Objective: To jointly estimate motor and non-motor outcomes of deep brain stimulation using presurgical quantitative susceptibility maps.
Background: Parkinson’s disease (PD) patients with motor complications are often considered for deep brain stimulation (DBS) surgery1. Candidate selection via the levodopa challenge test (LCT)2 estimates DBS response by measuring presurgical improvement from dosage changes. LCT is an inconsistent estimator3,4 and neuroimaging models5-15 attempt to predict motor or non-motor improvement, but joint symptom estimations are unexplored. As non-motor symptoms significantly impact quality of life16, joint motor and non-motor improvement predictions remain an unmet need. Quantitative susceptibility mapping (QSM)17,18 is an MRI contrast depicting whole brain19 iron20 and visualizing subthalamic nuclei (STN) in presurgical planning21. Radiomic spatial features in nuclei susceptibility correlate with PD progression22-24 and motor symptoms25, and may inform non-motor outcomes26,27.
Method: A novel presurgical QSM radiomics ([figure1], [figure2]) Lasso model is presented to predict both motor (unified Parkinson’s disease rating scale, UPDRS-III28) and non-motor (Beck Depression Inventory, BDI29) outcomes from STN features. From sample size of N=25, Tukey BDI outlier detection30 resulted in N=19 models trained (with optimal regularization and number of features) on N–1 cases.
Results: The model correlated ([figure3]) with motor r=0.71 and non-motor r=0.60 improvement (p<0.01). LCT failed to predict outcomes, with r=0.05 and r=-0.07, p>0.05. Features predictive [figure4] of motor improvement were primarily on wavelet images, as previously found31. The most predictive feature of non-motor outcomes was the gray level dependence matrix (GLDM) low gray level emphasis, separating (p<0.01) responders from non-responders; non-responders exhibit more low susceptibility regions ([figure5]) and less high susceptibility regions, with a lower mean STN susceptibility (p<0.01).
Conclusion: Predictive motor features are consistent with connections in texture features and symptom burden31,32. Predictive non-motor features link elevated susceptibility (suggestive of increased depression33) with reportedly improved BDI outcomes34,35. Both numerical predictions of both UPDRS-III and BDI improvements use the presurgical target visualization QSM36,37, offering a valuable alternative to LCT.
Pipeline overview of template and model creation
Proposed model; input QSM and output predictions
LCT and model motor and non-motor correlation
Predictive features by frequency in trained models
Separation by feature low susceptibility emphasis
References: 1. Bronstein JM, Tagliati M, Alterman RL, et al. Deep Brain Stimulation for Parkinson Disease: An Expert Consensus and Review of Key Issues. Archives of Neurology. 2011;68(2):165-165. doi:10.1001/archneurol.2010.260
2. Saranza G, Lang AE. Levodopa challenge test: indications, protocol, and guide. Journal of Neurology. 2020;doi:10.1007/s00415-020-09810-7
3. Lachenmayer L, Mürset M, Antih N, et al. Subthalamic and pallidal deep brain stimulation for Parkinson’s disease-meta-analysis of outcomes. npj Parkinson s Disease. 09/06 2021;7doi:10.1038/s41531-021-00223-5
4. Wolke R, Becktepe JS, Paschen S, et al. The Role of Levodopa Challenge in Predicting the Outcome of Subthalamic Deep Brain Stimulation. Movement Disorders Clinical Practice. 2023;10(8):1181-1191. doi:https://doi.org/10.1002/mdc3.13825
5. Horn A, Li N, Dembek TA, et al. Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging. NeuroImage. 2019/01/01/ 2019;184:293-316. doi:https://doi.org/10.1016/j.neuroimage.2018.08.068
