Deep brain stimulation (DBS) is an effective treatment for movement disorders, e.g. Parkinson’s disease. The quality of DBS treatment is dependent on the implantation accuracy of DBS electrode leads into target structures. However, brain shift during burr hole procedures has been documented and hypothesized to negatively impact treatment quality. Several approaches have been proposed to compensate for brain shift in DBS, namely microelectrode recording (MER) and interventional magnetic resonance (iMR) imaging. Though both demonstrate benefits in guiding accurate electrode placement, they suffer drawbacks such as prolonged procedures and in the latter, cost considerations. Hence, we are exploring a model-based brain shift compensation strategy in DBS to improve targeting accuracy for surgical navigation. Our method is a deformation-atlas-based approach, i.e. potential intraoperative deformations are pre-computed via biomechanical model under varying conditions, combined with an inverse problem driven by sparse intraoperative data for estimating volumetric brain deformations. In this preliminary feasibility study, we examine our model’s ability to predict brain shift in DBS by comparing with iMR in one patient. The evaluation includes: (1) a subsurface deformation comparison where subsurface shifts measured by iMR are compared to model-predicted counterparts; (2) a second comparison at surgical targets where the atlas-method is compared to deformations measured by non-rigid image-to-image registration using preoperative image and iMR. For the former, the model reduces alignment error from 8.6 ± 1.4 to 3.6 ± 0.8 mm, representing ~58.6% correction. For the latter, model estimated brain shifts at surgical targets are 2.4 and 0.6 mm, consistent with clinical observations.