Nada Yousif

The influence of reactivity of the electrode–brain interface on the crossing electric current in therapeutic deep brain stimulation

The use of deep brain stimulation (DBS) as an effective clinical therapy for a number of neurological disorders has been greatly hindered by the lack of understanding of the mechanisms which underlie the observed clinical improvement in patients. This problem is confounded by the difficulty of investigating the neuronal effects of DBS in situ, and the impossibility of measuring the induced current in vivo. In our recent computational work using a quasi-static finite element (FEM) model we have quantitatively shown that the properties of the depth electrode-brain interface (EBI) have a significant effect on the electric field induced in the brain volume surrounding the DBS electrode. In the present work, we explore the influence of the reactivity of the EBI on the crossing electric current using the Fourier-FEM approach to allow the investigation of waveform attenuation in the time domain. Results showed that the EBI affected the waveform shaping differently at different post-implantation stages, and that this in turn had implications on induced current distribution across the EBI. Furthermore, we investigated whether hypothetical waveforms, which were shown to have potential usefulness for neural stimulation but are not yet applied clinically, would have any advantage over the currently used square pulse. In conclusion, the influence of reactivity of the EBI on the crossing stimulation current in therapeutic DBS is significant, and affects the predictive estimation of current distribution around the implanted DBS electrode in the human brain.

Modeling the current distribution across the depth electrode–brain interface in deep brain stimulation

The mismatch between the extensive clinical use of deep brain stimulation (DBS), which is being used to treat an increasing number of neurological disorders, and the lack of understanding of the underlying mechanisms is confounded by the difficulty of measuring the spread of electric current in the brain in vivo. In this article we present a brief review of the recent computational models that simulate the electric current and field distribution in 3D space and, consequently, make estimations of the brain volume being modulated by therapeutic DBS. Such structural modeling work can be categorized into three main approaches: target-specific modeling, models of instrumentation and modeling the electrode–brain interface. Comments are made for each of these approaches with emphasis on our electrode–brain interface modeling, since the stimulating current must travel across the electrode–brain interface in order to reach the surrounding brain tissue and modulate the pathological neural activity. For future modeling work, a combined approach needs to be taken to reveal the underlying mechanisms, and both structural and dynamic models need to be clinically validated to make reliable predictions about the therapeutic effect of DBS in order to assist clinical practice.

The peri-electrode space is a significant element of the electrode–brain interface in deep brain stimulation: A computational study

Deep brain stimulation (DBS) is an increasingly used clinical treatment for various neurological disorders, particularly movement disorders such as Parkinsons disease. However, the mechanism by which these high frequency electrical pulses act on neuronal activity is unclear. Once the stimulating electrode is placed in situ, an electrode–brain interface (EBI) is created. To compensate for the lack of studies on the effects of this generic depth EBI on therapeutic DBS, we constructed a three-dimensional computational model of the EBI using the finite element method, in which the structural details and biophysical properties of the EBI are preserved. Our investigations focus on the peri-electrode space as a significant element of the EBI, and its physiological and pathological modulation, in particular by brain pulsation and giant cell formation. We also consider the difference between the current fields induced by different configurations of the quadripolar electrode contacts. These results quantitatively demonstrated that the peri-electrode space is a significant element of the EBI and its biophysical properties are modulated by brain pulsation and giant cell formation, as well as by the choice of electrode contact configuration. This study leads to a fuller understanding of the EBI and its effects on the crossing electric currents, and will ultimately lead to optimisation of the therapeutic effects of DBS.

Quantifying the effects of the electrode-brain interface on the crossing electric currents in deep brain recording and stimulation

A depth electrode-brain interface (EBI) is formed once electrodes are implanted into the human brain. We investigated the impact of the EBI on the crossing electric currents during both deep brain recording (DBR) and deep brain stimulation (DBS) over the acute, chronic and transitional stages post-implantation, in order to investigate and quantify the effect which changes at the EBI have on both DBR and DBS. We combined two complementary methods: (1) physiological recording of local field potentials via the implanted electrode in patients; and (2) computational simulations of an EBI model. Our depth recordings revealed that the physiological modulation of the EBI in the acute stage via brain pulsation selectively affected the crossing neural signals in a frequency-dependent manner, as the amplitude of the electrode potential was inversely correlated with that of the tremor-related oscillation, but not the beta oscillation. Computational simulations of DBS during the transitional period showed that the shielding effect of partial giant cell growth on the injected current could shape the field in an unpredictable manner. These results quantitatively demonstrated that physiological modulation of the EBI significantly affected the crossing currents in both DBR and DBS. Studying the microenvironment of the EBI may be a key step in investigating the mechanisms of DBR and DBS, as well as brain-computer interactions in general.

Investigating the depth electrode–brain interface in deep brain stimulation using finite element models with graded complexity in structure and solution

Deep brain stimulation (DBS) is an increasingly used surgical therapy for a range of neurological disorders involving the long-term electrical stimulation of various regions of the human brain in a disorder specific manner. Despite being used for the last 20 years, the underlying mechanisms are still not known, and disputed. In particular, when the electrodes are implanted into the human brain, an interface is created with changing biophysical properties which may impact on stimulation. We previously defined the electrode-brain interface (EBI) as consisting of three structural elements: the quadripolar DBS electrode, the peri-electrode space and the surrounding brain tissue. In order to understand more about the nature of this EBI, we used structural computational models of this interface, and estimated the effects of stimulation using coupled axon models. These finite element models differ in complexity, each highlighting a different feature of the EBIs effect on the DBS-induced electric field. We show that the quasi-static models are sufficient to demonstrate the difference between the acute and chronic clinical stages post-implantation. However, the frequency-dependent models are necessary as the waveform shaping has a major influence on the activation of neuronal fibres. We also investigate anatomical effects on the electric field, by taking specific account of the ventricular system in the human brain. Taken together, these models allow us to visualise the static, dynamic and target specific properties of the DBS-induced field in the surrounding brain regions.

