44.7.2. Hypothesis
Our hypothesis is that CN-NINM induces neuroplasticity by noninvasive stimulation of two major cranial nerves: trigeminal (CN-V) and facial (CN-VII). This stimulation excites a natural flow of neural impulses to the brainstem (pons varolii and medulla) and cerebellum via the lingual branch of the cranial nerve (CN-Vc), and chorda tympani branch of CN-VII, to effect changes in the function of these targeted brain structures (Ptito et al., 2005; Wildenberg et al., 2010).
The spatiotemporal trains of neural activation induced in these nerves eventually produce changes of neural activity in corresponding nuclei of the brainstem—at least in the sensory and spinal nuclei of trigeminal nuclei complex (the largest nuclei in the brainstem, extending from the midbrain to the nuclei of the descending spinal tracts), and the caudal part of the nucleus tractus solitarius where both stimulated nerves have direct projections.
Changes in neural activity were evidenced in the results of our pilot study, wherein we also developed a new fMRI signal processing method to yield high-resolution images of the pons, brainstem, and cerebellum beyond that previously reported, allowing observation of changes in functional activity in all of the regions of interest (Wildenberg et al., 2010, 2011a, 2011b, 2013). We are particularly interested in these specific changes (in pons, brainstem, and cerebellum) because these neural structures are the major sensory integration and movement control centers of the brain and therefore primary targets for neuromodulation.
We postulate that the intensive activation of these structures initiates a sequential cascade of changes in neighboring and/or connected nuclei by direct projections and collateral connections, by activation of brainstem interneuron circuitries (reticular formation of the brainstem), and/or by passive transmission of biochemical compounds in the intercellular space (release of neurotransmitters in the synaptic gaps). The stream of neural impulses lead to activation of corresponding neural networks and massive release of neurotransmitters that eventually activate the glial networks of the brainstem (responsible for maintenance of neuronal environment and synaptic gaps). This activation includes the trigeminal nuclei complex and nucleus tractus solitarius, vestibular nuclei complex, cochlear, facial, and hypoglossal nerve nuclei, and vermis of the cerebellum. This in turn, causes radiating therapeutic neurochemical and neurophysiological changes affecting both synaptic and extrasynaptic circuitries affecting information processing of afferent and efferent neural signals involved in movement control, including the cerebellum and nuclei of spinal motor pathways.
The temporal pattern of our observed retention effects is strikingly similar to the process well known in neuroscience literature for several decades as long term potentiation (LTP) and depression. Both processes were tested and verified in multiple animal models by analyzing changes in brain tissue samples, and both are in intensive use in different models of human processes of learning and memory as a basic mechanism of the synaptic plasticity of the brain (Abraham and Bear, 1996; Bear and Malenka, 1994; Dudek and Bear, 1993; Kirkwood et al., 1995; Larkman and Jack,1995; Levenes et al., 1998).
In brief, synaptic plasticity is a natural manifestation of using activity-dependent processes affecting structure and function of neuronal connections. As a result of such processes, multiple consequential adaptive changes are happening on different levels of brain organization (molecular, cellular, regional, and systemic), with different temporal patterns and dynamics (short and long) that reflects on multiple sensory and motor functions, cognitive performance, and behavior (Bi et al., 2006; Gisabella et al., 2005; Li et al., 2013; Shin et al., 2006, 2010; Tully and Bolshakov, 2010; Tully et al., 2007a, 2007b).
Intensive repetitive stimulation of neurons leads to corresponding activation of synaptic contacts on the axonal tree, including the whole complex of pre- and postsynaptic neurochemical mechanisms. Multiphasic fluctuations of postsynaptic potentials, frequently described as short-term activity-dependent synaptic plasticity (in the range of milliseconds, seconds, and minutes) has been shown are capable to enhance synaptic transmissions (Morice et al., 2008; Zucker, 1999; Zucker and Regehr, 2002).
In contrast, LTP is the phenomena of synaptic structural remodeling and formation of new synaptic contacts that is activated by high frequency stimulation (Buchs and Muller, 1996; Calverley and Jones, 1990; Engert and Bonhoeffer, 1999; Geinisman et al., 1991, 1993, 2000, 2001; Jones and Calverley, 1991; Jones et al., 1991; Maletic-Savatic et al., 1999). After 10–40 minutes of high-frequency stimulation (50–400 Hz, range of frequencies used in animal research) the number of synapses and proportion of multiple spine boutons can increase the efficiency of neural connections (Toni et al., 1999). Effects of LTP can continue during several hours and even days (Bliss and Gardner-Medwin, 1973; Bliss and Lomo, 1973).
In our experiments, using the PoNS device, prolonged and repetitive activation (20 minutes or more) of functional neuronal circuits (balance, gait) can initiate long-lasting processes of neuronal reorganization (similar to LTP), that we can see and measure in subjects’ behavior. The functional improvement after initial training sessions continues for several hours (see Figure 44.2). Multiple regular sequential training sessions lead to consistent increase of improved symptom duration and cumulative enhancement of affected functions.
This regular excitation may also increase the receptivity of numerous other neural circuitries and/or affect internal mechanisms of homeostatic self-regulation, according to contemporary concepts of synaptic plasticity. We cannot exclude also that this induces simultaneous activation of serotoninergic and noradrenalinergic regulation systems of the brain as well.
The result of this intervention is essentially brain plasticity on demand—a priming or upregulating of targeted neural structures to develop new functional pathways, which is the goal of neurorehabilitation and a primary means of functional recovery from permanent physical damage caused by stroke or trauma.
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44.8. CN-NINM TRAINING WITH THE PoNS DEVICE
CN-NINM training is an advanced level of neurorehabilitation therapy. It combines brain stimulation using the PoNS device with neuromuscular reeducation. Stimulation from the PoNS device enhances the brain’s innate capability toward improved function. The intervention is intended to produce beneficial neuroplastic changes by retraining mind-body connections.
The goal of CN-NINM training is to recover normal movement control. By combining brain activation with targeted physical training, we believe we are affecting neural pathways directly related to the task. Through experimentation in multiple studies with various populations (TBI, stroke, multiple sclerosis, Parkinson disease), we have found that the most effective way to train using this technology involves five main components:
I. Movement training
II. Balance training
III. Gait training
IV. Cognitive training
V. Breathing and awareness training (BAT)
Each component requires focused attention on tasks, with a goal of challenging the individual without pushing them beyond the point where they cannot continue training. Finding the “just right” challenge point is a key factor in achieving success with this intervention. In the presence of excessive fatigue, or absent adequate challenge, the PoNS stimulation may be less effective because the underlying, natural drivers of neuroplasticity are suboptimal.
We have achieved the best results using uninterrupted 20- to 25-minute training sessions with the PoNS device. We experimented with longer periods of continuous stimulation (e.g., 30, 40, 60 minutes), but found that these did not produce better results when compared with the 20-minute sessions.
An example of a day of training is found in Table 44.1. Figure 44.4 shows how the different training components can be combined temporally.