6. Goede LL, Oxenford S, Kroneberg D, et al. Linking Invasive and Noninvasive Brain Stimulation in Parkinson’s Disease: A Randomized Trial. Movement Disorders. 2024;doi:10.1002/mds.29940
7. Hirschmann J, Steina A, Vesper J, Florin E, Schnitzler A. Neuronal oscillations predict deep brain stimulation outcome in Parkinson’s disease. Brain Stimulation. 2022;15(3):792-802. doi:10.1016/j.brs.2022.05.008
8. Boutet A, Madhavan R, Elias GJB, et al. Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning. Nature Communications. 2021;12(1)doi:10.1038/s41467-021-23311-9
9. Li X, Xiong Y, Liu S, et al. Predicting the Post-therapy Severity Level (UPDRS-III) of Patients With Parkinson’s Disease After Drug Therapy by Using the Dynamic Connectivity Efficiency of fMRI. Frontiers in Neurology. 2019;10doi:10.3389/fneur.2019.00668
10. Liu Y, Xiao B, Zhang C, et al. Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson’s Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study. Front Neurosci. 2021;15:731109. doi:10.3389/fnins.2021.731109
11. Loehrer PA, Bopp MHA, Dafsari HS, et al. Microstructure predicts non-motor outcomes following deep brain stimulation in Parkinson’s disease. npj Parkinson’s Disease. 2024;10(1)doi:10.1038/s41531-024-00717-y
12. Roberts A, Zhang J, Tozlu C, et al. Radiomic prediction of Parkinson’s disease deep brain stimulation surgery outcomes using quantitative susceptibility mapping and label noise compensation. neuromodecJ. 2024/10/1 2024;doi:10.31641/nmj-ogms6387
13. Roberts AG, Zhang J, Akkus S, et al. Radiomic Prediction of Parkinson’s Disease Deep Brain Stimulation Surgery Motor and Non-motor Outcomes using Quantitative Susceptibility Mapping. presented at: Magnetic Resonance Phase, Susceptibility, and Electrical Properties Mapping; 2024;
14. Roberts AG, Zhang J, Tozlu C, et al. Quantitative Susceptibility Mapping Radiomics with Label Noise Compensation for Predicting Deep Brain Stimulation Outcomes in Parkinson’s Disease. Preprint. Cold Spring Harbor Laboratory; 2024. doi:10.1101/2024.12.26.24319663 https://dx.doi.org/10.1101/2024.12.26.24319663
15. Shang R, He L, Ma X, Ma Y, Li X. Connectome-Based Model Predicts Deep Brain Stimulation Outcome in Parkinson’s Disease. Frontiers in Computational Neuroscience. 2020;14doi:10.3389/fncom.2020.571527
16. Jost ST, Visser-Vandewalle V, Rizos A, et al. Non-motor predictors of 36-month quality of life after subthalamic stimulation in Parkinson disease. npj Parkinson’s Disease. 2021;7(1)doi:10.1038/s41531-021-00174-x
17. Liu J, Liu T, De Rochefort L, et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. NeuroImage. 2012;59(3):2560-2568. doi:10.1016/j.neuroimage.2011.08.082
18. De Rochefort L, Liu T, Kressler B, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: Validation and application to brain imaging. Magnetic Resonance in Medicine. 2010;63(1):194-206. doi:10.1002/mrm.22187
19. Roberts AG, Romano DJ, Şişman M, et al. Maximum spherical mean value filtering for whole‐brain QSM. Magnetic Resonance in Medicine. 2024;91(4):1586-1597. doi:10.1002/mrm.29963
20. Wang Y, Liu T. Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. Magnetic Resonance in Medicine. 2015;73(1):82-101. doi:10.1002/mrm.25358
21. Dimov AV, Gupta A, Kopell BH, Wang Y. High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping. Journal of Neurosurgery. 2019;131(2):360-367. doi:10.3171/2018.3.jns172145
22. Fu X, Deng W, Cui X, et al. Time-Specific Pattern of Iron Deposition in Different Regions in Parkinson’s Disease Measured by Quantitative Susceptibility Mapping. Frontiers in Neurology. 2021;12doi:10.3389/fneur.2021.631210
