J007 Neuroimaging-guided automated trajectory planning for direct neurotherapeutics delivery for Huntington’s disease

<h3>Background</h3> Convection-enhanced Delivery (CED) allows for administration of neurotherapeutic agents into the striatum to target the neurodegenerative process in Huntington’s Disease (HD).^{1 2} Cortical and striatal atrophy can limit the safety and accuracy of possible surgical trajectories. Neuroimaging-guided computer-assisted trajectory planning can aid in optimising delivery, by ensuring the maximum volume of distribution (Vdmax) within the target and minimising disruption to critical tissues. <h3>Aims</h3> Using imaging-based framework, investigate how progressive cortical and striatal atrophy affects CED planning in HD. <h3>Methods</h3> T1-weighted MRI images of pre-manifest HD patients were acquired on 3T scanner. Segmentation was performed as follows: (a) skull using SimNIBS charm,³ (b) cortex, ventricles, brainstem using FreeSurfer,⁴ (c) subcortical nuclei with FSL FIRST.⁵ Volumes were calculated using SamSeg.⁶ All possible trajectories to three targets (anterior caudate, anterior putamen, posterior putamen) on both hemispheres were evaluated. Trajectories were excluded: (1) from contralateral hemisphere, basal skull, eloquent cortex, sulcal surfaces, (2) within 2 mm of non-target nuclei, ventricles, and non-entry cortex. Estimation of Vdmax per trajectory was performed. <h3>Results</h3> Data analysing trajectories to the right anterior putamen in three subjects with various degrees of atrophy are presented (putamen/total intracranial volume: 0.0058;0.0028;0.0025) (figure 1). With increasing atrophy, the number of valid trajectories decreased (8:7:1), shortest trajectory became longer (58;62;64mm), Vdmax decreased (1.12;0.67;0.49mls). <h3>Conclusions</h3> Progressive atrophy limits trajectory options for CED. Our results raise the question regarding the timing of CED in disease evolution. The methods presented here will be applied to a larger cohort of HD patients for a more in-depth analysis. <h3>References</h3> Tabrizi SJ, Estevez-Fraga C, van Roon-Mom WMC, <i>et al</i>. Potential disease-modifying therapies for Huntington’s disease: lessons learned and future opportunities. <i>Lancet Neurol</i>. 2022;<b>21</b>(7):645-658. doi:10.1016/S1474-4422(22)00121-1 Bobo RH, Laske DW, Akbasak A, Morrison PF, Dedrick RL, Oldfield EH. Convection-enhanced delivery of macromolecules in the brain. <i>Proc Natl Acad Sci U S A</i>. Mar 15 1994;<b>91</b>(6):2076-80. doi:10.1073/pnas.91.6.2076. Puonti O, Van Leemput K, Saturnino GB, Siebner HR, Madsen KH, Thielscher A. Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling. <i>Neuroimage</i>, 2020;<b>219</b>:117044. Fischl, B. ‘FreeSurfer.’ <i>Neuroimage</i> 62.2 2012:774-781. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. <i>Neuroimage</i>. 2011;<b>56</b>(3):907-922. doi:10.1016/j.neuroimage.2011.02.046. Puonti O, Iglesias JE, Van Leemput K. Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. <i>Neuroimage</i>. 2016;<b>143</b>:235-249. doi:10.1016/j.neuroimage.2016.09.011.