Early Diagnosis of Parkinson’s Disease

Prevention of Parkinson's Disease: From Risk Factors to Early Interventions

Parkinson's disease (PD) is a debilitating neurological disorder characterized by progressively worsening motor dysfunction. Currently, available therapies merely alleviate symptoms, and there are no cures. Consequently, some researchers have now shifted their attention to identifying the modifiable risk factors of PD, with the intention of possibly implementing early interventions to prevent the development of PD. Four primary risk factors for PD are discussed including environmental factors (pesticides and heavy metals), lifestyle (physical activity and dietary intake), drug abuse, and individual comorbidities. Additionally, clinical biomarkers, neuroimaging, biochemical biomarkers, and genetic biomarkers could also help to detect prodromal PD. This review compiled available evidence that illustrates the relationship between modifiable risk factors, biomarkers, and PD. In summary, we raise the distinct possibility of preventing PD via early interventions of the modifiable risk factors and early diagnosis.

Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study

BACKGROUND

Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring; we attempt to further these works with structured tests taking into account 2D hand movement in addition to finger tapping. Our feasibility study uses keystroke and mouse movement data from a remotely conducted, structured, web-based test combined with self-reported PD status to create a predictive model for detecting the presence of PD.

OBJECTIVE

Analysis of finger tapping speed and accuracy through keyboard input and analysis of 2D hand movement through mouse input allowed differentiation between participants with and without PD. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease.

METHODS

Participants were recruited via email by the Hawaii Parkinson Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of 2D hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement.

RESULTS

Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. These selected features provided insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (SD 0.1663) and an average accuracy of 0.7429 (SD 0.1400) over 20 runs for predicting the presence of PD.

CONCLUSIONS

This preliminary feasibility study suggests the possibility of using technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.

Gut-Brain axis in Parkinson's disease etiology: The role of lipopolysaccharide

Recent studies highlight the initiation of Parkinson's disease (PD) in the gastrointestinal tract, decades before the manifestations in the central nervous system (CNS). This gut-brain axis of neurodegenerative diseases defines the critical role played by the unique microbial composition of the "second brain" formed by the enteric nervous system (ENS). Compromise in the enteric wall can result in the translocation of gut-microbiota along with their metabolites into the system that can affect the homeostatic machinery. The released metabolites can associate with protein substrates affecting several biological pathways. Among these, the bacterial endotoxin from Gram-negative bacteria, i.e., Lipopolysaccharide (LPS), has been implicated to play a definite role in progressive neurodegeneration. The molecular interaction of the lipid metabolites can have a direct neuro-modulatory effect on homeostatic protein components that can be transported to the CNS via the vagus nerve. α-synuclein (α-syn) is one such partner protein, the molecular interactions with which modulate its overall fibrillation propensity in the system. LPS interaction has been shown to affect the protein's aggregation kinetics in an alternative inflammatory pathway of PD pathogenesis. Several other lipid contents from the bacterial membranes could also be responsible for the initiation of α-syn amyloidogenesis. The present review will focus on the intermolecular interactions of α-syn with bacterial lipid components, particularly LPS, with a definite clinical manifestation in PD pathogenesis. However, deconvolution of the sequence of interaction events from the ENS to its propagation in the CNS is not easy or obvious. Nevertheless, the characterization of these lipid-mediated structures is a step towards realizing the novel targets in the pre-emptive diagnoses of PD. This comprehensive description should prompt the correlation of potential risk of amyloidogenesis upon detection of specific paradigm shifts in the microbial composition of the gut.

Optical coherence tomography measurements as potential imaging biomarkers for Parkinson's disease: A systematic review and meta-analysis

BACKGROUND AND PURPOSE

Retinal pathological changes may precede or accompany the deterioration of brain tissue in Parkinson's disease (PD). The purpose of this meta-analysis was to assess the usefulness of optical coherence tomography (OCT) measurements as potential imaging biomarkers for PD.

