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Updated: Dec 9, 2025

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
Published on: January 19, 2022
Basal ganglia and cerebellar circuits have distinct roles in blepharospasm
Amanda Glickman1, Phuong Nguyen1, Erika Shelton2
1School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO, 80045, USA.
Introduction:
To identify areas of brain activity associated with involuntary muscle contractions in patients with blepharospasm using functional MRI.
Methods:
15 patients with blepharospasm underwent 8-min resting state scans with spontaneous orbicularis oculi muscle contractions simultaneously recorded using MRI-compatible surface electromyography. Spasm severity and spasm onset/offset were modeled using the amplitude of the electromyography signal (EMG-Amp) and its first temporal derivative (EMG-Onset), respectively, and included in a multiple regression functional MRI analysis using SPM12. Primary outcome was within-group blood-oxygen-level dependent activations that co-varied with EMG-Amp and EMG-Onset following correction for multiple comparisons for an overall cluster corrected p < 0.05. Secondary analyses included testing for correlations between imaging findings and symptom severity, as measured by clinical dystonia rating scales, using an uncorrected voxel-level threshold of p < 0.001.
Results:
Imaging data from one subject were excluded due to excessive movement. EMG-Amp co-activated within the left sensorimotor cortex and cerebellum, as well as right lingual gyrus and superior temporal gyrus. EMG-Onset co-activated within the left posterior putamen/pallidum and a frontal eye field region in the left superior frontal gyrus. Symptom severity and EMG-Amp significantly co-varied in a small cluster within the left cerebellum.
Conclusion:
Our preliminary findings here suggest that cerebello-cortical circuits in blepharospasm could drive the intensity of eyelid spasms while basal ganglia circuits are associated with the triggering of spasms. This supports the network model for dystonia and identifies specific areas of involvement consistent with known brain regions responsible for control of movement.
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