Reviewed by: Stephen Halpin, Senior Clinical Research Fellow and Consultant in Rehabilitation Medicine, Academic Department of Rehabilitation Medicine, University of Leeds.
In the best tradition of Oxford Handbooks, this is a small book that packs a heavy punch. This third edition represents a significant expansion in scope and detail compared to the second edition of 2009. With over 650 pages miraculously compressed into less than two and a half centimetres, this edition adeptly fulfils its role in providing for jobbing clinicians a succinct, easily navigable overview of key clinical topics at their fingertips.
The volume is structured in two sections, the first of which ‘Common Clinical Approaches’ provides 25 chapters on cross-cutting areas from Communication, to Chronic Pain, Sexual Function and Mobility and Gait. Section 2 addresses ‘Condition-Specific Approaches’ in 16 chapters including Traumatic Brain Injury, Multiple Sclerosis, Prolonged Disorders of Consciousness and Amputee Rehabilitation. Where there is overlap, this is usefully signposted within the text, directing the reader to other relevant chapters. The text is helpfully presented, easy to scan and interspersed with many useful illustrations and diagrams.
The devotion of two chapters to musculoskeletal conditions, as well as the inclusion of Cancer Rehabilitation and Geriatric Rehabilitation reflects the editors’ timely desire to see the focus of rehabilitation as a medical specialty in the UK broaden to address conditions of greatest population burden. They will also be useful to rehabilitation practitioners in the traditional areas of neurological and spinal cord rehabilitation who find themselves working with increasingly complex conditions and co-morbidities.
Between the second and third editions the title has shifted from Clinical Rehabilitation to Rehabilitation Medicine, which is indicative of a change of emphasis towards greater rigorous pathophysiological detail and is accompanied by an authoritative, brisk editorial style. Medics, from students and junior doctors to specialists in Rehabilitation Medicine (but also Geriatrics, Neurology, Stroke Medicine and beyond) will be the main users of this handbook, but it also has much to offer to the whole multi-professional rehabilitation team.
The chapter authors are largely drawn from the UK and Australia, and sections on models of care and organisation of services reflect those settings. Inevitably, some details have already been superseded by new guidelines, for example that in TIA risk stratification, but in general this edition does an excellent job of succinctly bringing the reader up to date, and signposting further reading.
In its main aim of concisely presenting both the core principles and practical clinical details of Rehabilitation Medicine practice across an expanded scope of conditions, this handbook has certainly succeeded and it will undoubtedly become a familiar sight in MDT rooms and doctors’ offices across the rehabilitation landscape.
Fatigue after traumatic brain injury (TBI) is common, but often overlooked. But for people fighting their fatigue after brain injury day after day, fatigue is a major problem. This post-injury mental fatigue is characterized by limited energy reserves to accomplish ordinary daily activities. Persons who have not experienced this extreme exhaustion which may appear suddenly, and without previous warning during mental activity, do not understand the problem. This is especially difficult to understand as the fatigue may appear even after seemingly trivial mental activities which, for uninjured persons, are regarded as relaxing and pleasant, as reading a book or having a conversation with friends. A normal, well-functioning, brain performs mental activities simultaneously throughout the day, but after a brain injury, it takes greater energy levels to deal with cognitive and emotional situations.
In this chapter, we highlight mental fatigue after TBI. In the case of long-lasting mental fatigue, it could be the only factor that keeps people from returning to the full range of activities that they pursued prior to their injury with work, studies and social activities. We describe mental fatigue and suggest diagnostic criteria and we also give a theoretical explanation for this. At the end of the chapter, we discuss treatment strategies and give some examples of possible therapeutic alternatives which may alleviate the mental fatigue.
Normally, the brain works in an energy-efficient manner and prominent energy reserves are present. This is due to well-functioning ion channel and amino acid transport systems and other effective physiological processes. After brain injury, some of these systems are down-regulated, and when mental energy requirements are high the physiological processes do not function to their full capacity; these cease to function efficiently with a resultant energy loss. This may be an explanation as to why the mental fatigue appears.
1.1. When does mental fatigue occur?
Annually, about 100-300/100 000 individuals sustain a TBI, and most of the injuries are mild in severity . A majority of patients recover within one to three months following mild TBI [2, 3].
Fatigue is one of the most important long-lasting symptoms following TBI, and is most severe immediately after head injury. However it is difficult to arrive at any clear figure as to how common fatigue or, in particular, mental fatigue is. The reason for this is that different results have been obtained, and these are attributable to differences in definitions and differences in the methodology in the various studies. In follow-up studies, the frequency of prolonged fatigue varies from 16 up to 73 % [4–6]. There is no correlation between persistent fatigue and severity of the primary injury, age of the person at injury or time since injury [7, 8]. For those suffering from fatigue 3 months after the accident the fatigue remained relatively stable during longer periods . In particular, for those subjects who were suffering from the syndrome one year after the accident improvement in the fatigue was limited .
In the above reports, fatigue is discussed in terms of a single construct, i.e. not differentiated between the physical or mental aspects. In this chapter, we consider mental fatigue as a separate construct and we discuss its relationship to cognitive and emotional symptoms.
