Abstract
Cervical dystonia is a neurological disorder characterized by sustained, involuntary movements of the head and neck. Most cases of cervical dystonia are idiopathic, with no obvious cause, yet some cases are acquired, secondary to focal brain lesions. These latter cases are valuable as they establish a causal link between neuroanatomy and resultant symptoms, lending insight into the brain regions causing cervical dystonia and possible treatment targets. However, lesions causing cervical dystonia can occur in multiple different brain locations, leaving localization unclear. Here, we use a technique termed 'lesion network mapping', which uses connectome data from a large cohort of healthy subjects (resting state functional MRI, n = 1000) to test whether lesion locations causing cervical dystonia map to a common brain network. We then test whether this network, derived from brain lesions, is abnormal in patients with idiopathic cervical dystonia (n = 39) versus matched controls (n = 37). A systematic literature search identified 25 cases of lesion-induced cervical dystonia. Lesion locations were heterogeneous, with lesions scattered throughout the cerebellum, brainstem, and basal ganglia. However, these heterogeneous lesion locations were all part of a single functionally connected brain network. Positive connectivity to the cerebellum and negative connectivity to the somatosensory cortex were specific markers for cervical dystonia compared to lesions causing other neurological symptoms. Connectivity with these two regions defined a single brain network that encompassed the heterogeneous lesion locations causing cervical dystonia. These cerebellar and somatosensory regions also showed abnormal connectivity in patients with idiopathic cervical dystonia. Finally, the most effective deep brain stimulation sites for treating dystonia were connected to these same cerebellar and somatosensory regions identified using lesion network mapping. These results lend insight into the causal neuroanatomical substrate of cervical dystonia, demonstrate convergence across idiopathic and acquired dystonia, and identify a network target for dystonia treatment.