Patterned Brain Stimulation, What a Framework with Rhythmic and Noisy Components Might Tell Us about Recovery Maximization

Abstract

Brain stimulation is having remarkable impact on clinical neurology. Brain stimulation can modulate neuronal activity in functionally segregated circumscribed regions of the human brain. Polarity, frequency, and noise specific stimulation can induce specific manipulations on neural activity. In contrast to neocortical stimulation, deep-brain stimulation has become a tool that can dramatically improve the impact clinicians can possibly have on movement disorders. In contrast, neocortical brain stimulation is proving to be remarkably susceptible to intrinsic brain-states. Although evidence is accumulating that brain stimulation can facilitate recovery processes in patients with cerebral stroke, the high variability of results impedes successful clinical implementation. Interestingly, recent data in healthy subjects suggests that brain-state dependent patterned stimulation might help resolve some of the intrinsic variability found in previous studies. In parallel, other studies suggest that noisy “stochastic resonance” (SR)-like processes are a non-negligible component in non-invasive brain stimulation studies. The hypothesis developed in this manuscript is that stimulation patterning with noisy and oscillatory components will help patients recover from stroke related deficits more reliably. To address this hypothesis we focus on two factors common to both neural computation (intrinsic variables) as well as brain stimulation (extrinsic variables): noise and oscillation. We review diverse theoretical and experimental evidence that demonstrates that subject-function specific brain-states are associated with specific oscillatory activity patterns. These states are transient and can be maintained by noisy processes. The resulting control procedures can resemble homeostatic or SR processes. In this context we try to extend awareness for inter-individual differences and the use of individualized stimulation in the recovery maximization of stroke patients.

Retinal Origin of Electrically Evoked Potentials in Response to Transcorneal Alternating Current Stimulation in the Rat

“…[11][12][13][14] Thus, the details of the stimulation paradigm, such as electrode placement, stimulus polarity, frequency and pattern of stimulation, as well as the optimal time and duration of stimulation, require further refinement to maximize the therapeutic effects. 15 Noninvasive current stimulation is also an effective tool in the rehabilitation of visual impairments such as amblyopia, 16,17 hemianopia, 18 glaucoma, and other optic nerve neuropathies. [19][20][21] However, therapeutic stimulation protocols would significantly benefit from a better understanding of the functional mechanism(s) of such therapies, which would in turn speed up further refinement of more effective stimulation procedures.…”

Alternating Current Stimulation for Vision Restoration after Optic Nerve Damage: A Randomized Clinical Trial

“…Another issue of interest is the specificity of the range of current frequencies used for stimulation. In future studies it will be important to systematically study the effects of specific frequencies for parameter optimization [ 36 ]. Application of the optimal frequencies might improve efficacy and will also shed light on the role of specific brain oscillations in their ability to activate residual vision.…”

Nonphysiological factors in navigated TMS studies; Confounding covariates and valid intracortical estimates

“…Thus, on the one hand, MEP amplitude variability can be a severe limitation in brain stimulation studies [Rosler ; Wassermann ]. Yet on the other hand, CSE fluctuations are a ubiquitous phenomenon that requires further investigation [Patton and Amassian ; Schmidt et al ; Thut et al ]. In TMS studies, dissociating physical from physiological variance is a first step in this direction.…”

“…Brain states are understood to reflect transient dynamic changes of ongoing cortical excitability associated with specific patterns of function and dysfunction [Buch et al 2008;Thut et al 2011]. The manipulation of brain states might be a relevant goal of future recovery maximization algorithms [Jackson and Zimmermann 2012;Schmidt et al 2013;Thut et al 2011]. Thus, on the one hand, MEP amplitude variability can be a severe limitation in brain stimulation studies [Rosler 2001;Wassermann 2002].…”