Improves Motor Outcomes in Chronic Stroke – A Pilot Study | Psychology

FIGURE 1. Experimental setup and VR task. (A) The user works on a tabletop and arm movements are captured by augmented reality pattern tracking. These movements are mapped onto the movements of a virtual arm on the screen for the execution of the cancelation task. (B) The target stimuli can be letters, numbers, and symbols in black or different colors. The target stimuli in this picture are ordered by increasing complexity.

, , , , , , ,

Leave a comment

[Abstract+References] Non-invasive Cerebellar Stimulation: a Promising Approach for Stroke Recovery?

Abstract

Non-invasive brain stimulation (NIBS) combined with behavioral training is a promising strategy to augment recovery after stroke. Current research efforts have been mainly focusing on primary motor cortex (M1) stimulation. However, the translation from proof-of-principle to clinical applications is not yet satisfactory. Possible reasons are the heterogeneous properties of stroke, generalization of the stimulation protocols, and hence the lack of patient stratification. One strategy to overcome these limitations could be the evaluation of alternative stimulation targets, like the cerebellum. In this regard, first studies provided evidence that non-invasive cerebellar stimulation can modulate cerebellar processing and linked behavior in healthy subjects. The cerebellum provides unique plasticity mechanisms and has vast connections to interact with neocortical areas. Moreover, the cerebellum could serve as a non-lesioned entry to the motor or cognitive system in supratentorial stroke. In the current article, we review mechanisms of plasticity in the cortico-cerebellar system after stroke, methods for non-invasive cerebellar stimulation, and possible target symptoms in stroke, like fine motor deficits, gait disturbance, or cognitive impairments, and discuss strategies for multi-focal stimulation.

