While digital health foundations are in place and neuromodulation can benefit from digital health solutions, there remain practical impediments to seamless integration.
FREMONT, CA: Miniaturization of devices, the increasing complexity of activities, and improved brain response to stimulation drive technological advancement and innovation in neuromodulation. The size of neuromodulation devices has shrunk due to integrated circuits and batteries developments, while the functional requirements have increased. Surgical needs, better control over programming settings, and patient-interaction needs have driven advancement so far. Since neuromodulation therapies must address patients' symptoms outside of controlled clinical settings, future digital solutions must focus on addressing patients' demands beyond in-clinic evaluation while keeping clinicians informed.
Computational Models and Neuroimaging
While creative programming is excellent for fine-tuning post-operative therapy, imaging can help enhance clinical outcomes by better-targeted strategies. The subthalamic nucleus (STN), a prominent target for treating movement disorders, has a high degree of inter-individual structural heterogeneity. Atlas-based targeting is less precise than image-guided surgical planning. Image capture and computer modeling have improved electrode positioning accuracy. In addition, larger fields for structural magnetic resonance imaging (MRI) (1.5 teslas to 7 teslas) have become the standard of care for neuroimaging, allowing direct targeting of deep brain structures. Susceptibility-weighted imaging (SWI) and quantitative susceptibility mapping (QSM) are routinely used in surgical planning for MRI-guided DBS procedures.
Other current neuromodulation therapy challenges are addressed by integrated imaging modalities like DTI and image processing pipelines. The dentato-rubro-thalamic tract (DRT) is a target for essential tremor (ET). Studies show that improving the connection between the primary motor cortex and the superior posterolateral part of the STN improves tremor, whereas enhancing connectivity between the supplementary motor area and the superior posteromedial part of the STN improves bradykinesia.
Advanced computer simulations with imaging data are essential for visualizing potential stimulation effects. Bioelectric field models can be used to plan the lead placement and fine-tune DBS and SCS programming. Pre-surgical simulations address how prospective stimulation may affect target brain tissue. Post-surgical models can help identify poor stimulation patterns and aid in programming modification by examining the overlap between beneficial and harmful locations, as well as possible activated tissue volumes (VTAs). Models can be simple or complex, depending on the processing power and the desired outcome. The imaging quality utilized to build these models also dictates which parameters can be changed (for example, if DTI is available, tissue conductivities can be added). Using bioelectric field models can also help define safety criteria when testing novel stimulation paradigms like novel waveforms. Using computational models allows for parameter exploration and patient-specific optimization of neuromodulation therapy settings.