Posts Tagged Neuropsychology

[BOOK Chapter] Assessment and Rehabilitation Using Virtual Reality after Stroke: A Literature Review – Abstract + References

Abstract

This chapter presents the studies that have used virtual reality as an assessment or rehabilitation tool of cognitive functions following a stroke. To be part of this review, publications must have made a collection of data from individuals who have suffered a stroke and must have been published between 1980 and 2017. A total of 50 publications were selected out of a possible 143 that were identified in the following databases: Academic Search Complete, CINAHL, MEDLINE, PsychINFO, Psychological and Behavioural Sciences Collection. Overall, we find that most of the studies that have used virtual reality with stroke patients focused on attention, spatial neglect, and executive functions/multitasking. Some studies have focused on route representation, episodic memory, and prospective memory. Virtual reality has been used for training of cognitive functions with stroke patients, but also for their assessment. Overall, the studies support the value and relevance of virtual reality as an assessment and rehabilitation tool with people who have suffered a stroke. Virtual reality seems indeed an interesting way to better describe the functioning of the person in everyday life. Virtual reality also sometimes seems to be more sensitive than traditional approaches for detecting deficits in stroke people. However, it is important to pursue work in this emergent field in clinical neuropsychology.

References

  1. Ansuini, C., Pierno, A. C., Lusher, D., & Castiello, U. (2006). Virtual reality applications for the remapping of space in neglect patients. Restorative Neurology and Neuroscience, 24(4–6), 431–441.PubMedGoogle Scholar
  2. Baheux, K., Yoshikawa, M., Tanaka, A., Seki, K., & Handa, Y. (2004). Diagnosis and rehabilitation of patients with hemispatial neglect using virtual reality technology. Conference proceedings: Annual international conference of the IEEE engineering in medicine and biology society, 7, 4908–4911.Google Scholar
  3. Baheux, K., Yoshizawa, M., Tanaka, A., Seki, K., & Handa, Y. (2005). Diagnosis and rehabilitation of hemispatial neglect patients with virtual reality technology. Technology and health careOfficial Journal of the European Society for Engineering and Medicine, 13(4), 245–260.Google Scholar
  4. Baheux, K., Yoshizawa, M., Seki, K., & Handa, Y. (2006). Virtual reality pencil and paper test for neglect: A protocol. CyberPsychology & Behaviour, 9(2), 192–195.CrossRefGoogle Scholar
  5. Broeren, J., Samuelsson, H., Stibrant-Sunnerhagen, K., Blomstrand, C., & Rydmark, M. (2007). Neglect assessment as an application of virtual reality. Acta Neurlogica Scandinavica, 116, 157–163.CrossRefGoogle Scholar
  6. Brooks, B. M., Rose, F. D., Potter, J., Jayawardena, S., & Morling, A. (2004). Assessing stroke patients’ prospective memory using virtual reality. Brain Injury, 18(4), 391–401.CrossRefGoogle Scholar
  7. Buxbaum, L. J., Palermo, M., Mastrogiovanni, D., Read, M., Rosenberg-Pitonyak, E., Rizzo, A. A., & Coslett, H. (2008). Assessment of spatial attention and neglect with a virtual wheelchair navigation task. Journal of Clinical & Experimental Neuropsychology, 30(6), 650–660.CrossRefGoogle Scholar
  8. Buxbaum, L. J., Dawson, A. M., & Linsley, D. (2012). Reliability and validity of the virtual reality lateralized attention test in assessing hemispatial neglect in right-hemisphere stroke. Neuropsychology, 26(4), 430–441.CrossRefGoogle Scholar
