The basic goal of this study was to develop methods to combine patient-specific computational models of DBS with quantitative clinical evaluations. We present an example PSA generated with data from six PD patients with electrodes implanted in the subthalamic region. While the small patient population may limit the explicit value of this particular PSA, the general concept was successfully demonstrated. The continual of addition of new patient data into this PSA has potential to increase its utility for STN DBS for PD, as well as provide a foundation for investigation of other brain stimulation targets. Interestingly, the target volumes predicted by our PSA () match very well with the target volume used in Frankemolle et al. (Frankemolle et al., 2010
) to prospectively predict stimulation parameter settings that were in many was superior to settings defined via traditional clinical methods.
We attempted to define therapeutic stimulation targets by identifying regions of overlapping neural activation volumes associated with marked clinical improvement across multiple patients. Our analysis was performed without a priori assumptions on the anatomical entity(ies) directly stimulated. Instead we relied on a voxel-based approach to discretize the entire subthalamic region, thereby enabling correlative analysis between the level of clinical improvement and the probability of stimulation induced neural activation in a given voxel. While our results showed how this could be used to characterize improvement level, there are adjunct maps that capture other important information such as variability and statistical power, each of which vary on a per-voxel basis. In contrast to functional imaging methods such as fMRI where an equivalent amount of data can be gathered for each voxel, our analysis depends on different electrode positions and VTA sizes to sample different regions.
The methodology used in this study could be useful in any DBS application where the precise relationships between electrode placement, stimulation parameter settings, and clinical outcomes remain unclear. For example, clinical trials to evaluate DBS for treatment refractory depression are currently evaluating two different anatomical brain regions (anterior limb of the internal capsule (Malone et al., 2009
) or subcallosal cingulate gyrus white matter (Lozano et al., 2008
), but in either case limited information currently exists on the specific target stimulation volume for maximal therapeutic benefit. Data from those clinical trials could be used to create PSAs in a similar fashion as performed in this study and provide guidance for subsequent studies on DBS for neuropsychiatric disorders. Similarly, PSAs could be used to refine knowledge on common DBS targets such as the STN or globus pallidus. Our early stage results suggest that maximal therapeutic treatment of both bradykinesia and rigidity were associated with stimulation of the same general area (white matter dorsal to the STN). These results coincide well with previous conclusions derived from similar patient-specific neurostimulation models (Maks et al., 2009
; Frankemolle et al., 2010
). However, the PSA also indicated that these two symptoms may have distinct targets, which if substantiated by future studies with larger sample sizes and data from multiple institutions could have important implications for surgical targeting and device programming.
Possible Physiological Origin of Distinct Stimulation Targets
Numerous prior publications have concluded that the STN per se may not be the only stimulation target with the subthalamic region (Plaha et al., 2006
; Maks et al., 2009
). The unique contribution of this study was a methodology to quantitatively distinguish between stimulation targets on a per-symptom basis (). Given the currently achievable surgical accuracy for DBS electrode placement to within ~2 mm of the desired target (Maciunas et al., 1994
; Holloway et al., 2005
), our results suggest that it could be possible to tailor electrode placement to the specific target volume associated with a given symptom. Likewise it should be possible to effectively stimulate multiple targets with an electrode that can activate multiple target regions via advanced techniques such as current steering (Butson and McIntyre, 2008
) and/or directionally oriented electrode contacts.
The predominant neural elements within our predicted target regions are axonal in nature, embedded in fiber bundles consisting of efferent axons (e.g. emanating from STN, substantia nigra, ZI), afferent axons (e.g. arriving from pallidum, cortex), and fibers of passage (e.g. lenticular fasciculus, prelemniscal radiation). The electric field induced by DBS (Miocinovic et al., 2009
) is non-discriminately applied to all of these neural elements. Both theoretical (Miocinovic et al., 2006
) and experimental (Hashimoto et al., 2003
) results suggest that the stimulation generates propagating action potentials in these neurons. When neurons are stimulated, the action potential initiates in the axon, even if the neurons’ somata is in close proximity to the electrode (Nowak and Bullier, 1998
; McIntyre and Grill, 1999
). Therefore, our VTA calculations focus on axonal activation and are intended to be representative of the neural response to the broad spectrum of neural elements surrounding the DBS electrode.
While the exact therapeutic mechanisms of DBS remain unclear, a growing body of evidence suggests that stimulation-induced disruption of pathological oscillations throughout the basal-ganglia-thalamocortical circuit plays a major role (Li et al., 2007
; Guo et al., 2008
; Hahn et al., 2008
; Hahn and McIntyre, 2010
). Although speculative given our current understanding of the system, three axonal pathways of interest in the target region would be especially well suited to contribute the disruption of pathological oscillations. First are the efferent axons of the STN itself. Second is the lenticular fasciculus (LF), which courses dorsal to the STN and consists of pallidal outflow to the thalamus (Parent and Parent, 2005
; Miocinovic et al., 2006
). Third is antidromic activation of cortical afferents (Li et al., 2007
; Gradinaru et al., 2009
Cortical afferents and the LF have previously been implicated in the mechanisms of DBS and their relative anatomical arrangement in the subthalamic region provide for interesting connections to the results of this study (). Therapeutic benefits from rigidity can often be achieved through different contact locations in the subthalamic region, and much of the cortical inputs to the subthalamic region approach from the antero-dorsal direction over a dispersed area (Nambu et al., 1997
), corresponding to a relatively large target volume for rigidity (). Conversely, the LF is a more focused fiber bundle coursing by the postero-medial border of the STN on its way into thalamus (Parent and Parent, 2004
), corresponding to a relatively smaller target volume for bradykinesia (). While speculative, these two examples demonstrate how PSAs are a new tool to help identify possible neuroanatomical relationships that connect therapeutic outcomes and DBS technology.
