A dataset of multi-contrast population-averaged brain MRI atlases of a Parkinson׳s disease cohort

Abstract

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.

Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

“…Although the MNIPD25 atlas (Xiao et al, 2017) is PD-specific, the MR modality of the STN atlas templates (T 2 *W) is different from that of our standard clinical T 2 W data. This might have caused an error in registration between the atlas template and the clinical data; this atlas was associated with worse performance and smaller volume than using other atlases.…”

“…Our 7 T-ML method is compared with the following publicly available STN atlases in the literature to validate its reliability for patient-specific clinical MRI based STN-DBS targeting: (1) 7 T MRI STN atlas for use with 3 T MRI (7 T3 T) (Milchenko et al, 2018); (2) ultrahighfield atlas (UHFA) (Wang et al, 2016); (3) DBS intrinsic template atlas (DISTAL) (Ewert et al, 2017; Horn, Neumann, Degen, Schneider, & Kühn, 2017); and (4) population-averaged atlas that was made with 3 T MRI of 25 PD patients (MNIPD25) (Xiao et al, 2017). Data acquisition and template images for these atlases are summarized in Table 4.…”

“…Our 7 T-ML method is compared with the following publicly avail- (Ewert et al, 2017;Horn, Neumann, Degen, Schneider, & Kühn, 2017); and (4) population-averaged atlas that was made with 3 T MRI of 25 PD patients (MNIPD25) (Xiao et al, 2017). Data acquisition and template images for these atlases are summarized in Table 4.…”

“…A probabilistic approach to map DBS electrode coordinates onto the MNI space was presented in Horn, Kühn, et al (2017). More recently, to achieve anatomical precision in the MNI space, a histological atlas (Chakravarty, Bertrand, Hodge, Sadikot, & Collins, 2006) was merged with subcortical atlases based on multiple contrast 3 T MR sequences from PD patients (Xiao et al, 2017) and high resolution multimodal MRIs and structural connectivity data (Ewert et al, 2017). …”

A framework for evaluating correspondence between brain images using anatomical fiducials

“…The focal template misregistrations we have identified in fMRIPrep with AFIDs are meant to serve as a baseline for refinement in future versions that can be compared transparently and potentially incorporated for testing new versions as part of a continuous integration workflow. Using additional image contrasts (Xiao et al, 2017) or subcortical tissue priors (Ewert et al, 2019) to drive template registration have been demonstrated using conventional voxel overlap techniques to result in more optimal registrations that can also be tested using the AFIDs framework.…”