METHODS

PubMed, Embase, Web of Science and Cochrane Library databases were systematically searched for observational studies (published prior to 30 May 2020) comparing the OCT measurements between PD patients and healthy controls (HCs). Our main end-points were peripapillary retinal nerve fiber layer (pRNFL) thickness, macular ganglion cell complex thickness, macular thickness and macular volume. Pooled data were assessed by use of a random-effects model.

RESULTS

A total of 36 observational studies were identified that included 1712 patients with PD (2548 eyes) and 1778 HCs (2646 eyes). Compared with the HC group, the PD group showed a significant reduction in mean pRNFL thickness (weighted mean difference [WMD] -3.51 μm, 95% confidence interval [CI] -4.84, -2.18; p = 0.000), all quadrants at the pRNFL (WMD range -7.65 to -2.44 μm, all p < 0.05), macular fovea thickness (WMD -5.62 μm, 95% CI -7.37, -3.87; p = 0.000), all outer sector thicknesses at the macula (WMD range -4.68 to -4.10 μm, all p < 0.05), macular volume (WMD -0.21 mm³ , 95% CI -0.36, -0.06; p < 0.05) and macular ganglion cell complex thickness (WMD -4.18 μm, 95% CI -6.07, -2.29; p < 0.05).

CONCLUSIONS

Our pooled data confirmed robust associations between retinal OCT measurements and PD, highlighting the usefulness of OCT measurements as potential imaging biomarkers for PD.

Parkinson's Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks

Gait analysis plays a key role in the diagnosis of Parkinson’s Disease (PD), as patients generally exhibit abnormal gait patterns compared to healthy controls. Current diagnosis and severity assessment procedures entail manual visual examinations of motor tasks, speech, and handwriting, among numerous other tests, which can vary between clinicians based on their expertise and visual observation of gait tasks. Automating gait differentiation procedure can serve as a useful tool in early diagnosis and severity assessment of PD and limits the data collection to solely walking gait. In this research, a holistic, non-intrusive method is proposed to diagnose and assess PD severity in its early and moderate stages by using only Vertical Ground Reaction Force (VGRF). From the VGRF data, gait features are extracted and selected to use as training features for the Artificial Neural Network (ANN) model to diagnose PD using cross validation. If the diagnosis is positive, another ANN model will predict their Hoehn and Yahr (H&Y) score to assess their PD severity using the same VGRF data. PD Diagnosis is achieved with a high accuracy of 97.4% using simple network architecture. Additionally, the results indicate a better performance compared to other complex machine learning models that have been researched previously. Severity Assessment is also performed on the H&Y scale with 87.1% accuracy. The results of this study show that it is plausible to use only VGRF data in diagnosing and assessing early stage Parkinson’s Disease, helping patients manage the symptoms earlier and giving them a better quality of life.

[Changes in corneal nerves fibers in the early stages of Parkinson's disease according to in vivo confocal microscopy (preliminary report)]

One of the research directions of the so-called non-motor manifestations of Parkinson's disease (PD) is associated with the assessment of structural and functional changes in the organ of vision. An assessment of the state of thin non-myelinated corneal nerve fibers (CNF) in Parkinson's disease seems to be promising considering the neurodegenerative nature of the disease, as well as the possibility of objective intravital assessment of both functional and structural changes in CNF.

PURPOSE

To analyze the changes in the course and structure of corneal nerve fibers in the early stages of Parkinson's disease based on an objective algorithm of in vivo corneal confocal microscopy (CCM).

MATERIAL AND METHODS

The study was conducted on a group of 16 patients aged 39 to 66 years with verified diagnosis of PD. In addition to standard neurological and ophthalmological examinations, all patients underwent IVCCM on a Heidelberg Retinal Tomograph device with special Rostock Cornea Module (HRT3 RCM), followed by processing of the obtained images using a uniquely designed analysis algorithm.

