Essential tremor: what is beyond the oscillatory monosymptomatic illness?

Identifying and distinguishing of essential tremor and Parkinson's disease with grouped stability analysis based on searchlight-based MVPA

BACKGROUND

Since both essential tremor (ET) and Parkinson's disease (PD) are movement disorders and share similar clinical symptoms, it is very difficult to recognize the differences in the presentation, course, and treatment of ET and PD, which leads to misdiagnosed commonly.

PURPOSE

Although neuroimaging biomarker of ET and PD has been investigated based on statistical analysis, it is unable to assist the clinical diagnosis of ET and PD and ensure the efficiency of these biomarkers. The aim of the study was to identify the neuroimaging biomarkers of ET and PD based on structural magnetic resonance imaging (MRI). Moreover, the study also distinguished ET from PD via these biomarkers to validate their classification performance.

METHODS

This study has developed and implemented a three-level machine learning framework to identify and distinguish ET and PD. First of all, at the model-level assessment, the searchlight-based machine learning method has been used to identify the group differences of patients (ET/PD) with normal controls (NCs). And then, at the feature-level assessment, the stability of group differences has been tested based on structural brain atlas separately using the permutation test to identify the robust neuroimaging biomarkers. Furthermore, the identified biomarkers of ET and PD have been applied to classify ET from PD based on machine learning techniques. Finally, the identified biomarkers have been compared with the previous findings of the biology-level assessment.

RESULTS

According to the biomarkers identified by machine learning, this study has found widespread alterations of gray matter (GM) for ET and large overlap between ET and PD and achieved superior classification performance (PCA + SVM, accuracy = 100%).

CONCLUSIONS

This study has demonstrated the significance of a machine learning framework to identify and distinguish ET and PD. Future studies using a large data set are needed to confirm the potential clinical application of machine learning techniques to discern between PD and ET.

Depressive symptoms predict memory decline in Essential Tremor

INTRODUCTION

Essential tremor (ET), a common movement disorder, is characterized by motor, cognitive and psychiatric symptoms. Depressed mood, a symptom of ET, has historically been viewed as a psychological response to disability. However, depressive symptoms are emerging as a predictor of cognitive decline across several clinical populations. We examined if depressive symptoms predict decline in global cognition, memory, and executive functioning among older adults with ET.

METHODS

125 cognitively normal participants with ET completed three in-person assessments of cognition, mood, and motor symptoms at baseline, 18 months, and 36 months; baseline data were collected from July 2014-July 2016. Depressive symptoms were measured with the Geriatric Depression Scale. Cognitive functioning was measured via a 3-4 hour neuropsychological evaluation. Generalized linear regression models examined depressive symptoms as a predictor of decline in global cognition, executive functioning (EF), and memory.

RESULTS

Participants were grouped according to a median split (GDS <5 versus ≥ 5) due to the bimodal distribution of the data. In unadjusted models, depressive symptoms did not predict change in global cognition (b = -0.002, p = .502) or EF (b = 0.000, p = .931), however individuals with GDS ≥ 5 demonstrated faster memory decline in unadjusted (b = -0.008, p = .039) and adjusted models (b = -0.009, p = .019).

CONCLUSION

The presence of 5 or more depressive symptoms predicted mildly faster memory decline in cognitively normal older adults with ET over 36 months. We discuss potential mechanisms and clinical implications.

"Benign" tremor? A serial case report of 2.5 year progression from mild cognitive impairment to amnestic dementia following deep brain stimulator placement for essential tremor

Essential tremor (ET) is a prevalent neurological disorder with associated neuropsychological sequalae. Although cognitive deficits associated with ET are traditionally conceptualized as attention, processing speed, and executive impairments attributed to underlying frontal-subcortical dysfunction, emerging literature highlights the elevated frequency of progressive amnestic memory impairments in patients with ET. This case study centers around a 75-year-old woman with a 15-year history of ET who underwent deep brain stimulation (DBS) as well as three neuropsychological evaluations, one pre-surgically and two post-surgically at one and two-years post successful DBS surgery. Neuropsychological evaluation results revealed circumscribed mild and variable memory deficits pre-surgically and one-year post-surgically, However, two-years post-DBS, reliable change indices revealed significant declines in verbal/visual memory, consistent with an amnestic presentation, in addition to executive functions, aspects of higher-level language abilities, and overall IQ. This case study adds to a growing literature identifying a subset of ET patients with a neurodegenerative cognitive trajectory characterized by progressive, amnestic memory impairment. The case also highlights the importance of serial monitoring of cognition beyond the pre-surgical DBS workup to monitor for clinically significant decline(s).