Peaglet: A user-friendly probabilistic Kernel density estimation of intracranial cortical and subcortical stimulation sites

Background: Data on human brain function obtained with direct electrical stimulation (DES) in neurosurgical patients have been recently integrated and combined with modern neuroimaging techniques, allowing a connectome-based approach fed by intraoperative DES data. Within this framework is crucial to develop reliable methods for spatial localization of DES-derived information to be integrated within the neuroimaging workflow. New method: To this aim, we applied the Kernel Density Estimation for modelling the distribution of DES sites from different patients into the MNI space. The algorithm has been embedded in a MATLAB-based User Interface, Peaglet. It allows an accurate probabilistic weighted and unweighted estimation of DES sites location both at cortical level, by using shortest path calculation along the brain 3D geometric topology, and subcortical level, by using a volume-based approach. Results: We applied Peaglet to investigate spatial estimation of cortical and subcortical stimulation sites provided by recent brain tumour studies. The resulting NIfTI maps have been anatomically investigated with neuroimaging open-source tools. Comparison with existing methods: Peaglet processes differently cortical and subcortical data following their distinguishing geometrical features, increasing anatomical specificity of DES-related results and their reliability within neuroimaging environments. Conclusions: Peaglet provides a robust probabilistic estimation of the cortical and subcortical distribution of DES sites going beyond a region of interest approach, respecting cortical and subcortical intrinsic geometrical features. Results can be easily integrated within the neuroimaging workflow to drive connectomic analysis.