Julien Modolo

Postural sway and effect of levodopa in early Parkinson's disease

OBJECTIVE To characterize postural stability control and levodopa responsiveness in early Parkinsons disease (PD). METHODS Postural sway was studied during quiet stance in ten patients within six years of PD onset, both before (OFF) and after (ON) regular oral levodopa dosing. Postural sway was recorded using a force platform during 30 sec with eyes open, and six dependent variables were examined. RESULTS Mild baseline subclinical changes in postural sway were recorded in our patients. Clear benefit was observed in five out of six characteristics (mean sway, transversal sway, sagittal sway, sway intensity, and sway area) in the ON condition. CONCLUSION Postural control mechanisms are affected early in PD and modulated by dopamine.

Using a virtual cortical module implementing a neural field model to modulate brain rhythms in Parkinson's disease.

We propose a new method for selective modulation of cortical rhythms based on neural field theory, in which the activity of a cortical area is extensively monitored using a two-dimensional microelectrode array. The example of Parkinsons disease illustrates the proposed method, in which a neural field model is assumed to accurately describe experimentally recorded activity. In addition, we propose a new closed-loop stimulation signal that is both space- and time- dependent. This method is especially designed to specifically modulate a targeted brain rhythm, without interfering with other rhythms. A new class of neuroprosthetic devices is also proposed, in which the multielectrode array is seen as an artificial neural network interacting with biological tissue. Such a bio-inspired approach may provide a solution to optimize interactions between the stimulation device and the cortex aiming to attenuate or augment specific cortical rhythms. The next step will be to validate this new approach experimentally in patients with Parkinsons disease.

Pulsed magnetic field exposure induces lasting changes in neural network dynamics

How extremely low frequency (ELF) electromagnetic fields (such as power line exposure) impacts brain activity is today an intense area of research. One challenge is to unveil transduction mechanisms allowing ELF to interact with brain tissue. Thus, we present a cortical network model receiving internal and external stimuli. Using frequency analysis, we study how these stimuli durably modulate network dynamics depending on exposure duration, stimuli properties and transduction mechanisms. Our results indicate that these stimuli induce different responses in the frequency domain. Ultimately, such models might be useful in evaluating power line exposure thresholds, and in developing innovative brain stimulation methods.

Model-driven therapeutic treatment of neurological disorders: reshaping brain rhythms with neuromodulation

Electric stimulation has been investigated for several decades to treat, with various degrees of success, a broad spectrum of neurological disorders. Historically, the development of these methods has been largely empirical but has led to a remarkably efficient, yet invasive treatment: deep brain stimulation (DBS). However, the efficiency of DBS is limited by our lack of understanding of the underlying physiological mechanisms and by the complex relationship existing between brain processing and behaviour. Biophysical modelling of brain activity, describing multi-scale spatio-temporal patterns of neuronal activity using a mathematical model and taking into account the physical properties of brain tissue, represents one way to fill this gap. In this review, we illustrate how biophysical modelling is beginning to emerge as a driving force orienting the development of innovative brain stimulation methods that may move DBS forward. We present examples of modelling works that have provided fruitful insights in regards to DBS underlying mechanisms, and others that also suggest potential improvements for this neurosurgical procedure. The reviewed literature emphasizes that biophysical modelling is a valuable tool to assist a rational development of electrical and/or magnetic brain stimulation methods tailored to both the disease and the patients characteristics.

Is a computational model useful to understand the effect of deep brain stimulation in Parkinson's disease?

A growing number of computational models have been proposed over the last few years to help explain the therapeutic effect of deep brain stimulation (DBS) on motor disorders in Parkinsons disease (PD). However, none of these has been able to explain in a convincing manner the physiological mechanisms underlying DBS. Can these models really contribute to improving our understanding? The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD.

Using “Smart Stimulators” to Treat Parkinson’s Disease: Re-Engineering Neurostimulation Devices

Let’s imagine the cruise control of your car locked at 120 km/h on any road in any condition (city, country, highway, sunny or rainy weather), or your car air conditioner set on maximum cold in any temperature condition (even during a snowy winter): would you find it efficient? That would probably not be the most optimal strategy for a proper and comfortable driving experience. As surprising as this may seem, this is a pretty accurate illustration of how deep brain stimulation is used today to treat Parkinson’s disease motor symptoms and other neurological disorders such as essential tremor, obsessive-compulsive disorder, or epilepsy.

