J. Luis Lujan

Tractography-activation models applied to subcallosal cingulate deep brain stimulation

Deep brain stimulation (DBS) of the subcallosal cingulate white matter (SCCWM) is an experimental therapy for major depressive disorder (MDD). The specific axonal pathways that mediate the anti-depressant effects of DBS remain unknown. Patient-specific tractography-activation models (TAMs) are a new tool to help identify pathways modulated by DBS. TAMs consist of four basic components: 1) anatomical and diffusion-weighted imaging data acquired on the patient; 2) probabilistic tractography from the brain region surrounding the implanted DBS electrode; 3) finite element models of the electric field generated by the patient-specific DBS parameter settings; and 4) application of the DBS electric field to multi-compartment cable models of axons, with trajectories defined by the tractography, to predict action potential generation in specific pathways. This study presents TAM predictions from DBS of the SCCWM in one MDD patient. Our findings suggest that small differences in electrode location can generate substantial differences in the directly activated pathways.

Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation

OBJECTIVE Clinical deep brain stimulation (DBS) systems can be programmed with thousands of different stimulation parameter combinations (e.g. electrode contact(s), voltage, pulse width, frequency). Our goal was to develop novel computational tools to characterize the effects of stimulation parameter adjustment for DBS. APPROACH The volume of tissue activated (VTA) represents a metric used to estimate the spatial extent of DBS for a given parameter setting. Traditional methods for calculating the VTA rely on activation function (AF)-based approaches and tend to overestimate the neural response when stimulation is applied through multiple electrode contacts. Therefore, we created a new method for VTA calculation that relied on artificial neural networks (ANNs). MAIN RESULTS The ANN-based predictor provides more accurate descriptions of the spatial spread of activation compared to AF-based approaches for monopolar stimulation. In addition, the ANN was able to accurately estimate the VTA in response to multi-contact electrode configurations. SIGNIFICANCE The ANN-based approach may represent a useful method for fast computation of the VTA in situations with limited computational resources, such as a clinical DBS programming application on a tablet computer.

Axonal pathways linked to therapeutic and nontherapeutic outcomes during psychiatric deep brain stimulation.

The underlying hypothesis of our work is that specific clinical neuropsychiatric benefits can be achieved by selective activation of specific axonal pathways during deep brain stimulation (DBS). As such, the goal of this study was to develop a method for identifying axonal pathways whose activation is most likely necessary for achieving therapeutic benefits during DBS.

Automated 3-dimensional brain atlas fitting to microelectrode recordings from deep brain stimulation surgeries.

Objective: Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient. Methods: We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact. Results: The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance. Conclusions and Significance: DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.

The need for understanding and engaging the patient as consumer of products developed by neural engineering

Neural Engineering is a discipline at the intersection of neuroscience, engineering, and clinical care. Recent major efforts by government and industry aimed at bringing forth personalized therapies, increasing the potential of the neural engineering industry for future growth, eg. the National Institutes of Health (NIH) Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative and Stimulating Peripheral Activity to Relieve Conditions (SPARC) Common Fund Program, the Defense Advanced Research Projects Agency (DARPA) Electrical Prescriptions (ElectRx) and Systems-Based Neurotechnology for Emerging Therapies (SUBNETS) Programs, and the GlaxoSmithKline Bioelectric Medicines Initiative. However, the incremental development of neural technologies can easily become a case of advancing technology for its own sake. This mindset can lead to a solution looking for a problem, without taking into consideration the patient/consumer point of view.

Mechanisms of Action of Deep Brain Stimulation for the Treatment of Psychiatric Disorders

Deep brain stimulation (DBS) has recently emerged as a potential treatment for medically intractable psychiatric disease. Pilot clinical studies have been performed with DBS of the subcallosal cingulate (SCC) white matter and ventral anterior internal capsule/ventral striatum (VC/VS) for the treatment of depression and obsessive–compulsive disorder with encouraging results. However, little is known about the underlying neural response and network activity generated when DBS is applied to these targets. This chapter summarizes the current understanding of the axonal response to DBS, and discusses the general network architectures believed to underlie psychiatric disease. We use diffusion tensor imaging tractography to better understand axonal trajectories surrounding DBS electrodes implanted in the SCC and VC/VS. Finally, we attempt to reconcile various data sets by presenting generalized hypotheses on potential therapeutic mechanisms of DBS for the treatment of psychiatric disorders.