Posts Tagged Cognitive Rehabilitation

[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

[WEB SITE] Transcranial electrical stimulation shows promise for treating mild traumatic brain injury

 

Credit: copyright American Heart Association

Using a form of low-impulse electrical stimulation to the brain, documented by neuroimaging, researchers at the University of California San Diego School of Medicine, Veterans Affairs San Diego Healthcare System (VASDHS) and collaborators elsewhere, report significantly improved neural function in participants with mild traumatic brain injury (TBI).

Their findings are published online in the current issue of the journal Brain Injury.

TBI is a leading cause of sustained physical, cognitive, emotional and behavioral problems in both the civilian population (primarily due to , sports, falls and assaults) and among military personnel (blast injuries). In the majority of cases,  is deemed mild (75 percent of civilians, 89 percent of military), and typically resolves in days.

But in a significant percentage of cases, mild TBI and related post-concussive symptoms persist for months, even years, resulting in chronic, long-term cognitive and/or behavioral impairment.

Much about the pathology of mild TBI is not well understood, which the authors say has confounded efforts to develop optimal treatments. However, they note the use of passive neuro-feedback, which involves applying low-intensity pulses to the brain through transcranial  (LIP-tES), has shown promise.

In their pilot study, which involved six participants who had suffered mild TBI and experienced persistent post-concussion symptoms, the researchers used a version of LIP-tES called IASIS, combined with concurrent electroencephalography monitoring (EEG). The  effects of IASIS were assessed using magnetoencephalography (MEG) before and after treatment. MEG is a form of non-invasive functional imaging that directly measures brain neuronal electromagnetic activity, with high temporal resolution (1 ms) and high spatial accuracy (~3 mm at the cortex).

“Our previous publications have shown that MEG detection of abnormal brain slow-waves is one of the most sensitive biomarkers for mild  (concussions), with about 85 percent sensitivity in detecting concussions and, essentially, no false-positives in normal patients,” said senior author Roland Lee, MD, professor of radiology and director of Neuroradiology, MRI and MEG at UC San Diego School of Medicine and VASDHS. “This makes it an ideal technique to monitor the effects of concussion treatments such as LIP-tES.”

The researchers found that the brains of all six participants displayed abnormal slow-waves in initial, baseline MEG scans. Following treatment using IASIS, MEG scans indicated measurably reduced abnormal slow-waves. The participants also reported a significant reduction in post-concussion scores.

“For the first time, we’ve been able to document with neuroimaging the effects of LIP-tES treatment on brain functioning in mild TBI,” said first author Ming-Xiong Huang, PhD, professor in the Department of Radiology at UC San Diego School of Medicine and a research scientist at VASDHS. “It’s a small study, which certainly must be expanded, but it suggests new potential for effectively speeding the healing process in mild traumatic injuries.”

Source: Transcranial electrical stimulation shows promise for treating mild traumatic brain injury

, , , , , , , ,

Leave a comment

[Abstract] Exploiting Awareness for the Development of Collaborative Rehabilitation Systems

Abstract

Physical and cognitive rehabilitation is usually a challenging activity as people with any kind of deficit has to carry out tasks difficult due to their abilities damaged. Moreover, such difficulties become even harder while they have to work at home in an isolated manner. Therefore, the development of collaborative rehabilitation systems emerges as one of the best alternatives to mitigate such isolation and turn a difficult task into a challenging and stimulating one. As any other collaborative system, the need of being aware of other participants (their actions, locations, status, etc.) is paramount to achieve a proper collaborative experience. This awareness should be provided by using those feedback stimuli more appropriate according to the physical and cognitive abilities of the patients. This has led us to define an awareness interpretation for collaborative cognitive and physical systems. This has been defined by extending an existing proposal that has been already applied to the collaborative games field. Furthermore, in order to put this interpretation into practice, a case study based on an association image-writing rehabilitation pattern is presented illustrating how this cognitive rehabilitation task has been extended with collaborative features and enriched
with awareness information

Source: http://scholar.google.gr/scholar_url?url=http://downloads.hindawi.com/journals/misy/aip/4714328.pdf&hl=en&sa=X&scisig=AAGBfm3JVyov5uyNG4mj9U_e1I0YTQ_vvA&nossl=1&oi=scholaralrt

, , , ,

Leave a comment

[Abstract+References] Neuroplastic Changes Induced by Cognitive Rehabilitation in Traumatic Brain Injury: A Review 

Background. Cognitive deficits are among the most disabling consequences of traumatic brain injury (TBI), leading to long-term outcomes and interfering with the individual’s recovery. One of the most effective ways to reduce the impact of cognitive disturbance in everyday life is cognitive rehabilitation, which is based on the principles of brain neuroplasticity and restoration. Although there are many studies in the literature focusing on the effectiveness of cognitive interventions in reducing cognitive deficits following TBI, only a few of them focus on neural modifications induced by cognitive treatment. The use of neuroimaging or neurophysiological measures to evaluate brain changes induced by cognitive rehabilitation may have relevant clinical implications, since they could add individualized elements to cognitive assessment. Nevertheless, there are no review studies in the literature investigating neuroplastic changes induced by cognitive training in TBI individuals.

Objective. Due to lack of data, the goal of this article is to review what is currently known on the cerebral modifications following rehabilitation programs in chronic TBI.

Methods. Studies investigating both the functional and structural neural modifications induced by cognitive training in TBI subjects were identified from the results of database searches. Forty-five published articles were initially selected. Of these, 34 were excluded because they did not meet the inclusion criteria.

Results. Eleven studies were found that focused solely on the functional and neurophysiological changes induced by cognitive rehabilitation.