1.2. Mental fatigue is not a separate diagnostic entity
Mental fatigue is not an illness, rather it represents a mental sequel, probably due to a disturbance of higher brain functions, either physical or psychological in origin. It is included in, and defined within the diagnoses Mild cognitive impairment (F06.7), Neurasthenia (F48.0) and Posttraumatic brain syndrome (F07.2) .
1.3. Typical characteristics of mental fatigue
A typical characteristic of pathological mental fatigue after TBI is that the mental exhaustion becomes pronounced during sensory stimulation or when cognitive tasks are performed for extended periods without breaks. There is a drain of mental energy upon mental activity in situations in which there is an invasion of the senses with an overload of impressions, and in noisy and hectic environments. The person feels that their brain is overloaded after a tiny load. Another typical feature is a disproportionally long recovery time needed to restore the mental energy levels after being mentally exhausted. The mental fatigue is also dependent on the total activity level as well as the nature of the demands of daily activities. Fatigue often fluctuates during the day depending on the activities carried out. Thus, this fatigue is a dynamic process with variations in the mental energy level. The fatigue can appear very rapidly and, when it does, it is not possible for the affected person to continue the ongoing activity. Common associated symptoms include: impaired memory and concentration capacity, slowness of thinking, irritability, tearfulness, sound and light sensitivity, sensitivity to stress, sleep problems, lack of initiative and headache .
For many persons, this mental fatigue is the dominating factor which limits the person’s ability to lead a normal life with work and social activities. For most people, fatigue subsides after a period of time while, for others, this pathological fatigue persists for several months or years even after the brain injury has healed. Interestingly, however is that as many as 30% of family or friends interpreted fatigue as laziness .
Theories as to the mechanisms accounting for mental fatigue including our own theory, suggest that cognitive activities require more resources and are more energy-demanding after brain injury than usual [13, 14]. Thus, more extensive neural circuits are used in TBI victims compared to controls during a given mental activity . This indicates an increased cerebral effort after brain injury.
Therapist Luann Jacobs describes mild TBI and the lack of energy and lack of endurance that many can experience. As they are able to do what is normal and what appears normal, they run the risk that their symptoms will be misunderstood .
“Mild brain injury is a real misnomer, as it conveys the idea that nothing much is a problem when quite the opposite is more often true. It is called “mild” because, in fact, the mildly brain injured can walk, talk, eat and dress independently, often times drive a car, shop, cook, go to school, or even work.
What the term fails to account for is the inherent limits of how often, for how long (endurance), and the all-important, how consistently (e.g., every day, once a week) these activities can be performed. Even more elusive is the concept of how many of these daily activities can be done sequentially in a given day as is normal in the lives of people who are not brain injured.
The fatigue they feel defies description, going far beyond and far deeper than anything a non-brain-injured person would consider profound exhaustion.”
Outpatient therapy providers are under tremendous pressure to improve patient outcomes while at the same time lowering the cost of care. Although much of the focus around value-based care has been on driving clinician performance to meet key measures, it is just as crucial to look at ways to improve patient engagement as you evaluate strategies for the future. After all, studies show that engaged patients lead to better overall outcomes.
Patient engagement, however, is also one of the toughest areas to manage. While therapy is a critical component in the healing process, many patients are dealing with an injury or condition that will require weeks, if not months, of therapy. This can impact a patient’s motivation and willingness to follow their treatment through to the end. The important question to ask is “How can I successfully motivate my patients every step of the way?”
Download this free E-book from Net Health to learn four ways that you can motivate patients to stay active in their recovery so you can improve quality, generate better outcomes, AND boost clinic performance.
Many pharmacological treatments were proved effective in the treatment of panic disorder (PD), generalized anxiety disorder (GAD), social anxiety disorder (SAD), post-traumatic stress disorder (PTSD), and obsessive-compulsive disorder (OCD); still many patients do not achieve remission with these treatments. Neurostimulation techniques have been studied as promising alternatives or augmentation treatments to pharmacological and psychological therapies. The most studied neurostimulation method for anxiety disorders, PTSD, and OCD was repetitive transcranial magnetic stimulation (rTMS). This neurostimulation technique had the highest level of evidence for GAD. There were also randomized sham-controlled trials indicating that rTMS may be effective in the treatment of PTSD and OCD, but there were conflicting findings regarding these two disorders. There is indication that rTMS may be effective in the treatment of panic disorder, but the level of evidence is low. Deep brain stimulation (DBS) was most studied for treatment of OCD, but the randomized sham-controlled trials had mixed findings. Preliminary findings indicate that DBS could be affective for PTSD. There is weak evidence indicating that electroconvulsive therapy, transcranial direct current stimulation, vagus nerve stimulation, and trigeminal nerve stimulation could be effective in the treatment of anxiety disorders, PTSD, and OCD. Regarding these disorders, there is no support in the current literature for the use of neurostimulation in clinical practice. Large high-quality studies are warranted.
