Auditory and Olfactory Deficits in Essential Tremor - Review of the Current Evidence

Updates in essential tremor

Essential tremor (ET) is one of the most common tremor disorders and can be disabling in its affect on daily activities. There have been major breakthroughs in the treatment of tremor and ET is the subject of important ongoing research. This review will present recent advancements in the epidemiology, genetics, pathophysiology, diagnosis, comorbidities, and imaging of ET. Current and future treatment options in the management of ET will also be reviewed. The need for continued innovation and scientific inquiry to address the unmet needs of persons of ET will be highlighted.

Vestibular reflexes in essential tremor: abnormalities of ocular and cervical vestibular-evoked myogenic potentials are associated with the cerebellum and brainstem involvement

This study utilized cervical vestibular-evoked myogenic potentials tests (cVEMP) and ocular vestibular-evoked myogenic potentials tests (oVEMP) to investigate the vestibulocollic and vestibuloocular reflex arcs and to evaluate cerebellar and brainstem involvement) in essential tremor (ET). Eighteen cases with ET and 16 age- and gender-matched healthy control subjects (HCS) were included in the present study. Otoscopic and neurologic examinations were performed on all participants, and both cervical and ocular VEMP tests were performed. Pathological cVEMP results were increased in the ET group (64.7%) compared to the HCS (41,2%; p > 0.05). The latencies of P1 and N1 waves were shorter in the ET group than in HCS (p = 0.01 and p = 0.001). Pathological oVEMP responses were significantly higher in the ET group (72.2%) compared to the HCS (37.5%; p = 0.01). There was no statistically significant difference in oVEMP N1-P1 latencies between groups (p > 0.05). Because the ET group had high pathological responses to the oVEMP, but not the cVEMP, the upper brainstem pathways may be more affected by ET.

Challenging functional connectivity data: machine learning application on essential tremor recognition

BACKGROUND AND AIMS

This paper aimed to investigate the usefulness of applying machine learning on resting-state fMRI connectivity data to recognize the pattern of functional changes in essential tremor (ET), a disease characterized by slight brain abnormalities, often difficult to detect using univariate analysis.

METHODS

We trained a support vector machine with a radial kernel on the mean signals extracted by 14 brain networks obtained from resting-state fMRI scans of 18 ET and 19 healthy control (CTRL) subjects. Classification performance between pathological and control subjects was evaluated using a tenfold cross-validation. Recursive feature elimination was performed to rank the importance of the extracted features. Moreover, univariate analysis using Mann-Whitney U test was also performed.

RESULTS

The machine learning algorithm achieved an AUC of 0.75, with four networks (language, primary visual, cerebellum, and attention), which have an essential role in ET pathophysiology, being selected as the most important features for classification. By contrast, the univariate analysis was not able to find significant results among these two conditions.

CONCLUSION

The machine learning approach identifies the changes in functional connectivity of ET patients, representing a promising instrument to discriminate specific pathological conditions and find novel functional biomarkers in resting-state fMRI studies.