Neuroimaging in the early diagnosis of neurodegenerative disease

Hybrid nanostructures for neurodegenerative disease theranostics: the art in the combination of biomembrane and non-biomembrane nanostructures

The diagnosis of neurodegenerative diseases (NDDs) remains challenging, and existing therapeutic approaches demonstrate little efficacy. NDD drug delivery can be achieved through the utilization of nanostructures, hence enabling multimodal NDD theranostics. Nevertheless, both biomembrane and non-biomembrane nanostructures possess intrinsic shortcomings that must be addressed by hybridization to create novel nanostructures with versatile applications in NDD theranostics. Hybrid nanostructures display improved biocompatibility, inherent targeting capabilities, intelligent responsiveness, and controlled drug release. This paper provides a concise overview of the latest developments in hybrid nanostructures for NDD theranostics and emphasizes various engineering methodologies for the integration of diverse nanostructures, including liposomes, exosomes, cell membranes, and non-biomembrane nanostructures such as polymers, metals, and hydrogels. The use of a combination technique can significantly augment the precision, intelligence, and efficacy of hybrid nanostructures, therefore functioning as a more robust theranostic approach for NDDs. This paper also addresses the issues that arise in the therapeutic translation of hybrid nanostructures and explores potential future prospects in this field.

Clinical Application of Blood Biomarkers in Neurodegenerative Diseases-Present and Future Perspectives

Neurodegenerative diseases are a group of complex diseases characterized by a progressive loss of neurons and degeneration in different areas of the nervous system. They share similar mechanisms, such as neuroinflammation, oxidative stress, and mitochondrial injury, resulting in neuronal loss. One of the biggest challenges in diagnosing neurodegenerative diseases is their heterogeneity. Clinical symptoms are usually present in the advanced stages of the disease, thus it is essential to find optimal biomarkers that would allow early diagnosis. Due to the development of ultrasensitive methods analyzing proteins in other fluids, such as blood, huge progress has been made in the field of biomarkers for neurodegenerative diseases. The application of protein biomarker measurement has significantly influenced not only diagnosis but also prognosis, differentiation, and the development of new therapies, as it enables the recognition of early stages of disease in individuals with preclinical stages or with mild symptoms. Additionally, the introduction of biochemical markers into routine clinical practice may improve diagnosis and allow for a stratification group of people with higher risk, as well as an extension of well-being since a treatment could be started early. In this review, we focus on blood biomarkers, which could be potentially useful in the daily medical practice of selected neurodegenerative diseases.

Crater-like nanoelectrode arrays for electrochemical detection of dopamine release from neuronal cells

Stem cell therapy has shown great potential in treating various incurable diseases using conventional chemotherapy. Parkinson's disease (PD)-a neurodegenerative disease-has been reported to be caused by quantitative loss or abnormal functionality of dopaminergic neurons (DAnergic neurons). To date, stem cell therapies have shown some potential in treating PD throughex vivoengraftment of stem-cell-derived neurons. However, accurately identifying the differentiation and non-invasively evaluating the functionality and maturity of DAnergic neurons are formidable challenges in stem cell therapies. These strategies are important in enhancing the efficacy of stem cell therapies. In this study, we report a novel cell cultivation platform, that is, a nanocrater-like electrochemical nanoelectrode array (NCENA) for monitoring dopamine (DA) release from neurons to detect exocytotic DA release from DAnergic neurons. In particular, the developed NCENA has a nanostructure in which three-dimensional porous gold nanopillars are uniformly arranged on conductive electrodes. The developed NCENA exhibited great DA sensing capabilities with a linear range of 0.39-150μM and a limit of detection of 1.16μM. Furthermore, the nanotopographical cues provided by the NCENA are suitable for cell cultivation with enhanced cellular adhesion. Finally, we successfully analysed the functionality and maturity of differentiated neurons on the NCENA through its excellent sensing ability for exocytotic DA.

Pretargeted Imaging beyond the Blood-Brain Barrier-Utopia or Feasible?

