Central Nervous System

Author(s):  
Gautam Mehta ◽  
Bilal Iqbal

As with all neurological patients, you will be more likely to pick up the diagnosis if you take a step back and look at the whole patient. Take some time to assess their facial expressions, speech, tremor, and posture. A common instruction at this station, with the patient seated on a chair is ‘Look at this patient, and examine as appropriate’. Candidates are often baffled, when given this instruction. Often the patients with Parkinson’s disease are given specific instructions to interlock the fingers of both hands, or place hands flat on their lap to mask the tremor. Picking up an expressionless face and low volume monotonous speech from the outset will provide useful clues to the diagnosis. If you are not sure at this stage, proceed to examining the gait. Once you are certain, that this is Parkinson’s disease, you may proceed to demonstrate the other features. 1. Patients with Parkinson’s disease have characteristic expressionless facies (hypomimia), often described as ‘mask-like’. This is a manifestation of bradykinesia. There is a reduced blink rate. The glabellar tap (Myerson’s sign) is an unreliable sign and is not recommended in the examination. This involves tapping the patient’s forehead repeatedly. Normal subjects will stop blinking, but in Parkinson’s disease, the patient will continue to blink. The patient may be drooling saliva (resulting from dysphagia and sialorrhoea-due to autonomic dysfunction) 2. Patients may have soft speech (hypophonia). This is also a manifestation of bradykinesia, and characteristically, the speech is low-volume, monotonous and tremulous (appears slurred). 3. Blepharoclonus is tremor of the eyelids. This will only be demonstrated if the eyes are gently closed, as opposed to tightly closing the eyes. 4. The classic tremor is present at rest and asymmetrical (more marked on one side). It is classically described as being 4–6Hz and is the initial symptom in 60% of cases, although 20% of patients never have a tremor. The tremor may appear as a ‘pill-rolling’ motion of the hand or a simple oscillation of the hand or arm. It is easier to spot a tremor if you ask the patient to rest their arms in their lap in the semi-prone position.

2003 ◽  
Vol 17 (5) ◽  
pp. 759-778 ◽  
Author(s):  
Gwenda Simons ◽  
Heiner Ellgring ◽  
Marcia Smith Pasqualini

2014 ◽  
Vol 20 (3) ◽  
pp. 302-312 ◽  
Author(s):  
Aleksey I. Dumer ◽  
Harriet Oster ◽  
David McCabe ◽  
Laura A. Rabin ◽  
Jennifer L. Spielman ◽  
...  

AbstractGiven associations between facial movement and voice, the potential of the Lee Silverman Voice Treatment (LSVT) to alleviate decreased facial expressivity, termed hypomimia, in Parkinson's disease (PD) was examined. Fifty-six participants—16 PD participants who underwent LSVT, 12 PD participants who underwent articulation treatment (ARTIC), 17 untreated PD participants, and 11 controls without PD—produced monologues about happy emotional experiences at pre- and post-treatment timepoints (“T1” and “T2,” respectively), 1 month apart. The groups of LSVT, ARTIC, and untreated PD participants were matched on demographic and health status variables. The frequency and variability of facial expressions (Frequency and Variability) observable on 1-min monologue videorecordings were measured using the Facial Action Coding System (FACS). At T1, the Frequency and Variability of participants with PD were significantly lower than those of controls. Frequency and Variability increases of LSVT participants from T1 to T2 were significantly greater than those of ARTIC or untreated participants. Whereas the Frequency and Variability of ARTIC participants at T2 were significantly lower than those of controls, LSVT participants did not significantly differ from controls on these variables at T2. The implications of these findings, which suggest that LSVT reduces parkinsonian hypomimia, for PD-related psychosocial problems are considered. (JINS, 2014, 20, 1–11)


2021 ◽  
pp. 22-24
Author(s):  
V. Meenakshi ◽  
Saswathi Bhushan ◽  
T. Jyothirmayi

AIM: To evaluate tear lm status in cases of Parkinson's Disease and compare with a study group METHODS:50 patients of Parkinson's Disease and 50 age-gender matched controls were included in this study. Both groups underwent detailed history regarding dry eye symptoms,tear lm evaluation using slit-lamp bio-microscopy, uorescein staining, tear meniscus height, tear breakup time, Schirmer test, blink rate. Statistical analysis was done with Statistical Package for Social Sciences [SPSS] - Version 22.0 Released 2013 version RESULTS: There was a signicant difference between the various groups in terms of distribution of Meibomian Gland Disease 72.0% of the Case group as compared to 40% of control group had Meibomian Gland Disease,There was a signicant difference between the various groups in terms of distribution of Tear Meniscus Height <0.25Mm , Case group had the larger proportion of Tear Meniscus Height of <0.25Mm .There was a signicant difference between the various groups in terms of distribution of Tear Breakup Time <5 Sec, Schirmer's Test <5Mm in 5Min and Blink Rate <10 .There was no signicant difference between the various groups in terms of distribution of corneal Staining and dry eye symptoms. CONCLUSION: The study concluded that patient of Parkinson's disease had higher dry eye symptoms and Meibomian gland disease Also they have reduced Tear miniscus height,Tear lm break up time,Schirmer test I and Blink rate


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mohammad Rafayet Ali ◽  
Taylor Myers ◽  
Ellen Wagner ◽  
Harshil Ratnu ◽  
E. Ray Dorsey ◽  
...  

AbstractA prevalent symptom of Parkinson’s disease (PD) is hypomimia — reduced facial expressions. In this paper, we present a method for diagnosing PD that utilizes the study of micro-expressions. We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9 y/o, sd. 7.8) collected online through a web-based tool (www.parktest.net). In these videos, participants were asked to make three facial expressions (a smiling, disgusted, and surprised face) followed by a neutral face. Using techniques from computer vision and machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms. Logistic regression analysis revealed that participants with PD had less variance in AU6 (cheek raiser), AU12 (lip corner puller), and AU4 (brow lowerer) than non-PD individuals. An automated classifier using Support Vector Machine was trained on the variances and achieved 95.6% accuracy. Using facial expressions as a future digital biomarker for PD could be potentially transformative for patients in need of remote diagnoses due to physical separation (e.g., due to COVID) or immobility.


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