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In the last years there has been a growing interest in the applications ofdiffusion tractography for target identification in neurofunctional disordersfor an increasingly tailored approach. The growing diffusion ofwell-established neurosurgical procedures as deep brain stimulation (DBS) andtrans-cranial Magnetic Resonance-guided Focused Ultrasound (tcMRgFUS) favoredthis trend. Tractography can indeed provide more accurate, patient-specific,information about the targeted region if compared to stereotactic atlases. Onthe other hand, this tractography-based approachs is not veryphysician-friendly, and its heavily time consuming since needs several hoursfor Magnetic Resonance Imaging (MRI) data processing. In this study we proposea novel open-source deep learning framework called DeLTA-BIT (acronym ofDeep-learning Local TrActography for BraIn Targeting) for reconstructingthalamic probabilistic tractography maps enabling a personalized(patient-specific) target identification for functional neurological disorders.The proposed framework exploits two convolutional neural networks (CNNs) tosegment the thalamus and reconstruct the connectivity map between each voxelinside the thalamus and any user-specified target cortical or subcorticalmasks. These CNNs were trained, validated, and tested on the datasets from theHuman Connectome Project (HCP) database. Our results are comparable with thestate-of-the-art data-driven segmentation and probabilistic tractography anddemonstrate the high performance of proposed networks for personalized thalamictarget identification with a Dice coefficient of 0.93 +/- 0.01 for thalamussegmentation and a Dice Similarity Coefficient for the projection to theprecentral gyrus from the thalamus of 0.80 +/- 0.06. The code of these modelsis freely available on GitHub.

DeLTA-BIT: an open-source probabilistic tractography-based deep learning framework for thalamic targeting in functional neurological disorders