Conclusions. Outcomes showed that cerebral activation may be significantly modified by cognitive rehabilitation, in spite of the severity of the injury.

1. Laatsch L, Little D, Thulborn K. Changes in fMRI following cognitive rehabilitation in severe traumatic brain injury: a case study. Rehabil Psychol. 2004;49:262267. Google Scholar CrossRef
2. Voelbel GT, Genova HM, Chiaravalotti ND, Hoptman MJ. Diffusion tensor imaging of traumatic brain injury review: implications for neurorehabilitation. NeuroRehabilitation. 2012;31:281293. Google Scholar Medline
3. Kou Z, Iraji A. Imaging brain plasticity after trauma. Neural Regen Res. 2014;9:693700. Google Scholar CrossRef, Medline
4. Whyte J, Polansky M, Fleming M, Coslett HB, Cavallucci C. Sustained arousal and attention after traumatic brain injury. Neuropsychologia. 1995;33:797813. Google Scholar CrossRef, Medline
5. McAvinue L, O’Keeffe F, McMackin D, Robertson IH. Impaired sustained attention and error awareness in traumatic brain injury: implications for insight. Neuropsychol Rehabil. 2005;15:569587. Google Scholar CrossRef, Medline
6. Ziino C, Ponsford J. Selective attention deficits and subjective fatigue following traumatic brain injury. Neuropsychology. 2006;20:383390. Google Scholar CrossRef, Medline
7. Vakil E. The effect of moderate to severe traumatic brain injury (TBI) on different aspects of memory: a selective review. J Clin Exp Neuropsychol. 2005;27:9771021. Google Scholar CrossRef, Medline
8. Kennedy MR, Coelho C, Turkstra L, et al. Intervention for executive functions after traumatic brain injury: a systematic review, meta-analysis and clinical recommendations. Neuropsychol Rehabil. 2008;18:257299. Google Scholar CrossRef, Medline
9. Chen AJW, D’Esposito M. Traumatic brain injury: from bench to bedside to society. Neuron. 2010;66:1114. Google Scholar CrossRef, Medline
10. Tomaszczyk JC, Green NL, Frasca D, et al. Negative neuroplasticity in chronic traumatic brain injury and implications for neurorehabilitation. Neuropsychol Rev. 2014;24:409427. Google Scholar Medline
11. Chiaravalloti ND, Dobryakova E, Wylie GR, DeLuca J. Examining the efficacy of the modified story memory technique (mSMT) in persons with TBI using functional magnetic resonance imaging (fMRI): the TBI-MEM trial. J Head Trauma Rehabil. 2015;30:261269. Google Scholar CrossRef, Medline
12. Cicerone KD, Dahlberg C, Kalmar K, et al. Evidence-based cognitive rehabilitation: recommendations for clinical practice. Arch Phys Med Rehabil. 2000;81:15961615. Google Scholar CrossRef, Medline
13. Laatsch LK, Thulborn KR, Krisky CM, Shobat DM, Sweeney JA. Investigating the neurobiological basis of cognitive rehabilitation therapy with fMRI. Brain Inj. 2004;18:957974. Google Scholar CrossRef, Medline
14. Lemmens R, Jaspers T, Robberecht W, Thijs VN. Modifying expression of EphA4 and its downstream targets improves functional recovery after stroke. Hum Mol Genet. 2013;22:22142220. Google Scholar CrossRef, Medline
15. Faralli A, Bigoni M, Mauro A, Rossi F, Carulli D. Noninvasive strategies to promote functional recovery after stroke. Neural Plast. 2013;2013:854597. Google Scholar CrossRef, Medline
16. Lorber B, Howe ML, Benowitz LI, Irwin N. Mst3b, an Ste20-like kinase, regulates axon regeneration in mature CNS and PNS pathways. Nat Neurosci. 2009;12:14071414. Google Scholar CrossRef, Medline
17. Benowitz LI, Carmichael ST. Promoting axonal rewiring to improve outcome after stroke. Neurobiol Dis. 2010;37:259266. Google Scholar CrossRef, Medline
18. Chen H, Epstein J, Stern E. Neural plasticity after acquired brain injury: evidence from functional neuroimaging. PM R. 2010;2(12 suppl 2):S306S312. Google Scholar CrossRef, Medline
19. Sacco K, Gabbatore I, Geda E, et al. Rehabilitation of communicative abilities in patients with a history of TBI: behavioral improvements and cerebral changes in resting-state activity. Front Behav Neurosci. 2016;10:48. Google Scholar CrossRef, Medline
20. Cernich AN, Kurtz SM, Mordecai KL, Ryan PB. Cognitive rehabilitation in traumatic brain injury. Curr Treat Options Neurol. 2010;12:412423. Google Scholar CrossRef, Medline
21. Cicerone KD, Langenbahn DM, Braden C, et al. Evidence-based cognitive rehabilitation: updated review of the literature from 2003 through 2008. Arch Phys Med Rehabil. 2011;92:519530. Google Scholar CrossRef, Medline
22. Amen DG, Wu JC, Taylor D, Willeumier K. Reversing brain damage in former NFL players: implications for traumatic brain injury and substance abuse rehabilitation. J Psychoactive Drugs. 2011;43:15. Google Scholar CrossRef, Medline
23. Harch PG, Andrews SR, Fogarty EF, et al. A phase I study of low-pressure hyperbaric oxygen therapy for blast-induced post-concussion syndrome and post-traumatic stress disorder. J Neurotrauma. 2012;29:168185. Google Scholar CrossRef, Medline