Zugliani MM, Cabo MC, Nardi AE, Perna G, Freire RC. Pharmacological and neuromodulatory treatments for panic disorder: clinical trials from 2010 to 2018. Psychiatry Investig. 2019;16(1):50–8.PubMedPubMedCentralCrossRefGoogle Scholar
Freire RC, Nardi AE. The effect of neurostimulation in depression. In: Kim YK, editor. Understanding depression: contemporary issues, vol. 1. Singapore: Springer Singapore; 2018. p. 177–87.CrossRefGoogle Scholar
Freire RC, Cirillo PC, Nardi AE. Clinical application of neurostimulation in depression. In: Kim YK, editor. Understanding depression: contemporary issues, vol. 2. Singapore: Springer Singapore; 2018. p. 271–82.Google Scholar
Li H, Wang J, Li C, Xiao Z. Repetitive transcranial magnetic stimulation (rTMS) for panic disorder in adults. Cochrane Database Syst Rev. 2014;9:CD009083.Google Scholar
Koek RJ, Roach J, Athanasiou N, Van ‘t Wout-Frank M, Philip NS. Neuromodulatory treatments for post-traumatic stress disorder (PTSD). Prog Neuro-Psychopharmacol Biol Psychiatry. 2019;92:148–60.CrossRefGoogle Scholar
D’Urso G, Mantovani A, Patti S, Toscano E, de Bartolomeis A. Transcranial direct current stimulation in obsessive-compulsive disorder, posttraumatic stress disorder, and anxiety disorders. J ECT. 2018;34(3):172–81.PubMedCrossRefPubMedCentralGoogle Scholar
Milev RV, Giacobbe P, Kennedy SH, Blumberger DM, Daskalakis ZJ, Downar J, et al. Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the Management of Adults with major depressive disorder: section 4. Neurostimul Treat Can J Psychiat. 2016;61(9):561–75.CrossRefGoogle Scholar
Cirillo PC, Gold AK, Nardi AE, Ornelas AC, Nierenberg AA, Campodron J, et al. Transcranial magnetic stimulation in anxiety and trauma-related disorders: a systematic review and meta-analysis. Brain Behav. 2019; https://doi.org/10.1002/brb3.1284.
Diefenbach GJ, Bragdon LB, Zertuche L, Hyatt CJ, Hallion LS, Tolin DF, et al. Repetitive transcranial magnetic stimulation for generalised anxiety disorder: a pilot randomised, double-blind, sham-controlled trial. Br J Psychiatry. 2016;209(3):222–8.PubMedCrossRefPubMedCentralGoogle Scholar
Dilkov D, Hawken ER, Kaludiev E, Milev R. Repetitive transcranial magnetic stimulation of the right dorsal lateral prefrontal cortex in the treatment of generalized anxiety disorder: a randomized, double-blind sham controlled clinical trial. Prog Neuro-Psychopharmacol Biol Psychiatry. 2017;78:61–5.CrossRefGoogle Scholar
White D, Tavakoli S. Repetitive transcranial magnetic stimulation for treatment of major depressive disorder with comorbid generalized anxiety disorder. Ann Clin Psychiatry. 2015;27(3):192–6.PubMedPubMedCentralGoogle Scholar
Bystritsky A, Kaplan JT, Feusner JD, Kerwin LE, Wadekar M, Burock M, et al. A preliminary study of fMRI-guided rTMS in the treatment of generalized anxiety disorder. J Clin Psychiatry. 2008;69(7):1092–8.PubMedCrossRefPubMedCentralGoogle Scholar
Diefenbach GJ, Assaf M, Goethe JW, Gueorguieva R, Tolin DF. Improvements in emotion regulation following repetitive transcranial magnetic stimulation for generalized anxiety disorder. J Anxiety Disord. 2016;43:1–7.PubMedCrossRefPubMedCentralGoogle Scholar
Huang Z, Li Y, Bianchi MT, Zhan S, Jiang F, Li N, et al. Repetitive transcranial magnetic stimulation of the right parietal cortex for comorbid generalized anxiety disorder and insomnia: a randomized, double-blind, sham-controlled pilot study. Brain Stimul. 2018;11(5):1103–9.PubMedCrossRefPubMedCentralGoogle Scholar
Bystritsky A, Kerwin LE, Feusner JD. A preliminary study of fMRI-guided rTMS in the treatment of generalized anxiety disorder: 6-month follow-up. J Clin Psychiatry. 2009;70(3):431–2.PubMedCrossRefPubMedCentralGoogle Scholar
Isserles M, Shalev AY, Roth Y, Peri T, Kutz I, Zlotnick E, et al. Effectiveness of deep transcranial magnetic stimulation combined with a brief exposure procedure in post-traumatic stress disorder – a pilot study. Brain Stimul. 2013;6(3):377–83.PubMedCrossRefPubMedCentralGoogle Scholar
Nam DH, Pae CU, Chae JH. Low-frequency, repetitive Transcranial magnetic stimulation for the treatment of patients with posttraumatic stress disorder: a double-blind, sham-controlled study. Clin Psychopharmacol Neurosci. 2013;11(2):96–102.PubMedPubMedCentralCrossRefGoogle Scholar