  9. Cameirão, M. S., Faria, A. L., Paulino, T., Alves, J., & i Badia, S. B. (2016). The impact of positive, negative and neutral stimuli in a virtual reality cognitive-motor rehabilitation task: a pilot study with stroke patients. Journal of Neuroengineering and Rehabilitation, 13(1), 70.Google Scholar
  10. Carelli, L., Rusconi, M. L., Mattioli, F., Stampatori, C., Morganti, F., & Riva, G. (2009). Neuropsychological and virtual reality assessment in topographical disorientation. Annual Review of Cybertherapy and Telemedicine, 7, 230–233.Google Scholar
  11. Carelli, L., Rusconi, M. L., Scarabelli, C., Stampatori, C., Mattioli, F., & Riva, G. (2011). The transfer from survey (map-like) to route representations into virtual reality Mazes: effect of age and cerebral lesion. Journal Of Neuroengineering And Rehabilitation, 2011 Jan 31; Vol 8, pp 6 Electronic Publication.Google Scholar
  12. Castiello, U., Lusher, D., Burton, C., Glover, S., & Disler, P. (2004). Improving left hemispatial neglect using virtual reality. Neurology, 62, 1958–1962.CrossRefGoogle Scholar
  13. Cherniack, E. P. (2011). Not just fun and games: Applications of virtual reality in the identification and rehabilitation of cognitive disorders of the elderly. Disability and Rehabilitation: Assistive Technology, 6(4), 283–289.  https://doi.org/10.3109/17483107.2010.542570.CrossRefPubMedGoogle Scholar
  14. Crosbie, J. H., Lennon, S., Basford, J. R., & McDonough, S. M. (2007). Virtual reality in stroke rehabilitation: Still more virtual than real. Disability and Rehabilitation, 29(14), 1139–1146.CrossRefGoogle Scholar
  15. Dawson, A. M., Buxbaum, L. J., & Rizzo, A. A. (2008). The virtual reality lateralized attention test: Sensitivity and validity of a new clinical tool for assessing hemispatial neglect. Virtual Rehabilitation, 77–82.  https://doi.org/10.1109/ICVR.2008.4625140.
  16. Dvorkin, A. Y., Rymer, W. Z., Harvey, R. L., Bogey, R. A., & Patton, J. L. (2008). Assessment and monitoring of recovery of spatial neglect within a virtual environment. Proceedings of the IEEE Virtual Rehabilitation, 88–92.Google Scholar
  17. Dvorkin, A. Y., Bogey, R. A., Harvey, R. L., & Patton, J. L. (2012). Mapping the neglected space: Gradients of detection revealed by virtual reality. Neurorehabilitation and Neural Repair, 26(2), 120–131.  https://doi.org/10.1177/1545968311410068.CrossRefPubMedGoogle Scholar
  18. Edmans, J., Gladman, J., Walker, M., Sunderland, A., Porter, A., & Fraser, D. S. (2004). Mixed reality environments in stroke rehabilitation: Development as rehabilitation tools. International Journal on Disability and Human Development, 6(1), 39–45.Google Scholar
  19. Faria, A. L., Andrade, A., Soares, L., & i Badia, S. B. (2016). Benefits of virtual reality based cognitive rehabilitation through simulated activities of daily living: A randomized controlled trial with stroke patients. Journal of Neuroengineering and Rehabilitation, 13(1), 96.CrossRefGoogle Scholar
  20. Fordell, H., Bodin, K., Bucht, G., & Malm, J. (2011). A virtual reality test battery for assessment and screening of spatial neglect. Acta Neurologica Scandinavica, 123, 167–174.  https://doi.org/10.1111/j.1600-0404.2010.01390.x.CrossRefPubMedGoogle Scholar
  21. Gamito, P., Oliveira, J., Coelho, C., Morais, D., Lopes, P., Pacheco, J., & Barata, A. F. (2017). Cognitive training on stroke patients via virtual reality-based serious games. Disability and Rehabilitation, 39(4), 385–388.CrossRefGoogle Scholar