Pretargeting is a promising nuclear imaging technique that allows for the usage of antibodies (Abs) with enhanced imaging contrast and reduced patient radiation burden. It is based on bioorthogonal chemistry with the tetrazine ligation-a reaction between trans-cyclooctenes (TCOs) and tetrazines (Tzs)-currently being the most popular reaction due to its high selectivity and reactivity. As Abs can be designed to bind specifically to currently 'undruggable' targets such as protein isoforms or oligomers, which play a crucial role in neurodegenerative diseases, pretargeted imaging beyond the BBB is highly sought after, but has not been achieved yet. A challenge in this respect is that large molecules such as Abs show poor brain uptake. Uptake can be increased by receptor mediated transcytosis; however, it is largely unknown if the achieved brain concentrations are sufficient for pretargeted imaging. In this study, we investigated whether the required concentrations are feasible to reach. As a model Ab, we used the bispecific anti-amyloid beta (Aβ) anti-transferrin receptor (TfR) Ab 3D6scFv8D3 and conjugated it to a different amount of TCOs per Ab and tested different concentrations in vitro. With this model in hand, we estimated the minimum required TCO concentration to achieve a suitable contrast between the high and low binding regions. The estimation was carried out using pretargeted autoradiography on brain sections of an Alzheimer's disease mouse model. Biodistribution studies in wild-type (WT) mice were used to correlate how different TCO/Ab ratios alter the brain uptake. Pretargeted autoradiography showed that increasing the number of TCOs as well as increasing the TCO-Ab concentration increased the imaging contrast. A minimum brain concentration of TCOs for pretargeting purposes was determined to be 10.7 pmol/g in vitro. Biodistribution studies in WT mice showed a brain uptake of 1.1% ID/g using TCO-3D6scFv8D3 with 6.8 TCO/Ab. According to our estimations using the optimal parameters, pretargeted imaging beyond the BBB is not a utopia. Necessary brain TCO concentrations can be reached and are in the same order of magnitude as required to achieve sufficient contrast. This work gives a first estimate that pretargeted imaging is indeed possible with antibodies. This could allow the imaging of currently 'undruggable' targets and therefore be crucial to monitor (e.g., therapies for intractable neurodegenerative diseases).

Salivary biomarkers of neurodegenerative and demyelinating diseases and biosensors for their detection

Salivary analysis is gaining increasing interest as a novel and promising field of research for the diagnosis of neurodegenerative and demyelinating diseases related to aging. The collection of saliva offers several advantages, being noninvasive, stress-free, and repeatable. Moreover, the detection of biomarkers directly in saliva could allow an early diagnosis of the disease, leading to timely treatments. The aim of this manuscript is to highlight the most relevant researchers' findings relatively to salivary biomarkers of neurodegenerative and demyelinating diseases, and to describe innovative and advanced biosensing strategies for the detection of salivary biomarkers. This review is focused on five relevant aging-related neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, Huntington's disease, Multiple Sclerosis) and the salivary biomarkers most commonly associated with them. Advanced biosensors enabling molecular diagnostics for the detection of salivary biomarkers are presented, in order to stimulate future research in this direction and pave the way for their clinical application.

Neurofilament Light Chain as a Biomarker, and Correlation with Magnetic Resonance Imaging in Diagnosis of CNS-Related Disorders

The search for diagnostic and prognostic biomarkers for neurodegenerative conditions is of high importance, since these disorders may present difficulties in differential diagnosis. Biomarkers with high sensitivity and specificity are required. Neurofilament light chain (NfL) is a unique biomarker related to axonal damage and neural cell death, which is elevated in a number of neurological disorders, and can be detected in cerebrospinal fluid (CSF), as well as blood, serum, or plasma samples. Although the NfL concentration in CSF is higher than that in blood, blood measurement may be easier in practice due to its lesser invasiveness, reproducibility, and convenience. Many studies have investigated NfL in both CSF and serum/plasma as a potential biomarker of neurodegenerative disorders. Neuroimaging biomarkers can also potentially improve detection of CNS-related disorders at an early stage. Magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) are sensitive techniques to visualize neuroaxonal loss. Therefore, investigating the combination of NfL levels with indices extracted from MRI and DTI scans could potentially improve diagnosis of CNS-related disorders. This review summarizes the evidence for NfL being a reliable biomarker in the early detection and disease management in several CNS-related disorders. Moreover, we highlight the correlation between MRI and NfL and ask whether they can be combined.

