Ventral intermediate nucleus structural connectivity-derived segmentation: anatomical reliability and variability.

Area of Science:

• Neuroimaging
• Neurosurgery
• Computational Neuroscience

Background:

• The Ventral Intermediate Nucleus (Vim) is a key target for treating drug-refractory tremors.
• Existing Vim identification methods lack standardization, hindering reliable surgical targeting.
• Methodological variations in tractography-based pipelines complicate consistent results.

Purpose of the Study:

• To evaluate and compare different parcellation pipelines for identifying the Vim using tractography.
• To determine the most reliable pipeline for Vim identification with minimal inter-subject variability.
• To assess the spatial relationship between tractography-derived Vim and optimal stimulation points for essential tremor.

Main Methods:

• Tested four parcellation pipelines on high-quality and downsampled diffusion MRI datasets.
• Employed two diffusion signal modeling techniques and two voxel classification criteria.
• Selected the most reliable pipeline based on inter-subject variability and similarity to a histology-based Vim mask.

Main Results:

• A pipeline using higher-order signal modeling and threshold-based voxel classification proved most reliable across data quality.
• Vim identification was most accurate using precentral and dentate nucleus-thalamic connectivity.
• Tractography-derived targets showed significant shape and size discrepancies compared to histological ground truth.

Conclusions:

• Higher-order modeling and threshold-based classification provide the most reliable Vim identification for stereotactic neurosurgery.
• Connectivity-based thalamic segmentation requires careful consideration of data quality and pipeline choice.
• Further refinement of tractography methods is needed for precise Vim targeting in essential tremor treatment.