  22. Glover, S., & Castiello, U. (2006). Recovering space in unilateral neglect: A neurological dissociation revealed by virtual reality. Journal of Cognitive Neuroscience, 18(5), 833–843.CrossRefGoogle Scholar
  23. Guilbert, A., Clément, S., Martin, Y., Feuillet, A., & Moroni, C. (2016). Exogenous orienting of attention in hearing: A virtual reality paradigm to assess auditory attention in neglect patients. Experimental Brain Research, 234(10), 2893–2903.CrossRefGoogle Scholar
  24. Gupta, V., Knott, B. A., Kodgi, S., & Lathan, C. E. (2000). Using the “vreye” system for the assessment of unilateral visual neglect: Two case reports. Presence: Teleoperators & Virtual Environments, 9(3), 268–286.CrossRefGoogle Scholar
  25. Jannink, M. J. A., Aznar, M., de Kort, A. C., van de Vis, W., Veltink, P., & van der Kooij, H. (2009). Assessment of visuospatial neglect in stroke patients using virtual reality: A pilot study. International Journal of Rehabilitation Research, 32(4), 280–286.CrossRefGoogle Scholar
  26. Jovanovski, D., Zakzanis, K., Ruttan, L., Campbell, Z., Erb, S., & Nussbaum, D. (2012). Ecologically valid assessment of executive dysfunction using a novel virtual reality task in patients with acquired brain injury. Applied Neuropsychology, 19, 207–220.  https://doi.org/10.1080/09084282.2011.643956.CrossRefPubMedGoogle Scholar
  27. Kang, Y. J., Ku, J., Han, K., Kim, S. I., Yu, T. W., Lee, J. H., & Park, C. I. (2008). Development and clinical trial of virtual reality-based cognitive assessment in people with stroke: Preleminary study. CyberPsychology & Behaviour, 11(3), 329–339.CrossRefGoogle Scholar
  28. Katz, N., Ring, H., Naveh, Y., Kizony, R., Feintuch, U., & Weiss, P. L. (2005). Interactive virtual environment training for safe street crossing of right hemisphere stroke patients with unilateral spatial neglect. Disability and Rehabilitation, 27(20), 1235–1243.CrossRefGoogle Scholar
  29. Kim, K., Kim, J., Ku, J., Kim, D. Y., Chang, W. H., Shin, D. I., Lee, J. H., Kim, I. Y., & Kim, S. I. (2004). A virtual reality assessment and training system for unilateral neglect. CyberPsychology & Behaviour, 7, 742–749.CrossRefGoogle Scholar
  30. Kim, J., Kim, K., Kim, D. Y., Chang, W. H., Park, C.-I., Ohn, S. H., Han, K., Ku, J., Nam, S. W., Kim, I. Y., & Kim, S. I. (2007). Virtual environment training system for rehabilitation of stroke patients with unilateral neglect: Crossing the virtual street. CyberPsychology & Behaviour, 10(1), 7–15.CrossRefGoogle Scholar
  31. Kim, D. Y., Ku, J., Chang, W. H., Park, T. H., Lim, J. Y., Han, K., Kim, I. Y., & Kim, S. I. (2010). Assessment of post-stroke extrapersonal neglect using a three-dimensional immersive virtual street crossing program. Acta Neurologica Scandinavica, 121, 171–177.  https://doi.org/10.1111/j.1600-0404.2009.01194.x.CrossRefPubMedGoogle Scholar
  32. Kim, B. R., Chun, M. H., Kim, L. S., & Park, J. Y. (2011b). Effect of virtual reality on cognition in stroke patients. Annals of Rehabilitation Medicine, 35(3), 450–459.CrossRefGoogle Scholar
  33. Kim, Y. M., Chun, M. H., Yun, G. J., Song, Y. J., & Young, H. E. (2011a). The effect of virtual reality training on unilateral spatial neglect in stroke patients. Annals of Rehabilitation Medicine, 35(3), 309–315.CrossRefGoogle Scholar
  34. Knight, C., Alderman, N., & Burgess, P. W. (2002). Development of a simplified version of the multiple errands test for use in hospital settings. Neuropsychological Rehabilitation, 12(3), 231–255.CrossRefGoogle Scholar