24. Irimia A, Van Horn JD. Functional neuroimaging of traumatic brain injury: advances and clinical utility. Neuropsychiatr Dis Treat. 2015;11:23552365. Google Scholar CrossRef, Medline
25. Folmer RL, Billings CJ, Diedesch-Rouse AC, Gallun FJ, Lew HL. Electrophysiological assessments of cognition and sensory processing in TBI: applications for diagnosis, prognosis and rehabilitation. Int J Psychophysiol. 2011;82:415. Google Scholar CrossRef, Medline
26. Dockree PM, Robertson IH. Electrophysiological markers of cognitive deficits in traumatic brain injury: a review. Int J Psychophysiol. 2011;82:5360. Google Scholar CrossRef, Medline
27. Johnstone J, Thatcher RW. Quantitative EEG analysis and rehabilitation issues in mild traumatic brain injury. J Insur Med. 1991;23:228232. Google Scholar Medline
28. Stathopoulou S, Lubar JF. EEG changes in traumatic brain injured patients after cognitive rehabilitation. J Neurother. 2004;8:2151. Google Scholar CrossRef
29. Carter BG, Butt W. Are somatosensory evoked potentials the best predictor of outcome after severe brain injury? A systematic review. Intensive Care Med. 2005;31:765775. Google Scholar CrossRef, Medline
30. Strangman GE, O’Neil-Pirozzi TM, Supelana C, Goldstein R, Katz DI, Glenn MB. Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury. Front Hum Neurosci. 2010;4:182. Google Scholar CrossRef, Medline
31. Strangman GE, O’Neil-Pirozzi TM, Supelana C, Goldstein R, Katz DI, Glenn MB. Fractional anisotropy helps predicts memory rehabilitation outcome after traumatic brain injury. NeuroRehabilitation. 2012;31:295310. Google Scholar Medline
32. Strangman GE, O’Neil-Pirozzi TM, Goldstein R, et al. Prediction of memory rehabilitation outcomes in traumatic brain injury by using functional magnetic resonance imaging. Arch Phys Med Rehabil. 2008;89:974981. Google Scholar CrossRef, Medline
33. Chantsoulis M, Mirski A, Rasmus A, Kropotov JD, Pachalska M. Neuropsychological rehabilitation for traumatic brain injury patients. Ann Agric Environ Med. 2015;22:368379. Google Scholar CrossRef, Medline
34. Krawczyk DC, de la Plata CM, Schauer GF, et al. Evaluating the effectiveness of reasoning training in military and civilian chronic traumatic brain injury patients: study protocol. Trials. 2013;14:1. Google Scholar CrossRef, Medline
35. Arnemann KL, Chen AJ, Novakovic-Agopian T, Gratton C, Nomura EM, D’Esposito M. Functional brain network modularity predicts response to cognitive training after brain injury. Neurology. 2015;84:15681574. Google Scholar CrossRef, Medline
36. Becker F, Reinvang I. Event-related potentials indicate bi-hemispherical changes in speech sound processing during aphasia rehabilitation. J Rehabil Med. 2007;39:658661. Google Scholar CrossRef, Medline
37. Chen AJ, Novakovic-Agopian T, Nycum TJ, et al. Training of goal-directed attention regulation enhances control over neural processing for individuals with brain injury. Brain. 2011;134(pt 5):15411554. Google Scholar CrossRef, Medline
38. Halko MA, Datta A, Plow EB, Scaturro J, Bikson M, Merabet LB. Neuroplastic changes following rehabilitative training correlate with regional electrical field induced with tDCS. Neuroimage. 2011;57:885891. Google Scholar CrossRef, Medline
39. Laatsch L, Thomas J, Sychra J, Lin Q, Blend M. Impact of cognitive rehabilitation therapy on neuropsychological impairments as measured by brain perfusion SPECT: a longitudinal study. Brain Inj. 1997;11:851864. Google Scholar CrossRef, Medline
40. Castellanos NP, Paúl N, Ordóñez VE, et al. Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury. Brain. 2010;133(pt 8):23652381. Google Scholar CrossRef, Medline
41. Munivenkatappa A, Rajeswaran J, Indira Devi B, Bennet N, Upadhyay N. EEG neurofeedback therapy: can it attenuate brain changes in TBI? NeuroRehabilitation. 2014;35:481484. Google Scholar Medline
42. Sacco K, Cauda F, D’Agata F, et al. A combined robotic and cognitive training for locomotor rehabilitation: evidences of cerebral functional reorganization in two chronic traumatic brain injured patients. Front Hum Neurosci. 2011;5:146. Google Scholar CrossRef, Medline
43. Lima FP, Lima MO, Leon D, et al. fMRI of the sensorimotor cortex in patients with traumatic brain injury after intensive rehabilitation. Neurol Sci. 2011;32:633639. Google Scholar CrossRef, Medline
44. Garnett MR, Cadoux-Hudson TA, Styles P. How useful is magnetic resonance imaging in predicting severity and outcome in traumatic brain injury? Curr Opin Neurol. 2001;14:753757. Google Scholar CrossRef, Medline
45. Giaquinto S. Evoked potentials in rehabilitation. A review. Funct Neurol. 2004;19:219225. Google Scholar Medline
46. Muñoz-Cespedes JM, Rios-Lago M, Paul N, Maestu F. Functional neuroimaging studies of cognitive recovery after acquired brain damage in adults. Neuropsychol Rev. 2005;15:169183. Google Scholar CrossRef, Medline