Maximizing Clinical Outcomes
The use of DBS can be compared to the use of medication, in the sense that the neurostimulation “prescription” consists of synergistic interaction between the electrode location and stimulation parameter settings. The electrode location is chosen intra-operatively based on the patient anatomy, imaging data, and intra-operative electrophysiology (Machado et al., 2006
). The stimulation parameters are defined post-operatively, titrated to provide maximal therapeutic benefit with minimal side effects (Volkmann et al., 2006
). DBS PSAs could be used to assist both of these steps. Specifically, pre-operative targeting could be based not only on the patient anatomy but also on the patient’s primary symptoms. For example, the results of this study suggest that rigidity dominant patients might benefit from electrodes implanted more anteriorly than for bradykinesia dominant patients. Further, the target stimulation volumes could be combined with patient-specific VTA predictions to select stimulation parameters that optimally activate the target region (Butson et al., 2007b
; Frankemolle et al., 2010
). Such concepts could be integrated into surgical planning and clinical programming software technologies intended to provide cutting edge advances in image registration, brain atlases, and computational modeling to real world clinical applications (Finnis et al., 2003
; D’Haese et al., 2005
; Nowinski et al., 2005
; Butson et al., 2007a
; Miocinovic et al., 2007
; Yelnik et al., 2007
; Bardinet et al., 2009
Limitations and Sources of Error
While this study provides quantitative information about areas which should be stimulated and others that should be avoided, there are several limitations and possible sources of error in our analysis. With regard to the clinical evaluation, patients were tested with the ipsilateral stimulator turned on, programmed with therapeutic stimulation settings that were arrived at pragmatically based on clinical judgment and experience, independently of this study. While ipsilateral clinical benefit is a possible confound, we expect that such an effect on our results is minimal because the effects of contralateral stimulation would be superimposed on a constant, background reduction in symptoms. The primary outcomes of the patient evaluation were quantitative measures of bradykinesia and rigidity. Detailed analysis of overall clinical outcomes in terms of Hoehn & Yahr staging, UPDRS-III and quality of life measures was not performed, although these outcomes are amenable to analysis using a PSA and could be included in future work.
There are also methodological considerations with regard to the computational modeling. First, the spatial extent of the volumes of activation evaluated in this study was limited by the electrode locations and the size of the corresponding VTAs, which in turn were limited by the range of stimulation amplitudes that each patient could tolerate. However, given the distribution of electrode contacts across the various patients we were able to sample the entire subthalamic region and adequately explore locations associated with therapeutic benefit (). A second possible source of error stems from image coregistration. We attempted to minimize error between pre- and post-operative patient images by using widely accepted coregistration algorithms (ITK 3D registration in Analyze). This approach works well when registering images taken on the same patient by maximizing the similarity over the entire brain. For atlas registration, we used T1 weighted images for both the patient and atlas brains, and employed an approach that maximized accuracy in the immediate vicinity of the electrode relative to the surrounding anatomical nuclei (see Methods). Further, we took great care to confirm the relative placement of the electrode and surrounding nuclei in both the patient and atlas models by visual inspection. Another possible source of error was in our VTA predictions. VTAs were generated from detailed bioelectric field models combined with activation function-based predictions of the neural response to DBS. We have conducted extensive studies in attempts to validate the model predictions (Butson et al., 2007b
; Miocinovic et al., 2009
; Chaturvedi et al., 2010
); however, it should be noted that the neurostimulation models used in this study cannot capture all of the possible neural responses to DBS. Lastly, the small number of patients in this study precludes us from making definitive statements with regard to stimulation targets for STN DBS patients.
This study presents methods to construct a probabilistic atlas of stimulation-induced activation during DBS. These methods were developed during prospective clinical evaluation of patients with DBS in the STN region. However, we propose that this approach could be used to augment the study of DBS in any part of the brain and may prove useful in quantifying any number of motor and/or non-motor outcomes from DBS. DBS PSAs could be especially useful in the study of emerging indications such as epilepsy and neuropsychiatric disorders, accelerating the identification of optimal surgical targets and stimulation parameter settings.
- Probabilistic stimulation atlas for deep brain stimulation
- Patient-specific computational models define volume of tissue activated
- Activation volumes coupled with clinical outcomes measures to define target stimulation regions
- DBS for Parkinson’s disease is associated with target volumes dorsal to the subthalamic nucleus