  35. Lee, J. H., Ku, J., Cho, W., Hahn, W. Y., Kim, I. Y., Lee, S.-M., Kang, Y., Kim, D. Y., Yu, T., Wiederhold, B. K., Wiederhold, M., & Kim, S. I. (2003). A virtual reality system for the assessment and rehabilitation of the activities of daily living. CyberPsychology & Behaviour, 6(4), 383–388.CrossRefGoogle Scholar
  36. Lisa, L. P., Jughters, A., & Kerckhofs, E. (2013). The effectiveness of different treatment modalities for the rehabilitation of unilateral neglect in stroke patients: A systematic review. NeuroRehabilitation, 33, 611–620.  https://doi.org/10.3233/NRE-130986.CrossRefPubMedGoogle Scholar
  37. Maier, M., Bañuelos, N. L., Ballester, B. R., Duarte, E., & Verschure, P. F. (2017, July). Conjunctive rehabilitation of multiple cognitive domains for chronic stroke patients in virtual reality. In Rehabilitation Robotics (ICORR), 2017 international conference on (pp. 947–952). IEEE.Google Scholar
  38. Morganti, F. (2004). Virtual interaction in cognitive neuropsychology. Studies in Health, Technologies and Informatics, 99, 55–70.Google Scholar
  39. Myers, R. L., & Bierig, T. (2003). Virtual reality and left hemineglect: A technology for assessment and therapy. CyberPsychology & Behaviour, 3, 465–468.CrossRefGoogle Scholar
  40. Navarro, M_. D., Llorens, R., Noé, E., Ferri, J., & Alcaniz, M. (2013). Validation of a low-cost virtual reality system for training street-crossing. A comparative study in healthy, neglected and non-neglected stroke individuals. Neuropsychological Rehabilitation, 23(4), 597–618.  https://doi.org/10.1080/09602011.2013.806269.CrossRefPubMedGoogle Scholar
  41. Nir-Hadad, S. Y., Weiss, P. L., Waizman, A., Schwartz, N., & Kizony, R. (2017). A virtual shopping task for the assessment of executive functions: Validity for people with stroke. Neuropsychological Rehabilitation, 27(5), 808–833.CrossRefGoogle Scholar
  42. Pridmore, T., Cobb, S., Hilton, D., Green, J., & Eastgate, R. (2007). Mixed reality environments in stroke rehabilitation: Interfaces across the real-virtual divide. International Journal of Disability and Human Development, 6(1), 87–95.CrossRefGoogle Scholar
  43. Rand, D., Katz, N., Kizony, R., & Weiss, P. L. (2005). The virtual mall: a functional virtual environment for stroke rehabilitation. Annual Review of CyberTherapy and Telemedicine, 3, 193–198.Google Scholar
  44. Rand, D., Katz, N., & Weiss, P. L. (2007). Evaluation of virtual shopping in the VMall: Comparison of post-stroke participants to healthy control groups. Disability and Rehabilitation, 29, 1710–1719.CrossRefGoogle Scholar
  45. Rand, D., Basha-Abu Rukan, S., Weiss, P. L., & Katz, N. (2009a). Validation of the virtual MET as an assessment tool for executive functions. Neuropsychological Rehabilitation, 19, 583–602.CrossRefGoogle Scholar
  46. Rand, D., Weiss, P. L., & Katz, N. (2009b). Training multitasking in virtual supermarket: A novel intervention after stroke. The American Journal of Occupational Therapy, 63(5), 535–542.CrossRefGoogle Scholar
  47. Raspelli, S., Carelli, L., Morganti, F., Poletti, B., Corra, B., Silani, V., & Riva, G. (2010). Implementation of the multiple errands test in NeuroVR supermarket: A possible approach. Studies in Health Technology and Informatics, 154, 115–119.PubMedGoogle Scholar
  48. Raspelli, S., Pallavicini, F., Carelli, L., Morganti, F., Poletti, B., Corra, B., Silani, V., & Riva, G. (2011). Validation of a neuro virtual reality-based version of the multiple errands test for the assessment of executive functions. Annual Review of CyberTheraphy and Telemedicine, 9, 72–80.Google Scholar