Increased substantia nigra echogenicity correlated with visual hallucinations in Parkinson's disease: a Chinese population-based study

As a noninvasive technique, transcranial sonography (TCS) of substantia nigra (SN) has gradually showed its effectiveness not only in diagnosis but also in understanding clinical features of Parkinson’s Disease (PD). This study aimed to further evaluate TCS for clinical diagnosis of PD, and to explore the association between sonographic manifestations and visual hallucinations (VH). A total of 226 subjects including 141 PD patients and 85 controls were recruited. All participants received TCS. A series of rating scales to evaluate motor and non-motor symptoms were performed in PD patients. Results showed that 172 subjects were successfully assessed by TCS. The area of SN was greater in PD patients than that in controls (P < 0.001). As receiver-operating characteristic (ROC) curve analysis showed, the best cutoff value for the larger SN echogenicity size was 23.5 mm² (sensitivity 70.3%, specificity 77.0%). Patients with VH had larger SN area (P = 0.019), as well as higher Non-Motor Symptoms Scale (NMSS) scores (P = 0.018). Moreover, binary logistic regression analysis indicated that SN hyperechogenicity (odds ratio = 4.227, P = 0.012) and NMSS scores (odds ratio = 0.027, P = 0.042) could be the independent predictors for VH. In conclusion, TCS can be used as an auxiliary diagnostic tool for Parkinson’s disease. Increased SN echogenicity is correlated with VH in Parkinson’s disease, possibly because the brain stem is involved in the mechanism in the onset of VH. Further studies are needed to confirm these findings.

Neurotheranostics as personalized medicines

The discipline of neurotheranostics was forged to improve diagnostic and therapeutic clinical outcomes for neurological disorders. Research was facilitated, in largest measure, by the creation of pharmacologically effective multimodal pharmaceutical formulations. Deployment of neurotheranostic agents could revolutionize staging and improve nervous system disease therapeutic outcomes. However, obstacles in formulation design, drug loading and payload delivery still remain. These will certainly be aided by multidisciplinary basic research and clinical teams with pharmacology, nanotechnology, neuroscience and pharmaceutic expertise. When successful the end results will provide "optimal" therapeutic delivery platforms. The current report reviews an extensive body of knowledge of the natural history, epidemiology, pathogenesis and therapeutics of neurologic disease with an eye on how, when and under what circumstances neurotheranostics will soon be used as personalized medicines for a broad range of neurodegenerative, neuroinflammatory and neuroinfectious diseases.

Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records

The present work explores the diagnostic performance for depression of neural network classifiers analyzing the sound structures of laughter as registered from clinical patients and healthy controls. The main methodological novelty of this work is that simple sound variables of laughter are used as inputs, instead of electrophysiological signals or local field potentials (LFPs) or spoken language utterances, which are the usual protocols up-to-date. In the present study, involving 934 laughs from 30 patients and 20 controls, four different neural networks models were tested for sensitivity analysis, and were additionally trained for depression detection. Some elementary sound variables were extracted from the records: timing, fundamental frequency mean, first three formants, average power, and the Shannon-Wiener entropy. In the results obtained, two of the neural networks show a diagnostic discrimination capability of 93.02 and 91.15% respectively, while the third and fourth ones have an 87.96 and 82.40% percentage of success. Remarkably, entropy turns out to be a fundamental variable to distinguish between patients and controls, and this is a significant factor which becomes essential to understand the deep neurocognitive relationships between laughter and depression. In biomedical terms, our neural network classifier-based neuroprosthesis opens up the possibility of applying the same methodology to other mental-health and neuropsychiatric pathologies. Indeed, exploring the application of laughter in the early detection and prognosis of Alzheimer and Parkinson would represent an enticing possibility, both from the biomedical and the computational points of view.

Relation between functional connectivity and disability in multiple sclerosis: a non-linear model

OBJECTIVE

To characterize the relation between brain functional connectivity and disability in patients with multiple sclerosis; to investigate the existence of critical values of both disability and functional connectivity corresponding to exhaustion of functional adaptive mechanisms.

METHODS

Hundred-and-nineteen patients with no-to-severe disability and 42 healthy subjects were studied via 3T resting state functional MRI. Out of 116 regions extracted from Automated Anatomical Labeling atlas, pairs of regions whose functional connectivity correlated with Expanded Disability Status Score were identified. In patients, mathematical modeling was applied to find the best models describing Expanded-Disability-Status-Score vs structural or functional measures. Functional vs structural models intersecting points were identified.

