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Historical perspective: The pros and cons of conventional outcome measures in Parkinson's disease

Lim SY, Tan AH

Parkinsonism Relat Disord, 2018 Jan;46 Suppl 1:S47-S52.
PMID: 28793970 DOI: 10.1016/j.parkreldis.2017.07.029

BACKGROUND: Conventional outcome measures (COMs) in Parkinson's disease (PD) refer to rating scales, questionnaires, patient diaries and clinically-based tests that do not require specialized equipment.
METHODS: It is timely at this juncture - as clinicians and researchers begin to grapple with the "invasion" of digital technologies - to review the strengths and weaknesses of these outcome measures.
RESULTS: This paper discusses advances (including an enhanced understanding of PD itself, and the development of clinimetrics as a field) that have led to improvements in the COMs used in PD; their strengths and limitations; and factors to consider when selecting and using a measuring instrument.
CONCLUSIONS: It is envisaged that in the future, a combination of COMs and technology-based objective measures will be utilized, with different methods having their own strengths and weaknesses. Judgement is required on the part of the clinician and researcher in terms of which instrument(s) are appropriate to use, depending on the particular clinical or research setting or question.

Matched MeSH terms: Parkinson Disease/diagnosis*
Differentiating multiple-system atrophy from Parkinson's disease

Ramli N, Nair SR, Ramli NM, Lim SY

Clin Radiol, 2015 May;70(5):555-64.
PMID: 25752581 DOI: 10.1016/j.crad.2015.01.005

The purpose of this review is to illustrate the differentiating features of multiple-system atrophy from Parkinson's disease at MRI. The various MRI sequences helpful in the differentiation will be discussed, including newer methods, such as diffusion tensor imaging, MR spectroscopy, and nuclear imaging.

Matched MeSH terms: Parkinson Disease/diagnosis*
Fulltext A coumarin-based fluorescent probe as a central nervous system disease biomarker

Yap AC, Mahamad UA, Lim SY, Kim HJ, Choo YM

Sensors (Basel), 2014 Nov 10;14(11):21140-50.
PMID: 25390405 DOI: 10.3390/s141121140

Homocysteine and methylmalonic acid are important biomarkers for diseases associated with an impaired central nervous system (CNS). A new chemoassay utilizing coumarin-based fluorescent probe 1 to detect the levels of homocysteine is successfully implemented using Parkinson's disease (PD) patients' blood serum. In addition, a rapid identification of homocysteine and methylmalonic acid levels in blood serum of PD patients was also performed using the liquid chromatography-mass spectrometry (LC-MS). The results obtained from both analyses were in agreement. The new chemoassay utilizing coumarin-based fluorescent probe 1 offers a cost- and time-effective method to identify the biomarkers in CNS patients.

Matched MeSH terms: Parkinson Disease/diagnosis*
Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

Yuvaraj R, Murugappan M, Ibrahim NM, Sundaraj K, Omar MI, Mohamad K, et al.

Int J Psychophysiol, 2014 Dec;94(3):482-95.
PMID: 25109433 DOI: 10.1016/j.ijpsycho.2014.07.014

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

Matched MeSH terms: Parkinson Disease/diagnosis
Molecular diagnosis using multi drug delivery network and stability

Jalil MA, Innate K, Suwanpayak N, Yupapin PP, Ali J

Artif Cells Blood Substit Immobil Biotechnol, 2011 Dec;39(6):357-65.
PMID: 21999106 DOI: 10.3109/10731199.2011.618134

By using a pair of tweezers to generate the intense optical vortices within the PANDA ring resonator, the required molecules (drug volumes) can be trapped and moved dynamically within the molecular bus networks, in which the required diagnosis or drug delivery targets can be performed within the network. The advantage of the proposed system is that the proposed diagnostic method can perform within the tiny system (thin film device or circuit), which can be available for a human embedded device for diagnostic use. The channel spacing of the trapped volumes (molecules) within the bus molecular networks can be provided.

Matched MeSH terms: Parkinson Disease/diagnosis
Fulltext Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

Oung QW, Muthusamy H, Lee HL, Basah SN, Yaacob S, Sarillee M, et al.