47. Strangman G, O’Neil-Pirozzi TM, Burke D, et al. Functional neuroimaging and cognitive rehabilitation for people with traumatic brain injury. Am J Phys Med Rehabil. 2005;84:6275. Google Scholar CrossRef, Medline
48. Garcia AN, Shah MA, Dixon CE, Wagner AK, Kline AE. Biologic and plastic effects of experimental traumatic brain injury treatment paradigms and their relevance to clinical rehabilitation. PM R. 2011;3(6 suppl 1):S18S27. Google Scholar CrossRef, Medline
49. Marcano-Cedeño A, Chausa P, García A, Cáceres C, Tormos JM, Gómez EJ. Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients. Artif Intell Med. 2013;58:9199. Google Scholar CrossRef, Medline
50. Palacios EM, Sala-Llonch R, Junque C, et al. Resting-state functional magnetic resonance imaging activity and connectivity and cognitive outcome in traumatic brain injury. JAMA Neurol. 2013;70:845851. Google Scholar CrossRef, Medline
51. Hibino S, Mase M, Shirataki T, et al. Oxyhemoglobin changes during cognitive rehabilitation after traumatic brain injury using near infrared spectroscopy. Neurol Medico Chir (Tokyo). 2013;53:299303. Google Scholar CrossRef, Medline
52. Jiang Q. Magnetic resonance imaging and cell-based neurorestorative therapy after brain injury. Neural Regen Res. 2016;11:714. Google Scholar CrossRef, Medline
53. Reid LB, Boyd RN, Cunnington R, Rose SE. Interpreting intervention induced neuroplasticity with fMRI: the case for multimodal imaging strategies. Neural Plast. 2016;2016:2643491. Google Scholar CrossRef, Medline
54. Douglas DB, Iv M, Douglas PK, et al. Diffusion tensor imaging of TBI: potentials and challenges. Top Magn Reson Imaging. 2015;24:241251. Google Scholar CrossRef, Medline
55. Ham TE, Sharp DJ. How can investigation of network function inform rehabilitation after traumatic brain injury? Curr Opin Neurol. 2012;25:662669. Google Scholar CrossRef, Medline
56. Lerner A, Mogensen MA, Kim PE, Shiroishi MS, Hwang DH, Law M. Clinical applications of diffusion tensor imaging. World Neurosurg. 2014;82:96109. Google Scholar CrossRef, Medline
57. Shenton ME, Hamoda HM, Schneiderman JS, et al. A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav. 2012;6:137192. Google Scholar CrossRef, Medline
58. Strauss S, Hulkower M, Gulko E, et al. Current clinical applications and future potential of diffusion tensor imaging in traumatic brain injury. Top Magn Reson Imaging. 2015;24:353362. Google Scholar CrossRef, Medline
59. Sherer M, Stouter J, Hart T, et al. Computed tomography findings and early cognitive outcome after traumatic brain injury. Brain Inj. 2006;20:9971005. Google Scholar CrossRef, Medline
60. Sidaros A, Engberg AW, Sidaros K, et al. Diffusion tensor imaging during recovery from severe traumatic brain injury and relation to clinical outcome: a longitudinal study. Brain. 2008;131(pt 2):559572. Google Scholar CrossRef, Medline
61. Caglio M, Latini-Corazzini L, D’Agata F, et al. Virtual navigation for memory rehabilitation in a traumatic brain injured patient. Neurocase. 2012;18:123131. Google Scholar CrossRef, Medline
62. Laatsch L, Krisky C. Changes in fMRI activation following rehabilitation of reading and visual processing deficits in subjects with traumatic brain injury. Brain Inj. 2006;20:13671375. Google Scholar CrossRef, Medline
63. Kim YH, Yoo WK, Ko MH, Park CH, Kim ST, Na DL. Plasticity of the attentional network after brain injury and cognitive rehabilitation. Neurorehabil Neural Repair. 2009;23:468477. Google Scholar Link
64. Sacco K, Galetto V, Dimitri D, et al. Concomitant use of transcranial direct current stimulation and computer-assisted training for the rehabilitation of attention in traumatic brain injured patients: behavioral and neuroimaging results. Front Behav Neurosci. 2016;10:57. Google Scholar CrossRef, Medline
65. Musiek FE, Baran JA, Shinn J. Assessment and remediation of an auditory processing disorder associated with head trauma. J Am Acad Audiol. 2004;15:117132. Google Scholar CrossRef, Medline
66. Pachalska M, Łukowicz M, Kropotov JD, Herman-Sucharska I, Talar J. Evaluation of differentiated neurotherapy programs for a patient after severe TBI and long term coma using event-related potentials. Med Sci Monit. 2011;17:CS120CS128. Google Scholar CrossRef, Medline
67. Dundon NM, Dockree SP, Buckley V, et al. Impaired auditory selective attention ameliorated by cognitive training with graded exposure to noise in patients with traumatic brain injury. Neuropsychologia. 2015;75:7487. Google Scholar CrossRef, Medline
68. Nebel K, Wiese H, Stude P, de Greiff A, Diener HC, Keidel M. On the neural basis of focused and divided attention. Brain Res Cogn Brain Res. 2005;25:760776. Google Scholar CrossRef, Medline
69. Snyder SM, Hall JR. A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. J Clin Neurophysiol. 2006;23:440455. Google Scholar CrossRef, Medline
