Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery

Still no fucking clue how to possibly use this to help stroke recovery. Once again stroke survivors as guinea pigs.
http://journal.frontiersin.org/article/10.3389/fneur.2017.00058/full?

Paola Malerba¹*, Sofia Straudi²*, Felipe Fregni³, Maxim Bazhenov^1† and Nino Basaglia^2†

• ¹Department of Medicine, University of California San Diego, La Jolla, CA, USA
• ²Neuroscience and Rehabilitation Department, Ferrara University Hospital, Ferrara, Italy
• ³Center of Neuromodulation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA

Stroke is a leading cause of worldwide disability, and up to 75% of survivors suffer from some degree of arm paresis. Recently, rehabilitation of stroke patients has focused on recovering motor skills by taking advantage of use-dependent neuroplasticity, where high-repetition of goal-oriented movement is at times combined with non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS). Merging the two approaches is thought to provide outlasting clinical gains, by enhancing synaptic plasticity and motor relearning in the motor cortex primary area. However, this general approach has shown mixed results across the stroke population. In particular, stroke location has been found to correlate with the likelihood of success, which suggests that different patients might require different protocols. Understanding how motor rehabilitation and stimulation interact with ongoing neural dynamics is crucial to optimize rehabilitation strategies, but it requires theoretical and computational models to consider the multiple levels at which this complex phenomenon operate. In this work, we argue that biophysical models of cortical dynamics are uniquely suited to address this problem. Specifically, biophysical models can predict treatment efficacy by introducing explicit variables and dynamics for damaged connections, changes in neural excitability, neurotransmitters, neuromodulators, plasticity mechanisms, and repetitive movement, which together can represent brain state, effect of incoming stimulus, and movement-induced activity. In this work, we hypothesize that effects of tDCS depend on ongoing neural activity and that tDCS effects on plasticity may be also related to enhancing inhibitory processes. We propose a model design for each step of this complex system, and highlight strengths and limitations of the different modeling choices within our approach. Our theoretical framework proposes a change in paradigm, where biophysical models can contribute to the future design of novel protocols, in which combined tDCS and motor rehabilitation strategies are tailored to the ongoing dynamics that they interact with, by considering the known biophysical factors recruited by such protocols and their interaction.

Stroke Rehabilitation: Recruiting Changes in Brain Activity to Induce Mobility Recovery

Cortical Activity after a Stroke

Stroke, due to the interruption of blood supply to the brain determining an acute neurologic condition (1), is a major cause of disability worldwide, often resulting in limited motor recovery in the paretic upper limb. Up to 75% of survivors maintain an arm paresis even in a chronic stage, with substantial limitations during participation in daily life activities (2, 3). The hemiparesis, in which the movement ability is affected on a single side, is due to the interruption of the motor signal through the corticospinal tract (CST) to the spinal cord motor neurons. Stroke research in humans is performed using techniques, such as functional magnetic resonance (fMRI) characterized by an excellent spatial resolution or transcranial magnetic stimulation (TMS), which explores cortical excitability through the induction of an electromagnetic field (4) at a high temporal resolution. Animal models of stroke have been influential in describing functional map reorganization (5) via electrophysiology, pharmacology (6), and optogenetics (7, 8).

A stroke initiates a large amount of changes in cortical excitability, connectivity (i.e., the synaptic wiring within and across brain regions), and ultimately coding (i.e., the specific neural spiking patterns that encode for movement are likely different after stroke). These changes, although not completely understood, occur on different time scales: some immediately after the injury and some are slowly established on the course of months (the chronic phase). However, times at which a stroke is considered entering the chronic phase, or exiting the subacute phase, are not universally agreed upon. Since measured changes in neural properties have been shown to affect the chances of motor recovery (9, 10), the design of effective neurorestorative approaches requires knowledge of the mechanisms of brain injury and neural repair after stroke.

Early after a stroke, cell deaths results from several biological pathways, including toxicity induced by excessive excitability, ionic imbalance, inflammation, and apoptosis. In an early response to stroke, several neurotrophic factors are upregulated, and in the first 1–4 weeks local axonal sprouting, dendritic spine expansion and synaptogenesis occur (11). In humans and animals, the affected brain areas (in particular the CST) show decreased activation in TMS studies (10), with concurrent activation of the contralateral cortex (12). Such reduced activity is related to increase in GABAergic tonic inhibition close to the lesion, which has been hypothesized to be neuroprotective in the acute phase, to counterbalance the excitotoxic cascade (13). At the same time, fMRI studies show that bilateral activation in both the ipsilesional (affected) and controlesional (unaffected) hemispheres occurs, revealing the development of early cortical reorganization processes (14, 15). These findings suggest that a damaged brain is still plastic and possibly amenable to be influenced by experiences.

In a chronic stage after stroke, a new functional cerebral architecture is determined, based on several variables (side of lesion, age, pre-stroke comorbidities). Since the disruption of the cortical motor network triggers a major reassembly of inter- and intra-areal cortical networks, it is reasonable that some of the functions of the injured regions could be redistributed across the remaining cortical and subcortical motor network in due time (12). In fact, several weeks after stroke, functional map changes are consolidated (16). Specifically, correlations between structural motor cortex connectivity and motor impairment (17) or fMRI activation in ipsilesional primary and pre-motor cortex and good upper limb recovery (15) have been highlighted, and impaired motor function seems related to persistent contralesional M1 activation (18). Though a rebalance between hemispheres is considered a sign of good recovery in chronic phase, whether such bilateral activation is adaptive or maladaptive is still on debate (19, 20).

Recovery Depends on Network State

The progression of recovery can be seen as a relearning process of lost functions and as an adaptation and compensation of residual functions. Experimental animal data show that in absence of rehabilitation, functional spontaneous recovery occurs (21). However, it was limited and largely reflected the development of compensatory motor patterns far from normal kinematics.

The recovery process impinges on a damaged, reorganized network, and some of the changes in the acute to chronic phase after stroke can be predictive of rehabilitation outcomes. Even though a clear correlation between neurophysiological and neuroimaging findings and motor outcome in stroke survivors is not fully established, algorithms to predict motor recovery have been postulated (22). The imbalance between reduced excitability in the affected cortex and enhanced excitability of the unaffected hemisphere was predictive of motor recovery in a TMS study (9). In contrast, increases in contralesional primary motor cortex (M1) activity over the first 10 days after stroke correlated with the amount of spontaneous motor improvement in initially more impaired patients (23). Furthermore, repetitive training of the affected forelimb is related with a decreased motor representation of the intact hemisphere (24). These findings support the idea that after stroke an ipsilesional activity is rewired in patients with good recovery; whereas in patients severely impaired, the contralesional hemisphere can contribute to motor recovery.

Studies on animal models are essential to explore the time-windows more suitable to deliver rehabilitative interventions in order to achieve optimal neuroplastic changes (25, 26). For example, the upregulation of proteins occurs over a relatively narrow window of time after injury, which might be the optimal time to induce use-dependent cortical reorganization processes (16). Improvement in motor performance is associated with reorganization of cortical motor maps, but the temporal relationship between performance gains and map plasticity is not clear. Training-induced motor improvements are not reflected in motor maps until substantially later, suggesting that early motor training after stroke can help the evolving poststroke neural network (27). Also, in animals, a reduction of the increased tonic inhibition after injury induced functional recovery (6), which highlights the potential for animal models of stroke to provide new pharmacological targets.

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