Sensors (Basel), 2015 Aug 31;15(9):21710-45.
PMID: 26404288 DOI: 10.3390/s150921710

Parkinson's Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

Matched MeSH terms: Parkinson Disease/diagnosis*
Analysis of model-based and model-free CEST effect quantification methods for different medical applications

Foo LS, Yap WS, Hum YC, Manan HA, Tee YK

J Magn Reson, 2020 01;310:106648.
PMID: 31760147 DOI: 10.1016/j.jmr.2019.106648

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) holds great potential to provide new metabolic information for clinical applications such as tumor, stroke and Parkinson's Disease diagnosis. Many active research and developments have been conducted to translate this emerging MRI technique for routine clinical applications. In general, there are two CEST quantification techniques: (i) model-free and (ii) model-based techniques. The reliability of these quantification techniques depends heavily on the experimental conditions and quality of the collected data. Errors such as noise may lead to misleading quantification results and thus inaccurate diagnosis when CEST imaging becomes a standard or routine imaging scan in the future. This paper investigates the accuracy and robustness of these quantification techniques under different signal-to-noise (SNR) levels and magnetic field strengths. The quantified CEST effect before and after adding random Gaussian White Noise using model-free and model-based quantification techniques were compared. It was found that the model-free technique consistently yielded larger average percentage error across all tested parameters compared to its model-based counterpart, and that the model-based technique could withstand SNR of about 3 times lower than the model-free technique. When applied on noisy brain tumor, ischemic stroke, and Parkinson's Disease clinical data, the model-free technique failed to produce significant differences between normal and abnormal tissue whereas the model-based technique consistently generated significant differences. Although the model-free technique was less accurate and robust, its simplicity and thus speed would still make it a good approximate when the SNR was high (>50) or when the CEST effect was large and well-defined. For more accurate CEST quantification, model-based techniques should be considered. When SNR was low (<50) and the CEST effect was small such as those acquired from clinical field strength scanners, which are generally 3T and below, model-based techniques should be considered over model-free counterpart to maintain an average percentage error of less than 44% even under very noisy condition as tested in this work.

Matched MeSH terms: Parkinson Disease/diagnosis
Fulltext Comparison of computer-key-hold-time and alternating-finger-tapping tests for early-stage Parkinson's disease

Lan BL, Yeo JHW

PLoS One, 2019;14(6):e0219114.
PMID: 31247037 DOI: 10.1371/journal.pone.0219114

Giancardo et al. recently introduced the neuroQWERTY index (nQi), which is a novel motor index derived from computer-key-hold-time data using an ensemble regression algorithm, to detect early-stage Parkinson's disease. Here, we derive a much simpler motor index from their hold-time data, which is the standard deviation (SD) of the hold-time fluctuations, where fluctuation is defined as the difference between successive natural-log of hold time. Our results show the performance of the SD and nQi tests in discriminating early-stage subjects from controls do not differ, although the SD index is much simpler. There is also no difference in performance between the SD and alternating-finger-tapping tests.

Matched MeSH terms: Parkinson Disease/diagnosis*
Empirical Wavelet Transform Based Features for Classification of Parkinson's Disease Severity

Oung QW, Muthusamy H, Basah SN, Lee H, Vijean V

J Med Syst, 2017 Dec 29;42(2):29.
PMID: 29288342 DOI: 10.1007/s10916-017-0877-2

Parkinson's disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers - K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level - with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal's information.

Matched MeSH terms: Parkinson Disease/diagnosis*
Fulltext Biomechanical System Versus Observational Rating Scale for Parkinson's Disease Tremor Assessment

Chan PY, Mohd Ripin Z, Abdul Halim S, Kamarudin MI, Ng KS, Eow GB, et al.

Sci Rep, 2019 May 31;9(1):8117.
PMID: 31148550 DOI: 10.1038/s41598-019-44142-1

There is a lack of evidence that either conventional observational rating scale or biomechanical system is a better tremor assessment tool. This work focuses on comparing a biomechanical system and the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale in terms of test-retest reliability. The Parkinson's disease tremors were quantified by biomechanical system in joint angular displacement and predicted rating, as well as assessed by three raters using observational ratings. Qualitative comparisons of the validity and function are made also. The observational rating captures the overall severity of body parts, whereas the biomechanical system provides motion- and joint-specific tremor severity. The tremor readings of the biomechanical system were previously validated against encoders' readings and doctors' ratings; the observational ratings were validated with previous ratings on assessing the disease and combined motor symptoms rather than on tremor specifically. Analyses show that the predicted rating is significantly more reliable than the average clinical ratings by three raters. The comparison work removes some of the inconsistent impressions of the tools and serves as guideline for selecting a tool that can improve tremor assessment. Nevertheless, further work is required to consider more variabilities that influence the overall judgement.

