Sweetspot Explorer
Last updated
Last updated
This page is currently under construction.
Sweetspot Explorer allows users to identify local clusters that are associated with the clinical outcome following the DBS, e.g. % UPDRS-III improvement following STN-DBS for Parkinson's disease.
It has been developed to map anatomical hot- and cold-spots within DBS targeted regions.
The Sweetspot Explorer explains the outcome based on the stimulation location, which can be determined in several ways: as coordinates of active electrodes, weighted by the distance from the active electrode via a Gaussian function, as a binary VTA (thresholded here at 200V/m), or weighted by the strength of the electric field (thresholded here at 100V/m).
Sweetspot Explorer builds upon the Lead group and therefore, all the VTAs/E-fields have to be calculated (press the Calculate stats & VTA button in the Lead group interface) in order to use it. Click on the Visualize 3D button inside the Lead group and in the 3D viewer window, click on Add sweetspot analysis the button.
A new window with the Sweetspot Explorer user interface pops up, offering a selection of methods for further analysis. This window can be divided into 3 subparts: Interactive Model Setup, Visualization & Thresholds, and Crossvalidation & Prediction (Figure 1).
Interactive Model Setup (Image 1, arrow 1) allows users to define methods and parameters to generate the computational model. One can define the analysis level, subcohort selection, dependent variable selection, covariates, statistical tests, and the option to mirror VTAs/Efields (Image 2, arrows 1-8).
Analysis Level - allows users to simply select whether they want to use VTAs or E-Fields for their model. VTAs are stored in binary format [0 and 1], where all voxels within the VTA (voxels labelled as 1) are stimulated and all the voxels outside (voxels labelled as 0) are not stimulated. E-Fields are stored in a continuous fashion and contain information on the spatial distribution of the voltage. Based on the selection here, a further selection of options in "Inspire Analysis by ..." (Figure 2, arrow 1) will be determined.
VTA/E-Field Threshold [V/mm] - determines what effect will be considered as “stimulating” (Figure 2, arrow 2).
Voxels covered - Allows users to define the VTA/E-Field threshold required for voxels to be included in the model (Figure 2, arrow 3).
Variable of Interest (VOI), Normalize & Zero-Center VOI & Clean VOI from the following covariates - (Figure 2, arrows 4-6)
These options allow users to select one of the methods from previously published literature, or also create a custom model. The descriptions below were adapted from Dembek et al., 2022 and Elias et al., 2021.
Subcohorts can be created by selecting patients in the bottom window. This selection can be saved by clicking on Subcohorts (Image 2, arrow 10) and selecting one of the options: Create Subcohort from Selection or Create Subcohort from Inverse Selection.
If Mirror Data box is selected (Image 2, arrow 11), VTAs/Efields from one hemisphere will also be used for the second hemisphere (mirroring the effect).
The generated sweet and sour spots can be visualized and further adjusted during Visualization & Thresholding (Image 1, red arrow 2). Specifically, this step allows thresholding of the generated maps based on a predefined alpha level and correction for multiple comparisons (Image 3, arrows 1&2).
Users can select to visualize voxels positively or negatively correlated with your selected dependent variable (Image 3, arrow 3) and change their color (Image 3, arrow 4).
For more advanced post-hoc analysis, the option to export the generated maps in the form of NIfTI files is also available, by clicking on Export as NIfTI (Image 3, arrow 5). These NIfTI images can then be visualized and processed in Slicer. For further information please consult Sweet-sour spot.
After users finish the selection of all settings, pressing Refresh View or alternatively, ticking the Auto-Refresh box will refresh the view automatically (Image 3, arrow 7).
The final step across frameworks is Crossvalidation & Prediction (Image 1, red arrow 3). This step is crucial in establishing the validity of generated models within and generalizability across cohorts. Current validation strategies include permutation (Leave-Nothing-Out) approaches, as well as Leave-One-Patient-Out, Leave-One-Cohort-Out, and k-fold (randomized) cross-validations (Image 4, arrow 1). In addition, it is possible to customize this process and generate predictions for individual patients, as well as predefined subcohorts, cohorts, and sets (Image 4, arrow 2).
The drop-down window allows users to select a strategy, based on which predictions will be made. The current selection uses the mean of scores, the sum of scores, the peak of scores, and the peak of 5% of scores (Image 4, arrow 3).
If that option is selected, the results can also be post-hoc corrected for the group (Image 4, arrow 4).
Sweetspots corresponding to improvement.
Model results.
Cross-validation correlation plot.
Horn, A. et al. (2022) ‘Optimal deep brain stimulation sites and networks for cervical vs. generalized dystonia’, Proceedings of the National Academy of Sciences, 119(14), p. e2114985119. Available at: https://doi.org/10.1073/pnas.2114985119.
Neudorfer, C. et al. (2023) ‘Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks’, NeuroImage, 268, p. 119862. Available at: https://doi.org/10.1016/j.neuroimage.2023.119862.
Ríos, A.S. et al. (2022) ‘Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer’s disease’, Nature Communications, 13, p. 7707. Available at: https://doi.org/10.1038/s41467-022-34510-3.
Information on this page was taken from the book"Connectomic Deep Brain Stimulation" by Horn, 2022 and the following research publications:
Neudorfer C, Butenko K, Oxenford S, Rajamani N, Achtzehn J, Goede L, Hollunder B, Ríos AS, Hart L, Tasserie J, Fernando KB, Nguyen TAK, Al-Fatly B, Vissani M, Fox M, Richardson RM, van Rienen U, Kühn AA, Husch AD, Opri E, Dembek T, Li N, Horn A. Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks. Neuroimage. 2023 Mar;268:119862. doi: 10.1016/j.neuroimage.2023.119862. Epub 2023 Jan 5. PMID: 36610682; PMCID: PMC10144063.
Elias, G.J.B., Boutet, A., Joel, S.E., Germann, J., Gwun, D., Neudorfer, C., Gramer, R.M., Algarni, M., Paramanandam, V., Prasad, S., Beyn, M.E., Horn, A., Madhavan, R., Ranjan, M., Lozano, C.S., Kühn, A.A., Ashe, J., Kucharczyk, W., Munhoz, R.P., Giacobbe, P., Kennedy, S.H., Woodside, D.B., Kalia, S.K., Fasano, A., Hodaie, M. and Lozano, A.M. (2021), Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy. Ann Neurol, 89: 426-443. https://doi.org/10.1002/ana.25975
Dembek TA, Baldermann JC, Petry-Schmelzer JN, Jergas H, Treuer H, Visser-Vandewalle V, Dafsari HS, Barbe MT. Sweetspot Mapping in Deep Brain Stimulation: Strengths and Limitations of Current Approaches. Neuromodulation. 2022 Aug;25(6):877-887. doi: 10.1111/ner.13356. Epub 2022 Feb 10. PMID: 33476474.