Delayed and lasting effects of deep brain stimulation on locomotion in Parkinson's disease

Parkinsons disease (PD) is a neurodegenerative disorder characterized by a variety of motor signs affecting gait, postural stability, and tremor. These symptoms can be improved when electrodes are implanted in deep brain structures and electrical stimulation is delivered chronically at high frequency (>100 Hz). Deep brain stimulation (DBS) onset or cessation affects PD signs with different latencies, and the long-term improvements of symptoms affecting the body axis and those affecting the limbs vary in duration. Interestingly, these effects have not been systematically analyzed and modeled. We compare these timing phenomena in relation to one axial (i.e., locomotion) and one distal (i.e., tremor) signs. We suggest that during DBS, these symptoms are improved by different network mechanisms operating at multiple time scales. Locomotion improvement may involve a delayed plastic reorganization, which takes hours to develop, whereas rest tremor is probably alleviated by an almost instantaneous desynchronization of neural activity in subcortical structures. Even if all PD patients develop both distal and axial symptoms sooner or later, current computational models of locomotion and rest tremor are separate. Furthermore, a few computational models of locomotion focus on PD and none exploring the effect of DBS was found in the literature. We, therefore, discuss a model of a neuronal network during DBS, general enough to explore the subcircuits controlling locomotion and rest tremor simultaneously. This model accounts for synchronization and plasticity, two mechanisms that are believed to underlie the two types of symptoms analyzed. We suggest that a hysteretic effect caused by DBS-induced plasticity and synchronization modulation contributes to the different therapeutic latencies observed. Such a comprehensive, generic computational model of DBS effects, incorporating these timing phenomena, should assist in developing a more efficient, faster, durable treatment of distal and axial signs in PD.

Possible mechanisms of synaptic plasticity modulation by extremely low-frequency magnetic fields.

Understanding the biological mechanisms by which extremely low-frequency (ELF, < 300 Hz) magnetic fields (MFs) interact with human brain activity is an active field of research. Such knowledge is required by international agencies providing guidelines for general public and workers exposure to ELF MFs (such as ICNIRP, the International Commission on Non-Ionizing Radiation Protection). The identification of these interaction mechanisms is extremely challenging, since the effects of ELF MF exposure need to be monitored and understood at very different spatial (from micrometers to centimeters) and temporal (from milliseconds to minutes) scales. One possibility to overcome these issues is to develop biophysical models, based on the systems of mathematical equations describing the electric or metabolic activity of the brain tissue. Biophysical models of the brain activity offer the possibility to simulate how the brain tissue interacts with ELF MFs, in order to gain new insights into experimental data, and to test novel hypotheses regarding interaction mechanisms. This paper presents novel hypotheses regarding the effects of power line (60 Hz in North America) MFs on human brain activity, with arguments from biophysical models. We suggest a hypothetic chain of events that could bridge MF exposure with detectable effects on human neurophysiology. We also suggest novel directions of research in order to reach a convergence of biophysical models of brain activity and corresponding experimental data to identify interaction mechanisms.

Modulation of neuronal activity with extremely low-frequency magnetic fields: Insights from biophysical modeling

Time-varying magnetic stimulation of the central nervous system is nowadays a promising therapeutic approach already used to alleviate the symptoms in a variety of neurological disorders. Transcranial Magnetic Stimulation (TMS) is an example of a successful application involving specific patterns of magnetic field (MF) for therapeutic use, which provides clinical improvement in movement disorders or depression. Other neuromodulation strategies consist in proposing several orders of magnitude lower magnetic stimuli that are more flexible in terms of shape and frequency of the signal. However, the refinement of both of these techniques is limited due to the lack of understanding of the underlying mechanisms supporting the interaction between the magnetic stimulus and brain tissue. To provide insights into the modulation of neuronal activity by extremely low-frequency (ELF) MF, we present biophysical modeling results regarding 1) single neuron exposure to an ELF MF, and 2) neuronal network exposure to an ELF MF. These results shed light on the effect of ELF MFs on neuronal activity from the single cell to the network level, and illustrate the importance of a number of factors both in ELF MF characteristics and brain tissue properties in determining the outcome of the exposure. These principles may guide future therapeutic developments.

Effects of a 60 Hz Magnetic Field Exposure Up to 3000 μT on Human Brain Activation as Measured by Functional Magnetic Resonance Imaging.

Several aspects of the human nervous system and associated motor and cognitive processes have been reported to be modulated by extremely low-frequency (ELF, < 300 Hz) time-varying Magnetic Fields (MF). Due do their worldwide prevalence; power-line frequencies (60 Hz in North America) are of particular interest. Despite intense research efforts over the last few decades, the potential effects of 60 Hz MF still need to be elucidated, and the underlying mechanisms to be understood. In this study, we have used functional Magnetic Resonance Imaging (fMRI) to characterize potential changes in functional brain activation following human exposure to a 60 Hz MF through motor and cognitive tasks. First, pilot results acquired in a first set of subjects (N=9) were used to demonstrate the technical feasibility of using fMRI to detect subtle changes in functional brain activation with 60 Hz MF exposure at 1800 μT. Second, a full study involving a larger cohort of subjects tested brain activation during 1) a finger tapping task (N=20), and 2) a mental rotation task (N=21); before and after a one-hour, 60 Hz, 3000 μT MF exposure. The results indicate significant changes in task-induced functional brain activation as a consequence of MF exposure. However, no impact on task performance was found. These results illustrate the potential of using fMRI to identify MF-induced changes in functional brain activation, suggesting that a one-hour 60 Hz, 3000 μT MF exposure can modulate activity in specific brain regions after the end of the exposure period (i.e., residual effects). We discuss the possibility that MF exposure at 60 Hz, 3000 μT may be capable of modulating cortical excitability via a modulation of synaptic plasticity processes.