Watts BV, Landon B, Groft A, Young-Xu Y. A sham controlled study of repetitive transcranial magnetic stimulation for posttraumatic stress disorder. Brain Stimul. 2012;5(1):38–43.PubMedCrossRefPubMedCentralGoogle Scholar
Cohen H, Kaplan Z, Kotler M, Kouperman I, Moisa R, Grisaru N. Repetitive transcranial magnetic stimulation of the right dorsolateral prefrontal cortex in posttraumatic stress disorder: a double-blind, placebo-controlled study. Am J Psychiatry. 2004;161(3):515–24.PubMedCrossRefPubMedCentralGoogle Scholar
Osuch EA, Benson BE, Luckenbaugh DA, Geraci M, Post RM, McCann U. Repetitive TMS combined with exposure therapy for PTSD: a preliminary study. J Anxiety Disord. 2009;23(1):54–9.PubMedCrossRefPubMedCentralGoogle Scholar
Boggio PS, Rocha M, Oliveira MO, Fecteau S, Cohen RB, Campanha C, et al. Noninvasive brain stimulation with high-frequency and low-intensity repetitive transcranial magnetic stimulation treatment for posttraumatic stress disorder. J Clin Psychiatry. 2010;71(8):992–9.PubMedCrossRefPubMedCentralGoogle Scholar
Shivakumar V, Dinakaran D, Narayanaswamy JC, Venkatasubramanian G. Noninvasive brain stimulation in obsessive-compulsive disorder. Indian J Psychiatry. 2019;61(Suppl 1):S66–76.PubMedPubMedCentralGoogle Scholar
Shiozawa P, Leiva AP, Castro CD, da Silva ME, Cordeiro Q, Fregni F, et al. Transcranial direct current stimulation for generalized anxiety disorder: a case study. Biol Psychiatry. 2014;75(11):e17–8.PubMedCrossRefPubMedCentralGoogle Scholar
Saunders N, Downham R, Turman B, Kropotov J, Clark R, Yumash R, et al. Working memory training with tDCS improves behavioral and neurophysiological symptoms in pilot group with post-traumatic stress disorder (PTSD) and with poor working memory. Neurocase. 2015;21(3):271–8.PubMedCrossRefPubMedCentralGoogle Scholar
Van’t Wout M, Longo SM, Reddy MK, Philip NS, Bowker MT, Greenberg BD. Transcranial direct current stimulation may modulate extinction memory in posttraumatic stress disorder. Brain Behav. 2017;7(5):e00681.PubMedPubMedCentralCrossRefGoogle Scholar
Shiozawa P, Enokibara da Silva M, Cordeiro Q. Transcranial direct current stimulation (tDCS) for panic disorder: a case study. J Depress Anxiety. 2014;3(3):158.CrossRefGoogle Scholar
Heeren A, Billieux J, Philippot P, De Raedt R, Baeken C, de Timary P, et al. Impact of transcranial direct current stimulation on attentional bias for threat: a proof-of-concept study among individuals with social anxiety disorder. Soc Cogn Affect Neurosci. 2017;12(2):251–60.PubMedCrossRefPubMedCentralGoogle Scholar
Alonso P, Cuadras D, Gabriels L, Denys D, Goodman W, Greenberg BD, et al. Deep brain stimulation for obsessive-compulsive disorder: a meta-analysis of treatment outcome and predictors of response. PLoS One. 2015;10(7):e0133591.PubMedPubMedCentralCrossRefGoogle Scholar
Sousa MB, Reis T, Reis A, Belmonte-de-Abreu P. New-onset panic attacks after deep brain stimulation of the nucleus accumbens in a patient with refractory obsessive-compulsive and bipolar disorders: a case report. Revista brasileira de psiquiatria (Sao Paulo, Brazil: 1999). 2015;37(2):182–3.CrossRefGoogle Scholar
Shapira NA, Okun MS, Wint D, Foote KD, Byars JA, Bowers D, et al. Panic and fear induced by deep brain stimulation. J Neurol Neurosurg Psychiatry. 2006;77(3):410–2.PubMedPubMedCentralCrossRefGoogle Scholar
Okun MS, Mann G, Foote KD, Shapira NA, Bowers D, Springer U, et al. Deep brain stimulation in the internal capsule and nucleus accumbens region: responses observed during active and sham programming. J Neurol Neurosurg Psychiatry. 2007;78(3):310–4.PubMedCrossRefPubMedCentralGoogle Scholar
Langevin JP, Koek RJ, Schwartz HN, Chen JWY, Sultzer DL, Mandelkern MA, et al. Deep brain stimulation of the Basolateral amygdala for treatment-refractory posttraumatic stress disorder. Biol Psychiatry. 2016;79(10):e82–e4.PubMedCrossRefPubMedCentralGoogle Scholar
Daban C, Martinez-Aran A, Cruz N, Vieta E. Safety and efficacy of Vagus nerve stimulation in treatment-resistant depression. A systematic review. J Affect Disord. 2008;110(1–2):1–15.PubMedCrossRefPubMedCentralGoogle Scholar
George MS, Ward HE Jr, Ninan PT, Pollack M, Nahas Z, Anderson B, et al. A pilot study of vagus nerve stimulation (VNS) for treatment-resistant anxiety disorders. Brain Stimul. 2008;1(2):112–21.PubMedCrossRefPubMedCentralGoogle Scholar