 References

  1. 1.
    Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation. 2016;133(4):e38–360. https://doi.org/10.1161/CIR.0000000000000350.PubMedCrossRefGoogle Scholar
  2. 2.
    Blackburn DJ, Bafadhel L, Randall M, Harkness KA. Cognitive screening in the acute stroke setting. Age Ageing. 2013;42(1):113–6. https://doi.org/10.1093/ageing/afs116.PubMedCrossRefGoogle Scholar
  3. 3.
    Kotila M, Waltimo O, Niemi ML, Laaksonen R, Lempinen M. The profile of recovery from stroke and factors influencing outcome. Stroke. 1984;15(6):1039–44. https://doi.org/10.1161/01.STR.15.6.1039.PubMedCrossRefGoogle Scholar
  4. 4.
    Ramsey LE, Siegel JS, Lang CE, Strube M, Shulman GL, Corbetta M. Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav. 2017;1(3):38. https://doi.org/10.1038/s41562-016-0038.CrossRefGoogle Scholar
  5. 5.
    Rathore SS, Hinn AR, Cooper LS, Tyroler HA, Rosamond WD. Characterization of incident stroke signs and symptoms: findings from the atherosclerosis risk in communities study. Stroke. 2002;33(11):2718–21. https://doi.org/10.1161/01.STR.0000035286.87503.31.PubMedCrossRefGoogle Scholar
  6. 6.
    Stinear CM, Barber PA, Petoe M, Anwar S, Byblow WD. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012;135(8):2527–35. https://doi.org/10.1093/brain/aws146.PubMedCrossRefGoogle Scholar
  7. 7.
    Hummel FC, Cohen LG. Drivers of brain plasticity. Curr Opin Neurol. 2005;18(6):667–74. https://doi.org/10.1097/01.wco.0000189876.37475.42.PubMedCrossRefGoogle Scholar
  8. 8.
    Hummel F, Celnik P, Giraux P, Floel A, Wu WH, Gerloff C, et al. Effects of non-invasive cortical stimulation on skilled motor function in chronic stroke. Brain. 2005;128(3):490–9. https://doi.org/10.1093/brain/awh369.PubMedCrossRefGoogle Scholar
  9. 9.
    Lefaucheur JP, Antal A, Ayache SS, Benninger DH, Brunelin J, Cogiamanian F, et al. Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clin Neurophysiol. 2017;128(1):56–92. https://doi.org/10.1016/j.clinph.2016.10.087.PubMedCrossRefGoogle Scholar
  10. 10.
    Wessel MJ, Zimerman M, Hummel FC. Non-invasive brain stimulation: an interventional tool for enhancing behavioral training after stroke. Front Hum Neurosci. 2015;9:265. https://doi.org/10.3389/fnhum.2015.00265.
  11. 11.
    Tedesco Triccas L, Burridge JH, Hughes AM, Pickering RM, Desikan M, Rothwell JC, et al. Multiple sessions of transcranial direct current stimulation and upper extremity rehabilitation in stroke: a review and meta-analysis. Clin Neurophysiol. 2016;127(1):946–55. https://doi.org/10.1016/j.clinph.2015.04.067.PubMedCrossRefGoogle Scholar
  12. 12.
    Rossi C, Sallustio F, Di Legge S, Stanzione P, Koch G. Transcranial direct current stimulation of the affected hemisphere does not accelerate recovery of acute stroke patients. Eur J Neurol. 2013;20(1):202–4. https://doi.org/10.1111/j.1468-1331.2012.03703.x.PubMedCrossRefGoogle Scholar
  13. 13.
    Kapoor A, Lanctôt KL, Bayley M, Kiss A, Herrmann N, Murray BJ, et al. “Good outcome” isn’t good enough: cognitive impairment, depressive symptoms, and social restrictions in physically recovered stroke patients. Stroke. 2017;48(6):1688–90. https://doi.org/10.1161/STROKEAHA.117.016728.PubMedCrossRefGoogle Scholar
  14. 14.
    das Nair R, Cogger H, Worthington E, Lincoln NB. Cognitive rehabilitation for memory deficits after stroke: an updated review. Stroke. 2017;48(2):e28–9. https://doi.org/10.1161/STROKEAHA.116.015377.PubMedCrossRefGoogle Scholar
  15. 15.
    Miniussi C, Cappa SF, Cohen LG, Floel A, Fregni F, Nitsche MA, et al. Efficacy of repetitive transcranial magnetic stimulation/transcranial direct current stimulation in cognitive neurorehabilitation. Brain Stimulat. 2008;1(4):326–36. https://doi.org/10.1016/j.brs.2008.07.002.CrossRefGoogle Scholar
  16. 16.
    Elsner B, Kugler J, Pohl M, Mehrholz J. Transcranial direct current stimulation (tDCS) for improving activities of daily living, and physical and cognitive functioning, in people after stroke. Cochrane Database Syst Rev. 2016;3:CD009645. https://doi.org/10.1002/14651858.CD009645.pub3.
  17. 17.
    Ameli M, Grefkes C, Kemper F, Riegg FP, Rehme AK, Karbe H, et al. Differential effects of high-frequency repetitive transcranial magnetic stimulation over ipsilesional primary motor cortex in cortical and subcortical middle cerebral artery stroke. Ann Neurol. 2009;66(3):298–309. https://doi.org/10.1002/ana.21725.PubMedCrossRefGoogle Scholar
  18. 18.
    Carey JR, Deng H, Gillick BT, Cassidy JM, Anderson DC, Zhang L, et al. Serial treatments of primed low-frequency rTMS in stroke: characteristics of responders vs. nonresponders. Restor Neurol Neurosci. 2014;32(2):323–35. https://doi.org/10.3233/RNN-130358.PubMedPubMedCentralGoogle Scholar
  19. 19.
    Wagner T, Fregni F, Fecteau S, Grodzinsky A, Zahn M, Pascual-Leone A. Transcranial direct current stimulation: a computer-based human model study. NeuroImage. 2007;35(3):1113–24. https://doi.org/10.1016/j.neuroimage.2007.01.027.PubMedCrossRefGoogle Scholar
  20. 20.
    Lindenberg R, Zhu LL, Ruber T, Schlaug G. Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Hum Brain Mapp. 2012;33(5):1040–51. https://doi.org/10.1002/hbm.21266.PubMedCrossRefGoogle Scholar
  21. 21.
    Demirtas-Tatlidede A, Alonso-Alonso M, Shetty RP, Ronen I, Pascual-Leone A, Fregni F. Long-term effects of contralesional rTMS in severe stroke: safety, cortical excitability, and relationship with transcallosal motor fibers. NeuroRehabilitation. 2015;36(1):51–9. https://doi.org/10.3233/NRE-141191.PubMedGoogle Scholar
  22. 22.
    O’Shea J, Boudrias MH, Stagg CJ, Bachtiar V, Kischka U, Blicher JU, et al. Predicting behavioural response to TDCS in chronic motor stroke. NeuroImage. 2014;85(Pt 3):924–33. https://doi.org/10.1016/j.neuroimage.2013.05.096.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Bradnam LV, Stinear CM, Barber PA, Byblow WD. Contralesional hemisphere control of the proximal paretic upper limb following stroke. Cereb Cortex. 2012;22(11):2662–71. https://doi.org/10.1093/cercor/bhr344.PubMedCrossRefGoogle Scholar
  24. 24.
    Wang CC, Wang CP, Tsai PY, Hsieh CY, Chan RC, Yeh SC. Inhibitory repetitive transcranial magnetic stimulation of the contralesional premotor and primary motor cortices facilitate poststroke motor recovery. Restor Neurol Neurosci. 2014;32(6):825–35. https://doi.org/10.3233/RNN-140410.PubMedGoogle Scholar
  25. 25.
    Fregni F, Boggio PS, Mansur CG, Wagner T, Ferreira MJ, Lima MC, et al. Transcranial direct current stimulation of the unaffected hemisphere in stroke patients. Neuroreport. 2005;16(14):1551–5. https://doi.org/10.1097/01.wnr.0000177010.44602.5e.PubMedCrossRefGoogle Scholar
  26. 26.
    Kwon TG, Kim YH, Chang WH, Bang OY, Shin YI. Effective method of combining rTMS and motor training in stroke patients. Restor Neurol Neurosci. 2014;32(2):223–32. https://doi.org/10.3233/RNN-130313.PubMedGoogle Scholar
  27. 27.
    Cho JY, Lee A, Kim MS, Park E, Chang WH, Shin YI, et al. Dual-mode noninvasive brain stimulation over the bilateral primary motor cortices in stroke patients. Restor Neurol Neurosci. 2017;35(1):105–14. https://doi.org/10.3233/RNN-160669.PubMedGoogle Scholar
  28. 28.
    Boggio PS, Nunes A, Rigonatti SP, Nitsche MA, Pascual-Leone A, Fregni F. Repeated sessions of noninvasive brain DC stimulation is associated with motor function improvement in stroke patients. Restor Neurol Neurosci. 2007;25(2):123–9.PubMedGoogle Scholar
  29. 29.
    Carey MR. Synaptic mechanisms of sensorimotor learning in the cerebellum. Curr Opin Neurobiol. 2011;21(4):609–15. https://doi.org/10.1016/j.conb.2011.06.011.PubMedCrossRefGoogle Scholar
  30. 30.
    Cheron G, Dan B, Marquez-Ruiz J. Translational approach to behavioral learning: lessons from cerebellar plasticity. Neural Plast. 2013;2013:853654. https://doi.org/10.1155/2013/853654.
  31. 31.
    Bostan AC, Dum RP, Strick PL. Cerebellar networks with the cerebral cortex and basal ganglia. Trends Cogn Sci. 2013;17(5):241–54. https://doi.org/10.1016/j.tics.2013.03.003.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Manto MU. On the cerebello-cerebral interactions. The Cerebellum. 2006;5:286–8. https://doi.org/10.1080/14734220601003955.
  33. 33.
    Galea JM, Vazquez A, Pasricha N, de Xivry JJ, Celnik P. Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. Cereb Cortex. 2011;21(8):1761–70. https://doi.org/10.1093/cercor/bhq246.PubMedCrossRefGoogle Scholar
  34. 34.
    Theoret H, Haque J, Pascual-Leone A. Increased variability of paced finger tapping accuracy following repetitive magnetic stimulation of the cerebellum in humans. Neurosci Lett. 2001;306(1-2):29–32. https://doi.org/10.1016/S0304-3940(01)01860-2.PubMedCrossRefGoogle Scholar
  35. 35.
    Baron JC, Bousser MG, Comar D, Castaigne P. “Crossed cerebellar diaschisis” in human supratentorial brain infarction. Trans Am Neurol Assoc. 1981;105:459–61.PubMedGoogle Scholar
  36. 36.
    Szilagyi G, Vas A, Kerenyi L, Nagy Z, Csiba L, Gulyas B. Correlation between crossed cerebellar diaschisis and clinical neurological scales. Acta Neurol Scand. 2012;125(6):373–81. https://doi.org/10.1111/j.1600-0404.2011.01576.x.PubMedCrossRefGoogle Scholar
  37. 37.
    Gold L, Lauritzen M. Neuronal deactivation explains decreased cerebellar blood flow in response to focal cerebral ischemia or suppressed neocortical function. Proc Natl Acad Sci U A. 2002;99(11):7699–704. https://doi.org/10.1073/pnas.112012499.CrossRefGoogle Scholar
  38. 38.
    Kamouchi M, Fujishima M, Saku Y, Ibayashi S, Iida M. Crossed cerebellar hypoperfusion in hyperacute ischemic stroke. J Neurol Sci. 2004;225(1-2):65–9. https://doi.org/10.1016/j.jns.2004.07.004.PubMedCrossRefGoogle Scholar
  39. 39.
    Miura H, Nagata K, Hirata Y, Satoh Y, Watahiki Y, Hatazawa J. Evolution of crossed cerebellar diaschisis in middle cerebral artery infarction. J Neuroimaging. 1994;4(2):91–6. https://doi.org/10.1111/jon19944291.PubMedCrossRefGoogle Scholar
  40. 40.
    Takasawa M, Watanabe M, Yamamoto S, Hoshi T, Sasaki T, Hashikawa K, et al. Prognostic value of subacute crossed cerebellar diaschisis: single-photon emission CT study in patients with middle cerebral artery territory infarct. AJNR Am J Neuroradiol. 2002;23(2):189–93.PubMedGoogle Scholar
  41. 41.
    Bindman LJ, Lippold OC, Redfearn JW. Long-lasting changes in the level of the electrical activity of the cerebral cortex produced by polarizing currents. Nature. 1962;196(4854):584–5. https://doi.org/10.1038/196584a0.PubMedCrossRefGoogle Scholar
  42. 42.
    Lang N, Siebner HR, Ward NS, Lee L, Nitsche MA, Paulus W, et al. How does transcranial DC stimulation of the primary motor cortex alter regional neuronal activity in the human brain? Eur J Neurosci. 2005;22(2):495–504. https://doi.org/10.1111/j.1460-9568.2005.04233.x.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Schulz R, Frey BM, Koch P, Zimerman M, Bönstrup M, Feldheim J, et al. Cortico-cerebellar structural connectivity is related to residual motor output in chronic stroke. Cereb Cortex. 2017;27:635–45. https://doi.org/10.1093/cercor/bhv251.
  44. 44.
    Ugawa Y, Uesaka Y, Terao Y, Hanajima R, Kanazawa I. Magnetic stimulation over the cerebellum in humans. Ann Neurol. 1995;37(6):703–13. https://doi.org/10.1002/ana.410370603.PubMedCrossRefGoogle Scholar
  45. 45.
    Rothwell JC. Using transcranial magnetic stimulation methods to probe connectivity between motor areas of the brain. Hum Mov Sci. 2011;30(5):906–15. https://doi.org/10.1016/j.humov.2010.07.007.PubMedCrossRefGoogle Scholar
  46. 46.
    Kikuchi S, Mochizuki H, Moriya A, Nakatani-Enomoto S, Nakamura K, Hanajima R, et al. Ataxic hemiparesis: neurophysiological analysis by cerebellar transcranial magnetic stimulation. Cerebellum. 2012;11(1):259–63. https://doi.org/10.1007/s12311-011-0303-0.PubMedCrossRefGoogle Scholar
  47. 47.
    Ugawa Y, Terao Y, Hanajima R, Sakai K, Furubayashi T, Machii K, et al. Magnetic stimulation over the cerebellum in patients with ataxia. Electroencephalogr Clin Neurophysiol. 1997;104(5):453–8. https://doi.org/10.1016/S0168-5597(97)00051-8.PubMedCrossRefGoogle Scholar
  48. 48.
    Galea JM, Jayaram G, Ajagbe L, Celnik P. Modulation of cerebellar excitability by polarity-specific noninvasive direct current stimulation. J Neurosci. 2009;29(28):9115–22. https://doi.org/10.1523/JNEUROSCI.2184-09.2009.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Krakauer JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol. 2006;19(1):84–90. https://doi.org/10.1097/01.wco.0000200544.29915.cc.PubMedCrossRefGoogle Scholar
  50. 50.
    Nudo RJ, Wise BM, SiFuentes F, Milliken GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science. 1996;272(5269):1791–4. https://doi.org/10.1126/science.272.5269.1791.PubMedCrossRefGoogle Scholar
  51. 51.
    Askim T, Indredavik B, Vangberg T, Haberg A. Motor network changes associated with successful motor skill relearning after acute ischemic stroke: a longitudinal functional magnetic resonance imaging study. Neurorehabil Neural Repair. 2009;23(3):295–304. https://doi.org/10.1177/1545968308322840.PubMedCrossRefGoogle Scholar