RESULTS

A significant decrease in the directional anisotropy coefficient and an increase in the directional symmetry coefficient of the nerve fibers of the cornea were established (average values 3.15±1.08 and 0.92±0.04, respectively); in healthy individuals of the identical age range these indicators are 3.5±0.85 and 0.86±0.11, respectively. In addition, qualitative structural changes were noted, which consisted of an increase in the number of branches from the main nerve trunks, an increase in the tortuosity of CNF, multidirectionality, and "beaded" shape. In 9 cases, the presence of macrophages was revealed - dendritic Langerhans cells, which is an indirect sign of the inflammatory process.

CONCLUSION

The preliminary nature of the results obtained in this study and the need for further research in this area are related, on the one hand, to a small sample of observations and, on the other hand, to the criterion used to assess the status of CNF based on a comparative analysis with conditionally normal indicators. In the future, in order to solve the problem of the uniqueness of changes in CNF and the possibility of using these changes as a marker for PD progression, longitudinal studies are required to reveal the presence or absence of a correlation between the stage of the disease, the results of known monitoring methods (e.g. electromyography) and quantitative indicators of the status of CNF.

Automated Classification of Postural Control for Individuals With Parkinson's Disease Using a Machine Learning Approach: A Preliminary Study

The purposes of the study were (1) to compare postural sway between participants with Parkinson's disease (PD) and healthy controls and (2) to develop and validate an automated classification of PD postural control patterns using a machine learning approach. A total of 9 participants in the early stage of PD and 12 healthy controls were recruited. Participants were instructed to stand on a force plate and maintain stillness for 2 minutes with eyes open and eyes closed. The center of pressure data were collected at 50 Hz. Linear displacements, standard deviations, total distances, sway areas, and multiscale entropy of center of pressure were calculated and compared using mixed-model analysis of variance. Five supervised machine learning algorithms (ie, logistic regression, K-nearest neighbors, Naïve Bayes, decision trees, and random forest) were used to classify PD postural control patterns. Participants with PD exhibited greater center of pressure sway and variability compared with controls. The K-nearest neighbor method exhibited the best prediction performance with an accuracy rate of up to 0.86. In conclusion, participants with PD exhibited impaired postural stability and their postural sway features could be identified by machine learning algorithms.

Levodopa/carbidopa/entacapone for the treatment of early Parkinson's disease: a meta-analysis

Treatment of Parkinson's disease with levodopa/carbidopa/entacapone (LCE) has been studied for a long time. However, the efficacy and safety of LCE in the treatment of early Parkinson's disease (PD) still need to be assessed. Our objective was to do a meta-analysis of relevant randomized controlled trials (RCTs) to evaluate the efficacy and safety of LCE for early PD. PubMed, Embase, the Cochrane Library, and the Web of Science were searched for RCTs with "levodopa/carbidopa/entacapone" and "Parkinson's disease" as keywords. The search period was from inception to October 2018. The quality of included studies was strictly evaluated. We evaluated the quality of included studies strictly and six studies met all inclusion criteria. The results showed that LCE could improve activities of daily living and motor function in PD patients. However, LCE therapy was associated with higher risks of total AEs and single AEs compared with traditional therapy.

The role of optical coherence tomography in therapeutics and conditions, which primarily have systemic manifestations: a narrative review

Optical coherence tomography is designed to evaluate in vivo qualitative and quantitative changes of the anterior segment, optic nerve and the retina. Initial applications of this technology were confined mainly to ophthalmic diseases. However recently, numerous studies have evaluated its use in systemic conditions and in therapeutics where, optic nerve and retinal architecture can be assessed to monitor progression of systemic conditions and its response to treatment. This is a narrative review aimed at evaluating the debate surrounding the role of spectral domain optical coherence tomography, in systemic conditions where optic nerve affection can be measured and be used in the diagnosis, monitoring and assessment of treatment effect as a non-invasive, quick, novel technique.

