Bridging brain coordinates and machine learning for surgical targeting and morphometric mapping

COEX 

A deviation of 2 millimeters (mm) in deep brain stimulation (DBS) electrode positioning can result in variability of upwards of 60% in therapeutic benefit [1]. Suboptimal targeting may require reimplantation, which can pose additional risks. Localizing DBS targets is not always possible because of their small size, lack of contrast, and patient motion. Tools which involve non-linear alignment of an atlas to patient images, considered 'gold standard' for automatic localization, yield errors on the order of 2-3 mm [2] and highly depend on image quality (Figure 1A). Gadolinium enhanced T1w MRI (MRI-gad) is employed during DBS planning, as it helps with avoiding blood vessels. However, MRI-gad presents challenges during non-linear alignment [3]. Automatic localization of brain structures via machine learning (ML) offers faster and generalizable alternatives to registration approaches. However, limited studies cater ML to DBS targets while demonstrating generalizability in clinic (e.g. on MRI-gad) [4].
We validate an ML model (Figure 1) to localize surgical targets from the coordinates of salient brain landmarks [5] in patient space. Our approach is agnostic to field strength, generalizable to other brain regions, and enables more nuanced understanding of brain morphology that can be expressed in millimeters (Figure 2).