  49. Riva, G., Carelli, L., Gaggioli, A., Gorini, A., Vigna, C., Corsi, R., Faletti, G., & Vezzadini, L. (2009). NeuroVR 1.5 – a free virtual reality platform for the assessment and treatment in clinical psychology and neuroscience. Milan: Applied Technology for Neuro-Psychology Laboratory, Instituto Auxologico Italiano.Google Scholar
  50. Rose, F. D., Brooks, B. M., Attree, E. A., Parslow, D. M., Leadbetter, A. G., McNeil, J. E., Jayawardenas, S., Greenwood, R., & Potter, J. (1999). A preliminary investigation into the use of virtual environments in memory retraining after vascular brain injury: Indications for future strategy? Disability and Rehabilitation, 21(12), 548–554.CrossRefGoogle Scholar
  51. Rose, F. D., Brooks, B. M., & Rizzo, A. A. (2005). Virtual reality in brain damage rehabilitation: Review. CyberPsychology & Behaviour, 8(3), 241–262.CrossRefGoogle Scholar
  52. Rushton, S. K., Coles, K. L., & Wann, J. P. (1996). Virtual reality technology in the assessment and rehabilitation of unilateral visual neglect. 1st European Conference on Disability, Virtual Reality, and Associated Technologies. Maidenhead.Google Scholar
  53. Salva, A. M., Wiederhold, B. K., Alban, A. J., Hughes, C., Smith, E., Fidopiastis, C., & Wiederhold, M. D. (2009). Cognitive therapy using mixed reality for those impaired by cerebrovascular accident (CVA). Annual Review of Cybertherapy and Telemedicine, 7, 253–256.Google Scholar
  54. Shallice, T., & Burgess, P. W. (1991). Deficits in strategy application following frontal lobe damage in man. Brain, 114, 727–741.CrossRefGoogle Scholar
  55. Smith, J., Hebert, D., & Reid, D. (2007). Exploring the effects of virtual reality on unilateral neglect caused by stroke: Four case studies. Technology & Disability, 19, 29–40.CrossRefGoogle Scholar
  56. Tanaka, T., Sugihara, S., Nara, H., Ino, S., & Ifukube, T. (2005). A preliminary study of clinical assessment of the left unilateral spatial neglect using a head-mounted display system (HMD) in rehabilitation engineering technology. Journal of Neuroengineering & Rehabilitation, 2, 31–40.CrossRefGoogle Scholar
  57. Tanaka, T., Ifukube, T., Sugihara, S., & Izumi, T. (2010). A case study of new assessment and training of unilateral spatial neglect in stroke patients: Effect of visual image transformation and visual stimulation by using a head-mounted display system (HMD). Journal of Neuroengineering and Rehabilitation, 7(20), 1–8.Google Scholar
  58. Tsirlin, I., Dupierrix, E., Chokron, S., Coquillart, S., & Ohlmann, T. (2009). Uses of virtual reality for diagnosis, rehabilitation and study of unilateral spatial neglect: Review and analysis. CyberPsychology & Behaviour, 12(2), 175–181.CrossRefGoogle Scholar
  59. Weiss, P. L., Naveh, Y., & Katz, N. (2003). Design and testing of virtual environment to train stroke patients with unilateral spatial neglect to cross a street safely. Occupational Therapy International, 10(1), 39–55.CrossRefGoogle Scholar
  60. Williams, G. R., Jiang, J. G., Matchar, D. B., & Samsa, G. O. (1999). Incidence and occurrence of total (first-ever and recurrent) stroke. Stroke, 30, 2523–2528.CrossRefGoogle Scholar
  61. Yasuda, K., Muroi, D., Ohira, M., & Iwata, H. (2017). Validation of an immersive virtual reality system for training near and far space neglect in individuals with stroke: A pilot study. Topics in Stroke Rehabilitation, 24(7), 533–538.CrossRefGoogle Scholar