70. Barcelò F, Sanz M, Molina V, Rubia FJ. The Wisconsin Card Sorting Test and the assessment of frontal function: a validation study with event-related potentials. Neuropsychologia. 1997;35:399408. Google Scholar CrossRef, Medline
71. Barcelò F, Rubia FJ. Non-frontal P3b-like activity evoked by the Wisconsin card sorting test. Neuroreport. 1998;9:747751. Google Scholar CrossRef, Medline
72. Squire LR, Stark CE, Clark RE. The medial temporal lobe. Annu Rev Neurosci. 2004;27:279306. Google Scholar CrossRef, Medline
73. Coelho CA, Liles BZ, Duffy RJ. Impairments of discourse abilities and executive functions in traumatically brain-injured adults. Brain Inj. 1995;9:471477. Google Scholar CrossRef, Medline
74. Gabbatore I, Sacco K, Angeleri R, Zettin M, Bara BG, Bosco FM. Cognitive pragmatic treatment: a rehabilitative program for traumatic brain injury individuals. J Head Trauma Rehabil. 2015;30:E14E28. Google Scholar CrossRef, Medline
75. Duncan CC, Barry RJ, Connolly JF, et al. Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clin Neurophysiol. 2009;120:18831908. Google Scholar CrossRef, Medline
76. Rasmussen IA, Xu J, Antonsen IK, et al. Simple dual tasking recruits prefrontal cortices in chronic severe traumatic brain injury patients, but not in controls. J Neurotrauma. 2008;25:10571070. Google Scholar CrossRef, Medline
77. Mahncke HW, Connor BB, Appelman J, et al. Memory enhancement in healthy older adults using a brain plasticity-based training program: a randomized, controlled study. Proc Natl Acad Sci U S A. 2006;103:1252312528. Google Scholar CrossRef, Medline
78. Kim J, Whyte J, Patel S, et al. A perfusion fMRI study of the neural correlates of sustained-attention and working-memory deficits in chronic traumatic brain injury. Neurorehabil Neural Repair. 2012;26:870880. Google Scholar Link
79. Bryer EJ, Medaglia JD, Rostami S, Hillary FG. Neural recruitment after mild traumatic brain injury is task dependent: a meta-analysis. J Int Neuropsychol Soc. 2013;19:751762. Google Scholar CrossRef, Medline
80. Kleim JA, Jones TA, Schallert T. Motor enrichment and the induction of plasticity before or after brain injury. Neurochem Res. 2003;28:17571769. Google Scholar CrossRef, Medline
81. Friston KJ, Price CJ. Dynamic representations and generative models of brain function. Brain Res Bull. 2001;54:275285. Google Scholar CrossRef, Medline
82. Christodoulou C, DeLuca J, Ricker J, et al. Functional magnetic resonance imaging of working memory impairment after traumatic brain injury. J Neurol Neurosurg Psychiatry. 2001;71:161168. Google Scholar CrossRef, Medline
83. Sanchez-Carrion R, Fernandez-Espejo D, Junque C, et al. A longitudinal fMRI study of working memory in severe TBI patients with diffuse axonal injury. Neuroimage. 2008;43:421429. Google Scholar CrossRef, Medline
84. Sánchez-Carrión R, Gómez PV, Junqué C, et al. Frontal hypoactivation on functional magnetic resonance imaging in working memory after severe diffuse traumatic brain injury. J Neurotrauma. 2008;25:479494. Google Scholar CrossRef, Medline
85. McAllister AK, Katz LC, Lo DC. Neurotrophins and synaptic plasticity. Annu Rev Neurosci. 1999;22:295318. Google Scholar CrossRef, Medline
86. McAllister TW, Sparling MB, Flashman LA, Saykin AJ. Neuroimaging findings in mild traumatic brain injury. J Clin Exp Neuropsychol. 2001;23:775791. Google Scholar CrossRef, Medline
87. Scheibel RS, Newsome MR, Troyanskaya M, et al. Effects of severity of traumatic brain injury and brain reserve on cognitive-control related brain activation. J Neurotrauma. 2009;26:14471461. Google Scholar CrossRef, Medline
88. Turner GR, Levine B. Augmented neural activity during executive control processing following diffuse axonal injury. Neurology. 2008;71:812818. Google Scholar CrossRef, Medline
89. Turner GR, McIntosh AR, Levine B. Prefrontal compensatory engagement in TBI is due to altered functional engagement of existing networks and not functional reorganization. Front Syst Neurosci. 2011;5:9. Google Scholar CrossRef, Medline
90. Zhou Y, Milham MP, Lui YW, et al. Default-mode network disruption in mild traumatic brain injury. Radiology. 2012;265:882892. Google Scholar CrossRef, Medline
91. Sharp DJ, Scott G, Leech R. Network dysfunction after traumatic brain injury. Nat Rev Neurol. 2014;10:156166. Google Scholar CrossRef, Medline
92. Pandit AS, Expert P, Lambiotte R, et al. Traumatic brain injury impairs small-world topology. Neurology. 2013;80:18261833. Google Scholar CrossRef, Medline
93. Fork M, Bartels C, Ebert AD, Grubich C, Synowitz H, Wallesch CW. Neuropsychological sequelae of diffuse traumatic brain injury. Brain Inj. 2005;19:101108. Google Scholar CrossRef, Medline
94. Wallesch CW, Curio N, Kutz S, Jost S, Bartels C, Synowitz H. Outcome after mild-to-moderate blunt head injury: effects of focal lesions and diffuse axonal injury. Brain Inj. 2001;15:401412. Google Scholar CrossRef, Medline

