Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models

Abstract

Objective. The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop-imaging data, such analysis using preop- and intraop-imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography. Approach. The study examined six patients who had undergone interventional magnetic resonance-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift. Main results. The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R = −0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ∼2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2–3 mm are detrimental. Significance. The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.

Advances in the intraoperative delineation of malignant glioma margin

“…MRI is the essential preoperative examination for brain cancer patients and a salient basis for intraoperative navigation. However, due to the presence of brain shift, which is inevitably caused by the loss of cerebrospinal fluid, force-induced deformation of brain tissue, and so on, the utilization of preoperative image data to guide neuronavigation will inevitably lead to deviations ( 11 ). These deviations may lead to either residual tumor after resection or over-resection of normal brain tissue.…”

Accounting for brain shift during image-guided tumor resection surgeries: an intraoperative feasibility study

“…This biomechanical model has a long history of use within the context of brain shift compensation during tumor resection [18,19]. The model has been shown to predict brain shift due to cerebrospinal fluid changes that produce gravity-induced sag [19], effects from hyperosmotic drugs such as mannitol [20,21], effects from incorporating more resolved neuroanatomical structures in mdoels [20], effects of pneumocephalus in procedures such as deep brain stimulation [22,23], effects from tissue retraction and resection [24,25], and more recently the incorporation of compartmentalization [26] and tumor debulking effects [27]. With many of the studies above, the model has been embedded in an evolving inverse problem framework to enable real-time correction of brain shift.…”

Comparison of neural activation models to analyze the impact of brain deformations on neural pathways during DBS procedures

“…In later works 9,10 , the authors incorporated bioelectrical transport to their modeling scheme to evaluate changes in neural activation due to brain shift, using neural activation models. Interestingly, these activation models have been a considerable focus in various recent DBS research, and there is no consensus in the literature of their validity or accuracy 11 .…”

“…In a previous work 9 , the brain shift influence on neural pathways had been estimated using the AVT methodology. In this study, we intend to leverage neurophysiological models to improve our analyses of neural pathway recruitment, comparing our results to those obtained by a volume of tissue activation method, called the electric-field norm threshold [15][16][17] .…”