I can't see how any of this helps. I don't see where an objective damage starting point is measured so there is no way to know exactly who might be helped by this.
Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation
Alexander B. Remsik1,2,3*†, 
Leroy Williams Jr.1,4,5†, 
Klevest Gjini1,6, 
Keith Dodd1,7, 
Jaclyn Thoma1,8, 
Tyler Jacobson1,8, Matt Walczak1, 
Matthew McMillan1,7, 
Shruti Rajan1,9, 
Brittany M. Young1,3,8,10,11, 
Zack Nigogosyan1, Hemali Advani1, 
Rosaleena Mohanty1,12, 
Neelima Tellapragada1, 
Janerra Allen13, 
Mohsen Mazrooyisebdani1, Leo M. Walton7,8, 
Peter L. E. van Kan2, 
Theresa J. Kang1,6, Justin A. Sattin8, 
Veena A. Nair1, 
Dorothy Farrar Edwards2, 
Justin C. Williams7,14 and 
Vivek Prabhakaran1,6,8,9,11,15- 1Department of Radiology, University of Wisconsin–Madison, Madison, WI, United States
- 2Department of Kinesiology, University of Wisconsin–Madison, Madison, WI, United States
- 3Institute for Clinical and Translational Research, University of Wisconsin–Madison, Madison, WI, United States
- 4Department of Educational Psychology, University of Wisconsin–Madison, Madison, WI, United States
- 5Center for Women’s Health Research, University of Wisconsin–Madison, Madison, WI, United States
- 6Department of Neurology, University of Wisconsin–Madison, Madison, WI, United States
- 7Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI, United States
- 8Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- 9Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
- 10Clinical Neuroengineering Training Program, University of Wisconsin–Madison, Madison, WI, United States
- 11Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
- 12Department of Electrical and Computer Engineering, University of Wisconsin–Madison, Madison, WI, United States
- 13Department of Materials Science and Engineering, University of Wisconsin–Madison, Madison, WI, United States
- 14Department of Neurological Surgery, University of Wisconsin–Madison, Madison, WI, United States
- 15Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02098265.
Introduction
Stroke
Stroke is a leading cause of acquired adult long-term disability in the United States (Benjamin et al., 2017)
and occurs when blood supply to the brain is compromised, leading to
functional deficits that may affect activities of daily living (ADLs).
Approximately 85% of patients who suffer and survive a new or recurrent
stroke in the United States each year require rehabilitation (Yang et al., 2017). Six months post-stroke, nearly 50% of survivors have some residual motor deficits (Benjamin et al., 2017). By 2050, stroke burden on the United States economy will approach $2.2 trillion (Benjamin et al., 2017).
Despite advances in acute stroke care, the estimated direct and
indirect costs of stroke continue to escalate and are disproportionally
associated with long-term care and rehabilitation (Benjamin et al., 2017).
Current standard of care seems insufficiently developed to treat
long-term motor deficits, potentially further burdening patients as
untreated motor impairment can lead to deconditioning and
underutilization of the affected upper extremity (UE), a consequence
deemed, learned non-use (LNU) (Schaechter, 2004).
Customary Care and the Opportunities for Improvement
Several rehabilitation techniques are traditionally used
for stroke recovery including conventional physical-occupational-speech
therapies, provided in acute care settings as well as newer motor
therapies such as constraint-induced movement therapy (CIMT),
robot-aided therapy, transcranial direct current stimulation (tDCS),
transcranial magnetic stimulation (TMS), and virtual reality (VR) (Kollen et al., 2006; Lindenberg et al., 2010; Fleet et al., 2014; Young et al., 2014a,b,c; Laver et al., 2015; Song et al., 2015; Babaiasl et al., 2016; Smith and Stinear, 2016).
Importantly, a much different level of evidence exists for CIMT and
traditional therapies than experimental therapies such as tDCS and
VR-based approaches. Existing pharmacological treatments, Botox
injections for example, and traditional physical therapy methods
primarily serve to treat symptoms associated with stroke (Benjamin et al., 2017)
and may not focus on bringing about basic changes to the underlying
impaired brain function associated with relevant post-stroke
pathologies. Patients with UE motor impairment traditionally receive
rehabilitation regimens that involve passive, repetitive movement of the
impaired limb without directly linking brain activity to these
movements (Dromerick et al., 2009).
Whereas passive movement repetition can be an effective rehabilitation
strategy, recovery can be slow, and suboptimal. In contrast, linking
brain activity to movement is important for motor skill learning (e.g.,
walking, running, throwing, writing, etc.) and the formation of central
to peripheral connections. Leveraging this innate and robust motor
learning circuitry, harnessing brain plasticity (Thakor, 2013), may be the next step toward improve patient outcomes.
Motor Recovery
Research suggests that motor recovery post-stroke,
similar to motor learning, requires specific internal and external
environmental conditions (Power et al., 2011; Wenger et al., 2017).
For example, lesion load is a limiting factor as sufficient existing
neural-architecture is needed for motor recovery to occur (Power et al., 2011).
