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Review
. 2016 Dec 27:10:584.
doi: 10.3389/fnins.2016.00584. eCollection 2016.

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

Max O Krucoff  1 , Shervin Rahimpour  1 , Marc W Slutzky  2 , V Reggie Edgerton  3 , Dennis A Turner  4
Affiliations
Review

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

Max O Krucoff et al. Front Neurosci. .

Abstract

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required-a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery.

Keywords: brain-machine interface (BMI); neural interface; neural regeneration; neural repair; neural stimulation; neuroplasticity; neurorehabilitation; spinal cord stimulation.

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Figures

Figure 1
Figure 1
Injury environment timeline. Blue, acute phase; Red, subacute phase; Black, chronic phase. ICP, intracranial pressure.
Figure 2
Figure 2
Intra- and extracellular mechanisms of neuronal growth and inhibition. Blue, associated with neuronal growth; Red, associated with neuronal inhibition; Black, modulates both neuronal growth and inhibition. PTEN, phosphatase and tensin homolog; cAMP, cyclic adenosine monophosphate; GAP43, growth associated protein 43; GDF10, growth differentiation factor 10; CAP23, cytoskeleton-associated protein; ARG1, arginase 1; SPRR1, small proline-rich protein 1; HSPB1, heat shock protein family B (small) member 1; MARCKS, myristoylated alanine-rich C-kinase substrate; SCG10, superior cervical ganglion 10; NgR, nogo receptor; CSPG, chondroitin sulfate proteoglycans; NG2, neural/glial antigen 2; MAG, myelin-associated glycoprotein; OMgp, oligodendrocyte-myelin glycoprotein; CNTF, ciliary neurotrophic factor; OPN, osteopontin; IGF1, insulin-like growth factor 1.
Figure 3
Figure 3
Principal component analysis (PCA). The example reduces 22 joint-position variables of the wrist and fingers to 3 dimensions that represent the state of the hand. (A) Experimental task where the patient is cued to move his hand into one of the three configurations shown. (B) 22 independent variables of hand movement are recorded and reduced to 3 principle components (PCs). Results are plotted as hand position and hand velocity in 3-dimensional PC space. (C) Greater than 80% variance of hand movement is accounted for using only the first three PCs (Krucoff and Slutzky, 2011). MCP, metacarpophalangeal joint; PIP, proximal interphalangeal joint; DIP, distal interphalangeal joint.
Figure 4
Figure 4
Assistive vs. rehabilitative BMIs. The assistive BMI uses brain signals to bypass a neural lesion and generate an intended action. The rehabilitative BMI pairs goal-oriented tasks with positive feedback and works to re-activate lesioned circuits to create plasticity for long-lasting functional improvement.
Figure 5
Figure 5
Recording and stimulating modalities for neural signals. (A) Functional MRI (fMRI), (B) functional Near-Infrared Spectroscopy (fNIRS), (C) scalp electrodes (EEG), (D) epidural electrodes (FP), (E) subdural electrodes (ECoG), (F) intracortical electrodes (AP, or spikes), (G) muscle electrodes (EMG), (H) intraspinal electrodes (AP, or spikes), (I) spinal epidural electrodes (FP). EEG, electroencephalography; FP, field potential; ECoG, electrocorticography; AP, action potential; EMG, electromyography.
Figure 6
Figure 6
Neurorehabilitation strategies. (A) Partial weight-based therapy (PWBT), (B) constraint-induced movement therapy (CIMT) in a patient with a right hemispheric injury, (C) cortical neurostimulation in a patient with a right hemispheric injury, (D) biofeedback in a patient with a left hemispheric injury. CPU, computer processing unit; EEG, electroencephalography; fMRI, functional magnetic resonance imaging.
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