Cook IA, Abrams M, Leuchter AF. Trigeminal nerve stimulation for comorbid posttraumatic stress disorder and major depressive disorder. Neuromodulation. 2016;19(3):299–305.PubMedCrossRefPubMedCentralGoogle Scholar
Trevizol AP, Shiozawa P, Albuquerque Sato I, da Silva ME, de Barros Calfat EL, Alberto RL, et al. Trigeminal nerve stimulation (TNS) for Post-traumatic stress disorder: a case study. Brain Stimul. 2015;8(3):676–8.PubMedCrossRefPubMedCentralGoogle Scholar
Trevizol AP, Shiozawa P, Sato IA, Calfat EL, Alberto RL, Cook IA, et al. Trigeminal nerve stimulation (TNS) for generalized anxiety disorder: a case study. Brain Stimul. 2015;8(3):659–60.PubMedCrossRefPubMedCentralGoogle Scholar
Trevizol AP, Taiar I, Malta RC, Sato IA, Bonadia B, Cordeiro Q, et al. Trigeminal nerve stimulation (TNS) for social anxiety disorder: a case study. Epilepsy Behav. 2016;56:170–1.PubMedCrossRefPubMedCentralGoogle Scholar
Trevizol AP, Sato IA, Cook IA, Lowenthal R, Barros MD, Cordeiro Q, et al. Trigeminal nerve stimulation (TNS) for panic disorder: an open label proof-of-concept trial. Brain Stimul. 2016;9(1):161–2.PubMedCrossRefPubMedCentralGoogle Scholar
Ahmadi N, Moss L, Simon E, Nemeroff CB, Atre-Vaidya N. Efficacy and long-term clinical outcome of comorbid posttraumatic stress disorder and major depressive disorder after electroconvulsive therapy. Depress Anxiety. 2016;33(7):640–7.PubMedCrossRefPubMedCentralGoogle Scholar
Margoob MA, Ali Z, Andrade C. Efficacy of ECT in chronic, severe, antidepressant- and CBT-refractory PTSD: an open, prospective study. Brain Stimul. 2010;3(1):28–35.PubMedCrossRefPubMedCentralGoogle Scholar
Rosenquist PB, Youssef NA, Surya S, McCall WV. When all else fails: the use of electroconvulsive therapy for conditions other than major depressive episode. Psychiatr Clin North Am. 2018;41(3):355–71.PubMedCrossRefPubMedCentralGoogle Scholar
Fontenelle LF, Coutinho ES, Lins-Martins NM, Fitzgerald PB, Fujiwara H, Yucel M. Electroconvulsive therapy for obsessive-compulsive disorder: a systematic review. J Clin Psychiatry. 2015;76(7):949–57.PubMedCrossRefPubMedCentralGoogle Scholar
Garrido A. Electroconvulsive therapy in severe obsessive-compulsive disorder. Eur Psychiatry. 1998;13(Suppl 4):236s–7s.CrossRefGoogle Scholar
Dubois JC. Obsessions and mood: apropos of 43 cases of obsessive neurosis treated with antidepressive chemotherapy and electroshock. Ann Med Psychol (Paris). 1984;142(1):141–51.Google Scholar
Fontani V, Mannu P, Castagna A, Rinaldi S. Social anxiety disorder: radio electric asymmetric conveyor brain stimulation versus sertraline. Patient Prefer Adherence. 2011;5:581–6.PubMedPubMedCentralGoogle Scholar
Kuhn J, Lenartz D, Huff W, Lee S, Koulousakis A, Klosterkoetter J, Sturm V. Remission of alcohol dependency following deep brain stimulation of the nucleus accumbens: valuable therapeutic implications? J Neurol Neurosurg Psychiatry. 2007;78(10):1152–3.CrossRefGoogle Scholar
Elsevier Health Sciences, Apr 14, 2011 – Medical – 318 pages
Learning Radiology: Recognizing the Basics, 2nd Edition, is an image-filled, practical, and clinical introduction to this integral part of the diagnostic process. William Herring, MD, a skilled radiology teacher, masterfully covers everything you need to know to effectively interpret medical images. Learn the latest on ultrasound, MRI, CT, and more, in a time-friendly format with brief, bulleted text and abundant high-quality images. Then ensure your mastery of the material with additional online content, bonus images, and self-assessment exercises at http://www.studentconsult.com.
Identify a wide range of common and uncommon conditions based upon their imaging findings.Quickly grasp the fundamentals you need to know through easy-access bulleted text and more than 700 images.
Arrive at diagnoses by following a pattern recognition approach, and logically overcome difficult diagnostic challenges with the aid of decision trees.
This book is the first comprehensive, authoritative reference that provides a broad and comprehensive overview of Enhanced Recovery After Surgery (ERAS). Written by experts in the field, chapters analyze elements of care that are both generic and specific to various surgeries. It covers the patient journey through such a program, commencing with optimization of the patient’s condition, patient education, and conditioning of their expectations.