via Assessment and Rehabilitation Using Virtual Reality after Stroke: A Literature Review | SpringerLink

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[Abstract] Employment stability in the first 5 years after moderate to severe traumatic brain injury

Abstract

Objective

To characterize employment stability and identify predictive factors of employment stability in working-age individuals after moderate to severe traumatic brain injury (TBI) that may be clinically addressed.

Design

Longitudinal observational study of an inception cohort from the Traumatic Brain Injury Model Systems National Database (TBIMS-NDB) using data at years 1, 2, and 5 post-TBI.

Setting

Inpatient rehabilitation centers with telephone follow-up.

Participants

Individuals enrolled in the TBIMS-NDB since 2001, aged 18 to 59, with employment data at two or more follow-up interviews at years 1, 2, and 5 (N=5,683).

Interventions

Not applicable.

Main Outcome Measure

Employment stability, categorized using post-TBI employment data as no paid employment (53.25%), stably (27.20%), delayed (10.24%), or unstably (9.31%) employed.

Results

Multinomial regression analyses identified predictive factors of employment stability, including younger age, white race, less severe injuries, pre-injury employment, higher annual earnings, male sex, higher education, transportation independence post-injury, and no anxiety or depression at 1-year post-TBI.

Conclusions

Employment stability serves as an important measure of productivity post-TBI. Psychosocial, clinical, environmental, and demographic factors predict employment stability post-TBI. Notable predictors include transportation independence as well as presence of anxiety and depression at year 1 post-TBI as potentially modifiable intervention targets.

via Employment stability in the first 5 years after moderate to severe traumatic brain injury – Archives of Physical Medicine and Rehabilitation

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[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?

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[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?

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[Systematic review] The potential of real-time fMRI neurofeedback for stroke rehabilitation – Full Text

Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation.

1. Introduction

The number of stroke survivors is continuously increasing with the ageing of the population: about 15 million people worldwide suffer from stroke every year, of whom 5 million die, whereas another 5 million become chronically disabled (WHO, 2012). Behavioural deficits in cognitive and motor domains are highly prevalent and persistent in stroke survivors (Bickerton et al., 2014; Demeyere, Riddoch, Slavkova, Bickerton, & Humphreys, 2015; Demeyere et al., 2016; Jaillard, Naegele, Trabucco-Miguel, LeBas, & Hommel, 2009; Planton et al., 2012; Verstraeten, Mark, & Sitskoorn, 2016). Neurophysiological and neuroimaging studies suggested that stroke causes network-wide changes across structurally intact regions, directly or indirectly connected to the site of infarction (Carrera & Tononi, 2014; Carter et al., 2010; Gillebert & Mantini, 2013; Grefkes et al., 2008; Ward & Cohen, 2004). Disruptions in even one of the many networks or brain regions implicated in the different aspects of motor function and cognition can have a major impact on quality of life (Achten, Visser-Meily, Post, & Schepers, 2012; Hochstenbach, Mulder, Limbeek, Donders, & Schoonderwaldt, 1998). Accordingly, both local tissue damage and secondary changes in brain function should be considered when developing rehabilitation strategies to improve the recovery rate and generally increase the quality of life in stroke survivors (Chechlacz, Mantini, Gillebert, & Humphreys, 2015; Chechlacz et al., 2013; Corbetta et al., 2015; Gillebert & Mantini, 2013). In this regard, the use of neurofeedback may be a promising approach.

1.1. Neurofeedback

Neurofeedback works as a closed loop system that provides real-time information regarding the participant’s own brain activity and/or connectivity, which can be used to develop self-learning strategies to modulate these brain signals (Weiskopf, Mathiak, et al., 2004). It follows the principle of operant conditioning, a method of learning that occurs through reinforcing specific behaviour with rewards and punishments (Skinner, 1938). If the participant learns to control activity of the brain areas targeted through neurofeedback, this may ultimately lead to a measurable behavioural change that is related to the function of those areas (DeCharms et al., 2005; Haller, Birbaumer, & Veit, 2010; Hartwell et al., 2016).

The origins of neurofeedback are rooted in electroencephalography (EEG), which measures dynamic changes of electrical potentials over the participant’s scalp (Nowlis & Kamiya, 1970). This technique is portable and inexpensive, and provides estimates of brain activity at high temporal resolution. EEG neurofeedback has been widely used over the years to induce long-lasting behavioural changes, both in healthy volunteers and in patients (Gruzelier, 2014; Nelson, 2007). However, because of the low spatial resolution associated with this technique, it is very challenging to selectively target brain areas of interest. As such, the effects of EEG neurofeedback are often not specific (Rogala et al., 2016; Scharnowski & Weiskopf, 2015). Other neuroimaging techniques used for neurofeedback include magnetoencephalography (MEG) (Buch et al., 2012; Okazaki et al., 2015) and functional near-infrared spectroscopy (fNIRS) (Kober et al., 2014; Mihara et al., 2013). However, as also for EEG, their spatial resolution is relatively limited and they do not permit to target precise brain regions.