23. Shahmaei V, Faeghi F, Mohammadbeigi A, Hashemi H, Ashrafi F. Evaluation of iron deposition in brain basal ganglia of patients with Parkinson’s disease using quantitative susceptibility mapping. European Journal of Radiology Open. 2019;6:169-174. doi:10.1016/j.ejro.2019.04.005
24. Zeng W, Cai J, Zhang L, Peng Q. Iron Deposition in Parkinson’s Disease: A Mini-Review. Cellular and Molecular Neurobiology. 2024;44(1)doi:10.1007/s10571-024-01459-4
25. Huang W, Ogbuji R, Zhou L, Guo L, Wang Y, Kopell BH. Motoric impairment versus iron deposition gradient in the subthalamic nucleus in Parkinson’s disease. J Neurosurg. Jul 1 2021;135(1):284-290. doi:10.3171/2020.5.JNS201163
26. Duan X, Xie Y, Zhu X, et al. Quantitative Susceptibility Mapping of Brain Iron Deposition in Patients With Recurrent Depression. Psychiatry Investigation. 2022;19(8):668-675. doi:10.30773/pi.2022.0110
27. Yao S, Zhong Y, Xu Y, et al. Quantitative Susceptibility Mapping Reveals an Association between Brain Iron Load and Depression Severity. Frontiers in Human Neuroscience. 2017;11doi:10.3389/fnhum.2017.00442
28. Goetz CG, Tilley BC, Shaftman SR, et al. Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): Scale presentation and clinimetric testing results. Movement Disorders. 2008;23(15):2129-2170. doi:10.1002/mds.22340
29. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An Inventory for Measuring Depression. Archives of General Psychiatry. 1961;4(6):561-571. doi:10.1001/archpsyc.1961.01710120031004
30. Seo S. A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets. 2006:
31. Zhao W, Yang C, Tong R, et al. Relationship Between Iron Distribution in Deep Gray Matter Nuclei Measured by Quantitative Susceptibility Mapping and Motor Outcome After Deep Brain Stimulation in Patients With Parkinson’s Disease. Journal of Magnetic Resonance Imaging. 2023;58(2):581-590. doi:10.1002/jmri.28574
32. Li G, Zhai G, Zhao X, et al. 3D texture analyses within the substantia nigra of Parkinson’s disease patients on quantitative susceptibility maps and R2∗ maps. NeuroImage. 2019/03/01/ 2019;188:465-472. doi:https://doi.org/10.1016/j.neuroimage.2018.12.041
33. Shibukawa S, Kan H, Honda S, et al. Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia. Translational Psychiatry. 2024;14(1)doi:10.1038/s41398-024-02862-7
34. Hu T, Xie H, Diao Y, et al. Effects of Subthalamic Nucleus Deep Brain Stimulation on Depression in Patients with Parkinson’s Disease. Journal of Clinical Medicine. 2022;11(19):5844. doi:10.3390/jcm11195844
35. Birchall EL, Walker HC, Cutter G, et al. The effect of unilateral subthalamic nucleus deep brain stimulation on depression in Parkinson’s disease. Brain Stimulation. 2017;10(3):651-656. doi:10.1016/j.brs.2016.12.014
36. Rasouli J, Ramdhani R, Panov FE, et al. Utilization of Quantitative Susceptibility Mapping for Direct Targeting of the Subthalamic Nucleus During Deep Brain Stimulation Surgery. Operative neurosurgery (Hagerstown, Md). Apr 1 2018;14(4):412-419. doi:10.1093/ons/opx131
37. Liu T, Eskreis-Winkler S, Schweitzer AD, et al. Improved subthalamic nucleus depiction with quantitative susceptibility mapping. Radiology. Oct 2013;269(1):216-23. doi:10.1148/radiol.13121991
To cite this abstract in AMA style:
A. Roberts, S. Akkus, M. Spadaccia, C. Tozlu, D. Romano, P. Spincemaille, Y. Wang, B. Kopell. Joint Prediction of Motor and Non-motor Deep Brain Stimulation Outcomes using Quantitative Susceptibility Mapping [abstract]. Mov Disord. 2025; 40 (suppl 1). https://www.mdsabstracts.org/abstract/joint-prediction-of-motor-and-non-motor-deep-brain-stimulation-outcomes-using-quantitative-susceptibility-mapping/. Accessed October 5, 2025.« Back to 2025 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/joint-prediction-of-motor-and-non-motor-deep-brain-stimulation-outcomes-using-quantitative-susceptibility-mapping/