Societal Burden and Persisting Unmet Needs of Parkinson’s Disease

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Machine Learning for Neurodegenerative Disorder Diagnosis — Survey of Practices and Launch of Benchmark Dataset

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s, constitute a major factor in long-term disability and are becoming more and more a serious concern in developed countries. As there are, at present, no effective therapies, early diagnosis along with avoidance of misdiagnosis seem to be critical in ensuring a good quality of life for patients. In this sense, the adoption of computer-aided-diagnosis tools can offer significant assistance to clinicians. In the present paper, we provide in the first place a comprehensive recording of medical examinations relevant to those disorders. Then, a review is conducted concerning the use of Machine Learning techniques in supporting diagnosis of neurodegenerative diseases, with reference to at times used medical datasets. Special attention has been given to the field of Deep Learning. In addition to that, we communicate the launch of a newly created dataset for Parkinson’s disease, containing epidemiological, clinical and imaging data, which will be publicly available to researchers for benchmarking purposes. To assess the potential of the new dataset, an experimental study in Parkinson’s diagnosis is carried out, based on state-of-the-art Deep Neural Network architectures and yielding very promising accuracy results.

Handwriting Analysis in Parkinson's Disease: Current Status and Future Directions

BACKGROUND

The majority of patients with Parkinson's disease (PD) have handwriting abnormalities. Micrographia (abnormally small letter size) is the most commonly reported and easily detectable handwriting abnormality in patients with PD. However, micrographia is perhaps the tip of the iceberg representing the handwriting abnormalities in PD. Digitizing tablet technology, which has evolved over the last 2 decades, has made it possible to study the pressure and kinematic features of handwriting. This has resulted in a surge of studies investigating graphomotor impairment in patients with PD.

METHODS

The objectives of this study were to review the evolution of the kinematic analysis of handwriting in PD and to provide an overview of handwriting abnormalities observed in PD along with future directions for research in this field. Articles for review were searched from the PubMed and SCOPUS databases.

RESULTS

Digitizing tablet technologies have resulted in a shift of focus from the analysis of only letter size to the analysis of several kinematic features of handwriting. Studies based on the kinematic analysis of handwriting have revealed that patients with PD may have abnormalities in velocity, fluency, and acceleration in addition to micrographia. The recognition of abnormalities in several kinematic parameters of handwriting has given rise to the term PD dysgraphia. In addition, certain kinematic properties potentially may be helpful in distinguishing PD from other parkinsonian disorders.

CONCLUSION

The journey from micrographia to PD dysgraphia is indeed a paradigm shift. Further research is warranted to gain better insight into the graphomotor impairments in PD and their clinical implications.

Posttranslational modifications of blood-derived alpha-synuclein as biochemical markers for Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder known for the typical motor features associated. Pathologically, it is characterized by the intracellular accumulation of alpha-synuclein (aSyn) in Lewy bodies and Lewy neurites. Currently, there are no established biochemical markers for diagnosing or for following disease progression, a major limitation for the clinical practice. Posttranslational modifications (PTMs) in aSyn have been identified and implicated on its pathobiology. Since aSyn is abundant in blood erythrocytes, we aimed to evaluate whether PTMs of aSyn in the blood might hold value as a biomarker for PD. We examined 58 patients with PD and 30 healthy age-matched individuals. We found that the levels of Y125 phosphorylated, Y39 nitrated, and glycated aSyn were increased in PD, while those of SUMO were reduced. A combinatory analysis of the levels of these PTMs resulted in an increased sensitivity, with an area under curve (AUC) of 0.843 for PD versus healthy controls, and correlated with disease severity and duration. We conclude that the levels of these selected PTMs hold strong potential as biochemical markers for PD. Ultimately, our findings might facilitate the monitoring of disease progression in clinical trials, opening the possibility for developing more effective therapies against PD.