RESULTS

Disability had direct linear relation with lesion load (r = 0.40, p < 5E-6), inverse of thalamic volume (r = 0.31 p < 1E-3) and functional connectivity in bi-frontal pairs of regions (r > 0.40, p < 0.04), while being non-linearly associated with functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions (F > 1.73, p < 0.02). Structural vs functional models intersecting points corresponded to Expanded Disability Status Score of 3.0. 85% of patients scoring more than 3.0 showed functional connectivity in cerebello-temporal and cerebello-frontal pairs of regions below confidence intervals (z = [2.28-2.88] 95% CI) measured in healthy subjects.

CONCLUSIONS

Functional brain connectivity changes may represent mechanisms of adaptation to structural damage and inflammation and may be not always clinically beneficial. Functional connectivity decreases in comparison with structural measure at Expanded Disability Status Score greater than 3.0, which may be critical and indicate exhaustion of compensatory mechanisms.

Computational Techniques for Eye Movements Analysis towards Supporting Early Diagnosis of Alzheimer's Disease: A Review

An opportune early diagnosis of Alzheimer's disease (AD) would help to overcome symptoms and improve the quality of life for AD patients. Research studies have identified early manifestations of AD that occur years before the diagnosis. For instance, eye movements of people with AD in different tasks differ from eye movements of control subjects. In this review, we present a summary and evolution of research approaches that use eye tracking technology and computational analysis to measure and compare eye movements under different tasks and experiments. Furthermore, this review is targeted to the feasibility of pioneer work on developing computational tools and techniques to analyze eye movements under naturalistic scenarios. We describe the progress in technology that can enhance the analysis of eye movements everywhere while subjects perform their daily activities and give future research directions to develop tools to support early AD diagnosis through analysis of eye movements.

Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference

OBJECTIVES

In recent years, large-scale longitudinal neuroimaging studies have improved our understanding of healthy ageing and pathologies including Alzheimer's disease (AD). A particular focus of these studies is group differences and identification of participants at risk of deteriorating to a worse diagnosis. For this, statistical analysis using linear mixed-effects (LME) models are used to account for correlated observations from individuals measured over time. A Bayesian framework for LME models in AD is introduced in this paper to provide additional insight often not found in current LME volumetric analyses.

SETTING AND PARTICIPANTS

Longitudinal neuroimaging case study of ageing was analysed in this research on 260 participants diagnosed as either healthy controls (HC), mild cognitive impaired (MCI) or AD. Bayesian LME models for the ventricle and hippocampus regions were used to: (1) estimate how the volumes of these regions change over time by diagnosis, (2) identify high-risk non-AD individuals with AD like degeneration and (3) determine probabilistic trajectories of diagnosis groups over age.

RESULTS

We observed (1) large differences in the average rate of change of volume for the ventricle and hippocampus regions between diagnosis groups, (2) high-risk individuals who had progressed from HC to MCI and displayed similar rates of deterioration as AD counterparts, and (3) critical time points which indicate where deterioration of regions begins to diverge between the diagnosis groups.

CONCLUSIONS

To the best of our knowledge, this is the first application of Bayesian LME models to neuroimaging data which provides inference on a population and individual level in the AD field. The application of a Bayesian LME framework allows for additional information to be extracted from longitudinal studies. This provides health professionals with valuable information of neurodegeneration stages, and a potential to provide a better understanding of disease pathology.

A future perspective on neurodegenerative diseases: nasopharyngeal and gut microbiota

Neurodegenerative diseases are considered a serious life-threatening issue regardless of age. Resulting nerve damage progressively affects important activities, such as movement, coordination, balance, breathing, speech and the functioning of vital organs. Reports on the subject have concluded that neurodegenerative disease can be caused by mutations of susceptible genes, alcohol consumption, toxins, chemicals and other unknown environmental factors. Although several diagnostic techniques can be used to determine aetiologies, the process is difficult and often fails. Research shows that nasopharyngeal and gut microbiota play important roles in brain to spinal cord coordination. However, no conclusive epidemiologic evidence is available on the roles played by respiratory and gut microbiota in the development of neurodegenerative diseases. Thus, understanding the connection between respiratory and gut microbiota and the nervous system could provide information on causal links. The present review describes future perspectives on the role played by nasopharyngeal and gut microbiota in the development of neurodegenerative diseases.