Source: Neuroplastic Changes Induced by Cognitive Rehabilitation in Traumatic Brain Injury: A ReviewNeurorehabilitation and Neural Repair – Valentina Galetto, Katiuscia Sacco, 2017

, , , , , ,

Leave a comment

[ARTICLE] Neurofeedback as a form of cognitive rehabilitation therapy following stroke: A systematic review – Full Text

Neurofeedback therapy (NFT) has been used within a number of populations however it has not been applied or thoroughly examined as a form of cognitive rehabilitation within a stroke population.

Objectives for this systematic review included:

  • i) identifying how NFT is utilized to treat cognitive deficits following stroke,
  • ii) examining the strength and quality of evidence to support the use of NFT as a form of cognitive rehabilitation therapy (CRT) and
  • iii) providing recommendations for future investigations.

Searches were conducted using OVID (Medline, Health Star, Embase + Embase Classic) and PubMed databases. Additional searches were completed using the Cochrane Reviews library database, Google Scholar, the University of Toronto online library catalogue, ClinicalTrials.gov website and select journals. Searches were completed Feb/March 2015 and updated in June/July/Aug 2015. Eight studies were eligible for inclusion in this review.

Studies were eligible for inclusion if they:

  • i) were specific to a stroke population,
  • ii) delivered CRT via a NFT protocol,
  • iii) included participants who were affected by a cognitive deficit(s) following stroke (i.e. memory loss, loss of executive function, speech impairment etc.).

NFT protocols were highly specific and varied within each study. The majority of studies identified improvements in participant cognitive deficits following the initiation of therapy. Reviewers assessed study quality using the Downs and Black Checklist for Measuring Study Quality tool; limited study quality and strength of evidence restricted generalizability of conclusions regarding the use of this therapy to the greater stroke population.

Progression in this field requires further inquiry to strengthen methodology quality and study design. Future investigations should aim to standardize NFT protocols in an effort to understand the dose-response relationship between NFT and improvements in functional outcome. Future investigations should also place a large emphasis on long-term participant follow-up.

Introduction

In 2011, stroke was identified as the third leading cause of death among Canadians (5.5%, 13 283 deaths), and considered to be the leading cause of neurological disability in Canadian adults [12]. Although stroke occurrence is most common in individuals aged 70 and older, stroke incidence for individuals over the age of 50 has increased by 24% and 13% in individuals over the age of 60, in the last decade [3]. Following a stroke, patients typically enter rehabilitation programs (i.e. physical therapy, occupational therapy, etc.) to address a multitude of physical, emotional and cognitive deficits [45]. Many rehabilitation interventions initiated following stroke primarily target functional motor impairments. In reviewing the literature, few investigations have been published that aim to target cognitive deficits, despite 40% of stroke survivors experiencing a decline in cognitive function (especially memory) following stroke [6].

The brain is a highly complex and organized organ therefore the extent of impairment and deficits that follow stroke are largely dependent on lesion severity and location [7]. Physiologically these impairments are a result of the loss of neuronal circuits and connections linked to the relevant sensory, motor, and cognitive functions [89]. Furthermore, it is thought that the neurological recovery that occurs following a stroke is a direct result of brain plasticity and it’s ability to repair and reorganize [10]. Some evidence exists for the initiation of reparative functions in the brain in as little as a few hours following a stroke [1112]. In respect to recovery trajectories following stroke, ninety-five percent of stroke patients reach their peak language recovery within 6 weeks of a stroke, and within 3 months for hemispatial neglect [1314]. Deficits that do not spontaneously resolve contribute to the large number of individuals requiring long term care following stroke (i.e. rehabilitative therapy) [1516]. Occupational and physical rehabilitation programs target functional and mobility issues however, in addition to these impairments patients experience a wide range of cognitive and neurological deficits. Individuals with impairments of this nature often require cognitive rehabilitation therapy (CRT).

CRT encompasses any intervention targeting the restoration, remediation and adaptation of cognitive functions including: attention, concentration, memory, comprehension, communication, reasoning, problem solving, planning, initiation, judgement, self-monitoring and awareness [17]. CRT can be offered in a variety of settings such as rehabilitation hospitals, community care facilities, private residences as well as the workplace [18]. Although cognitive therapy has been around since the early 19th century, the 1970’s marked the most recent biofeedback movement in CRT [18]. Traditionally used to treat muscular impairments (via electromyography (EMG) feedback) biofeedback has taken on a new form known as neurofeedback therapy (NFT). NFT targets the brain and cognitive functions through the use of electroencephalography (EEG), hence neurofeedback is sometimes referred to as EEG biofeedback [19]. In classical NFT, EEG and brainwave activity is provided as a visual or auditory cue to the user [6]. Using these cues the user can consciously adapt their brainwave activity to reach targeted training thresholds. NFT relies on operant conditioning to stimulate the neuroplastic abilities of the brain [2021]. Physiologically stimulating specific band frequencies over damaged areas stimulates cortical metabolism [19]. NFT is also used to counter excessive slow wave activity (i.e. theta waves and sometimes alpha waves) that typically follow stroke [21]. An alternative form of NFT known as nonlinear dynamical neurofeedback has also been used to restore homeostasis to the brain. This form of NFT requires no conscious effort from the participant to adapt their brainwaves in any particular direction (i.e. the participant maintains a passive role). Modalities like NeurOptimal® utilize Functional Targeting to provide the brain with “… information about itself which allows the brain to assemble it’s own, best organizing strategies moment by moment” [22]. In the context of this review, the studies included herein concern the use of classical NFT only.

To date, NFT has been used extensively to treat cognitive deficits associated with other neurological disorders and illnesses including: mild traumatic brain injury [23], ADD/ADHD [24], Epilepsy [25], Autism Spectrum Disorders [2627], Dyslexia [28], Fibromyalgia [29], Depression [30], and opiate additions [31]. Despite promising NFT outcomes within these populations, NFT has not been thoroughly evaluated for use in a stroke population. The aim of this systematic review was to thoroughly evaluate the available evidence pertinent to understanding the effectiveness of NFT as a form of CRT following stroke. To achieve this objective a number of research questions were established to guide this review:

  1. Among a stroke population, how is NFT utilized to treat cognitive deficits?
  2. Among identified NFT interventions targeting a stroke population, what is the quality and strength of evidence to support the use of NFT as a form of CRT following stroke?
  3. Based on the available NFT evidence for use in stroke populations, what recommendations can be made for future research?