Structural learning in feedforward and feedback control

For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control.

Evaluating the impact of the deep brain stimulation induced electric field on subthalamic neurons: A computational modelling study

Deep brain stimulation (DBS) is an effective surgical treatment used to alleviate the symptoms of neurological disorders, most commonly movement disorders. However, the mechanism of how the applied stimulus pulses interact with the surrounding neuronal elements is not yet clearly understood, slowing progress and development of this promising therapeutic technology. To extend previous approaches of using isolated, myelinated axon models used to estimate the effect of DBS, we propose that taking into account entire neurons will reveal stimulation induced effects overlooked by previous studies. We compared the DBS induced volume of tissue activated (VTA) using arrays of whole cell models of subthalamic nucleus (STN) excitatory neurons consisting of a cell body and an anatomically accurate dendritic tree, to the common models of axon arrays. Our results demonstrate that STN neurons have a higher excitation threshold than axons, as stimulus amplitudes 10 times as large elicit a VTA range a fifth of the distance from the electrode surface. However, the STN neurons do show a change in background firing rate in response to stimulation, even when they are classified as sub-threshold by the VTA definition. Furthermore the whole neuron models are sensitive to regions of high current density, as the distribution of firing is centred on the electrode contact edges These results demonstrate the importance of accurate neuron models for fully appreciating the spatial effects of DBS on the immediate surrounding brain volume within small distances of the electrode, which are overlooked by previous models of isolated axons and individual neurons.

Vestibular Activation Differentially Modulates Human Early Visual Cortex and V5/MT Excitability and Response Entropy

Head movement imposes the additional burdens on the visual system of maintaining visual acuity and determining the origin of retinal image motion (i.e., self-motion vs. object-motion). Although maintaining visual acuity during self-motion is effected by minimizing retinal slip via the brainstem vestibular-ocular reflex, higher order visuovestibular mechanisms also contribute. Disambiguating self-motion versus object-motion also invokes higher order mechanisms, and a cortical visuovestibular reciprocal antagonism is propounded. Hence, one prediction is of a vestibular modulation of visual cortical excitability and indirect measures have variously suggested none, focal or global effects of activation or suppression in human visual cortex. Using transcranial magnetic stimulation-induced phosphenes to probe cortical excitability, we observed decreased V5/MT excitability versus increased early visual cortex (EVC) excitability, during vestibular activation. In order to exclude nonspecific effects (e.g., arousal) on cortical excitability, response specificity was assessed using information theory, specifically response entropy. Vestibular activation significantly modulated phosphene response entropy for V5/MT but not EVC, implying a specific vestibular effect on V5/MT responses. This is the first demonstration that vestibular activation modulates human visual cortex excitability. Furthermore, using information theory, not previously used in phosphene response analysis, we could distinguish between a specific vestibular modulation of V5/MT excitability from a nonspecific effect at EVC.

The role of cortical feedback in the generation of the temporal receptive field responses of lateral geniculate nucleus neurons: a computational modelling study

The influence of cortical feedback on thalamic visual responses has been a source of much discussion in recent years. In this study we examine the possible role of cortical feedback in shaping the spatiotemporal receptive field (STRF) responses of thalamocortical (TC) cells in the lateral geniculate nucleus (LGN) of the thalamus. A population-based computational model of the thalamocortical network is used to generate a representation of the STRF response of LGN TC cells within the corticothalamic feedback circuit. The cortical feedback is shown to have little influence on the spatial response properties of the STRF organization. However, the model suggests that cortical feedback may play a key role in modifying the experimentally observed biphasic temporal response property of the STRF, that is, the reversal over time of the polarity of ON and OFF responses of the centre and surround of the receptive field, in particular accounting for the experimentally observed mismatch between retinal cells and TC cells in respect of the magnitude of the second (rebound) phase of the temporal response. The model results also show that this mismatch may result from an anti-phase corticothalamic feedback mechanism.

A population-based model of the nonlinear dynamics of the thalamocortical feedback network displays intrinsic oscillations in the spindling (7–14 Hz) range

The thalamocortical network is modelled using the Wilson–Cowan equations for neuronal population activity. We show that this population model with biologically derived parameters possesses intrinsic nonlinear oscillatory dynamics, and that the frequency of oscillation lies within the spindle range. Spindle oscillations are an early sleep oscillation characterized by high‐frequency bursts of action potentials followed by a period of quiescence, at a frequency of 7–14 Hz. Spindles are generally regarded as being generated by intrathalamic circuitry, as decorticated thalamic slices and the isolated thalamic reticular nucleus exhibit spindles. However, the role of cortical feedback has been shown to regulate and synchronize the oscillation. Previous modelling studies have mainly used conductance‐based models and hence the mechanism relied upon the inclusion of ionic currents, particularly the T‐type calcium current. Here we demonstrate that spindle‐frequency oscillatory activity can also arise from the nonlinear dynamics of the thalamocortical circuit, and we use bifurcation analysis to examine the robustness of this oscillation in terms of the functional range of the parameters used in the model. The results suggest that the thalamocortical circuit has intrinsic nonlinear population dynamics which are capable of providing robust support for oscillatory activity within the frequency range of spindle oscillations.