Position Emission Tomography/Single-Photon Emission Tomography Neuroimaging for Detection of Premotor Parkinson's Disease

Premotor Parkinson's disease (PD) refers to a prodromal stage of Parkinson's disease (PD) during which nonmotor clinical features may be present. Currently, it is difficult to make an early diagnosis for premotor PD. Molecular imaging with position emission tomography (PET) or single-photon emission tomography (SPECT) offers a wide variety of tools for overcoming this difficulty. Indeed, molecular imaging techniques may play a crucial role in diagnosing, monitoring and evaluating the individuals with the risk for PD. For example, dopaminergic dysfunctions can be identified by detecting the expression of vesicular monoamine transporter (VMAT2) and aromatic amino acid decarboxylase (AADC) to evaluate the conditions of dopaminergic terminals functions in high-risk individuals of PD. This detection provides a sensitive and specific measurement of nonmotor symptoms (NMS) such as olfactory dysfunction, sleep disorders, and psychiatric symptoms in the high-risk patients, especially at the premotor phase. Molecular imaging technique is capable of detecting the dysfunction of serotonergic, noradrenergic, and cholinergic systems that are typically associated with premotor manifestations. This review discusses the importance of SPECT/PET applications in the detection of premotor markers preceding motor abnormalities with highlighting their great potential for early and accurate diagnosis of premotor symptoms of PD and its scientific significance.

Transcranial sonography of the substantia nigra and its correlation with DAT-SPECT in the diagnosis of Parkinson's disease

INTRODUCTIONS

Transcranial sonography (TCS) of the substantia nigra is a new and promising method to diagnose Parkinson's disease (PD) but its effectiveness is controversial.

METHODS

All 55 PD patients involved in the study underwent single photon emission computed tomography (SPECT) imaging using the labeled dopamine transporter radiotracer (99m)Tc-TRODAT-1 to assess nigrostriatal dopaminergic function. The echogenicity of the substantia nigra was measured by TCS in all patients who received DAT-SPECT scanning. Finally, statistical analysis was carried out to determine the diagnostic accuracy of TCS as well as its correlation with (99m)Tc-TRODAT-1 SPECT, its positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

Contralateral striatal (99m)Tc-TRODAT-1 uptake was significantly reduced compared to ipsilateral striatal uptake, and had a negative correlation with UPDRS-Ⅲ(r = -0.334, p = 0.013), disease duration (r = -0.393, p = 0.003) and H-Y stage (r = -0.330, p = 0.014). After TCS measurement, the contralateral SN echogenic area was similar to the ipsilateral SN echogenic area (27.77 ± 13.19 vs 25.98 ± 11.94 mm(2), p = 0.402, n = 24). No correlation was identified between TCS and UPDRS-Ⅲ (r = 0.383, p = 0.065), disease duration (r = 0.371, p = 0.075) or H-Y stage (r = 0.259, p = 0.222). The sensitivity and specificity of SN TCS for the diagnosis of PD were calculated as 64.70% and 60% according to DAT-SPECT, respectively, while the positive predictive value and negative predictive value was calculated as 91.67% and 20%, respectively.

CONCLUSIONS

Compared to DAT-SPECT, TCS is a non-radioactive and convenient procedure to perform. In our investigation, TCS had no correlation with DAT-SPECT. However, the high positive predictive value of TCS highlights its possible utility as a routine diagnostic test.

Conventional 3T brain MRI and diffusion tensor imaging in the diagnostic workup of early stage parkinsonism

Introduction

The aim of this study is to evaluate whether the diagnostic accuracy of 3 T brain MRI is improved by region of interest (ROI) measures of diffusion tensor imaging (DTI), to differentiate between neurodegenerative atypical parkinsonism (AP) and Parkinson’s disease (PD) in early stage parkinsonism.

