Parkinson׳s disease (PD) is a neurodegenerative disease that primarily affects the motor functions of the patients. Research and surgical treatment of PD (e.g., deep brain stimulation) often require human brain atlases for structural identification or as references for anatomical normalization. However, two pitfalls exist for many current atlases used for PD. First, most atlases do not represent the disease-specific anatomy as they are based on healthy young subjects. Second, subcortical structures, such as the subthalamic nucleus (STN) used in deep brain stimulation procedures, are often not well visualized. The dataset described in this Data in Brief is a population-averaged atlas that was made with 3 T MRI scans of 25 PD patients, and contains 5 image contrasts: T1w (FLASH & MPRAGE), T2*w, T1–T2* fusion, phase, and an R2* map. While the T1w, T2*w, and T1–T2* fusion templates provide excellent anatomical details for both cortical and sub-cortical structures, the phase and R2* map contain bio-chemical features. Probabilistic tissue maps of whiter matter, grey matter, and cerebrospinal fluid are provided for the atlas. We also manually segmented eight subcortical structures: caudate nucleus, putamen, globus pallidus internus and externus (GPi & GPe), thalamus, STN, substantia nigra (SN), and the red nucleus (RN). Lastly, a co-registered histology-derived digitized atlas containing 123 anatomical structures is included. The dataset is made freely available at the MNI data repository accessible through the link http://nist.mni.mcgill.ca/?p=1209.

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The proposed unbiased templates are more representative of the population of interest and can benefit both the surgical planning and research of PD.

Abstract. We propose an automated method for preoperative trajectory planning of deep brain stimulation image-guided neurosurgery. Our framework integrates multi-modal MRI analysis (T1w, SWI, TOF-MRA) to determine an optimal trajectory to DBS targets (subthalamic nuclei and globus pallidus interna) while avoiding critical brain structures for prevention of hemorrhages, loss of function and other complications. Results show that our method is well suited to aggregate many surgical constraints and allows the analysis of thousands of trajectories in less than 1/10th of the time for manual planning. Finally, a qualitative evaluation of computed trajectories resulted in the identification of potential new constraints, which are not addressed in the current literature, to better mimic the decision-making of the neurosurgeon during DBS planning.