Organized into nine parts, this book discusses metabolic responses to surgery, anaesthetic contributions, and optimal fluid management after surgery. Chapters are supplemented with examples of ERAS pathways and practical tips on post-operative pain control, feeding, mobilization, and criteria for discharge.
Enhanced Recovery After Surgery: A Complete Guide to Optimizing Outcomes is an indispensable manual that thoroughly explores common post-operative barriers and challenges.
Each year, 795,000 stroke patients suffer a new or recurrent stroke and 235,000 severe traumatic brain injuries (TBIs) occur in the US. These patients are susceptible to a combination of significant motor, sensory, and cognitive deficits, and it becomes difficult or impossible for them to perform activities of daily living due to residual functional impairments. Recently, sensorimotor rhythm (SMR)-based brain–computer interface (BCI)-controlled functional electrical stimulation (FES) has been studied for restoration and rehabilitation of motor deficits. To provide future neuroergonomists with the limitations of current BCI-controlled FES research, this chapter presents the state-of-the-art SMR-based BCI-controlled FES technologies, such as current motor imagery (MI) training procedures and guidelines, an EEG-channel montage used to decode MI features, and brain features evoked by MI.
Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008). Filter Bank Common Spatial Pattern (FBCSP) in brain-computer interface. In IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2390–2397.Google Scholar
Ang, K. K., Guan, C., Ang, Kai Keng, & Guan, Cuntai. (2015). Brain-computer interface for neurorehabilitation of upper limb after stroke. Proceedings of the IEEE,103(6), 944–953.CrossRefGoogle Scholar
Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007). A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. Journal of Neural Engineering,4(2), R32–R57.CrossRefGoogle Scholar
Berrar, D., Bradbury, I., & Dubitzky, W. (2006). Avoiding model selection bias in small-sample genomic datasets. Bioinformatics, 22(10), 1245–1250. Oxford Univ Press.Google Scholar
Blanchard, G., & Blankertz, B. (2004). BCI competition 2003—Data set IIa: Spatial patterns of self-controlled brain rhythm modulations. IEEE Transactions on Biomedical Engineering,51(6), 1062–1066.CrossRefGoogle Scholar
Choi, I., Bond, K., Krusienski, D., & Nam, C. S. (2015). Comparison of stimulation patterns to elicit steady-state somatosensory evoked potentials (SSSEPs): Implications for hybrid and SSSEP-based BCIs. Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on (pp. 3122–3127).Google Scholar
Choi, I., Bond, K., & Nam, C. S. (2016). A hybrid BCI-controlled FES system for hand-wrist motor function. IEEE International Conference on Systems, Man, and Cybernetics.Google Scholar
Daly, J. J., Cheng, R., Rogers, J., Litinas, K., Hrovat, K., & Dohring, M. (2009). Feasibility of a new application of noninvasive brain computer interface (BCI): A case study of training for recovery of volitional motor control after stroke. Journal of Neurologic Physical Therapy, 33(4), 203–211.Google Scholar
Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods,134(1), 9–21.CrossRefGoogle Scholar
Delorme, A., Makeig, S., & Sejnowski, T. (2001). Automatic artifact rejection for EEG data using high-order statistics and independent component analysis. Proceedings of the third international ICA conference (pp. 9–12).Google Scholar
Doucet, B. M., Lam, A., & Griffin, L. (2012). Neuromuscular electrical stimulation for skeletal muscle function. Yale J Biol Med,85(2), 201–215.Google Scholar
Elnady, A. M., Zhang, X., Xiao, Z. G., Yong, X., Randhawa, B. K., Boyd, L., & Menon, C. (2015). A single-session preliminary evaluat on of an affordable BCI-controlled arm exoskeleton and motor-proprioception platform. Frontiers in Human Neuroscience, 9, 168. Switzerland.Google Scholar
Ferree, T. C., Clay, M. T., & Tucker, D. M. (2001). The spatial resolution of scalp EEG. Neurocomputing,38–40, 1209–1216.CrossRefGoogle Scholar
Forrester, B. J., & Petrofsky, J. S. (2004). Effect of electrode size, shape, and placement during electrical stimulation. Journal of Applied Research,4(2), 346–354.Google Scholar
Gordon, C. C., Churchill, T., Clauser, C. E., Bradtmiller, B., & McConville, J. T. (1989). Anthropometric survey of US army personnel: methods and summary statistics 1988.Google Scholar
Gu, Y., Dremstrup, K., & Farina, D. (2009). Single-trial discrimination of type and speed of wrist movements from EEG recordings. Clinical Neurophysiology, 120(8), 1596–1600. International Federation of Clinical Neurophysiology.Google Scholar
Hamedi, M., Salleh, S.-H., & Noor, A. M. (2016). Electroencephalographic motor imagery brain connectivity analysis for BCI: A review. Neural Computation,28(6), 999–1041.MathSciNetCrossRefGoogle Scholar
Hyvärinen, a, & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural networks : the official journal of the International Neural Network Society, 13(4–5), 411–430.Google Scholar
