Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

Modeling the volume of tissue activated in deep brain stimulation and its clinical influence: a review

Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.

Mechanical behavior and microstructure of porcine brain tissues under pulsed electric fields

Pulsed electric fields are extensively utilized in clinical treatments, such as subthalamic deep brain stimulation, where electric field loading is in direct contact with brain tissue. However, the alterations in brain tissue's mechanical properties and microstructure due to changes in electric field parameters have not received adequate attention. In this study, the mechanical properties and microstructure of the brain tissue under pulsed electric fields were focused on. Herein, a custom indentation device was equipped with a module for electric field loading. Parameters such as pulse amplitude and frequency were adjusted. The results demonstrated that following an indentation process lasting 5 s and reaching a depth of 1000 μm, and a relaxation process of 175 s, the average shear modulus of brain tissue was reduced, and viscosity decreased. At the same amplitude, high-frequency pulsed electric fields had a smaller effect on brain tissue than low-frequency ones. Furthermore, pulsed electric fields induced cell polarization and reduced the proteoglycan concentration in brain tissue. As pulse frequency increased, cell polarization diminished, and proteoglycan concentration decreased significantly. High-frequency pulsed electric fields applied to brain tissue were found to reduce impedance fluctuation amplitude. This study revealed the effect of pulsed electric fields on the mechanical properties and microstructure of ex vivo brain tissue, providing essential information to promote the advancement of brain tissue electrotherapy in clinical settings.

Thirty Years of Global Deep Brain Stimulation: "Plus ça change, plus c'est la même chose"?

BACKGROUND

The advent of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease 30 years ago has ushered a global breakthrough of DBS as a universal method for therapy and research in wide areas of neurology and psychiatry. The literature of the last three decades has described numerous concepts and practices of DBS, often branded as novelties or discoveries. However, reading the contemporary publications often elicits a sense of déjà vu in relation to several methods, attributes, and practices of DBS. Here, we review various applications and techniques of the modern-era DBS and compare them with practices of the past.

SUMMARY

Compared with modern literature, publications of the old-era functional stereotactic neurosurgery, including old-era DBS, show that from the very beginning multidisciplinarity and teamwork were often prevalent and insisted upon, ethical concerns were recognized, brain circuitries and rational for brain targets were discussed, surgical indications were similar, closed-loop stimulation was attempted, evaluations of surgical results were debated, and controversies were common. Thus, it appears that virtually everything done today in the field of DBS bears resemblance to old-time practices, or has been done before, albeit with partly other tools and techniques. Movement disorders remain the main indications for modern DBS as was the case for lesional surgery and old-era DBS. The novelties today consist of the STN as the dominant target for DBS, the tremendous advances in computerized brain imaging, the sophistication and versatility of implantable DBS hardware, and the large potential for research.

KEY MESSAGES

Many aspects of contemporary DBS bear strong resemblance to practices of the past. The dominant clinical indications remain movement disorders with virtually the same brain targets as in the past, with one exception: the STN. Other novel brain targets - that are so far subject to DBS trials - are the pedunculopontine nucleus for gait freezing, the anteromedial internal pallidum for Gilles de la Tourette and the fornix for Alzheimer's disease. The major innovations and novelties compared to the past concern mainly the unmatched level of research activity, its high degree of sponsorship, and the outstanding advances in technology that have enabled multimodal brain imaging and the miniaturization, versatility, and sophistication of implantable hardware. The greatest benefit for patients today, compared to the past, is the higher level of precision and safety of DBS, and of all functional stereotactic neurosurgery.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders

Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.

How Sample Size Impacts Probabilistic Stimulation Maps in Deep Brain Stimulation

Probabilistic stimulation maps of deep brain stimulation (DBS) effect based on voxel-wise statistics (p-maps) have increased in literature over the last decade. These p-maps require correction for Type-1 errors due to multiple testing based on the same data. Some analyses do not reach overall significance, and this study aims to evaluate the impact of sample size on p-map computation. A dataset of 61 essential tremor patients treated with DBS was used for the investigation. Each patient contributed with four stimulation settings, one for each contact. From the dataset, 5 to 61 patients were randomly sampled with replacement for computation of p-maps and extraction of high- and low-improvement volumes. For each sample size, the process was iterated 20 times with new samples generating in total 1140 maps. The overall p-value corrected for multiple comparisons, significance volumes, and dice coefficients (DC) of the volumes within each sample size were evaluated. With less than 30 patients (120 simulations) in the sample, the variation in overall significance was larger and the median significance volumes increased with sample size. Above 120 simulations, the trends stabilize but present some variations in cluster location, with a highest median DC of 0.73 for n = 57. The variation in location was mainly related to the region between the high- and low-improvement clusters. In conclusion, p-maps created with small sample sizes should be evaluated with caution, and above 120 simulations in single-center studies are probably required for stable results.

Probabilistic maps for deep brain stimulation - Impact of methodological differences

BACKGROUND

Group analysis of patients with deep brain stimulation (DBS) has the potential to help understand and optimize the treatment of patients with movement disorders. Probabilistic stimulation maps (PSM) are commonly used to analyze the correlation between tissue stimulation and symptomatic effect but are applied with different methodological variations.

OBJECTIVE

To compute a group-specific MRI template and PSMs for investigating the impact of PSM model parameters.

METHODS

Improvement and occurrence of dizziness in 68 essential tremor patients implanted in caudal zona incerta were analyzed. The input data includes the best parameters for each electrode contact (screening), and the clinically used settings. Patient-specific electric field simulations (n = 488) were computed for all DBS settings. The electric fields were transformed to a group-specific MRI template for analysis and visualization. The different comparisons were based on PSMs representing occurrence (N-map), mean improvement (M-map), weighted mean improvement (wM-map), and voxel-wise t-statistics (p-map). These maps were used to investigate the impact from input data (clinical/screening settings), clustering methods, sampling resolution, and weighting function.

RESULTS

Screening or clinical settings showed the largest impacts on the PSMs. The average differences of wM-maps were 12.4 and 18.2% points for the left and right sides respectively. Extracting clusters based on wM-map or p-map showed notable variation in volumes, while positioning was similar. The impact on the PSMs was small from weighting functions, except for a clear shift in the positioning of the wM-map clusters.

CONCLUSION

The distribution of the input data and the clustering method are most important to consider when creating PSMs for studying the relationship between anatomy and DBS outcome.