Methods

We performed a prospective observational cohort study of 60 patients presenting with early stage parkinsonism and initial uncertain diagnosis. At baseline, patients underwent a 3 T brain MRI including DTI. After clinical follow-up (mean 28.3 months), diagnoses could be made in 49 patients (30 PD and 19 AP). Conventional brain MRI was evaluated for regions of atrophy and signal intensity changes. Tract-based spatial statistics and ROI analyses of DTI were performed to analyze group differences in mean diffusivity (MD) and fractional anisotropy (FA), and diagnostic thresholds were determined. Diagnostic accuracy of conventional brain MRI and DTI was assessed with the receiver operating characteristic (ROC).

Results

Significantly higher MD of the centrum semiovale, body corpus callosum, putamen, external capsule, midbrain, superior cerebellum, and superior cerebellar peduncles was found in AP. Significantly increased MD of the putamen was found in multiple system atrophy–parkinsonian form (MSA-P) and increased MD in the midbrain and superior cerebellar peduncles in progressive supranuclear palsy (PSP). The diagnostic accuracy of brain MRI to identify AP as a group was not improved by ROI measures of MD, though the diagnostic accuracy to identify MSA-P was slightly increased (AUC 0.82 to 0.85).

Conclusion

The diagnostic accuracy of brain MRI to identify AP as a group was not improved by the current analysis approach to DTI, though DTI measures could be of added value to identify AP subgroups.

Salivary DJ-1 could be an indicator of Parkinson's disease progression

Objective: The goal of the current investigation was to explore whether salivary DJ-1 could be a potential biomarker for monitoring disease progression in Parkinson's disease (PD) by evaluating the association between salivary DJ-1 concentrations and nigrostriatal dopaminergic function.

Methods: First, in 74 patients with PD and 12 age-matched normal controls, single photon emission computed tomography (SPECT) imaging with labeled dopamine transporters (DAT) (^99mTc-TRODAT-1), which has been used for measuring DAT density in PD was prformed. Then, the DJ-1 level in their saliva was analyzed by quantitative and sensitive Luminex assay and compared to caudate or putamen DAT density. Finally, based on the above, our cross-section study was carried out in 376 research volunteers (285 patients with PD and 91 healthy controls) to measure salivary DJ-1 level.

Results: From our analysis, we found a correlation between salivary concentration of DJ-1 and putamen nucleus uptake of ^99mTc-TRODAT-1 in the PD group. Although salivary DJ-1 levels were not affected by UPDRS scores, gender, age, and pharmacotherapy, DJ-1 levels in H&Y 4 stage of PD were higher than those in H&Y 1-3 stage as well as those in healthy controls. Salivary DJ-1 also decreased significantly in mixed type PD patients compared to the tremor-dominant type (TDT) and akinetic-rigid dominant type (ARDT) PD patients.

Conclusions: According to the investigation in a large cohort, we reported for the first time the prognostic potential of the salivary DJ-1 as a biomarker for evaluating nigrostriatal dopaminergic function in PD.

Combination of olfactory test and substantia nigra transcranial sonopraphy in the differential diagnosis of Parkinson's disease: a pilot study from China

OBJECTIVES

Both hyposmia and substania nigra (SN) hyperechogenicity on trascranial sonography (TCS) were risk markers for idiopathic Parkinson's disease (PD), which was beneficial to the differential diagnosis of the disease. However, each of their single diagnostic value is often limited. The purpose of present study was to explore whether the combination of olfactory test and TCS of SN could enhance the differential diagnostic power in Chinese patients with PD.

METHODS

Thirty-seven patients with PD and twenty-six patients with essential tremor (ET) were evaluated on 16-item odor identification test from extended version of sniffin' sticks and TCS of SN. The frequency of hyposmia and SN hyperechogenicity in each group was compared. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the two clinical biomarkers were analyzed.

RESULTS

The frequency of hyposmia in patients with PD was significantly higher than in patients with ET (62.2% VS. 3.8%, P = 0.000). The frequency of SN hyperechogenicity in patients with PD was significantly higher than in ET subjects (48.6% VS. 15.4%, P = 0.006). The combination of hyposmia and SN hyperechogenicity (if either one or both present) discriminated patients with PD from ET with a sensitivity of 78.4% and 29.7%, specificity of 80.8% and 100%, PPV of 85.3% and 100%, and NPV of 72.4% and 50.0%, respectively.

CONCLUSIONS

Our preliminary data suggested that the combination of hyposmia and SN hyperechogenicity could improve the diagnostic potential for discriminating Chinese patients with PD from ET.