 

The primary outcome of interest in this review was to identify if cognitive symptom complaints could be ameliorated following the initiation of NFT in a sub-acute and chronic post-stroke population. Secondary outcomes aimed to assess study quality, methodology and strength of support for use of NFT in this population.

Continue —> Neurofeedback as a form of cognitive rehabilitation therapy following stroke: A systematic review

Fig 1. PRISMA flow diagram.

, , ,

Leave a comment

[WEB PAGE] What Is PTSD? – PTSD: National Center for PTSD

What Is PTSD?

PTSD (posttraumatic stress disorder) is a mental health problem that some people develop after experiencing or witnessing a life-threatening event, like combat, a natural disaster, a car accident, or sexual assault.

It’s normal to have upsetting memories, feel on edge, or have trouble sleeping after this type of event. At first, it may be hard to do normal daily activities, like go to work, go to school, or spend time with people you care about. But most people start to feel better after a few weeks or months.

If it’s been longer than a few months and you’re still having symptoms, you may have PTSD. For some people, PTSD symptoms may start later on, or they may come and go over time.

What factors affect who develops PTSD?

PTSD can happen to anyone. It is not a sign of weakness. A number of factors can increase the chance that someone will have PTSD, many of which are not under that person’s control. For example, having a very intense or long-lasting traumatic event or getting injured during the event can make it more likely that a person will develop PTSD. PTSD is also more common after certain types of trauma, like combat and sexual assault.

Personal factors, like previous traumatic exposure, age, and gender, can affect whether or not a person will develop PTSD. What happens after the traumatic event is also important. Stress can make PTSD more likely, while social support can make it less likely.

What are the symptoms of PTSD?

PTSD symptoms usually start soon after the traumatic event, but they may not appear until months or years later. They also may come and go over many years. If the symptoms last longer than four weeks, cause you great distress, or interfere with your work or home life, you might have PTSD.

There are four types of symptoms of PTSD (en Español), but they may not be exactly the same for everyone. Each person experiences symptoms in their own way.

  1. Reliving the event (also called re-experiencing symptoms). You may have bad memories or nightmares. You even may feel like you’re going through the event again. This is called a flashback.
  2. Avoiding situations that remind you of the event. You may try to avoid situations or people that trigger memories of the traumatic event. You may even avoid talking or thinking about the event.
  3. Having more negative beliefs and feelings. The way you think about yourself and others may change because of the trauma. You may feel guilt or shame. Or, you may not be interested in activities you used to enjoy. You may feel that the world is dangerous and you can’t trust anyone. You might be numb, or find it hard to feel happy.
  4. Feeling keyed up (also called hyperarousal). You may be jittery, or always alert and on the lookout for danger. Or, you may have trouble concentrating or sleeping. You might suddenly get angry or irritable, startle easily, or act in unhealthy ways (like smoking, using drugs and alcohol, or driving recklessly.

Can children have PTSD?

Children can have PTSD too. They may have symptoms described above or other symptoms depending on how old they are. As children get older, their symptoms are more like those of adults. Here are some examples of PTSD symptoms in children:

  • Children under 6 may get upset if their parents are not close by, have trouble sleeping, or act out the trauma through play.
  • Children age 7 to 11 may also act out the trauma through play, drawings, or stories. Some have nightmares or become more irritable or aggressive. They may also want to avoid school or have trouble with schoolwork or friends.
  • Children age 12 to 18 have symptoms more similar to adults: depression, anxiety, withdrawal, or reckless behavior like substance abuse or running away.

What other problems do people with PTSD experience?

People with PTSD may also have other problems. These include:

  • Feelings of hopelessness, shame, or despair
  • Depression or anxiety
  • Drinking or drug problems
  • Physical symptoms or chronic pain
  • Employment problems
  • Relationship problems, including divorce

In many cases, treatments for PTSD will also help these other problems, because they are often related. The coping skills you learn in treatment can work for PTSD and these related problems.

Will people with PTSD get better?

“Getting better” means different things for different people. There are many different treatment options for PTSD. For many people, these treatments can get rid of symptoms altogether. Others find they have fewer symptoms or feel that their symptoms are less intense. Your symptoms don’t have to interfere with your everyday activities, work, and relationships.

What treatments are available?

There are two main types of treatment, psychotherapy (sometimes called counseling or talk therapy) and medication. Sometimes people combine psychotherapy and medication.

Psychotherapy for PTSD

Psychotherapy, or counseling, involves meeting with a therapist. There are different types of psychotherapy:

  • Cognitive behavioral therapy (CBT) is the most effective treatment for PTSD. There are different types of CBT, such as cognitive therapy and exposure therapy.
    • One type is Cognitive Processing Therapy (CPT) where you learn skills to understand how trauma changed your thoughts and feelings. Changing how you think about the trauma can change how you feel.
    • Another type is Prolonged Exposure (PE) where you talk about your trauma repeatedly until memories are no longer upsetting. This will help you get more control over your thoughts and feelings about the trauma. You also go to places or do things that are safe, but that you have been staying away from because they remind you of the trauma.
  • A similar kind of therapy is called Eye Movement Desensitization and Reprocessing (EMDR), which involves focusing on sounds or hand movements while you talk about the trauma. This helps your brain work through the traumatic memories.

