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Background: Childhood-onset dystonia is often progressive and severely impairs a child´s life. The pathophysiology is very heterogeneous and treatment responses vary in patients with dystonia. Factors influencing treatment effects remain to be elucidated. We hypothesize that differences in brain connectivity and fiber coherence contribute to the heterogeneity in treatment response among pediatric patients with inherited and acquired dystonia.Methods: Twenty patients with childhood-onset dystonia were retrospectively recruited including twelve patients with inherited or idiopathic, and eight patients with acquired dystonia (mean age 10 years; 8 female/12 male). Fiber density between the internal part of the globus pallidus and selective target regions, as well as the diffusion measures of fractional anisotropy (FA) and mean diffusivity (MD) were analyzed and compared between different etiologies.Results: Patients with acquired dystonia presented higher fiber density to the premotor cortex and putamen and lower FA values in the thalamus compared to patients with inherited/idiopathic dystonia. MD in the premotor cortex was higher in patients with acquired dystonia, while it was lower in the thalamus.Conclusion: Diffusion MRI reveals microstructural and network alterations in patients with dystonia of different etiologies.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.