Parkinson's disease classification using gait analysis via deterministic learning

Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). In this paper, we propose a method to classify (diagnose) patients with PD and healthy control subjects using gait analysis via deterministic learning theory. The classification approach consists of two phases: a training phase and a classification phase. In the training phase, gait characteristics represented by the gait dynamics are derived from the vertical ground reaction forces under the usual and self-selected paces of the subjects. The gait dynamics underlying gait patterns of healthy controls and PD patients are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. The gait patterns of healthy controls and PD patients constitute a training set. In the classification phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of gait dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gait pattern of a certain PD patient to be classified (diagnosed), a set of classification errors are generated. The average L₁ norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test PD gait pattern according to the smallest error principle. When the gait patterns of 93 PD patients and 73 healthy controls are classified with five-fold cross-validation method, the accuracy, sensitivity and specificity of the results are 96.39%, 96.77% and 95.89%, respectively. Based on the results, it may be claimed that the features and the classifiers used in the present study could effectively separate the gait patterns between the groups of PD patients and healthy controls.

Reversible Pharmacological Induction of Motor Symptoms in MPTP-Treated Mice at the Presymptomatic Stage of Parkinsonism: Potential Use for Early Diagnosis of Parkinson's Disease

A crucial event in the pathogenesis of Parkinson's disease is the death of dopaminergic neurons of the nigrostriatal system, which are responsible for the regulation of motor function. Motor symptoms first appear in patients 20-30 years after the onset of the neurodegeneration, when there has been a loss of an essential number of neurons and depletion of compensatory reserves of the brain, which explains the low efficiency of treatment. Therefore, the development of a technology for the diagnosing of Parkinson's disease at the preclinical stage is of a high priority in neurology. In this study, we have developed at an experimental model a fundamentally novel for neurology approach for diagnosis of Parkinson's disease at the preclinical stage. This methodology, widely used for the diagnosis of chronic diseases in the internal medicine, is based on the application of a challenge test that temporarily increases the latent failure of a specific functional system, thereby inducing the short-term appearance of clinical symptoms. The provocation test was developed by a systemic administration of α-methyl-p-tyrosine (αMpT), a reversible inhibitor of tyrosine hydroxylase to MPTP-treated mice at the presymptomatic stage of parkinsonism. For this, we first selected a minimum dose of αMpT, which caused a decrease of the dopamine level in the striatum of normal mice below the threshold at which motor dysfunctions appear. Then, we found the maximum dose of αMpT at which a loss of dopamine in the striatum of normal mice did not reach the threshold level, and motor behavior was not impaired. We showed that αMpT at this dose induced a decrease of the dopamine concentration in the striatum of MPTP-treated mice at the presymptomatic stage of parkinsonism below a threshold level that results in the impairment of motor behavior. Finally, we proved that αMpT exerts a temporal and reversible influence on the nigrostriatal dopaminergic system of MPTP-treated mice with no long-term side effects on other catecholaminergic systems. Thus, the above experimental data strongly suggest that αMpT-based challenge test might be considered as the provocation test for Parkinson's disease diagnosis at the preclinical stage in the future clinical trials.

Transcranial brain parenchymal sonography in neurodegenerative and psychiatric diseases

Transcranial sonography is a highly sensitive noninvasive sonographic method for detection of early and specific echogenic changes in basal ganglia of patients with some neurodegenerative diseases. Transcranial sonography showed substantia nigra hyperechogenicity as a typical echo feature in idiopathic Parkinson disease and lenticular nucleus hyperechogenicity as a characteristic finding in atypical parkinsonian syndromes. Brain stem raphe hypoechogenicity or interruption has been shown to be highly prevalent in patients with unipolar depression as well as depression associated with certain neurodegenerative diseases. Transcranial sonography also revealed basal ganglia hyperechoic changes in movement disorders with trace metal accumulation such as Wilson disease, some entities of neurodegeneration with brain iron accumulation, as well as several forms of spinocerebellar ataxia. Transcranial sonography is a valuable neuro imaging method for early and differential diagnosis and follow-up of patients with neurodegenerative and psychiatric diseases.