Medications for PTSD

Medications can be effective too. SSRIs (selective serotonin reuptake inhibitors) and SNRIs (serotonin-norepinephrine reuptake inhibitors), which are also used for depression, are effective for PTSD. Another medication called Prazosin has been found to be helpful in decreasing nightmares related to the trauma.

IMPORTANT: Benzodiazepines and atypical antipsychotics should generally be avoided for PTSD treatment because they do not treat the core PTSD symptoms and can be addictive.

Visit Site —> What Is PTSD? – PTSD: National Center for PTSD

, , , , , , , , , ,

Leave a comment

[BLOG POST] Driving After Stroke: Is it Safe? – Saebo

After having a stroke, many survivors are eager to start driving again. Driving offers independence and the ability to go where you want to go on your own schedule, so it is no surprise that survivors want to get back behind the wheel rather than rely on someone else for their transportation needs.

Unfortunately, having a stroke can have lasting effects that make driving more difficult. A survivor might not be aware of all of the effects of their stroke and could misjudge their ability to drive safely. Driving against a doctor’s orders after a stroke is not only dangerous, it may even be illegal. Many stroke survivors successfully regain their ability to safely drive after a stroke, but it is important that they do not attempt to drive until they are cleared by their healthcare provider.

 

How Stroke Affects the Ability to Drive

Having a stroke can affect an individual’s ability to drive in numerous ways, whether it be because of physical challenges, cognitive changes, or other challenges.

 

Physical Challenges

Physical-Challenges

After a stroke, it’s common to experience weakness or paralysis on one side of the body, depending on which side of the brain the stroke occurred. More than half of all stroke survivors also experience post-stroke pain. Minor physical challenges may be overcome with adaptive driving equipment, but severe challenges like paralysis or contracture can seriously affect an individual’s ability to drive.

 

Cognitive Effects

cognitive

Driving requires a combination of cognitive skills, including memory, concentration, problem solving, judgement, multitasking, and the ability to make quick decisions. A stroke can cause cognitive changes that limit the ability to do many of those things.

 

Vision Problems

vision

As many as two-thirds of stroke victims experience vision impairments as a result of a stroke. This can include vision loss, blurred vision, and visual processing problems. Stroke survivors with vision problems should not drive until their problems are resolved and they have been cleared by a doctor.

 

Fatigue

fatigue

Fatigue is a common physical condition after a stroke that affects between 40 and 70 percent of stroke survivors. Fatigue can arrive without warning, so it is dangerous to drive when suffering from post-stroke fatigue.

 

Warning Signs of Unsafe Driving

 

Stroke survivors are not always aware of how their stroke has limited their ability to drive. If they are choosing to drive after their stroke against their doctor’s advice, it is important for them and their loved ones to look out for warning signs that they might not be ready to start driving. Here are some of the common warning signs to look out for:

  • Driving faster or slower than the posted speed or the wrong speed for the current driving conditions
  • Consistently asking for instruction and help from passengers
  • Ignoring posted signs or signals
  • Making slow or poor decisions
  • Becoming easily frustrated or confused
  • Getting lost in familiar areas
  • Being in an accident or having close calls
  • Drifting into other lanes

 

If you or your loved one is showing any of these warning signs, immediately stop yourself or them from driving until your or their driving is tested.

 

Driving Again After a Stroke

Before a stroke survivor begins driving again, they should speak with their doctor or therapist to discuss whether or not it would be safe for them to continue driving. Many states require mandatory reporting by a physician to the DMV if their patient has impairments that may affect their driving after a stroke. Even if their doctor clears them to drive, they still will likely need to be evaluated by the DMV before they regain their driving privileges.

 

Driver rehabilitation specialists are available to help stroke survivors evaluate their driving ability from behind the wheel. There are also driver’s training programs that provide a driving evaluation, classroom instruction, and suggestions for modifying a car to the individual driver’s needs. For instance, an occupational therapist can provide a comprehensive in-clinic evaluation of a client’s current skills and deficits relative to driving.

 

From there a client could be sent for an in-vehicle assessment for further evaluation by a certified driver rehabilitation specialist (CDRS). They can assess driving skills in a controlled and safe environment. An in-vehicle driving test is the most thorough way to gauge a driver’s abilities. Each assessment takes about 1 hour and involves driving with a trained evaluator or driving in a computer simulator.

 

The “behind-the-wheel” evaluation will include testing for changes in key performance areas such as attention, memory, vision, reaction time, and coordination. After this assessment the CDRS can determine if the client is safe to drive, can not drive at all, or may drive with additional recommendations.

 

Often times clients may require certain modifications to their car in order to drive safely. In addition, some clients may benefit from on-going classroom training and simulation training in order to meet safety standards. These are all services that a driver rehabilitation specialist can provide. To help find these resources, The Association for Driver Rehabilitation Specialists has a directory of certified driver rehabilitation specialists, driver rehabilitation specialists, and mobility equipment dealers and manufacturers.

 

Get Back Behind the Wheel

Many stroke survivors successfully drive after a stroke; however, not all are able to. While reclaiming independence is important, staying safe is the greatest concern. It is important for stroke survivors to listen to their doctors and wait until they are fully ready before attempting to drive again. With some hard work and patience, getting back behind the wheel is possible.

 


All content provided on this blog is for informational purposes only and is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. If you think you may have a medical emergency, call your doctor or 911 immediately. Reliance on any information provided by the Saebo website is solely at your own risk.

Source: Driving After Stroke: Is it Safe? | Saebo

, , , , ,

Leave a comment

%d bloggers like this: