Towards topography-driven stereotactic targeting of the zona incerta
Presented During:
Wednesday, June 25, 2025: 6:21 PM - 6:33 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
5
Submission Type:
Abstract Submission
Authors:
Roy Haast1,2,3, Jason Kai4,5,3, Alaa Taha3,6, Violet Liu3,7, Greydon Gilmore3,6,7,8, Maxime Guye1,2, Ali Khan3,5,6,7, Jonathan Lau3,6,7,8
Institutions:
1Aix Marseille Univ, CNRS, CRMBM, Marseille, France, 2APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France, 3Robarts Research Institute, Western University, London, Ontario, Canada, 4Child Mind Institute, New York, NY, USA, 5Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada, 6School of Biomedical Engineering, Western University, London, Ontario, Canada, 7Graduate Program in Neuroscience, Western University, London, Ontario, Canada, 8Division of Neurosurgery, Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
First Author:
Roy Haast
Aix Marseille Univ, CNRS, CRMBM|APHM, Hôpital Universitaire Timone, CEMEREM|Robarts Research Institute, Western University
Marseille, France|Marseille, France|London, Ontario, Canada
Aix Marseille Univ, CNRS, CRMBM|APHM, Hôpital Universitaire Timone, CEMEREM|Robarts Research Institute, Western University
Marseille, France|Marseille, France|London, Ontario, Canada
Co-Author(s):
Jason Kai
Child Mind Institute|Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University|Robarts Research Institute, Western University
New York, NY, USA|London, Ontario, Canada|London, Ontario, Canada
Child Mind Institute|Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University|Robarts Research Institute, Western University
New York, NY, USA|London, Ontario, Canada|London, Ontario, Canada
Alaa Taha
Robarts Research Institute, Western University|School of Biomedical Engineering, Western University
London, Ontario, Canada|London, Ontario, Canada
Robarts Research Institute, Western University|School of Biomedical Engineering, Western University
London, Ontario, Canada|London, Ontario, Canada
Violet Liu
Robarts Research Institute, Western University|Graduate Program in Neuroscience, Western University
London, Ontario, Canada|London, Ontario, Canada
Robarts Research Institute, Western University|Graduate Program in Neuroscience, Western University
London, Ontario, Canada|London, Ontario, Canada
Greydon Gilmore
Robarts Research Institute, Western University|School of Biomedical Engineering, Western University|Graduate Program in Neuroscience, Western University|Division of Neurosurgery, Department of Clinical Neurological Sciences, Western University
London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada
Robarts Research Institute, Western University|School of Biomedical Engineering, Western University|Graduate Program in Neuroscience, Western University|Division of Neurosurgery, Department of Clinical Neurological Sciences, Western University
London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada
Maxime Guye
Aix Marseille Univ, CNRS, CRMBM|APHM, Hôpital Universitaire Timone, CEMEREM
Marseille, France|Marseille, France
Aix Marseille Univ, CNRS, CRMBM|APHM, Hôpital Universitaire Timone, CEMEREM
Marseille, France|Marseille, France
Ali Khan
Robarts Research Institute, Western University|Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University|School of Biomedical Engineering, Western University|Graduate Program in Neuroscience, Western University
London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada
Robarts Research Institute, Western University|Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University|School of Biomedical Engineering, Western University|Graduate Program in Neuroscience, Western University
London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada
Jonathan Lau
Robarts Research Institute, Western University|School of Biomedical Engineering, Western University|Graduate Program in Neuroscience, Western University|Division of Neurosurgery, Department of Clinical Neurological Sciences, Western University
London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada
Robarts Research Institute, Western University|School of Biomedical Engineering, Western University|Graduate Program in Neuroscience, Western University|Division of Neurosurgery, Department of Clinical Neurological Sciences, Western University
London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada|London, Ontario, Canada
Introduction:
The zona incerta (ZI)–a brain structure implicated in various functions (Mitrofanis, 2005)–is a target for deep brain stimulation (DBS) in essential tremor (ET) (Plaha et al., 2011) and Parkinson's disease (Blomstedt et al., 2018). Yet, precise stereotactic ZI targeting remains challenging due to poor direct visualization. We propose a novel approach by combining (i) our previous in vivo labeling of the human ZI based on 7 Tesla MRI T1 mapping (Lau et al., 2020) and (ii) cortico-incertal structural connectivity maps derived from high-quality diffusion MRI (dMRI) datasets of the Human Connectome Project (HCP) (Van Essen et al., 2012).
Methods:
Minimally preprocessed anatomical and dMRI data from 173 HCP subjects were used to construct a coordinate system. ZI and cortical regions were defined using probabilistic and surface-based parcellations (HCP-MMP1.0) and mapped to each subject's native space (Glasser et al., 2016; Lau et al., 2020). Probabilistic tractography using FSL's probtrackx, seeded from ZI voxels to cortical targets, provided cortico-incertal tracts. Resulting tracts were transformed to template space (MNI152NLin6Asym) to optimize inter-subject ZI overlap.