Visual dysfunction in patients with Parkinson's disease and essential tremor

The aim of this study was to determine the specificity and sensitivity of the Pelli-Robson and Ishihara diagnostic methods in differing Parkinson's disease from essential tremor compared to DaTSCAN (dopamine transporter scan) findings. The intention was to investigate whether visual dysfunction appears in the early state of Parkinson's disease. Therefore, we included patients with the symptomatology of parkinsonism lasting between 6 and 12 months. The study included 164 patients of which 59 (36.0%) suffered from Parkinson's disease, 51 (31.1%) from essential tremor, and 54 (32.9%) healthy patients which presented the control group. The specificity of Pelli-Robson test in confirming Parkinson's disease was 53% and the sensitivity 81.4%. The specificity of Ishihara test in confirming Parkinson's disease was 88.2%, and sensitivity 55.9%. We found that the colour and contrast dysfunction are present as the earliest symptoms of Parkinson's disease. In this study the Pelli-Robson test is highly sensitive and the Ishihara tables are highly specific in the differential diagnosis between Parkinson's disease and essential tremor, but neither of these methods fulfils the criteria for the validity of a test. We suggest performing both of these methods to evaluate which patients are indicated for DaTSCAN.

Retinal nerve fiber layer thickness changes in Parkinson disease: a meta-analysis

BACKGROUND

Parkinson disease (PD) is a neurodegenerative process that leads to a selective loss of dopaminergic neurons, mainly in the basal ganglia of the brain. Numerous studies have analyzed the ability of optical coherence tomography (OCT) to detect retinal nerve fiber layer (RNFL) thickness abnormalities and changes in PD, but the results have not always been consistent. Therefore, we carried out a meta-analysis to evaluate the RNFL thickness measured with OCT in PD.

METHODS AND FINDINGS

Case-control studies were selected through an electronic search of the Cochrane Controlled Trials Register, PUBMED and EMBASE. For the continuous outcomes, we calculated the weighted mean difference (WMD) and 95% confidence interval (CI). The statistical analysis was performed by RevMan 5.0 software. Thirteen case-control studies were included in the present meta-analysis, containing a total of 644 eyes in PD patients and 604 eyes in healthy controls. The results of our study showed that there was a significant reduction in average RNFL thickness in patients with PD compared to healthy controls (WMD = -5.76, 95% CI: -8.99 to -2.53, P = 0.0005). Additionally, differences of RNFL thickness in superior quadrant (WMD = -4.44, 95% CI: -6.93 to -1.94, P = 0.0005), inferior quadrant (WMD = -7.56, 95% CI: -11.33 to -3.78, P<0.0001), nasal quadrant (WMD = -3.12, 95% CI: -5.63 to -0.61, P = 0.01) and temporal quadrant (WMD = -4.63, 95% CI: -7.20 to -2.06, P = 0.0004) were all significant between the two groups.

CONCLUSION

In view of these results and the noninvasive nature of OCT technology, we surmise that OCT could be a useful tool for evaluating the progression of the Parkinson disease.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01928212.

Optical coherence tomography in parkinsonian syndromes

Background/Objective

Parkinson's disease (PD) and the atypical parkinsonian syndromes multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) are movement disorders associated with degeneration of the central nervous system. Degeneration of the retina has not been systematically compared in these diseases.

Methods

This cross-sectional study used spectral-domain optical coherence tomography with manual segmentation to measure the peripapillar nerve fiber layer, the macular thickness, and the thickness of all retinal layers in foveal scans of 40 patients with PD, 19 with MSA, 10 with CBS, 15 with PSP, and 35 age- and sex-matched controls.

Results

The mean paramacular thickness and volume were reduced in PSP while the mean RNFL did not differ significantly between groups. In PSP patients, the complex of retinal ganglion cell- and inner plexiform layer and the outer nuclear layer was reduced. In PD, the inner nuclear layer was thicker than in controls, MSA and PSP. Using the ratio between the outer nuclear layer and the outer plexiform layer with a cut-off at 3.1 and the additional constraint that the inner nuclear layer be under 46 µm, we were able to differentiate PSP from PD in our patient sample with a sensitivity of 96% and a specificity of 70%.

Conclusion

Different parkinsonian syndromes are associated with distinct changes in retinal morphology. These findings may serve to facilitate the differential diagnosis of parkinsonian syndromes and give insight into the degenerative processes of patients with atypical parkinsonian syndromes.