Recovery likely manifests either by the return of function to surviving
neural architecture, or via neural reorganization and neural network
remapping of proximal (i.e., near-by) neural architecture (Gazzaniga, 2005; Jones, 2017).
Perhaps such processes may even be related. If neuroplasticity in the
motor system, though likely attenuated by age, is continuous (Gazzaniga, 2005) over the life course (Power et al., 2011; Wenger et al., 2017),
long-studied learning theories such as Hebbian plasticity and classical
conditioning might be better integrated in treatment designs to aid
recovery of stroke impaired UE motor capacities (Power et al., 2011; Remsik et al., 2016). The incorporation of neurorehabilitation techniques has yielded operational clinical therapies and devices (Pfurtscheller et al., 1997, 2005; Neuper and Pfurtscheller, 2001; Pineda, 2005; Felton et al., 2007; Schalk et al., 2008; Power et al., 2011; Kuiken et al., 2013; Young et al., 2014a; Wenger et al., 2017).
As a number of existing approaches suffer from issues of high cost,
passive movement repetition, large equipment, personnel and time
constraints it is crucial efforts are made to pursue more expedient and
efficacious means of rehabilitation, improve our quality of care, and
better serve our survivors.
Sensorimotor Rhythms
Human brain rhythms associated with motor output,
sensorimotor rhythms (SMRs), are recorded superficial to the motor and
somatosensory cortical strip of the brain (electrode sites C3 and C4)
and originate according to homuncular organization (Pfurtscheller et al., 1997; Birbaumer et al., 2006).
At the motor cortical strip (generally, Brodmann areas 3–6), each brain
hemisphere desynchronizes with imagined, attempted, and also
preparation of movement. This phenomenon is known as event-related
desynchronization (ERD). Specific frequency bands have been associated
with specific aspects of event-related motor behaviors (Pfurtscheller et al., 1997, 2005; Felton et al., 2007; Schalk et al., 2008; Song et al., 2014; Young et al., 2014b).
In normal effortful movement, Mu rhythms of the contralateral cortex
are desynchronized and attenuated (ERD) as movements are planned and
executed (Pfurtscheller et al., 1997).
This is followed by an increased presence of Beta rhythm ERD in the
contralateral motor cortex which is associated with the later stages of
motor command output and control (Pineda, 2005).
After the completion, or at the cessation of movement, the SMRs in Mu
and Beta frequency bands synchronize (ERS). ERD and ERS were key
elements in the development and use of early BCIs for the rehabilitation
of motor functions (Pfurtscheller et al., 1997, 2005; Nam et al., 2011).
The early designs confirmed that ERD or ERS in specific spatial areas
and neural networks (e.g., thalamocortical networks, frontoparietal
networks) associated with a task or triggered events can be utilized to
control a device or output command (Pfurtscheller et al., 1997; Neuper and Pfurtscheller, 2001).
Mu and Beta sensorimotor rhythms (SMRs) in human subjects
are recorded exclusively over sensorimotor areas at frequencies of
about 10–20 Hz (Pfurtscheller et al., 1997; Birbaumer et al., 2006). Two basic strategies in SMR-based control have been introduced for motor rehabilitation in stroke patients: motor imagery (Wolpaw et al., 1991; Ortner et al., 2012; Irimia et al., 2016) and attempted movement-based approaches (Wolpaw et al., 1991; Schalk et al., 2004; Young et al., 2014a,b,c).
Either approach utilizes essentially overlapping neural architecture to
provide input signals (electrophysiological recordings by the EEG cap)
to the BCI. The authors of this study designed the protocol to utilize
attempted hand movements during the intervention according to the logic
that a motor therapy intended to restore volitional motor function of
the affected UE should utilize voluntary attempted movements of that
impaired hand in a continuous effort to improve the participant’s UE
capacity and performance.
Brain–Computer Interface (BCI) and Electroencephalography for Assistive Design
Noninvasive brain–computer interfaces (BCIs), which
utilize ancillary adjuvant peripheral devices and electrical muscle
stimulation, as well as invasive BCI approaches with electrodes
implanted in the skull, have been introduced (Wolpaw et al., 1991; Leuthardt et al., 2004; Schalk et al., 2004, 2008; McFarland et al., 2006; Felton et al., 2007)
as contemporary intervention and rehabilitation techniques following
neural disease or trauma, such as stroke. Devices similar to what was
utilized in this research are controlled by input signals generated by
scalp electroencephalographic (EEG) recordings from electrodes
superficial to the sensorimotor cortices. EEG signals associated with
various components of voluntary movement are identified and translated
into a device command or specified output (Pfurtscheller et al., 1997, 2005; Felton et al., 2007; Schalk et al., 2008; Wilson et al., 2012), like activation of an FES pad (Song et al., 2014; Young et al., 2014a,b).
BCIs can monitoring volitional modulation of electrical brain rhythms
and execute an augmentative, facilitative, or rehabilitative command in
the presence or absence of such signals.
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