For decades, research on recovery from acute vestibular deficits has focused on compensatory mechanisms in the brainstem vestibular nuclei and associated brainstem/cerebellar pathways. In contrast, recent prospective clinical studies identified elevated autonomic arousal and anxiety as the primary predictors of failed recovery and prolonged vestibular symptoms (e.g. dizziness, imbalance, hypersensitivity to motion stimuli) in chronic vestibular disorders following acute vestibular events. Thus, adverse vestibular-autonomic interactions appear to precipitate and perpetuate chronic vestibular disorders. Pathways linking vestibular nuclei to the amygdalae via parabrachial and associated brainstem autonomic nuclei are thought to underlie these disorders. Greater knowledge of vestibular-autonomic interactions, when translated into early clinical interventions, promises to maximize recovery from acute vestibular disorders, an important public health goal, by reducing the incidence of chronic vestibular disorders and improving their treatment. Progress is hampered by a critical barrier: neural circuitry of brainstem vestibular-autonomic processes is underspecified in living humans, despite extensive research in animal models. Existing in vivo brain imaging methods lack sufficient sensitivity and contrast to localize key brainstem vestibular and autonomic nuclei, such as the vestibular nuclei complex, periaqueductal gray, raphe magnus, lateral and medial parabrachial nuclei, and solitary nucleus. To surmount this barrier, the central aim of the proposed research is to generate in living healthy subjects an original probabilistic neuroimaging atlas of vestibular and autonomic nuclei in standard stereotaxic space and to map their benchmark connectivity diagram (?connectome?) at rest and during vestibular stimulation using advanced imaging technology (7 T and 3 T Connectome scanners). This proposal builds on our recently published 7 T work in living humans, which yielded the probabilistic atlas and connectome of two An (periaqueductal gray, raphe magnus) and nine other brainstem nuclei of the arousal and motor systems. We propose to extend this atlas and connectome to include the vestibular nuclei complex and three additional autonomic nuclei (lateral and medial parabrachial nuclei, and solitary nucleus), as well as to use vestibular stimulation to pioneeringly test hypothesis driven functional connectivity changes in brainstem vestibular and autonomic nuclei. Thus, our project will provide two important new tools, a structural atlas and connectome for studying the vestibular nuclei, multiple autonomic nuclei, and their interactions in living humans. The ability of localizing vestibular and autonomic nuclei in neuroimages and of investigating vestibular-autonomic pathways in living humans will enhance our understanding of how brainstem vestibular-autonomic processes relate to the patho- physiologic mechanism causing chronic vestibular disorders and to their treatment outcomes.
Despite accumulating evidence indicates that changes in the connectivity pathways of brainstem vestibular and autonomic nuclei are involved in the pathological processes causing chronic vestibular disorders, existing imaging methods are incapable of resolving details of these brainstem structures in vivo. In this application, we plan to generate in living healthy subjects a pioneering probabilistic neuroimaging atlas of brainstem vestibular and autonomic nuclei by the use of advanced imaging technology (7 Tesla scanner) and methods, and to use the atlas as a tool to map in vivo vestibular and autonomic brainstem connectivity pathways in humans. The proposed research lays a critical foundation for future scientific and clinical inquiry in living humans into how brainstem vestibular-autonomic processes relate to the pathological mechanism causing chronic vestibular disorders and to their treatment outcomes.
|Satpute, Ajay B; Kragel, Philip A; Barrett, Lisa Feldman et al. (2018) Deconstructing arousal into wakeful, autonomic and affective varieties. Neurosci Lett :|