Optimal deep brain stimulation sites and networks for cervical vs. generalized dystonia

Abstract

Dystonia is a debilitating disease with few treatment options. One effective option is deep brain stimulation (DBS) to the internal pallidum. While cervical and generalized forms of isolated dystonia have been targeted with a common approach to the posterior third of the nucleus, large-scale investigations regarding optimal stimulation sites and potential network effects have not been carried out. Here, we retrospectively studied clinical results following DBS for cervical and generalized dystonia in a multicenter cohort of 80 patients. We model DBS electrode placement based on pre- and postoperative imaging and introduce an approach to map optimal stimulation sites to anatomical space. Second, we investigate which tracts account for optimal clinical improvements, when modulated. Third, we investigate distributed stimulation effects on a whole-brain functional connectome level. Our results show marked differences of optimal stimulation sites that map to the somatotopic structure of the internal pallidum. While modulation of the striatopallidofugal axis of the basal ganglia accounted for optimal treatment of cervical dystonia, modulation of pallidothalamic bundles did so in generalized dystonia. Finally, we show a common multisynaptic network substrate for both phenotypes in the form of connectivity to the cerebellum and somatomotor cortex. Our results suggest a brief divergence of optimal stimulation networks for cervical vs. generalized dystonia within the pallidothalamic loop that merge again on a thalamo-cortical level and share a common whole-brain network.

Keywords: connectomics; deep brain stimulation; dystonia; movement disorders; neuromodulation.

Fig. 1.

Functional-anatomical model leading to the core hypothesis for the present study. (A) Basal-ganglia model in context of a reinforcement-learning context. The left side shows the main axis of the basal ganglia (actor) with a three-layer model in which both striatum and subthalamic nucleus form entry nodes and GPi and substantia nigra pars reticularis (SNr) serve as output ganglia, feeding information back (via the thalamus) to the cortex and passing it on to brainstem centers (BS). Dopaminergic input serves as one of multiple critics to reinforce successful motor behavior. Adapted from ref. . (B) Translation of the model to the anatomical domain based on information shown in SI Appendix, Fig. S1. The striatopallidofugal system and pallidothalamic fibers serve as the main axis (actor) and receive feedback from dopaminergic centers, especially the substantia nigra pars compacta (SNc). Pallidal receptive fields reside in a 90° angle to the striatopallidofugal fiber system, pallidothalamic output tracts traverse the main axis in equally orthogonal fashion. (C) Hypothesis generation for the present study based on anatomical considerations. Two scenarios are possible (shown as cut-out box from B). (Upper) Active contacts (pink) of top-responding patients are located along the direction of striatopallidonigral fibers. In this case, our results would reveal activation of these fibers to best account for clinical outcome. (Lower) Instead, active contacts (cyan) could also be located along pallidothalamic tracts (ansa lenticularis; a.l. and fasciculus lenticularis; f.l.). In this case, our results would reveal activation of these fibers to best account for clinical outcomes.

Fig. 2.

Overview of the three methods applied. (A) DBS sweetspot mapping. Based on DBS electrode localizations carried out with Lead-DBS, E-fields were estimated using a finite element approach based on the long-term stimulation parameters applied in each patient. E-fields were then warped into MNI space. For each voxel, the E-field vector magnitudes and clinical improvements were rank-correlated, leading to a map with positive and negative associations (sweet and sour spots). (B) DBS fiber filtering. Again, E-fields were pooled in standard space and the group was set into relationship with all of 26,800 tracts forming a predefined set of normative pathways (17). Sum E-field magnitudes along each tract were aggregated for each patient and again rank-correlated with clinical improvements, attributing positive vs. negative weights to each tract (sweet and sour tracts). (C) DBS network mapping. Seeding blood-oxygen level-dependent signals from each E-field in a database of 1,000 healthy brains led to functional connectivity maps that were averaged to form a functional connectivity “fingerprint” for each patient. Voxels in these were correlated with clinical improvements to create an R-map model of optimal network connectivity.

Fig. 3.

Reconstructions of DBS electrode placement of the five cohorts color-coded by center (Top). Active DBS contacts of the group shown in synopsis with an ultrahigh-resolution template of the human brain (27).

Fig. 4.

Sweetspot mapping of cervical (red) vs. generalized (blue) subcohorts matches somatotopic organization of the GPi as defined by Nambu (12). Voxels are color-coded by the degree of correlation between percent improvements of either TWSTRS (cervical, hot colors) or BFMDRS (generalized, cool colors) and shown on multiple axial (Upper) and coronal/sagittal (Lower) slides on top of the BigBrain template (50). The last panel (Lower Right) shows the homuncular representation of the pallidum following reports by Nambu (12), which stated that neurons responding to the orofacial, forelimb, and hindlimb regions of motor cortex are located along the ventral-to-dorsal axis in the GPi.

Fig. 5.

Tracts associated with optimal outcome for patients with cervical (Left) and generalized (Right) dystonia. (A) On a broader scale (slightly lower threshold), modulation of corticofugal tracts from the somatomotor head and neck region weas associated with optimal outcomes in cervical dystonia, while tracts from the whole somatotopical domain were associated with generalized dystonia. (B) On a localized level (slightly higher threshold), in cervical dystonia, striatopallidofugal tracts of the posterior comb system were associated with optimal outcomes. In contrast, fibers from the fasciculus lenticularis were negatively associated. In generalized dystonia, both pallidothalamic bundles (ansa and fasciculus lenticularis) were associated with optimal outcomes, as was a more anterior portion of the comb system. (C) Across the cervical and generalized cohorts, the degree of how fittingly the identified networks were modulated by each patient's E-field correlated with clinical improvements. While these correlation analyses are of circular nature, a permutation statistic (Bottom) showed superior model fits for unpermuted vs. permuted improvement values. AL: ansa lenticularis, FL: fasciculus lenticularis.

Fig. 6.

DBS network-mapping results based on normative rs-fMRI data. Red regions show connections positively correlated with clinical improvements, blue regions the opposite. Crucially, optimal networks in cervical vs. generalized dystonia differed substantially but both included positive connections to cerebellum and midbrain regions and negative to somatomotor, temporal and inferior frontal cortices (as revealed by both the combined and agreement maps).