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Linking Essential Tremor to the Cerebellum--Neuroimaging Evidence

Academic Article
Publication Date:
2016
abstract:
Essential tremor (ET) is the most common pathological tremor disorder in the world, and post-mortem evidence has shown that the cerebellum is the most consistent area of pathology in ET. In the last few years, advanced neuroimaging has tried to confirm this evidence. The aim of the present review is to discuss to what extent the evidence provided by this field of study may be generalised. We performed a systematic literature search combining the terms ET with the following keywords: MRI, VBM, MRS, DTI, fMRI, PET and SPECT. We summarised and discussed each study and placed the results in the context of existing knowledge regarding the cerebellar involvement in ET. A total of 51 neuroimaging studies met our search criteria, roughly divided into 19 structural and 32 functional studies. Despite clinical and methodological differences, both functional and structural imaging studies showed similar findings but without defining a clear topography of neurodegeneration. Indeed, the vast majority of studies found functional and structural abnormalities in several parts of the anterior and posterior cerebellar lobules, but it remains to be established to what degree these neural changes contribute to clinical symptoms of ET. Currently, advanced neuroimaging has confirmed the involvement of the cerebellum in pathophysiological processes of ET, although a high variability in results persists. For this reason, the translation of this knowledge into daily clinical practice is again partially limited, although new advanced multivariate neuroimaging approaches (machine-learning) are proving interesting changes of perspective.
Iris type:
01.09 Rassegna della letteratura scientifica in rivista (Literature review)
Keywords:
Essential tremor; Cerebellum; Voxel-based-morphometry; Diffusion tensor imaging; Functional magnetic resonance imaging; Fractional anisotropy; Magnetic resonance imaging; Positron emission tomography; Machine learning
List of contributors:
Quattrone, Aldo; Cerasa, Antonio
Authors of the University:
CERASA ANTONIO
Handle:
https://iris.cnr.it/handle/20.500.14243/316764
Published in:
CEREBELLUM (LOND., ONLINE)
Journal
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