The following is a summary of the methods paper:
When work on Lead-DBS was started in 2012, we began collecting subcortical atlases of the brain to best define anatomy. For instance, the ATAG atlas was made freely available by the Forstmann group in Amsterdam.
Over the years, with growing experience, we noticed that none of the available atlases was precisely suited for use in Lead-DBS, none of them was explicitly built for our toolbox.
One limitation of most available atlases (when used in Lead-DBS) can be seen in the figure below:
Your patient data will (if quality allows) show a similar hypointense region that defines (part) of the STN.
- If you use a nonlinear deformation algorithm (like the Advanced Normalization Tools, FSL- or SPM-based implementations in Lead-DBS) to register the hypointense region of your patient’s brain to the MNI template AND
- If data & algorithms were perfect => this would lead to the hypointense region of your patient’s brain exactly matching the STN of the template.
If you now combine this image with an atlas that doesn’t exactly fit the MNI template, a systematic mismatch will result!
Above: This figure further illustrates the systematic error introduced by a misplaced atlas (i.e. an atlas not precisely matching the template that is used for normalization).
Above conclusions forced us to create an atlas that is “made for Lead-DBS”
We wanted to create an atlas that is i) very precisely aligned to the ICBM 2009b MNI template and ii) accurate enough to visualize all structures relevant for DBS surgery. This resulted in three separate stages.
Stage 1: A multimodal region-growing algorithm applied to MNI templates
In this stage, the T1-, T2-, PD- and T2rlx weighted templates were segmented simultaneously, incorporating all information in parallel. First, a multimodel region-growing-like algorithm was applied that produced probability maps. These maps were then used to manually segment subthalamic nucleus, red nucleus, internal and external pallidum directly within the space of the template series.
Stage 2: Precise co-registration of histology, using Stage 1 as additional anchor points
To “fill the gaps” between the regions defined on the template itself, a precise histological atlas was graciously provided by Mallar Chakravarty (Douglas Institute) and Louis Collins (MNI). To align this data as precisely as possible to the MNI space, manual structures were used as additional anchorpoints in a nonlinear pseudo-MRI to template warp.
Stage 3: Further parcellation of core DBS targets by means of structural connectivity
The final stage was to further subdivide the primary DBS targets, namely the STN and the GPi by means of structural connectivity. This has been done in multiple studies for the STN, subdividing it into sensorimotor, associative and limbic subregions. It has not been done for the GPi, potentially given it’s closeness to efferent and afferent structures. We used the efferent connectivity of the GPi that projects to thalamic functional zones as they were defined in the famous Behrens 2003 study.
The final result is an atlas that is i) precisely coregistered to the ICBM 2009b nonlinear asymmetric MNI template and ii) very detailed (based on histology and structural connectivity).