Kayser, J., & Tenke, C. E. (2003). Optimizing PCA methodology for ERP component identification and measurement: Theoretical rationale and empirical evaluation. Clinical Neurophysiology,114(12), 2307–2325.CrossRefGoogle Scholar
Kim, T., Kim, S., & Lee, B. (2016). Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: A randomized controlled trial. Occupational therapy international, 23(1), 39–47. England.Google Scholar
Lawrence, M. (2009). Transcutaneous electrode technology for neuroprostheses, (18213).Google Scholar
Lee, H., & Choi, S. (2003). PCA + HMM + SVM for EEG pattern classification. Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 1(2), 1–4.Google Scholar
Liu, Y., Li, M., Zhang, H., Wang, H., Li, J., Jia, J., Wu, Y., et al. (2014). A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training. Journal of neuroscience methods, 222, 238–249. Elsevier.Google Scholar
Looned, R., Webb, J., Xiao, Z. G., & Menon, C. (2014). Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation. Journal of neuroengineering and rehabilitation, 11, 51. England.Google Scholar
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007). A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering,4(2), R1–R13.CrossRefGoogle Scholar
Lyons, G. M., Leane, G. E., Clarke-Moloney, M., O’Brien, J. V., & Grace, P. A. (2004). An investigation of the effect of electrode size and electrode location on comfort during stimulation of the gastrocnemius muscle. Medical Engineering & Physics,26(10), 873–878.CrossRefGoogle Scholar
McGie, S. C., Zariffa, J. J., Popovic, M. R., & Nagai, M. K. (2015). Short-term neuroplastic effects of brain-controlled and muscle-controlled electrical stimulation. Neuromodulation, 18(3), 233–240. United States.Google Scholar
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews,4(1), 1.CrossRefGoogle Scholar
Mukaino, M., Ono, T., Shindo, K., Fujiwara, T., Ota, T., Kimura, A., Liu, M., et al. (2014). Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. Journal of rehabilitation medicine, 46(4), 378–382. Sweden: Medical Journals Limited.Google Scholar
Müller, G. R. R., Neuper, C., Rupp, R., Keinrath, C., Gerner, H. J. J., & Pfurtscheller, G. (2003). Event-related beta EEG changes during wrist movements induced by functional electrical stimulation of forearm muscles in man. Neuroscience Letters,340(2), 143–147.CrossRefGoogle Scholar
Nam, C. S., Lee, J., Bahn, S., Li, Y., & Choi, I. (2014). Brain-computer interface supported collaborative work. Proceedings of 5th International Brain-Computer Interface Meeting.Google Scholar
Nam, C. S., Moore, M., Choi, I., & Li, Y. (2015). Designing better, cost-effective brain-computer interfaces. Ergonomics in Design: The Quarterly of Human Factors Applications, 23(4), 13–19. SAGE.Google Scholar
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors.Google Scholar
Noirhomme, Q., Lesenfants, D., Gomez, F., Soddu, A., Schrouff, J., Garraux, G., Luxen, A., et al. (2014). Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions. NeuroImage: Clinical, 4, 687–694.CrossRefGoogle Scholar
Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: fully automated statistical thresholding for EEG artifact rejection. Journal of neuroscience methods, 192(1), 152–162. Elsevier.Google Scholar
Novi, Q., Guan, C., Dat, T. H., & Xue, P. (2007). Sub-band common spatial pattern (SBCSP) for brain-computer interface. Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, 204–207.Google Scholar
Pfurtscheller, G., & Lopes, F. H. (1999). Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology,110, 1842–1857.CrossRefGoogle Scholar
Pfurtscheller, G., Müller-Putz, G. R., Pfurtscheller, J. J., Rupp, R. R., Muller-Putz, G. R., Pfurtscheller, J. J., Rupp, R. R., et al. (2005). EEG-based asynchronous BCI controls functional electrical stimulation in a tetraplegic patient. EURASIP Journal on Applied Signal Processing, 2005(19), 3152–3155. Hindawi, USA.Google Scholar
Pfurtscheller, G., Müller, G. R., Pfurtscheller, J. J., Gerner, H. J. J., Rupp, R. R., Muller, G. R., Pfurtscheller, J. J., et al. (2003). “Thought”—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience letters, 351(1), 33–36. Ireland.CrossRefGoogle Scholar
Pfurtscheller, G., & Neuper, C. (2006). Future prospects of ERD/ERS in the context of brain—computer interface (BCI) developments. Progress in Brain Research,159, 433–437.CrossRefGoogle Scholar
Pfurtscheller, G., Solis-Escalante, T., Ortner, R., Linortner, P., & Muller-Putz, G. R. (2010). Self-paced operation of an SSVEP-based orthosis with and without an imagery-based “brain switch”: A feasibility study towards a hybrid BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering,18(4), 409–414.CrossRefGoogle Scholar
Polikar, R. (2006). Ensemble based systems in decision making. Circuits and Systems Magazine, IEEE,6(3), 21–45.CrossRefGoogle Scholar
Powers, J. C., Bieliaieva, K., Wu, S., & Nam, C. S. (2015). The human factors and ergonomics of P300-based brain-computer interfaces. Brain sciences, 5(3), 318–56. Switzerland.Google Scholar
Reynolds, C., Osuagwu, B. A., & Vuckovic, A. (2015). Influence of motor imagination on cortical activation during functional electrical stimulation. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 126(7), 1360–1369. Netherlands.Google Scholar
