Biophysical Principles and Computational Modeling of Deep Brain Stimulation.

BACKGROUND: Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community. OBJECTIVE: In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS. RESULTS: Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response. CONCLUSION: This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease.

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson's disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.

Laryngeal Dystonia: Multidisciplinary Update on Terminology, Pathophysiology, and Research Priorities.

OBJECTIVE: To delineate research priorities for improving clinical management of laryngeal dystonia, the NIH convened a multidisciplinary panel of experts for a 1-day workshop to examine the current progress in understanding its etiopathophysiology and clinical care. METHODS: The participants reviewed the current terminology of disorder and discussed advances in understanding its pathophysiology since a similar workshop was held in 2005. Clinical and research gaps were identified, and recommendations for future directions were delineated. RESULTS: The panel unanimously agreed to adopt the term "laryngeal dystonia" instead of "spasmodic dysphonia" to reflect the current progress in characterizations of this disorder. Laryngeal dystonia was recognized as a multifactorial, phenotypically heterogeneous form of isolated dystonia. Its etiology remains unknown, whereas the pathophysiology likely involves large-scale functional and structural brain network disorganization. Current challenges include the lack of clinically validated diagnostic markers and outcome measures and the paucity of therapies that address the disorder pathophysiology. CONCLUSION: Research priorities should be guided by challenges in clinical management of laryngeal dystonia. Identification of disorder-specific biomarkers would allow the development of novel diagnostic tools and unified measures of treatment outcome. Elucidation of the critical nodes within neural networks that cause or modulate symptoms would allow the development of targeted therapies that address the underlying pathophysiology. Given the rarity of laryngeal dystonia, future rapid research progress may be facilitated by multicenter, national and international collaborations.

Microscale Physiological Events on the Human Cortical Surface.

Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.

Simultaneous scalp EEG improves seizure lateralization during unilateral intracranial EEG evaluation in temporal lobe epilepsy.

PURPOSE: To determine if simultaneous bilateral scalp EEG (scEEG) can accurately detect a contralateral seizure onset in patients with unilateral intracranial EEG (IEEG) implantation. METHODS: We evaluated 39 seizures from 9 patients with bitemporal epilepsy who underwent simultaneous scEEG and IEEG (SSIEEG). To simulate conditions of unilateral IEEG implantation with a missed contralateral seizure onset, we analyzed the IEEG recording contralateral to the seizure onset (CL- IEEG), in conjunction with simultaneous scEEG. The following criteria were evaluated between scEEG and CL- IEEG (1) latency: the time to onset of EEG seizure (2) location: concordance of ictal onset zones and (3) pattern: congruence of EEG morphology and frequency. RESULTS: SSIEEG correctly lateralized 36/39 (92.3%) seizures compared to 13/39 (33.3%) seizures using CL- IEEG alone (OR = 24.0, p < 0.01), 33 (84.6%) seizures using scEEG alone (OR = 2.2, p = 0.29) and 26 (66.9%) seizures using time of clinical onset alone (OR = 6.0, p = 0.01). For the three criteria evaluated, (1) 22/39 (56.4%) seizures had an earlier onset on the scEEG, compared to CL- IEEG; (2) lack of congruence of location of seizure onset was noted in 33/39 (84.6%) of the seizures; and (3) 22/39 (56.4%) seizures did not have a congruent ictal pattern. CONCLUSIONS: The chronological, topographic and morphologic features of SSIEEG can accurately detect the hemisphere of seizure onset in most cases with unilateral IEEG implantation. SSIEEG is significantly better than, IEEG, scEEG or clinical onset alone in this scenario. We propose that SSIEEG should be considered in all cases of intractable focal epilepsy undergoing unilateral IEEG evaluation.

Interventional MRI-Guided Deep Brain Stimulation Lead Implantation.

Current knowledge of the functional anatomy of the subthalamic nucleus and globus pallidus, discovered through microelectrode recording and postoperative imaging, justifies purely anatomic targeting for deep brain stimulation (DBS). Interventional MRI (iMRI)-DBS is more anatomically accurate than traditional awake procedures and has similar clinical outcomes without increased risk or increased operative times. iMRI lead implantation allows patients to receive DBS therapy who cannot tolerate or do not agree to undergo an awake procedure. This article describes considerations for iMRI-DBS implantation in the subthalamic nucleus and globus pallidus, including patient selection, technique of electrode placement, expected outcomes, and potential complications.

Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions.

Dynamics of human subthalamic neuron phase-locking to motor and sensory cortical oscillations during movement.

Coupled oscillatory activity recorded between sensorimotor regions of the basal ganglia-thalamocortical loop is thought to reflect information transfer relevant to movement. A neuronal firing-rate model of basal ganglia-thalamocortical circuitry, however, has dominated thinking about basal ganglia function for the past three decades, without knowledge of the relationship between basal ganglia single neuron firing and cortical population activity during movement itself. We recorded activity from 34 subthalamic nucleus (STN) neurons, simultaneously with cortical local field potentials and motor output, in 11 subjects with Parkinson's disease (PD) undergoing awake deep brain stimulator lead placement. STN firing demonstrated phase synchronization to both low- and high-beta-frequency cortical oscillations, and to the amplitude envelope of gamma oscillations, in motor cortex. We found that during movement, the magnitude of this synchronization was dynamically modulated in a phase-frequency-specific manner. Importantly, we found that phase synchronization was not correlated with changes in neuronal firing rate. Furthermore, we found that these relationships were not exclusive to motor cortex, because STN firing also demonstrated phase synchronization to both premotor and sensory cortex. The data indicate that models of basal ganglia function ultimately will need to account for the activity of populations of STN neurons that are bound in distinct functional networks with both motor and sensory cortices and code for movement parameters independent of changes in firing rate.NEW & NOTEWORTHY Current models of basal ganglia-thalamocortical networks do not adequately explain simple motor functions, let alone dysfunction in movement disorders. Our findings provide data that inform models of human basal ganglia function by demonstrating how movement is encoded by networks of subthalamic nucleus (STN) neurons via dynamic phase synchronization with cortex. The data also demonstrate, for the first time in humans, a mechanism through which the premotor and sensory cortices are functionally connected to the STN.