Group-average connectivity matrices were derived by quantifying streamline counts between ZI voxels and cortical parcels. Spectral clustering (k=6) based on cosine similarity parcellated the ZI, aligning with rodent models (Romanowski et al., 1985) and offering fine-grained targeting granularity. ZI connectivity gradients were extracted using diffusion map embedding (BrainSpace, Fig. 1) (Vos de Wael et al., 2020).
Stimulation volumes from a single ET DBS case were reconstructed using Lead-DBS 3.0 (Horn & Kühn, 2015) to evaluate connectivity within stimulation voxels. The patient, experiencing complete symptom resolution without side effects at 1-year follow-up, can aid in identifying optimal stimulation targets based on cortico-incertal connectivity.
Group-average connectivity matrices were derived by quantifying streamline counts between ZI voxels and cortical parcels. Spectral clustering (k=6) based on cosine similarity parcellated the ZI, aligning with rodent models (Romanowski et al., 1985) and offering fine-grained targeting granularity. ZI connectivity gradients were extracted using diffusion map embedding (BrainSpace, Fig. 1) (Vos de Wael et al., 2020).
Stimulation volumes from a single ET DBS case were reconstructed using Lead-DBS 3.0 (Horn & Kühn, 2015) to evaluate connectivity within stimulation voxels. The patient, experiencing complete symptom resolution without side effects at 1-year follow-up, can aid in identifying optimal stimulation targets based on cortico-incertal connectivity.
Results:
The primary gradient (G1) distinguished rostral ZI (rZI, blue) from caudal ZI (cZI, red), reflecting connectivity from primary sensorimotor areas (high G1) to anterior prefrontal regions, including the frontal pole (low G1). The secondary gradient (G2) identified voxels connected to premotor and dorsolateral prefrontal areas (low G2) versus the rest of the brain (high G2). Spectral clusters aligned with G1, ranging from cluster 1 (lowest G1) to cluster 6 (highest, Fig. 2a-c).
To evaluate ZI connectivity maps for stereotactic targeting, we compared stimulation volumes of a successful caudal ZI DBS case with the gradient coordinate space and clusters (Fig. 2d-e). Combining G1 and G2 values showed clear separation of the k=6 spectral clustering solution. This is reflected by an average Silhouette score of 0.324 (95% CI [0.311 – 0.337]) and 0.284 (95% CI [0.269 – 0.299]) for left and right hemispheres, which ranges from -1, indicating incorrect clustering, to 1, indicating highly dense clustering. Scores around zero indicate overlapping clusters. For the left hemisphere, ZI voxels within stimulation volumes showed mean G1 and G2 scores of 0.066 ± 0.058 and -0.130 ± 0.036, respectively, overlapping predominantly with cluster 4 (~64%). For the right hemisphere, mean G1 and G2 scores were 0.174 ± 0.045 and -0.046 ± 0.061, overlapping clusters 4 (~22%) and 5 (~20%).
To evaluate ZI connectivity maps for stereotactic targeting, we compared stimulation volumes of a successful caudal ZI DBS case with the gradient coordinate space and clusters (Fig. 2d-e). Combining G1 and G2 values showed clear separation of the k=6 spectral clustering solution. This is reflected by an average Silhouette score of 0.324 (95% CI [0.311 – 0.337]) and 0.284 (95% CI [0.269 – 0.299]) for left and right hemispheres, which ranges from -1, indicating incorrect clustering, to 1, indicating highly dense clustering. Scores around zero indicate overlapping clusters. For the left hemisphere, ZI voxels within stimulation volumes showed mean G1 and G2 scores of 0.066 ± 0.058 and -0.130 ± 0.036, respectively, overlapping predominantly with cluster 4 (~64%). For the right hemisphere, mean G1 and G2 scores were 0.174 ± 0.045 and -0.046 ± 0.061, overlapping clusters 4 (~22%) and 5 (~20%).
Conclusions:
We present structural connectivity gradients and spectral clusters as a reference framework for DBS targeting in the ZI. These topographic maps enable visualization of stimulation sites relative to cortico-incertal connections, and emphasize the predominant connectivity of the stimulated volume to dorsal prefrontal and primary sensorimotor cortices. Compared to spectral clustering, the 2D gradient coordinate space offers a unique ZI visualization to refine targeting, optimize therapeutic windows, and complement emerging strategies to identify DBS sweet spots (Hollunder et al., 2024).
Brain Stimulation:
Deep Brain Stimulation 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
White Matter Anatomy, Fiber Pathways and Connectivity
Keywords:
Movement Disorder
MRI
Sub-Cortical
Other - Topography
1|2Indicates the priority used for review
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