The field of neurofeedback has rapidly developed and delved into new avenues by the introduction of real-time functional magnetic resonance imaging (rt-fMRI) technology (Cox, Jesmanowicz, & Hyde, 1995). Accordingly, in the past years there has been a steady increase of studies focussing on rt-fMRI neurofeedback applications to induce behavioural changes (Sulzer et al., 2013). Rt-fMRI neurofeedback uses the blood-oxygenation level-dependent (BOLD) signal to present contingent feedback to the participant and to enable modulation of brain activity (Fig. 1). Various acquisition parameters are available, and chosen based on a trade-off between spatial and temporal resolution, and signal-to-noise ratio (Weiskopf, Scharnowski, et al., 2004). The analysis is performed almost immediately or with a delay of a few seconds depending on the available computational resources. With a much higher spatial resolution than EEG, fMRI allows for a refined delineation of both cortical and subcortical target regions. These properties can be valuable for neurofeedback applications (Stoeckel et al., 2014). Recent studies suggest that rt-fMRI is a mature technology to use in the context of neurofeedback training (for a review, see e.g., Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014; Weiskopf, 2012). As a result, doors are being opened to the application of rt-fMRI neurofeedback in ameliorating disrupted brain functions in stroke survivors.[…]

Fig. 1

Fig. 1. Real-time fMRI neurofeedback is a closed-loop system that can be used to voluntarily modulate brain-activity through the principle of operant conditioning. (A) The participants use self-generated or prior instructed strategies to attempt to change their brain activity. (B) fMRI data are acquired and (C) processed in real-time. Computer programs select the relevant signals and (D) return these to the participants after varied degrees of pre-processing to allow them to adjust their control strategies.

Continue —>  The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review – ScienceDirect

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[BOOK] The Role of Technology in Clinical Neuropsychology – Google Books

Front CoverNeuropsychology as a field has been slow to embrace and exploit the potential offered by technology to either make the assessment process more efficient or to develop new capabilities that augment the assessment of cognition.

The Role of Technology in Clinical Neuropsychology details current efforts to use technology to enhance cognitive assessment with an emphasis on developing expanded capabilities for clinical assessment. The first sections of the book provide an overview of current approaches to computerized assessment along with newer technologies to assess behavior. The next series of chapters explores the use of novel technologies and approaches in cognitive assessment as they relate to developments in telemedicine, mobile health, and remote monitoring including developing smart environments. While still largely office-based, health care is increasingly moving out of the office with an increased emphasis on connecting patients with providers, and providers with other providers, remotely.

Chapters also address the use of technology to enhance cognitive rehabilitation by implementing conceptually-based games to teach cognitive strategies and virtual environments to measure outcomes. Next, the chapters explore the use of virtual reality and scenario-based assessment to capture critical aspects of performance not assessed by traditional means and the implementation of neurobiological metrics to enhance patient assessment. Chapters also address the use of imaging to better define cognitive skills and assessment methods along with the integration of cognitive assessment with imaging to define the functioning of brain networks. The final section of the book discusses the ethical and methodological considerations needed for adopting advanced technologies for neuropsychological assessment.

Authored by numerous leading figures in the field of neuropsychology, this volume emphasizes the critical role that virtual environments, neuroimaging, and data analytics will play as clinical neuropsychology moves forward in the future.

Source: The Role of Technology in Clinical Neuropsychology – Google Books

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[BOOK] Neuropsychology: A Review of Science and Practice – Google Books

 Front Cover
Neuropsychology: A Review of Science and Practice, Volume 2
 edited by Emeritus Associate Professor Sandra Koffler, Sandra Koffler, Adjunct Associate Professor Joel Morgan, Joel Morgan, Bernice Marcopulos, Manfred F. Greiffenstein

Neuropsychology: A Review of Science and Practice – Google Books.

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