Matched MeSH terms: Parkinson Disease/diagnosis*
A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging

Nair SR, Tan LK, Mohd Ramli N, Lim SY, Rahmat K, Mohd Nor H

Eur Radiol, 2013 Jun;23(6):1459-66.
PMID: 23300042 DOI: 10.1007/s00330-012-2759-9

OBJECTIVE: To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD).
METHODS: 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3.
RESULTS: Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified.
CONCLUSION: Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD.
KEY POINTS: • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

Matched MeSH terms: Parkinson Disease/diagnosis*
Validation of an informant-based cognitive screening tool for Parkinson disease

Chee KY, Ong KY, Mak CY, Yacob S, Yeo SC, Thrichelam N, et al.

Asia Pac Psychiatry, 2017 Dec;9(4).
PMID: 28326670 DOI: 10.1111/appy.12278

INTRODUCTION: The objective of this study was to establish the psychometric properties of the AD8 Dementia Screening Interview in patients with Parkinson disease (PD) with or without cognitive impairment using the Montreal Cognitive Assessment Tool (MoCA) for comparison.
METHODS: The AD8 was translated into Malay for Malay-speaking participants. A correlation analysis and a receiver operator characteristic curve were generated to establish the psychometric properties of the AD8 in relation to the MoCA.
RESULTS: One hundred fifty patients and their caretakers completed the AD8 and MoCA. Using a cutoff score of 1/8, the AD8 had 81% sensitivity and 59% specificity for the detection of cognitive impairment in PD. With a cutoff score of 2/8, the AD8 had 83% specificity and 64% sensitivity. The area under the receiver operator characteristic curve was 80%, indicating good-to-excellent discriminative ability.
DISCUSSION: These findings suggest that the AD8 can reliably differentiate between cognitively impaired and cognitively normal patients with PD and is a useful caregiver screening tool for PD.

Matched MeSH terms: Parkinson Disease/diagnosis*
Earlier onset of motor complications in Parkinson's patients with comorbid diabetes mellitus

Mohamed Ibrahim N, Ramli R, Koya Kutty S, Shah SA

Mov Disord, 2018 12;33(12):1967-1968.
PMID: 30427552 DOI: 10.1002/mds.27526
Matched MeSH terms: Parkinson Disease/diagnosis
A new hybrid intelligent system for accurate detection of Parkinson's disease

Hariharan M, Polat K, Sindhu R

Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.

Matched MeSH terms: Parkinson Disease/diagnosis*
Polar-phase indices of perioral muscle reciprocity during syllable production in Parkinson's disease

Chu SY, Barlow SM, Lee J, Wang J

Int J Speech Lang Pathol, 2017 12;19(6):616-627.
PMID: 28425760 DOI: 10.1080/17549507.2016.1265587

PURPOSE: This research characterised perioral muscle reciprocity and amplitude ratio in lower lip during bilabial syllable production [pa] at three rates to understand the neuromotor dynamics and scaling of motor speech patterns in individuals with Parkinson's disease (PD).
METHOD: Electromyographic (EMG) signals of the orbicularis oris superior [OOS], orbicularis oris inferior [OOI] and depressor labii inferioris [DLI] were recorded during syllable production and expressed as polar-phase notations.
RESULT: PD participants exhibited the general features of reciprocity between OOS, OOI and DLI muscles as reflected in the EMG during syllable production. The control group showed significantly higher integrated EMG amplitude ratio in the DLI:OOS muscle pairs than PD participants. No speech rate effects were found in EMG muscle reciprocity and amplitude magnitude across all muscle pairs.
CONCLUSION: Similar patterns of muscle reciprocity in PD and controls suggest that corticomotoneuronal output to the facial nucleus and respective perioral muscles is relatively well-preserved in our cohort of mild idiopathic PD participants. Reduction of EMG amplitude ratio among PD participants is consistent with the putative reduction in the thalamocortical activation characteristic of this disease which limits motor cortex drive from generating appropriate commands which contributes to bradykinesia and hypokinesia of the orofacial mechanism.

Matched MeSH terms: Parkinson Disease/diagnosis

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