Rohm, M., Muller-Putz, G. R., Kreilinger, A., von Ascheberg, A., & Rupp, R. (2010). A hybrid-Brain Computer Interface for control of a reaching and grasping neuroprosthesis. Biomedizinische Technik, 55(suppl. 1). Fachverlag Schiele & Schon GmbH, Germany.Google Scholar
Rohm, M., Schneiders, M., Müller, C., Kreilinger, A., Kaiser, V., Müller-Putz, G. R., Rupp, R. R. R., et al. (2013). Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury. Artificial Intelligence in Medicine, 59(2), 133–142. Netherlands: Elsevier Science B.V., Netherlands.Google Scholar
Roset, S. A., Gant, K., Prasad, A., & Sanchez, J. C. (2014). An adaptive brain actuated system for augmenting rehabilitation. Frontiers in neuroscience, 8, 415. Switzerland.Google Scholar
Rosner, B. (2015). Fundamentals of biostatistics. Nelson Education.Google Scholar
Schalk, G., & Mellinger, J. (2010). A practical guide to brain–computer interfacing with BCI2000: General-purpose software for brain-computer interface research, data acquisition, stimulus presentation, and brain monitoring. Springer Science & Business Media.Google Scholar
Sun, S., Zhang, C., & Zhang, D. (2007). An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters,28(15), 2157–2163.CrossRefGoogle Scholar
Tan, H. G., Shee, C. Y., Kong, K. H., Guan, C., Ang, W. T., et al. (2011). EEG controlled neuromuscular electrical stimulation of the upper limb for stroke patients. Frontiers of Mechanical Engineering, 6(1), 71–81. SP Higher Education Press, Germany.Google Scholar
Vuckovic, A., Wallace, L., & Allan, D. B. (2015). Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. Journal of neurologic physical therapy : JNPT, 39(1), 3–14. United States.Google Scholar
Wang, D., Miao, D., & Blohm, G. (2012). Multi-class motor imagery EEG decoding for brain-computer interfaces. Frontiers in Neuroscience, 6(OCT), 1–13.Google Scholar
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology,113(6), 767–791.CrossRefGoogle Scholar
Young, B. M., Nigogosyan, Z., Walton, L. M., Remsik, A., Song, J., Nair, V. A., Tyler, M. E., et al. (2015). Dose-response relationships using brain-computer interface technology impact stroke rehabilitation. Frontiers in human neuroscience, 9, 361. Switzerland.Google Scholar
Young, B. M., Nigogosyan, Z., Nair, V. A., Walton, L. M., Song, J., Tyler, M. E., Edwards, D. F., et al. (2014). Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability. Frontiers in neuroengineering, 7, 18. Switzerland.Google Scholar
Zickler, C., Riccio, A., Leotta, F., Hillian-Tress, S., Halder, S., Holz, E., Staiger-Salzer, P., et al. (2011). A brain-computer interface as input channel for a standard assistive technology software. Clinical EEG and neuroscience, 42(4), 236–244.CrossRefGoogle Scholar
Rehabilitation is a process of recovery of an individual from disabling or functionally limiting condition, whether temporary or irreversible, participates to regain maximal function, independence, and restoration . The purpose of rehabilitation is to prevent and slow down the loss of function of the body, improve and restore the function, compensation for the lost function, and maintenance of the current function.
Hemiparesis is a symptom of residual weakness in half of the body, including the upper extremity, which affects the majority of post stroke survivors. Upper limb function is essential for daily life and reduction in movements can lead to tremendous decline in quality of life and independence. Current treatments, such as physiotherapy, aim to improve motor functions, however due to increasing NHS pressure, growing recognition on mental health, and close scrutiny on disease spending there is an urgent need for new approaches to be developed rapidly and sufficient resources devoted to stroke disease. Fortunately, a range of digital technologies has led to revived rehabilitation techniques in captivating and stimulating environments. To gain further insight, a meta-analysis literature search was carried out using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method. Articles were categorized and pooled into the following groups; pro/anti/neutral for the use of digital technology. Additionally, most literature is rationalised by quantitative and qualitative findings. Findings displayed, the majority of the inclusive literature is supportive of the use of digital technologies in the rehabilitation of upper extremity following stroke. Overall, the review highlights a wide understanding and promise directed into introducing devices into a clinical setting. Analysis of all four categories; (1) Digital Technology, (2) Virtual Reality, (3) Robotics and (4) Leap Motion displayed varying qualities both—pro and negative across each device. Prevailing developments on use of these technologies highlights an evolutionary and revolutionary step into utilizing digital technologies for rehabilitation purposes due to the vast functional gains and engagement levels experienced by patients. The influx of more commercialised and accessible devices could alter stroke recovery further with initial recommendations for combination therapy utilizing conventional and digital resources.