  52. 52.
    Doyon J, Benali H. Reorganization and plasticity in the adult brain during learning of motor skills. Curr Opin Neurobiol. 2005;15(2):161–7. https://doi.org/10.1016/j.conb.2005.03.004.PubMedCrossRefGoogle Scholar
  53. 53.
    Hardwick RM, Rottschy C, Miall RC, Eickhoff SB. A quantitative meta-analysis and review of motor learning in the human brain. NeuroImage. 2013;67:283–97. https://doi.org/10.1016/j.neuroimage.2012.11.020.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Cantarero G, Spampinato D, Reis J, Ajagbe L, Thompson T, Kulkarni K, et al. Cerebellar direct current stimulation enhances on-line motor skill acquisition through an effect on accuracy. J Neurosci. 2015;35(7):3285–90. https://doi.org/10.1523/JNEUROSCI.2885-14.2015.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Wessel MJ, Zimerman M, Timmermann JE, Heise KF, Gerloff C, Hummel FC. Enhancing consolidation of a new temporal motor skill by cerebellar noninvasive stimulation. Cereb Cortex. 2016;26(4):1660–7. https://doi.org/10.1093/cercor/bhu335.PubMedCrossRefGoogle Scholar
  56. 56.
    Di Lazzaro V, Restuccia D, Molinari M, Leggio MG, Nardone R, Fogli D, et al. Excitability of the motor cortex to magnetic stimulation in patients with cerebellar lesions. J Neurol Neurosurg Psychiatry. 1994;57(1):108–10. https://doi.org/10.1136/jnnp.57.1.108.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Liepert J, Kucinski T, Tuscher O, Pawlas F, Baumer T, Weiller C. Motor cortex excitability after cerebellar infarction. Stroke. 2004;35(11):2484–8. https://doi.org/10.1161/01.STR.0000143152.45801.ca.PubMedCrossRefGoogle Scholar
  58. 58.
    De Vico FF, Clausi S, Leggio M, Chavez M, Valencia M, Maglione AG, et al. Interhemispheric connectivity characterizes cortical reorganization in motor-related networks after cerebellar lesions. Cerebellum. 2017;16:358–75. https://doi.org/10.1007/s12311-016-0811-z.
  59. 59.
    Koziol LF, Budding D, Andreasen N, D’Arrigo S, Bulgheroni S, Imamizu H, et al. Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum Lond Engl. 2014;13(1):151–77. https://doi.org/10.1007/s12311-013-0511-x.CrossRefGoogle Scholar
  60. 60.
    Sui R, Zhang L. Cerebellar dysfunction may play an important role in vascular dementia. Med Hypotheses. 2012;78:162–5. https://doi.org/10.1016/j.mehy.2011.10.017.
  61. 61.
    Chida K, Ogasawara K, Aso K, Suga Y, Kobayashi M, Yoshida K, et al. Postcarotid endarterectomy improvement in cognition is associated with resolution of crossed cerebellar hypoperfusion and increase in 123I-iomazenil uptake in the cerebral cortex: a SPECT study. Cerebrovasc Dis Basel Switz. 2010;29(4):343–51. https://doi.org/10.1159/000278930.CrossRefGoogle Scholar
  62. 62.
    Rastogi A, Cash R, Dunlop K, Vesia M, Kucyi A, Ghahremani A, et al. Modulation of cognitive cerebello-cerebral functional connectivity by lateral cerebellar continuous theta burst stimulation. NeuroImage. 2017;158:48–57. https://doi.org/10.1016/j.neuroimage.2017.06.048.PubMedCrossRefGoogle Scholar
  63. 63.
    Desmond JE, Chen SHA, Shieh PB. Cerebellar transcranial magnetic stimulation impairs verbal working memory. Ann Neurol. 2005;58(4):553–60. https://doi.org/10.1002/ana.20604.PubMedCrossRefGoogle Scholar
  64. 64.
    Balsters JH, Ramnani N. Cerebellar plasticity and the automation of first-order rules. J Neurosci. 2011;31(6):2305–12. https://doi.org/10.1523/JNEUROSCI.4358-10.2011.PubMedCrossRefGoogle Scholar
  65. 65.
    van Dun K, Bodranghien F, Manto M, Marien P. Targeting the cerebellum by noninvasive neurostimulation: a review. Cerebellum. 2017;16(3):695–741. https://doi.org/10.1007/s12311-016-0840-7.PubMedCrossRefGoogle Scholar
  66. 66.
    Fritsch B, Reis J, Martinowich K, Schambra HM, Ji Y, Cohen LG, et al. Direct current stimulation promotes BDNF-dependent synaptic plasticity: potential implications for motor learning. Neuron. 2010;66(2):198–204. https://doi.org/10.1016/j.neuron.2010.03.035.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Antal A, Paulus W. Transcranial alternating current stimulation (tACS). Front Hum Neurosci. 2013;7:317. https://doi.org/10.3389/fnhum.2013.00317.
  68. 68.
    Valero-Cabre A, Payne BR, Pascual-Leone A. Opposite impact on 14C-2-deoxyglucose brain metabolism following patterns of high and low frequency repetitive transcranial magnetic stimulation in the posterior parietal cortex. Exp Brain Res. 2007;176(4):603–15. https://doi.org/10.1007/s00221-006-0639-8.PubMedCrossRefGoogle Scholar
  69. 69.
    Huang YZ, Chen RS, Rothwell JC, Wen HY. The after-effect of human theta burst stimulation is NMDA receptor dependent. Clin Neurophysiol. 2007;118(5):1028–32. https://doi.org/10.1016/j.clinph.2007.01.021.PubMedCrossRefGoogle Scholar
  70. 70.
    Naro A, Bramanti A, Leo A, Manuli A, Sciarrone F, Russo M, et al. Effects of cerebellar transcranial alternating current stimulation on motor cortex excitability and motor function. Brain Struct Funct. 2017;222(6):2891–906. https://doi.org/10.1007/s00429-016-1355-1.PubMedCrossRefGoogle Scholar
  71. 71.
    Morellini N, Grehl S, Tang A, Rodger J, Mariani J, Lohof AM, et al. What does low-intensity rTMS do to the cerebellum? Cerebellum. 2015;14(1):23–6. https://doi.org/10.1007/s12311-014-0617-9.PubMedCrossRefGoogle Scholar
  72. 72.
    Koch G, Mori F, Marconi B, Codeca C, Pecchioli C, Salerno S, et al. Changes in intracortical circuits of the human motor cortex following theta burst stimulation of the lateral cerebellum. Clin Neurophysiol. 2008;119(11):2559–69. https://doi.org/10.1016/j.clinph.2008.08.008.PubMedCrossRefGoogle Scholar
  73. 73.
    Doeltgen SH, Young J, Bradnam LV. Anodal direct current stimulation of the cerebellum reduces cerebellar brain inhibition but does not influence afferent input from the hand or face in healthy adults. Cerebellum. 2016;15(4):466–74. https://doi.org/10.1007/s12311-015-0713-5.PubMedCrossRefGoogle Scholar
  74. 74.
    Naro A, Leo A, Russo M, Cannavo A, Milardi D, Bramanti P, et al. Does transcranial alternating current stimulation induce cerebellum plasticity? Feasibility, safety and efficacy of a novel electrophysiological approach. Brain Stimul. 2016;9(3):388–95. https://doi.org/10.1016/j.brs.2016.02.005.PubMedCrossRefGoogle Scholar
  75. 75.
    Popa T, Russo M, Meunier S. Long-lasting inhibition of cerebellar output. Brain Stimul. 2010;3(3):161–9. https://doi.org/10.1016/j.brs.2009.10.001.PubMedCrossRefGoogle Scholar
  76. 76.
    Oliveri M, Koch G, Torriero S, Caltagirone C. Increased facilitation of the primary motor cortex following 1 Hz repetitive transcranial magnetic stimulation of the contralateral cerebellum in normal humans. Neurosci Lett. 2005;376(3):188–93. https://doi.org/10.1016/j.neulet.2004.11.053.PubMedCrossRefGoogle Scholar
  77. 77.
    Fierro B, Giglia G, Palermo A, Pecoraro C, Scalia S, Brighina F. Modulatory effects of 1 Hz rTMS over the cerebellum on motor cortex excitability. Exp Brain Res. 2007;176(3):440–7. https://doi.org/10.1007/s00221-006-0628-y.PubMedCrossRefGoogle Scholar
  78. 78.
    Langguth B, Eichhammer P, Zowe M, Landgrebe M, Binder H, Sand P, et al. Modulating cerebello-thalamocortical pathways by neuronavigated cerebellar repetitive transcranial stimulation (rTMS). Neurophysiol Clin. 2008;38(5):289–95. https://doi.org/10.1016/j.neucli.2008.08.003.PubMedCrossRefGoogle Scholar
  79. 79.
    Torriero S, Oliveri M, Koch G, Caltagirone C, Petrosini L. Interference of left and right cerebellar rTMS with procedural learning. J Cogn Neurosci. 2004;16(9):1605–11. https://doi.org/10.1162/0898929042568488.PubMedCrossRefGoogle Scholar
  80. 80.
    Hoffland BS, Bologna M, Kassavetis P, Teo JT, Rothwell JC, Yeo CH, et al. Cerebellar theta burst stimulation impairs eyeblink classical conditioning. J Physiol. 2012;590(4):887–97. https://doi.org/10.1113/jphysiol.2011.218537.PubMedCrossRefGoogle Scholar
  81. 81.
    Li Voti P, Conte A, Rocchi L, Bologna M, Khan N, Leodori G, et al. Cerebellar continuous theta-burst stimulation affects motor learning of voluntary arm movements in humans. Eur J Neurosci. 2014;39(1):124–31. https://doi.org/10.1111/ejn.12391.PubMedCrossRefGoogle Scholar
  82. 82.
    Sebastian R, Saxena S, Tsapkini K, Faria AV, Long C, Wright A, et al. Cerebellar tDCS: a novel approach to augment language treatment post-stroke. Front Hum Neurosci. 2017;10:695. https://doi.org/10.3389/fnhum.2016.00695.
  83. 83.
    Kim WS, Jung SH, Oh MK, Min YS, Lim JY, Paik NJ. Effect of repetitive transcranial magnetic stimulation over the cerebellum on patients with ataxia after posterior circulation stroke: a pilot study. J Rehabil Med. 2014;46(5):418–23. https://doi.org/10.2340/16501977-1802.PubMedCrossRefGoogle Scholar
  84. 84.
    Bonni S, Ponzo V, Caltagirone C, Koch G. Cerebellar theta burst stimulation in stroke patients with ataxia. Funct Neurol. 2014;29(1):41–5. https://doi.org/10.11138/FNeur/2014.29.1.041.
  85. 85.
    Bikson M, Inoue M, Akiyama H, Deans JK, Fox JE, Miyakawa H, et al. Effects of uniform extracellular DC electric fields on excitability in rat hippocampal slices in vitro. J Physiol. 2004;557(1):175–90. https://doi.org/10.1113/jphysiol.2003.055772.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Creutzfeldt OD, Fromm GH, Kapp H. Influence of transcortical d-c currents on cortical neuronal activity. Exp Neurol. 1962;5(6):436–52. https://doi.org/10.1016/0014-4886(62)90056-0.PubMedCrossRefGoogle Scholar
  87. 87.
    Nitsche MA, Paulus W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol. 2000;527(Pt 3):633–9. https://doi.org/10.1111/j.1469-7793.2000.t01-1-00633.x.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Ferrucci R, Brunoni AR, Parazzini M, Vergari M, Rossi E, Fumagalli M, et al. Modulating human procedural learning by cerebellar transcranial direct current stimulation. Cerebellum. 2013;12(4):485–92. https://doi.org/10.1007/s12311-012-0436-9.PubMedCrossRefGoogle Scholar
  89. 89.
    Pope PA, Miall RC. Task-specific facilitation of cognition by cathodal transcranial direct current stimulation of the cerebellum. Brain Stimul. 2012;5(2):84–94. https://doi.org/10.1016/j.brs.2012.03.006.PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Oldrati V, Schutter DJLG. Targeting the human cerebellum with transcranial direct current stimulation to modulate behavior: a meta-analysis. Cerebellum. 2017. https://doi.org/10.1007/s12311-017-0877-2.
  91. 91.
    Block HJ, Celnik P. Can cerebellar transcranial direct current stimulation become a valuable neurorehabilitation intervention? Expert Rev Neurother. 2012;12(11):1275–7. https://doi.org/10.1586/ern.12.121.PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Celnik P. Understanding and modulating motor learning with cerebellar stimulation. Cerebellum. 2015;14(2):171–4. https://doi.org/10.1007/s12311-014-0607-y.PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    Ferrucci R, Cortese F, Priori A. Cerebellar tDCS: how to do it. Cerebellum. 2015;14(1):27–30. https://doi.org/10.1007/s12311-014-0599-7.PubMedCrossRefGoogle Scholar
  94. 94.
    Grimaldi G, Argyropoulos GP, Bastian A, Cortes M, Davis NJ, Edwards DJ, et al. Cerebellar transcranial direct current stimulation (ctDCS): a novel approach to understanding cerebellar function in health and disease. Neuroscientist. 2016;22(1):83–97. https://doi.org/10.1177/1073858414559409.PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    van Dun K, Bodranghien FC, Marien P, Manto MU. tDCS of the cerebellum: where do we stand in 2016? Technical issues and critical review of the literature. Front Hum Neurosci. 2016;10:199. https://doi.org/10.3389/fnhum.2016.00199.
  96. 96.
    Antal A, Boros K, Poreisz C, Chaieb L, Terney D, Paulus W. Comparatively weak after-effects of transcranial alternating current stimulation (tACS) on cortical excitability in humans. Brain Stimul. 2008;1(2):97–105. https://doi.org/10.1016/j.brs.2007.10.001.PubMedCrossRefGoogle Scholar
  97. 97.
    Moliadze V, Antal A, Paulus W. Boosting brain excitability by transcranial high frequency stimulation in the ripple range. J Physiol. 2010;588(24):4891–904. https://doi.org/10.1113/jphysiol.2010.196998.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Helfrich RF, Schneider TR, Rach S, Trautmann-Lengsfeld SA, Engel AK, Herrmann CS. Entrainment of brain oscillations by transcranial alternating current stimulation. Curr Biol. 2014;24(3):333–9. https://doi.org/10.1016/j.cub.2013.12.041.PubMedCrossRefGoogle Scholar
  99. 99.
    Zaehle T, Rach S, Herrmann CS. Transcranial alternating current stimulation enhances individual alpha activity in human EEG. PLoS One. 2010;5(11):e13766. https://doi.org/10.1371/journal.pone.0013766.PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Polania R, Nitsche MA, Korman C, Batsikadze G, Paulus W. The importance of timing in segregated theta phase-coupling for cognitive performance. Curr Biol. 2012;22(14):1314–8. https://doi.org/10.1016/j.cub.2012.05.021.PubMedCrossRefGoogle Scholar
  101. 101.
    Antal A, Herrmann CS. Transcranial alternating current and random noise stimulation: possible mechanisms. Neural Plast. 2016;2016:3616807. http://doi.org/10.1155/2016/3616807.
  102. 102.
    Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007;55(2):187–99. https://doi.org/10.1016/j.neuron.2007.06.026.PubMedCrossRefGoogle Scholar
  103. 103.
    Rossi S, Hallett M, Rossini PM, Pascual-Leone A. Safety of TMSCG. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol. 2009;120(12):2008–39. https://doi.org/10.1016/j.clinph.2009.08.016.PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Huang YZ, Edwards MJ, Rounis E, Bhatia KP, Rothwell JC. Theta burst stimulation of the human motor cortex. Neuron. 2005;45(2):201–6. https://doi.org/10.1016/j.neuron.2004.12.033.PubMedCrossRefGoogle Scholar
  105. 105.
    Miall RC, Christensen LO. The effect of rTMS over the cerebellum in normal human volunteers on peg-board movement performance. Neurosci Lett. 2004;371(2-3):185–9. https://doi.org/10.1016/j.neulet.2004.08.067.PubMedCrossRefGoogle Scholar
  106. 106.
    Koch G. Repetitive transcranial magnetic stimulation: a tool for human cerebellar plasticity. Funct Neurol. 2010;25(3):159–63.PubMedGoogle Scholar
  107. 107.
    Minks E, Kopickova M, Marecek R, Streitova H, Bares M. Transcranial magnetic stimulation of the cerebellum. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2010;154(2):133–9. https://doi.org/10.5507/bp.2010.020.PubMedCrossRefGoogle Scholar
  108. 108.
    Ivry RB, Keele SW, Diener HC. Dissociation of the lateral and medial cerebellum in movement timing and movement execution. Exp Brain Res. 1988;73(1):167–80. https://doi.org/10.1007/BF00279670.PubMedCrossRefGoogle Scholar
  109. 109.
    Stoodley CJ, MacMore JP, Makris N, Sherman JC, Schmahmann JD. Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke. NeuroImage Clin. 2016;12:765–75. https://doi.org/10.1016/j.nicl.2016.10.013.PubMedPubMedCentralCrossRefGoogle Scholar
  110. 110.
    Machado AG, Cooperrider J, Furmaga HT, Baker KB, Park HJ, Chen Z, et al. Chronic 30-Hz deep cerebellar stimulation coupled with training enhances post-ischemia motor recovery and peri-infarct synaptophysin expression in rodents. Neurosurgery. 2013;73(2):344–53. https://doi.org/10.1227/01.neu.0000430766.80102.ac.
  111. 111.
    Jorgensen HS. The Copenhagen Stroke Study experience. J Stroke Cerebrovasc Dis. 1996;6(1):5–16. https://doi.org/10.1016/S1052-3057(96)80020-6.PubMedCrossRefGoogle Scholar
  112. 112.
    Beyaert C, Vasa R, Frykberg GE. Gait post-stroke: pathophysiology and rehabilitation strategies. Neurophysiol Clin. 2015;45(4-5):335–55. https://doi.org/10.1016/j.neucli.2015.09.005.PubMedCrossRefGoogle Scholar
  113. 113.
    Chieffo R, Comi G, Leocani L. Noninvasive neuromodulation in poststroke gait disorders: rationale, feasibility, and state of the art. Neurorehabil Neural Repair. 2015;30:71–82. https://doi.org/10.1177/1545968315586464.
  114. 114.
    Jayaram G, Tang B, Pallegadda R, Vasudevan EV, Celnik P, Bastian A. Modulating locomotor adaptation with cerebellar stimulation. J Neurophysiol. 2012;107(11):2950–7. https://doi.org/10.1152/jn.00645.2011.PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Fernandez L, Albein-Urios N, Kirkovski M, McGinley JL, Murphy AT, Hyde C, et al. Cathodal transcranial direct current stimulation (tDCS) to the right cerebellar hemisphere affects motor adaptation during gait. Cerebellum. 2017;16(1):168–77. https://doi.org/10.1007/s12311-016-0788-7.PubMedCrossRefGoogle Scholar
  116. 116.
    Naro A, Milardi D, Cacciola A, Russo M, Sciarrone F, La Rosa G, et al. What do we know about the influence of the cerebellum on walking ability? Promising findings from transcranial alternating current stimulation. Cerebellum. 2017;16(4):859–67. https://doi.org/10.1007/s12311-017-0859-4.PubMedCrossRefGoogle Scholar
  117. 117.
    Nijsse B, Visser-Meily JM, van Mierlo ML, Post MW, de Kort PL, van Heugten CM. Temporal evolution of Poststroke cognitive impairment using the Montreal Cognitive Assessment. Stroke. 2017;48(1):98–104. https://doi.org/10.1161/STROKEAHA.116.014168.PubMedCrossRefGoogle Scholar
  118. 118.
    Dichgans M, Leys D. Vascular cognitive impairment. Circ Res. 2017;120(3):573–91. https://doi.org/10.1161/CIRCRESAHA.116.308426.PubMedCrossRefGoogle Scholar
  119. 119.
    Brainin M, Tuomilehto J, Heiss WD, Bornstein NM, Bath PM, Teuschl Y, et al. Post-stroke cognitive decline: an update and perspectives for clinical research. Eur J Neurol. 2015;22(2):229–238, e13-6https://doi.org/10.1111/ene.12626.PubMedCrossRefGoogle Scholar
  120. 120.
    Bodranghien F, Bastian A, Casali C, Hallett M, Louis ED, Manto M, et al. Consensus paper: revisiting the symptoms and signs of cerebellar syndrome. Cerebellum. 2016;15(3):369–91. https://doi.org/10.1007/s12311-015-0687-3.PubMedPubMedCentralCrossRefGoogle Scholar
  121. 121.
    Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain. 1998;121(Pt 4):561–79. https://doi.org/10.1093/brain/121.4.561.PubMedCrossRefGoogle Scholar
  122. 122.
    Ferrucci R, Giannicola G, Rosa M, Fumagalli M, Boggio PS, Hallett M, et al. Cerebellum and processing of negative facial emotions: cerebellar transcranial DC stimulation specifically enhances the emotional recognition of facial anger and sadness. Cogn Emot. 2012;26(5):786–99. https://doi.org/10.1080/02699931.2011.619520.PubMedCrossRefGoogle Scholar
  123. 123.
    Turkeltaub PE, Swears MK, D’Mello AM, Stoodley CJ. Cerebellar tDCS as a novel treatment for aphasia? Evidence from behavioral and resting-state functional connectivity data in healthy adults. Restor Neurol Neurosci. 2016;34(4):491–505. https://doi.org/10.3233/RNN-150633.PubMedPubMedCentralGoogle Scholar
  124. 124.
    Boehringer A, Macher K, Dukart J, Villringer A, Pleger B. Cerebellar transcranial direct current stimulation modulates verbal working memory. Brain Stimul. 2013;6(4):649–53. https://doi.org/10.1016/j.brs.2012.10.001.PubMedCrossRefGoogle Scholar
  125. 125.
    Ferrucci R, Marceglia S, Vergari M, Cogiamanian F, Mrakic-Sposta S, Mameli F, et al. Cerebellar transcranial direct current stimulation impairs the practice-dependent proficiency increase in working memory. J Cogn Neurosci. 2008;20(9):1687–97. https://doi.org/10.1162/jocn.2008.20112.PubMedCrossRefGoogle Scholar
  126. 126.
    Macher K, Bohringer A, Villringer A, Pleger B. Cerebellar-parietal connections underpin phonological storage. J Neurosci. 2014;34(14):5029–37. https://doi.org/10.1523/JNEUROSCI.0106-14.2014.PubMedCrossRefGoogle Scholar
  127. 127.
    Grimaldi G, Oulad Ben Taib N, Manto M, Bodranghien F. Marked reduction of cerebellar deficits in upper limbs following transcranial cerebello-cerebral DC stimulation: tremor reduction and re-programming of the timing of antagonist commands. Front Syst Neurosci. 2014;8:9. https://doi.org/10.3389/fnsys.2014.00009.
  128. 128.
    Ramnani N. The primate cortico-cerebellar system: anatomy and function. Nat Rev Neurosci. 2006;7(7):511–22. https://doi.org/10.1038/nrn1953.PubMedCrossRefGoogle Scholar
  129. 129.
    Manto M, Marien P. Schmahmann’s syndrome—identification of the third cornerstone of clinical ataxiology. Cerebellum Ataxias. 2015;2(1):2. https://doi.org/10.1186/s40673-015-0023-1.PubMedPubMedCentralCrossRefGoogle Scholar
  130. 130.
    Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. NeuroImage. 2009;44(2):489–501. https://doi.org/10.1016/j.neuroimage.2008.08.039.PubMedCrossRefGoogle Scholar
  131. 131.
    Schutter DJ, van Honk J. The cerebellum on the rise in human emotion. Cerebellum. 2005;4(4):290–4. https://doi.org/10.1080/14734220500348584.PubMedCrossRefGoogle Scholar
  132. 132.
    Kelly RM, Strick PL. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J Neurosci. 2003;23(23):8432–44.PubMedGoogle Scholar
  133. 133.
    Jurgens U. The efferent and afferent connections of the supplementary motor area. Brain Res. 1984;300(1):63–81. https://doi.org/10.1016/0006-8993(84)91341-6.PubMedCrossRefGoogle Scholar
  134. 134.
    Akkal D, Dum RP, Strick PL. Supplementary motor area and presupplementary motor area: targets of basal ganglia and cerebellar output. J Neurosci. 2007;27(40):10659–73. https://doi.org/10.1523/JNEUROSCI.3134-07.2007.PubMedCrossRefGoogle Scholar
  135. 135.
    Brodal P. The corticopontine projection in the rhesus monkey. Origin and principles of organization. Brain. 1978;101(2):251–83. https://doi.org/10.1093/brain/101.2.251.PubMedCrossRefGoogle Scholar
  136. 136.
    Hashimoto M, Takahara D, Hirata Y, Inoue K, Miyachi S, Nambu A, et al. Motor and non-motor projections from the cerebellum to rostrocaudally distinct sectors of the dorsal premotor cortex in macaques. Eur J Neurosci. 2010;31(8):1402–13. https://doi.org/10.1111/j.1460-9568.2010.07151.x.PubMedCrossRefGoogle Scholar
  137. 137.
    Middleton FA, Strick PL. Dentate output channels: motor and cognitive components. Prog Brain Res. 1997;114:553–66. https://doi.org/10.1016/S0079-6123(08)63386-5.PubMedCrossRefGoogle Scholar
  138. 138.
    Clower DM, Dum RP, Strick PL. Basal ganglia and cerebellar inputs to “AIP”. Cereb Cortex. 2005;15(7):913–20. https://doi.org/10.1093/cercor/bhh190.PubMedCrossRefGoogle Scholar
  139. 139.
    Prevosto V, Graf W, Ugolini G. Cerebellar inputs to intraparietal cortex areas LIP and MIP: functional frameworks for adaptive control of eye movements, reaching, and arm/eye/head movement coordination. Cereb Cortex. 2010;20(1):214–28. https://doi.org/10.1093/cercor/bhp091.PubMedCrossRefGoogle Scholar
  140. 140.
    Anand BK, Malhotra CL, Singh B, Dua S. Cerebellar projections to limbic system. J Neurophysiol. 1959;22(4):451–7.PubMedGoogle Scholar
  141. 141.
    Snider RS, Maiti A. Cerebellar contributions to the Papez circuit. J Neurosci Res. 1976;2(2):133–46. https://doi.org/10.1002/jnr.490020204.PubMedCrossRefGoogle Scholar
  142. 142.
    Zimerman M, Nitsch M, Giraux P, Gerloff C, Cohen LG, Hummel FC. Neuroenhancement of the aging brain: restoring skill acquisition in old subjects. Ann Neurol. 2013;73(1):10–5. https://doi.org/10.1002/ana.23761.PubMedCrossRefGoogle Scholar
  143. 143.
    Samaei A, Ehsani F, Zoghi M, Hafez Yosephi M, Jaberzadeh S. Online and offline effects of cerebellar transcranial direct current stimulation on motor learning in healthy older adults: a randomized double-blind sham-controlled study. Eur J Neurosci. 2017;45(9):1177–85. https://doi.org/10.1111/ejn.13559.PubMedCrossRefGoogle Scholar
  144. 144.
    Ehsani F, Bakhtiary AH, Jaberzadeh S, Talimkhani A, Hajihasani A. Differential effects of primary motor cortex and cerebellar transcranial direct current stimulation on motor learning in healthy individuals: a randomized double-blind sham-controlled study. Neurosci Res. 2016;112:10–9. https://doi.org/10.1016/j.neures.2016.06.003.PubMedCrossRefGoogle Scholar
  145. 145.
    Fregni F, Boggio PS, Nitsche M, Bermpohl F, Antal A, Feredoes E, et al. Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory. Exp Brain Res. 2005;166(1):23–30. https://doi.org/10.1007/s00221-005-2334-6.PubMedCrossRefGoogle Scholar
  146. 146.
    Miler JA, Meron D, Baldwin DS, Garner M. The effect of prefrontal transcranial direct current stimulation on attention network function in healthy volunteers. Neuromodulation. 2017. https://doi.org/10.1111/ner.12629.
  147. 147.
    Hulst T, John L, Kuper M, van der Geest JN, Goricke SL, Donchin O, et al. Cerebellar patients do not benefit from cerebellar or M1 transcranial direct current stimulation during force field reaching adaptation. J Neurophysiol. 2017;118(2):732–48. https://doi.org/10.1152/jn.00808.2016.PubMedCrossRefGoogle Scholar
  148. 148.
    Jalali R, Miall RC, Galea JM. No consistent effect of cerebellar transcranial direct current stimulation (tDCS) on visuomotor adaptation. J Neurophysiol. 2017;118(2):655–65. https://doi.org/10.1152/jn.00896.2016.PubMedCrossRefGoogle Scholar
  149. 149.
    Spielmann K, van der Vliet R, van de Sandt-Koenderman WM, Frens MA, Ribbers GM, Selles RW, et al. Cerebellar cathodal transcranial direct stimulation and performance on a verb generation task: a replication study. Neural Plast. 2017;2017:1254615. https://doi.org/10.1155/2017/1254615.
  150. 150.
    Verhage MC, Avila EO, Frens MA, Donchin O, van der Geest JN. Cerebellar tDCS does not enhance performance in an implicit categorization learning task. Front Psychol. 2017;8:476. https://doi.org/10.3389/fpsyg.2017.00476.
  151. 151.
    Cooper IS. Twenty-five years of experience with physiological neurosurgery. Neurosurgery. 1981;9(2):190–200. https://doi.org/10.1227/00006123-198108000-00017.PubMedCrossRefGoogle Scholar
  152. 152.
    Oulad Ben Taib N, Manto M. Trains of epidural DC stimulation of the cerebellum tune corticomotor excitability. Neural Plast. 2013;2013:613197. https://doi.org/10.1155/2013/613197.
  153. 153.
    Teixeira MJ, Cury RG, Galhardoni R, Barboza VR, Brunoni AR, Alho E, et al. Deep brain stimulation of the dentate nucleus improves cerebellar ataxia after cerebellar stroke. Neurology. 2015;85(23):2075–6. https://doi.org/10.1212/WNL.0000000000002204.PubMedCrossRefGoogle Scholar

via Non-invasive Cerebellar Stimulation: a Promising Approach for Stroke Recovery? | SpringerLink

, , , , ,

Leave a comment

[Abstract] Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

Background

Cognitive impairment after stroke is common and can cause disability with a high impact on quality of life and independence. Cognitive rehabilitation is a therapeutic approach designed to improve cognitive functioning after central nervous system’s injuries. Computerized cognitive rehabilitation (CCR) uses multimedia and informatics resources to optimize cognitive compromised performances. The aim of this study is to evaluate the effects of pc cognitive training with Erica software in patients with stroke.

Methods

We studied 35 subjects (randomly divided into 2 groups), affected by either ischemic or hemorrhagic stroke, having attended from January 2013 to May 2015 the Laboratory of Robotic and Cognitive Rehabilitation of Istituto di Ricerca e Cura a Carattere Scientifico Neurolesi in Messina. Cognitive dysfunctions were investigated through a complete neuropsychological battery, administered before (T0) and after (T1) each different training.

Results

At T0, all the patients showed language and cognitive deficits, especially in attention process and memory abilities, with mood alterations. After the rehabilitation program (T1), we noted a global cognitive improvement in both groups, but a more significant increase in the scores of the different clinical scales we administered was found after CCR.

Conclusions

Our data suggest that cognitive pc training by using the Erica software may be a useful methodology to increase the post-stroke cognitive recovery.

 

via Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

, , , ,

Leave a comment

[WEB SITE] Hospital wins patent in VR treatment for cognitive disorders.

A local hospital is drawing attention by winning a patent in cognitive rehabilitation treatment using a 3D virtual reality (VR) technology.

The Gil Medical Center and Gachon University’s industry-university cooperation foundation said on Monday they registered the patent in “a method and system using 3D virtual reality for the treatment of cognitive impairment.” Professor Lee Ju-kang of Gachon University Gil Medical Center’s physical medicine and rehabilitation department had developed the system.

The invention allows doctors to treat a wide range of cognitive disorders, including dementia, with all the different kinds of virtual space. Physicians expect better treatment results with the new technology, which offers virtual areas such as homes that are more familiar to patients than hospital’s treatment rooms.

To build 3D background information, the user of the program should visit the patient’s home and scan it first. Then, the user can save it as a database.

“Existing dementia treatments are quite limited, as most of them focus on prevention of further progress rather than on cure. Thus, it is becoming more important to use rehabilitation treatment to prevent dementia-derived adjustment disorders or accidents in daily life,” the medical center stated in the patent explanation.

“Existing treatments include cognitive rehabilitation offered in a limited environment such as hospital’s treatment room and cognitive training through a few computer programs, which are far from real life,” it went on to say. “By generating 3D virtual reality, we have developed a system to give patients easier access to necessary environment and targets and treat their cognitive impairment.”

Earlier, the hospital unveiled a plan to open a “VR Life Center” next January to treat patients with post-traumatic stress disorder and panic disorder.

“If we combine VR technology with medical treatment software, we can reenact an environment, which is difficult to visit in reality and expect better treatment results,” the hospital said. “VR treatments have already been used as a psychological treatment for a phobia and an addiction and have proven effective.”

via Hospital wins patent in VR treatment for cognitive disorders – Korea Biomedical Review

, , ,

Leave a comment

[Abstract] Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

Background

Cognitive impairment after stroke is common and can cause disability with a high impact on quality of life and independence. Cognitive rehabilitation is a therapeutic approach designed to improve cognitive functioning after central nervous system’s injuries. Computerized cognitive rehabilitation (CCR) uses multimedia and informatics resources to optimize cognitive compromised performances. The aim of this study is to evaluate the effects of pc cognitive training with Erica software in patients with stroke.

Methods

We studied 35 subjects (randomly divided into 2 groups), affected by either ischemic or hemorrhagic stroke, having attended from January 2013 to May 2015 the Laboratory of Robotic and Cognitive Rehabilitation of Istituto di Ricerca e Cura a Carattere Scientifico Neurolesi in Messina. Cognitive dysfunctions were investigated through a complete neuropsychological battery, administered before (T0) and after (T1) each different training.

Results

At T0, all the patients showed language and cognitive deficits, especially in attention process and memory abilities, with mood alterations. After the rehabilitation program (T1), we noted a global cognitive improvement in both groups, but a more significant increase in the scores of the different clinical scales we administered was found after CCR.

Conclusions

Our data suggest that cognitive pc training by using the Erica software may be a useful methodology to increase the post-stroke cognitive recovery.

 

via Improving Cognitive Function in Patients with Stroke: Can Computerized Training Be the Future?

, , , ,

Leave a comment

[Abstract+References] A Serious Games Platform for Cognitive Rehabilitation with Preliminary Evaluation

Abstract

In recent years Serious Games have evolved substantially, solving problems in diverse areas. In particular, in Cognitive Rehabilitation, Serious Games assume a relevant role. Traditional cognitive therapies are often considered repetitive and discouraging for patients and Serious Games can be used to create more dynamic rehabilitation processes, holding patients’ attention throughout the process and motivating them during their road to recovery. This paper reviews Serious Games and user interfaces in rehabilitation area and details a Serious Games platform for Cognitive Rehabilitation that includes a set of features such as: natural and multimodal user interfaces and social features (competition, collaboration, and handicapping) which can contribute to augment the motivation of patients during the rehabilitation process. The web platform was tested with healthy subjects. Results of this preliminary evaluation show the motivation and the interest of the participants by playing the games.

References

  1. 1.
    Burke, J. W., McNeill, M. D. J., Charles, D. K., Morrow, P. J., Crosbie, J. H., and McDonough, S. M., Optimising engagement for stroke rehabilitation using serious games. Vis. Comput. 25:1085–1099, 2009.CrossRefGoogle Scholar
  2. 2.
    Burke, J. W., McNeill, M. D. J., Charles, D. K., Morrow, P. J., Crosbie, J. H., McDonough, S. M. Augmented reality games for upper-limb stroke rehabilitation. In: 2010 second international conference on games and virtual worlds for serious applications (VS-GAMES). pp. 75–78. 2010.Google Scholar
  3. 3.
    Maclean, N., Pound, P., Wolfe, C., and Rudd, A., Qualitative analysis of stroke patients’ motivation for rehabilitation. Br. Med. J. 321:1051–1054, 2000.CrossRefGoogle Scholar
  4. 4.
    Krichevets, A. N., Sirotkina, E. B., Yevsevicheva, I. V., and Zeldin, L. M., Computer games as a means of movement rehabilitation. Disabil. Rehabil. 17:100–105, 1995.CrossRefPubMedGoogle Scholar
  5. 5.
    Rego, P., Moreira, P. M., Reis, L. P., Serious games for rehabilitation: a survey and a classification towards a taxonomy. In: 5th Iberian conference on information systems and technologies. Vol. I. pp. 349–354. Santiago de Compostela, Spain, 2010.Google Scholar
  6. 6.
    Rego, P. A., Moreira, P. M., Reis, L. P., New forms of interaction in serious games for rehabilitation. In: Cruz-Cunha, M. M., (Ed.), Handbook of research on serious games as educational, business, and research tools: development and design. IGI Global, 2012.Google Scholar
  7. 7.
    Rego, P. A., Moreira, P. M., and Reis, L. P., A serious games framework for health rehabilitation. Int. J. Healthc. Inf. Syst. Inf. (IJHISI) 9:1–21, 2014.CrossRefGoogle Scholar
  8. 8.
    Rego, P. A., Moreira, P. M., Reis, L. P., Architecture for serious games in health rehabilitation. In: Rocha, Á., Correia, A. M., Tan, F. B., Stroetmann, K. A.. (Eds.), New perspectives in information systems and technologies, volume 2, Vol. 276. pp. 307–317. Springer International Publishing, 2014.Google Scholar
  9. 9.
    Mendes, L., Dores, A. R., Rego, P. A., Moreira, P. M., Barbosa, F., Reis, L. P., Viana, J., Coelho, A., and Sousa, A., Virtual centre for the rehabilitation of road accident victims (VICERAVI). In: Rocha, A., CalvoManzano, J., Reis, L. P., and Cota, M. P. (Eds.), 7th Iberian conference on information systems and technologies (CISTI 2012), vol. I. AISTI, Madrid, pp. 817–822, 2012.Google Scholar
  10. 10.
    Rocha, R., Reis, L. P., Rego, P. A., Moreira, P. M., Serious games for cognitive rehabilitation: Forms of interaction and social dimension. In: 2015 10th Iberian conference on information systems and technologies (CISTI). pp. 1–6. 2015.Google Scholar
  11. 11.
    Alankus, G., Lazar, A., May, M., Kelleher, C., Towards customizable games for stroke rehabilitation. In: Proceedings of the SIGCHI conference on human factors in computing systems. pp. 2113–2122. ACM, Atlanta, Georgia, USA, 2010.Google Scholar
  12. 12.
    Ma, M., and Bechkoum, K., Serious games for movement therapy after stroke. IEEE international conference on systems, man and cybernetics. International Convention & Exhibition Center, Suntec Singapore, pp. 1872–1877, 2008.Google Scholar
  13. 13.
    Karray, F., Alemzadeh, M., Saleh, J. A., and Arab, M. N., Human-computer interaction: overview on state of the art. Int. J. Smart Sens. Intell. Syst. 1:137–159, 2008.Google Scholar
  14. 14.
    Oviatt, S., Multimodal interfaces. In: Julie, A. J., Andrew, S., (Eds.), The human-computer interaction handbook, pp. 286–304. L. Erlbaum Associates Inc, 2003.Google Scholar
  15. 15.
    Rego, P. A., Moreira, P. M., Reis, L. P., Natural user interfaces in serious games for rehabilitation: a prototype and playability study. In: Rocha, Á., Gonçalves, R., Cota, M. P., Reis, L. P., (Eds.), First Iberian Workshop on Serious Games and Meaningful Play (SGaMePlay’2011) – Proceedings of the 6th iberian conference on information systems and technologies, Vol. I. pp. 229–232. Chaves, Portugal, 2011.Google Scholar
  16. 16.
    Rego, P. A., Moreira, P. M., Reis, L. P., Natural and multimodal user interfaces in serious games for health rehabilitation. In: MASH’14: Multi-agent systems for healthcare / AAMAS’14 – 13th international conference on autonomous agents and multiagent systems. IFAMAAS, 2014.Google Scholar
  17. 17.
    Jaimes, A., and Sebe, N., Multimodal human-computer interaction: a survey. Comput. Vis. Image Underst. 108:116–134, 2007.CrossRefGoogle Scholar
  18. 18.
    Jain, J., Lund, A., Wixon, D., The future of natural user interfaces. In: CHI ‘11 extended abstracts on human factors in computing systems. pp. 211–214. ACM, 1979527, 2011.Google Scholar
  19. 19.
    Chai, J. Y., Hong, P., Zhou, M. X., A probabilistic approach to reference resolution in multimodal user interfaces. In: Proceedings of the 9th international conference on intelligent user interfaces. pp. 70–77. ACM, Funchal, Madeira, Portugal, 2004.Google Scholar
  20. 20.
    Faria, B. M., Reis, L. P., Lau, N., Soares, J. C., and Vasconcelos, S., Patient classification and automatic configuration of an intelligent wheelchair. In: Filipe, J., and Fred, A. (Eds.), Agents and artificial intelligence, vol. 358. Springer, Berlin Heidelberg, pp. 268–282, 2013.CrossRefGoogle Scholar
  21. 21.
    Johnston, M., Bangalore, S., MATCHkiosk: a multimodal interactive city guide. In: Proceedings of the ACL 2004 on Interactive poster and demonstration sessions. pp. 33. Association for Computational Linguistics, 2004.Google Scholar
  22. 22.
    Ibrahim, A., and Johansson, P., Multimodal dialogue systems: a case study for interactive TV. In: Carbonell, N., and Stephanidis, C. (Eds.), Universal access theoretical perspectives, practice, and experience: 7th ERCIM international workshop on user interfaces for all, Paris, France, October 24–25, 2002, revised papers. Springer Berlin Heidelberg, Berlin, pp. 209–218, 2003.CrossRefGoogle Scholar
  23. 23.
    Morikawa, C., and Lyons, M. J., Design and evaluation of vision-based head and face tracking interfaces for assistive input. In: Georgios, K. (Ed.), Assistive technologies and computer access for motor disabilities. IGI Global, Hershey, pp. 180–205, 2014.CrossRefGoogle Scholar
  24. 24.
    Ronzhin, A., Karpov, A., Assistive multimodal system based on speech recognition and head tracking. In: Proceedings of 13th European Signal Processing Conference. 2005Google Scholar
  25. 25.
    Reis, L., Faria, B., Vasconcelos, S., Lau, N., Invited paper: multimodal interface for an intelligent wheelchair. In: Ferrier, J.-L., Gusikhin, O., Madani, K., Sasiadek, J., (Eds.), Informatics in control, automation and robotics, Vol. 325. pp. 1–34. Springer International Publishing, 2015Google Scholar
  26. 26.
    Ogiela, M. R., and Hachaj, T., Natural user interfaces in medical image analysis: cognitive analysis of brain and carotid artery images. Springer International Publishing, Switzerland, 2014.Google Scholar
  27. 27.
    Steinberg, G., Natural user interfaces. In: ACM SIGCHI conference on human factors in computing systems. 2012.Google Scholar
  28. 28.
    Faria, B. M., Reis, L. P., Lau, N., Moreira, A. P., Petry, M., Ferreira, L. M., Intelligent wheelchair driving: bridging the gap between virtual and real intelligent wheelchairs. In: Pereira, F., Machado, P., Costa, E., Cardoso, A., (Eds.), Progress in artificial intelligence. Vol. 9273, pp. 445–456. Springer International Publishing, 2015.Google Scholar
  29. 29.
    Faria, B. M., Reis, L. P., Lau, N., A methodology for creating an adapted command language for driving an intelligent wheelchair. J. Intell. Robot. Syst. 80, 2015.Google Scholar
  30. 30.
    Faria, B., Reis, L., and Lau, N., Adapted control methods for cerebral palsy users of an intelligent wheelchair. J. Intell. Robot. Syst. 77:299–312, 2015.CrossRefGoogle Scholar
  31. 31.
    Faria, B. M., Silva, A., Faias, J., Reis, L. P., Lau, N., Intelligent wheelchair driving: a comparative study of cerebral palsy adults with distinct boccia experience. In: Rocha, Á., Correia, A. M., Tan, F. B., Stroetmann, K. A., (Eds.), New perspectives in information systems and technologies, volume 2. Vol. 276. pp. 329–340. Springer International Publishing, 2014.Google Scholar
  32. 32.
    Faria, B. M., Vasconcelos, S., and Reis, L. P., Evaluation of distinct input methods of an intelligent wheelchair in simulated and real environments: a performance and usability study. Assist. Technol. Off. J. RESNA 25:88–98, 2013.CrossRefGoogle Scholar
  33. 33.
    Faria, B., Reis, L., Teixeira, S., Faias, J., Lau, N., Intelligent wheelchair simulator for users’ training cerebral palsy children’s case study. In: 8th Iberian conference on information systems and technologies (CISTI). 2013.Google Scholar
  34. 34.
    Faria, B. M., Vasconcelos, S., Reis, L. P., Lau, N., A methodology for creating intelligent wheelchair users’ profiles. In: ICAART 2012 – 4th International conference on agents and artificial intelligence. pp. 171–179. 2012.Google Scholar
  35. 35.
    Moussa, M. B., Magnenat-Thalmann, N., Applying affect recognition in serious games: the playmancer project. In: Egges, A., Geraerts, R., Overmars, M., (Eds.), Motion in games. pp. 53–62. Springer, 2009.Google Scholar
  36. 36.
    Gerling, K., Livingston, I., Nacke, L., Mandryk, R., Full-body motion-based game interaction for older adults. In: Proceedings of the SIGCHI conference on human factors in computing systems. pp. 1873–1882. ACM, Austin, Texas, USA, 2012.Google Scholar
  37. 37.
    Chang, Y.-J., Chen, S.-F., and Chuang, A.-F., A gesture recognition system to transition autonomously through vocational tasks for individuals with cognitive impairments. Res. Dev. Disabil. 32:2064–2068, 2011.CrossRefPubMedGoogle Scholar
  38. 38.
    Ciger, J., Herbeliny, B., Thalmannz, D., Evaluation of gaze tracking technology for social interaction in virtual environments. In: Proceedings of the 2nd workshop on modeling and motion capture techniques for virtual environments (CAPTECH04). 2004.Google Scholar
  39. 39.
    Jacob, R. J. K., Karn, K. S., Eye tracking in human-computer interaction and usability research: ready to deliver the promises. The mind’s eye: cognitive the mind’s eye: cognitive and applied aspects of eye movement research. pp. 573–603. 2003.Google Scholar
  40. 40.
    Mohamed, A. O., Silva, M. P. D., Courboulay, V., A history of eye gaze tracking. Tech. Rep.2008.Google Scholar
  41. 41.
    Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., and Taylor, J. G., Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18:32–80, 2001.CrossRefGoogle Scholar
  42. 42.
    Li, S. Z., and Jain, A. K., Handbook of face recognition. Springer Science & Business Media, Germany, 2011.CrossRefGoogle Scholar
  43. 43.
    Menache, A., Understanding motion capture for computer animation and video games. Morgan Kaufmann, 2000.Google Scholar
  44. 44.
    Kirishima, T., Sato, K., and Chihara, K., Real-time gesture recognition by learning and selective control of visual interest points. IEEE Trans. Pattern Anal. Mach. Intell. 27:351–364, 2005.CrossRefPubMedGoogle Scholar
  45. 45.
    Gavrila, D. M., The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73:82–98, 1999.CrossRefGoogle Scholar
  46. 46.
    Bradski, G. R., Computer vision face tracking for use in a perceptual user interface. In: Proceedings of the fourth IEEE workshop on applications of computer vision (WACV’98). 1998.Google Scholar
  47. 47.
    Wachs, J. P., Kölsch, M., Stern, H., and Edan, Y., Vision-based hand-gesture applications. Commun. ACM 54:60–71, 2011.CrossRefGoogle Scholar
  48. 48.
    Microsoft kinect for Windows. Available: https://developer.microsoft.com/en-us/windows/kinect, 2016.
  49. 49.
    Leap motion. Available: https://www.leapmotion.com/ 2016.
  50. 50.
    Duchowski, A. T., A breadth-first survey of eye-tracking applications. Behav. Res. Methods Instrum. Comput. 34:455–470, 2002.CrossRefPubMedGoogle Scholar
  51. 51.
    Duchowski, A., Eye tracking methodology: theory and practice. Springer Science & Business Media, 2007.Google Scholar
  52. 52.
    Bulling, A., and Gellersen, H., Toward mobile Eye-based human-computer interaction. IEEE Pervasive Comput. 9:8–12, 2010.CrossRefGoogle Scholar
  53. 53.
    Dickie, C., Vertegaal, R., Sohn, C., Cheng, D., Eyelook: using attention to facilitate mobile media consumption. In: Proceedings of the 18th annual ACM symposium on user interface software and technology. pp. 103–106. ACM, Seattle, WA, USA, 2005.Google Scholar
  54. 54.
    Zhai, S., Morimoto, C., Ihde, S., Manual and gaze input cascaded (MAGIC) pointing. In: Proceedings of the SIGCHI conference on Human factors in computing systems: the CHI is the limit. pp. 246–253. ACM, Pittsburgh, Pennsylvania, United States, 1999.Google Scholar
  55. 55.
    Tobii. Available: http://www.tobii, 2015.
  56. 56.
    Schneiderman, R., Accuracy, apps advance speech recognition [special reports]. IEEE Signal Process. Mag. 32:12–125, 2015.CrossRefGoogle Scholar
  57. 57.
    Schroeder, M. R., Computer speech: recognition, compression, synthesis. Springer Science & Business Media, 2004.Google Scholar
  58. 58.
    Igarashi, T., Hughes, J. F., Voice as sound: using non-verbal voice input for interactive control. In: Proceedings of the 14th annual ACM symposium on User interface software and technology. pp. 155–156. ACM, Orlando, Florida, 2001.Google Scholar
  59. 59.
    Sporka, A. J., Kurniawan, S. H., and Slavík, P., Non-speech operated emulation of keyboard. In: Clarkson, J., Langdon, P., and Robinson, P. (Eds.), Designing accessible technology. Springer London, London, pp. 145–154, 2006.CrossRefGoogle Scholar
  60. 60.
    Bilmes, J. A., Li, X., Malkin, J., Kilanski, K., Wright, R., Kirchhoff, K., Subramanya, A., Harada, S., Landay, J. A., Dowden, P., Chizeck, H., The vocal joystick: a voice-based human-computer interface for individuals with motor impairments. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. pp. 995–1002. Association for Computational Linguistics, 2005.Google Scholar
  61. 61.
    Poláček, O., Sporka, A. J., and Míkovec, Z., Measuring performance of a predictive keyboard operated by humming. In: Miesenberger, K., Karshmer, A., Penaz, P., and Zagler, W. (Eds.), Computers helping people with special needs: 13th international conference, ICCHP 2012, Linz, Austria, July 11-13, 2012, proceedings, part II. Springer Berlin Heidelberg, Berlin, pp. 467–474, 2012.CrossRefGoogle Scholar
  62. 62.
    Harada, S., Wobbrock, J. O., and Landay, J. A., Voice games: investigation into the use of Non-speech voice input for making computer games more accessible. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., and Winckler, M. (Eds.), Human-computer interaction – INTERACT 2011: 13th IFIP TC 13 international conference, Lisbon, Portugal, September 5-9, 2011, proceedings, part I. Springer Berlin Heidelberg, Berlin, pp. 11–29, 2011.CrossRefGoogle Scholar
  63. 63.
    Sporka, A. J., Kurniawan, S. H., Mahmud, M., Slavík, P., Non-speech input and speech recognition for real-time control of computer games. In: Proceedings of the 8th international ACM SIGACCESS conference on computers and accessibility. pp. 213–220. ACM, Portland, Oregon, USA, 2006.Google Scholar
  64. 64.
    Pierre-Yves, O., The production and recognition of emotions in speech: features and algorithms. Int. J. Hum. Comput. Stud. 59:157–183, 2003.CrossRefGoogle Scholar
  65. 65.
    Ververidis, D., and Kotropoulos, C., Emotional speech recognition: resources, features, and methods. Speech Comm. 48:1162–1181, 2006.CrossRefGoogle Scholar
  66. 66.
    Schiel, F., Steininger, S., Türk, U., The SmartKom multimodal corpus at BAS. In: Proc. 3rd Int. Conf. on Language Resources and Evaluation (LREC 2002). pp. 35–41. 2002.Google Scholar
  67. 67.
    France, D. J., Shiavi, R. G., Silverman, S., Silverman, M., and Wilkes, M., Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans. Biomed. Eng. 47:829–837, 2000.CrossRefPubMedGoogle Scholar
  68. 68.
    Ozdas, A., Shiavi, R. G., Silverman, S. E., Silverman, M. K., and Wilkes, D. M., Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk. IEEE Trans. Biomed. Eng. 51:1530–1540, 2004.CrossRefPubMedGoogle Scholar
  69. 69.
    Schröder, M., Heylen, D., and Poggi, I., Perception of non-verbal emotional listener feedback. In: Hoffmann, R., and Mixdorff, H. (Eds.), Speech prosody 2006, vol. 40. TUDpress, Dresden, pp. 43–46, 2006.Google Scholar
  70. 70.
    Kostoulas, T., Mporas, I., Kocsis, O., Ganchev, T., Katsaounos, N., Santamaria, J. J., Jimenez-Murcia, S., Fernandez-Aranda, F., and Fakotakis, N., Affective speech interface in serious games for supporting therapy of mental disorders. Exp. Syst. Appl. 39:11072–11079, 2012.CrossRefGoogle Scholar
  71. 71.
    Hayward, V., Astley, O. R., Cruz-Hernandez, M., Grant, D., and Robles-De-La-Torre, G., Haptic interfaces and devices. Sens. Rev. 24:16–29, 2004.CrossRefGoogle Scholar
  72. 72.
    Göger, D., Weiß, K., Burghart, C., Wörn, H., Sensitive skin for a humanoid robot. In: Proceedings of the 2006 international conference on human-centered robotic systems. 2006.Google Scholar
  73. 73.
    AAPB. Available: http://www.aapb.org/, 2011.
  74. 74.
    Conconi, A., Ganchev, T., Kocsis, O., Papadopoulos, G., Fernandez-Aranda, F., Jimenez-Murcia, S., PlayMancer: a serious gaming 3D environment. In: International conference on automated solutions for cross media content and multi-channel distribution (AXMEDIS ‘08). pp. 111–117. Institute of Electrical and Electronics Engineers (IEEE), 2008.Google Scholar
  75. 75.
    Nacke, L. E., Kalyn, M., Lough, C., Mandryk, R .L., Biofeedback game design: using direct and indirect physiological control to enhance game interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 103–112. ACM, Vancouver, BC, Canada, 2011.Google Scholar
  76. 76.
    Kuikkaniemi, K., Laitinen, T., Turpeinen, M., Saari, T., Kosunen, I., Ravaja, N., The influence of implicit and explicit biofeedback in first-person shooter games. In: Proceedings of the SIGCHI conference on human factors in computing systems. pp. 859–868. ACM, Atlanta, Georgia, USA, 2010.Google Scholar
  77. 77.
    Flynn, S., Palma, P., and Bender, A., Feasibility of using the Sony PlayStation 2 gaming platform for an individual poststroke: a case report. J. Neurol. Phys. Ther. 31:180–189, 2007.CrossRefPubMedGoogle Scholar
  78. 78.
    Saposnik, G., Teasell, R., Mamdani, M., Hall, J., McIlroy, W., Cheung, D., Thorpe, K., Cohen, L., and Bayley, M., Effectiveness of virtual reality using Wii gaming technology in stroke rehabilitation: a pilot randomized clinical trial and proof of principle. Stroke41:1477–1484, 2010.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Nintendo: Wii console. Available: http://www.nintendo.com/wii/console, 2014.
  80. 80.
    Sony: playstation move. Available: http://pt.playstation.com/psmove/, 2014.
  81. 81.
    Vanacken, L., Notelaers, S., Raymaekers, C., Coninx, K., van den Hoogen, W., Jsselsteijn, W. I., Feys, P., Game-based collaborative training for arm rehabilitation of MS patients: a proof-of-concept game. In: Proceedings of the GameDays 2010. pp. 65–75. 2010.Google Scholar
  82. 82.
    Battocchi, A., Gal, E., Ben Sasson, A., Painesi, F., Venuti, P., Zancanaro, M., Weiss, P. L., Collaborative puzzle game – an interface for studying collaboration and social interaction for children who are typically developed or who have autistic spectrum disorder. In: Proceedings of the 7th International Conference series on disability, virtual reality and associated technologies (ICDVRAT). pp. 127–134. 2008.Google Scholar
  83. 83.
    Battocchi, A., Pianesi, F., Tomasini, D., Zancanaro, M., Esposito, G., Venuti, P., Sasson, A. B., Gal, E., Weiss, P. L., Collaborative puzzle game: a tabletop interactive game for fostering collaboration in children with Autism Spectrum Disorders (ASD). In: Proceedings of the ACM international conference on interactive tabletops and surfaces. pp. 197–204. ACM, Banff, Alberta, Canada, 2009.Google Scholar
  84. 84.
    Caglio, M., Latini-Corazzini, L., D’agata, F., Cauda, F., Sacco, K., Monteverdi, S., Zettin, M., Duca, S., and Geminiani, G., Video game play changes spatial and verbal memory: rehabilitation of a single case with traumatic brain injury. Cogn. Process. 10:195–197, 2009.CrossRefGoogle Scholar
  85. 85.
    Cameirão, M. S., Badia, S. B., Zimmerli, L., Oller, E. D., and Vershure, P. F. M. J., The rehabilitation gaming system: a review. Stud. Health Technol. Inform. 145:65–83, 2009.PubMedGoogle Scholar
  86. 86.
  87. 87.
    Maia, L., Gaspar, C., Azevedo, M., Loureiro, M. J., and Silva, C. F., Reabilitação cognitiva assistida por computador: o programa RehaCom e a sua utilização no GEARNeurop. Psiquiatr. Clín. 25:83–105, 2004.Google Scholar
  88. 88.
    Parrot software. Available: http://www.parrotsoftware.com/, 2016.
  89. 89.
    Fundación intras. Available: http://www.intras.es/index.php?id=75, 2014.
  90. 90.
    StatCounter: GlobalStats. Available: http://gs.statcounter.com/#browser-ww-monthly-201409-201509-bar, 2015.
  91. 91.
    Bangor, A., Kortum, P., and Miller, J., Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4:114–123, 2009.Google Scholar

via A Serious Games Platform for Cognitive Rehabilitation with Preliminary Evaluation | SpringerLink

, , , , ,

Leave a comment

[BLOG POST] Thinking & Memory After Stroke – Saebo

Whether you’re awake or asleep, your brain is continuously active. Vast amounts of information—thoughts, moments, feelings, etc.—are sent to your brain, where they are filtered and stored, and it’s important for your brain to be working properly in order to place them in the right spots.

After surviving a stroke, there is a possibility that some of the brain’s vital functions could be damaged, which makes its processes more difficult to carry out, potentially causing harmful issues for the patient. In many stroke cases, issues with thinking and memory are likely to occur, but there are ways to rebuild brain power and regain a healthy lifestyle over time.

Common Problems After a Stroke

Due to physical trauma to the brain, it’s common to experience a variety of issues. Daily actions, like executing a simple task or reacting to external situations, can become difficult to navigate. These kinds of challenges may include watching a television show, reading a book, following through with a task from start to finish, remembering what others have just told you, troubles with directions, executing simple instructions, and even cooking for yourself. If these don’t sound cumbersome enough, along with a slew of physical hurdles lies a deeper obstacle of impaired cognition.
Continue reading our previous post Most Common Questions Answered for more common stroke recovery questions & answers.

Cognitive Problems After a Stroke

Impairments dealing with cognition refer to mental actions and operations that the brain cannot fully sort out. Basically, there is a lack of communication when it comes to gaining information and understanding through vital pathways—thoughts, experiences, and the senses. Because of this, a stroke survivor can possibly mimic symptoms of someone who has dementia or memory loss.

Depending on which side of the brain is most affected by a stroke, different symptoms can occur. For example, someone with a right-brain stroke can exhibit complications with problem solving. In addition, they may confuse information or muddle up the order of details of an event. For those who are left-brain impacted, there may be a significant change to their short-term memory. In this case, a survivor may have a hard time learning new things and will most likely have to be reminded of something many times. That being said, there are ways to help improve cognitive abilities with patience and repetition, and it all starts with rebuilding memory.

Memory Loss After a Stroke

Not only is it common for stroke survivors, but memory loss can be an issue for anyone. Factors like old age and physical accidents can contribute to its deterioration, so understanding its processes can provide a better scope of what to expect.

Types of memory loss may include:

  • Difficulty speaking and understanding language
  • Visual confusion with faces, objects, and directions
  • Trouble with new information and tasks
  • Inability to think clearly

Although these issues may seem challenging, keep in mind that one’s memory has the capability to heal itself over time with the help of mental exercises. Daily routines of mental stimulation may aid in rebuilding awareness and focus, and the best part is that these activities can be enjoyable. There are ways to incorporate a variety of exercises into your life that can make a big difference towards a healthy recovery. Remember, memory symptoms have the potential to last for years, so it’s unlikely that progress will be made overnight, but consistency can set the pace for improvement.

Something else to keep in mind is that techniques for improving after memory loss are considered experimental. In most stroke cases, treatments are designed to help prevent further damage, so if you or a loved one feel like treatments aren’t working, consult with your doctor about taking medications that may assist in rehabilitation.

Ways to Stimulate the Brain

The good news is that there are many options to increase your brain power, and they are all useful in more ways than one! For instance, taking up a new hobby that involves both the mind and body is a great way to work your brain muscles. In addition, performing various physical movements shows a huge correlation with growth in mental and physical strength. Along with these methods, great improvements of mental health can be made by following a routine. Simple tasks like writing things down, designating certain spots for items, and overall repetition provide stability and reassurance.

Apps

Rather than focusing all your attention on classic methods of brain stimulation, try technology; it can be an immediate and fun way to see results. On a smartphone or tablet you’ll find countless apps available that can help improve memory and speech, set reminders for medications and appointments, and help manage other illnesses or issues that you may have. With today’s growing technology, apps are both widely accessible and easy to use, giving you freedom to develop your own regiment of “app rehab.”

Here are some of our favorite apps to try out:

What’s the Difference?

In this game, two pictures will appear on the screen, and it’s your job to use your finger and circle any differences you spot on the image below compared to the image above. As you move from one level to the next, the differences will be harder to find! This game will improve your awareness and perception skills with every round.

Thinking Time Pro

Designed by Harvard and UC Berkeley neuroscientists, this app uses four different scientific games to enhance your memory, attention, reasoning, and overall cognitive skills. The best part about this app is that you can set the difficulty level to move at your own pace.

Fit Brains Trainer

Ranked as one of the best educational apps in the world, Fit Brains Trainer stimulates your cognitive and emotional intelligence through a variety of brain games, workout sessions, and personalized status reports based on your performance.

Eidetic

For the ultimate boost in memorization, Eidetic utilizes a technique known as “spaced repetition” to aid you in memorizing loads of information. Whether you want to remember someone’s phone number or a recipe you just found online, this app will do the trick.

Support Leads to Progress

If you or a loved one is suffering from issues pertaining to thinking and memory, know that there are treatments out there to make improvements. With patience and understanding, a stroke survivor can eventually reach a level of fulfillment in life, but it’s difficult to get there alone. More than anything, a survivor will need encouragement in order to believe that progress can be made. With the support of friends and family, and help from various exercises and technologies, development is certainly possible

via Thinking & Memory After Stroke | Saebo

, , , ,

Leave a comment

%d bloggers like this: