Differential diagnosis of parkinsonian syndromes using dopamine transporter and perfusion SPECT

Machine-learning assisted multiplex detection of catecholamine neurotransmitters with a colorimetric sensor array

Catecholamine neurotransmitters (CNs), such as dopamine (DA), epinephrine (EP), norepinephrine (NEP), and levodopa (LD), are recognized as the primary biomarkers of a variety of neurological illnesses. Therefore, simultaneous monitoring of these biomarkers is highly recommended for clinical diagnosis and treatment. In this study, a high-performance colorimetric artificial tongue has been proposed for the multiplex detection of CNs. Different aggregation behaviors of gold nanoparticles in the presence of CNs under various buffering conditions generate unique fingerprint response patterns. Under various buffering conditions, the distinct acidity constants of CNs, and consequently their predominant species at a given pH, drive the aggregation of gold nanoparticles (AuNPs). The utilization of machine learning algorithms in this design enables classification and quantification of CNs in various samples. The response profile of the array was analyzed using the linear discriminant analysis algorithm for classification of CNs. This colorimetric sensor array is capable of accurately distinguishing between individual neurotransmitters and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit (FOMs) and linear ranges of 0.6-9 μM (R² = 0.99) for DA, 0.1-10 μM (R² = 0.99) for EP, 0.1-9 μM (R² = 0.99) for NEP and 1-70 μM (R² = 0.99) for LD demonstrated the potential applicability of the developed sensor array in precise and accurate determination of CNs. Finally, the feasibility of the array was validated in human urine samples as a complex biological fluid with LODs of 0.3, 0.5, 0.2, and 1.9 μM for DA, EP, NEP, and LD, respectively.

Brain Networks, Clinical Manifestations, and Neuroimaging of Cognitive Disorders: The Role of Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Other Advanced Neuroimaging Tests

In this article, we briefly discuss imaging modalities used in clinical settings for neuroanatomical characterization and for diagnosis of the underlying disease. We then discuss how each neuroimaging tool can be used in the context of clinical syndromes. The major underlying causes relevant to our discussion include Alzheimer disease, Lewy body disease, cerebrovascular disease, frontotemporal degeneration, autoimmune diseases, and systemic or metabolic derangements.

Validation of dynamic [18F]FE-PE2I PET for estimation of relative regional cerebral blood flow: a comparison with [15O]H2O PET

BACKGROUND

Dopamine transporter (DAT) imaging is used in the diagnostic work-up in suspected parkinsonian syndromes and dementia with Lewy bodies but cannot differentiate between these syndromes, and an extra brain imaging examination of the regional cerebral blood flow (rCBF) or glucose metabolism is often needed for differential diagnosis. The requirement of two different imaging examinations is resource-consuming and inconvenient for the patients. Therefore, imaging of both cortical blood flow and DAT imaging with the same radiotracer would be more convenient and cost-effective. The aim of this study was to test whether relative regional cerebral blood flow (rCBF_R) can be measured with the DAT-specific positron emission tomography (PET) tracer [¹⁸F]FE-PE2I (FE-PE2I), by validation with cerebral perfusion measured with [¹⁵O]H₂O PET (H₂O).

METHODS

The rCBF_R was quantified by kinetic modeling for FE-PE2I (R1) and H₂O (F). The R1 was calculated using the simplified reference tissue model, and F was calculated with a modified Koopman double-integration method. The linear relationship and intraclass correlation (ICC) between R1 and F were tested in image data derived from 29 patients with recent onset parkinsonism and 30 healthy controls.

RESULTS

There was a strong linear correlation across all subjects between R1 and F in the frontal, parietal, temporal, cingulate and occipital cortex as well as in the striatum (r  ≥  0.731-0.905, p  < 0.001) with a good-to-excellent ICC, ranging from 0.727 to 0.943 (p < 0.001).

CONCLUSIONS

Our results suggest that FE-PE2I may be used as a proxy for cerebral perfusion, thus potentially serving as a radiotracer for assessment of both DAT availability and rCBF_R in one single dynamic scan. This could be valuable in the differential diagnosis of parkinsonian syndromes.

TRIAL REGISTRATION

EUDRA-CT 2015-003045-26. Registered 23 October 2015 https://www.clinicaltrialsregister.eu/ctr-search/search?query=2015-003045-26.

The Strengths and Obstacles in the Differential Diagnosis of Progressive Supranuclear Palsy—Parkinsonism Predominant (PSP-P) and Multiple System Atrophy (MSA) Using Magnetic Resonance Imaging (MRI) and Perfusion Single Photon Emission Computed Tomography (SPECT)

Multiple System Atrophy—Parkinsonism Predominant (MSA-P) and Progressive Supranuclear Palsy—Parkinsonism Predominant (PSP-P) are the clinical manifestations of atypical parkinsonism. Currently, there are no efficient in vivo methods available relating to neuroimaging or biochemical analysis in the examination of these entities. Among the advanced methods available, using positron emission tomography is constrained by high cost and low accessibility. In this study the authors examined patients with two types of atypical parkinsonism—MSA-P and PSP-P, which are difficult to differentiate, especially in the early years of their development. The aim of this study was to assess whether the examination of patients in the period following the early years (3–6-year duration of symptoms) could be enhanced by perfusion single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI) or evaluation of cognitive abilities. Extended examination using MRI and perfusion SPECT showed that the evaluation of the mesencephalon/pons ratio, mesencephalic volume decrease, the Magnetic Resonance Parkinsonism Index (MRPI) and frontal perfusion should be considered more feasible than screening cognitive evaluation in MSA-P and PSP-P with a 3–6-year duration of symptoms.

Diagnostic accuracy of dual-phase 18F-FP-CIT PET imaging for detection and differential diagnosis of Parkinsonism

Delayed phase 18F-FP-CIT PET (dCIT) can assess the striatal dopamine transporter binding to detect degenerative parkinsonism (DP). Early phase 18F-FP-CIT (eCIT) can assess the regional brain activity for differential diagnosis among parkinsonism similar with 18F-FDG PET. We evaluated the diagnostic performance of dual phase 18F-FP-CIT PET (dual CIT) and 18F-FDG PET compared with clinical diagnosis in 141 subjects [36 with idiopathic Parkinson's disease (IPD), 77 with multiple system atrophy (MSA), 18 with progressive supranuclear palsy (PSP), and 10 with non-DP)]. Visual assessment of eCIT, dCIT, dual CIT, 18F-FDG and 18F-FDG PET with dCIT was in agreement with the clinical diagnosis in 61.7%, 69.5%, 95.7%, 81.6%, and 97.2% of cases, respectively. ECIT showed about 90% concordance with non-DP and MSA, and 8.3% and 27.8% with IPD and PSP, respectively. DCIT showed ≥ 88% concordance with non-DP, IPD, and PSP, and 49.4% concordance with MSA. Dual CIT showed ≥ 90% concordance in all groups. 18F-FDG PET showed ≥ 90% concordance with non-DP, MSA, and PSP, but only 33.3% concordance with IPD. The combination of 18F-FDG and dCIT yielded ≥ 90% concordance in all groups. Dual CIT may represent a powerful alternative to the combination of 18F-FDG PET and dCIT for differential diagnosis of parkinsonian disorders.

Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy

PURPOSE

To construct multivariate radiomics models using hybrid ¹⁸F-FDG PET/MRI for distinguishing between Parkinson's disease (PD) and multiple system atrophy (MSA).

METHODS

Ninety patients (60 with PD and 30 with MSA) were randomized to training and test sets in a 7:3 ratio. All patients underwent ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/MRI to simultaneously obtain metabolic images (¹⁸F-FDG), structural MRI images (T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and T2-weighted fluid-attenuated inversion recovery (T2/FLAIR)) and functional MRI images (susceptibility-weighted imaging (SWI) and apparent diffusion coefficient). Using PET and five MRI sequences, we extracted 1172 radiomics features from the putamina and caudate nuclei. The radiomics signatures were constructed with the least absolute shrinkage and selection operator algorithm in the training set, with progressive optimization through single-sequence and double-sequence radiomics models. Multivariable logistic regression analysis was used to develop a clinical-radiomics model, combining the optimal multi-sequence radiomics signature with clinical characteristics and SUV values. The diagnostic performance of the models was assessed by receiver operating characteristic and decision curve analysis (DCA).

RESULTS

The radiomics signatures showed favourable diagnostic efficacy. The optimal model comprised structural (T1WI), functional (SWI) and metabolic (¹⁸F-FDG) sequences (Radscore_{FDG_T1WI_SWI}) with the area under curves (AUCs) of the training and test sets of 0.971 and 0.957, respectively. The integrated model, incorporating Radscore_{FDG_T1WI_SWI}, three clinical symptoms (disease duration, dysarthria and autonomic failure) and SUV_max, demonstrated satisfactory calibration and discrimination in the training and test sets (0.993 and 0.994, respectively). DCA indicated the highest clinical benefit of the clinical-radiomics integrated model.

CONCLUSIONS

The radiomics signature with metabolic, structural and functional information provided by hybrid ¹⁸F-FDG PET/MRI may achieve promising diagnostic efficacy for distinguishing between PD and MSA. The clinical-radiomics integrated model performed best.

Classification of Parkinson's disease based on multi-modal features and stacking ensemble learning

BACKGROUND

Early diagnosis of Parkinson's disease (PD) enables timely treatment of patients and helps control the course of the disease. An efficient and reliable approach is therefore needed to develop for improving the clinical ability to diagnose this disease.

NEW METHOD

We proposed a two-layer stacking ensemble learning framework with fusing multi-modal features in this study, for accurately identifying early PD with healthy control (HC). To begin with, we investigated relative importance of multi-modal neuroimaging (T1 weighted image (T1WI), diffusion tensor imaging (DTI)) and early clinical assessment to classify PD and HC. Next, a two-layer stacking ensemble framework was proposed: at the first layer, we evaluated advantages of these four base classifiers: support vector machine (SVM), random forests (RF), K-nearest neighbor (KNN) and artificial neural network (ANN); at the second layer, a logistic regression (LR) classifier was applied to classify PD. The performance of the proposed model was evaluated by comparing with traditional ensemble models.

RESULTS

The proposed method performed an accuracy of 96.88 %, a precision of 100 %, a recall of 95 % and a F₁ score of 97.44 % respectively for identifying PD and HC.

COMPARISON WITH EXISTING METHOD

The classification results showed that the proposed model achieved a superior performance in comparison with traditional ensemble models.

CONCLUSION

The stacking ensemble model with efficiently and effectively integrate multiple base classifiers performed higher accuracy than each single traditional model. The method developed in this study provided a novel strategy to enhance the accuracy of diagnosis and early detection of PD.

Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

Parkinson's disease (PD) and atypical Parkinsonian syndromes are progressive heterogeneous neurodegenerative diseases that share clinical characteristic of parkinsonism as a common feature, but are considered distinct clinicopathological disorders. Based on the predominant protein aggregates observed within the brain, these disorders are categorized as, (1) α-synucleinopathies, which include PD and other Lewy body spectrum disorders as well as multiple system atrophy, and (2) tauopathies, which comprise progressive supranuclear palsy and corticobasal degeneration. Although, great strides have been made in neurodegenerative disease research since the first medical description of PD in 1817 by James Parkinson, these disorders remain a major diagnostic and treatment challenge. A valid diagnosis at early disease stages is of paramount importance, as it can help accommodate differential prognostic and disease management approaches, enable the elucidation of reliable clinicopathological relationships ideally at prodromal stages, as well as facilitate the evaluation of novel therapeutics in clinical trials. However, the pursuit for early diagnosis in PD and atypical Parkinsonian syndromes is hindered by substantial clinical and pathological heterogeneity, which can influence disease presentation and progression. Therefore, reliable neuroimaging biomarkers are required in order to enhance diagnostic certainty and ensure more informed diagnostic decisions. In this article, an updated presentation of well-established and emerging neuroimaging biomarkers are reviewed from the following modalities: (1) structural magnetic resonance imaging (MRI), (2) diffusion-weighted and diffusion tensor MRI, (3) resting-state and task-based functional MRI, (4) proton magnetic resonance spectroscopy, (5) transcranial B-mode sonography for measuring substantia nigra and lentiform nucleus echogenicity, (6) single photon emission computed tomography for assessing the dopaminergic system and cerebral perfusion, and (7) positron emission tomography for quantifying nigrostriatal functions, glucose metabolism, amyloid, tau and α-synuclein molecular imaging, as well as neuroinflammation. Multiple biomarkers obtained from different neuroimaging modalities can provide distinct yet corroborative information on the underlying neurodegenerative processes. This integrative "multimodal approach" may prove superior to single modality-based methods. Indeed, owing to the international, multi-centered, collaborative research initiatives as well as refinements in neuroimaging technology that are currently underway, the upcoming decades will mark a pivotal and exciting era of further advancements in this field of neuroscience.

Differences in cerebellar perfusion between Parkinson's disease and multiple system atrophy

INTRODUCTION

Objective biomarkers are required for differential diagnosis of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).

OBJECTIVE

We aimed to determine if cerebellar blood flow, measured using N-isopropyl-[¹²³I] p-iodoamphetamine single photon emission computed tomography (¹²³I -IMP-SPECT), was useful for differentiating between PD, MSA and PSP.

METHODS

Twenty-four patients with PD, seventeen patients with MSA with predominant parkinsonian features (MSA-P), sixteenth patients with MSA with predominant cerebellar ataxia (MSA-C) and eight patients with PSP were enrolled. Twenty-seven normal controls' data were used for the calculation of z score. All patients underwent ¹²³I -IMP-SPECT, and data were analyzed using a three-dimensional-stereotactic surface projection program.

RESULTS

Cerebellar perfusion in MSA-P (MSA-P vs PD, P = .002; MSA-P vs PSP, P < .001) and MSA-C (MSA-C vs PD, P < .001; MSA-C vs PSP, P < .001) were significantly decreased compared with PD or PSP. There was no significant difference in perfusion between PD and PSP groups (P = .061). The area under the receiver operating characteristic curve for cerebellar perfusion between MSA-P and PD was 0.858.

CONCLUSION

Our findings revealed that cerebellar perfusion by ¹²³I-IMP-SPECT was useful for differentiating between PD and MSA-P.

Thalamic and cerebellar hypoperfusion in single photon emission computed tomography may differentiate multiple system atrophy and progressive supranuclear palsy

Neuroimaging in the context of examining atypical parkinsonian tauopathies is an evolving matter. Positron emission tomography and single photon emission computed tomography (SPECT) bring tools, which may be reasonable in supplementary examination, however, cannot be interpreted as a criterion standard for correct diagnosis. The aim of this observational study was to assess the differentiating potential of perfusion SPECT in 3 types of atypical parkinsonisms: multiple system atrophy parkinsonian type (MSA-P), corticobasal syndrome (CBS), and progressive supranuclear palsy (PSP). The study was carried out using the comparison of standard deviations of perfusion in patients from these 3 groups. Data obtained from 10 patients with clinical diagnosis MSA-P, 14 patients with CBS and 21 patients with PSP, which were analyzed using Tukey honest significant difference post-hoc test, revealed significant differences of perfusion P < .05 between MSA-P and PSP within the cerebellum and thalamus. No significant differences between CBS and PSP were observed.

Early Detection of Parkinson’s Disease by Using SPECT Imaging and Biomarkers

Precise and timely diagnosis of Parkinson’s disease is important to control its progression among subjects. Currently, a neuroimaging technique called dopaminergic imaging that uses single photon emission computed tomography (SPECT) with 123I-Ioflupane is popular among clinicians for detecting Parkinson’s disease in early stages. Unlike other studies, which consider only low-level features like gray matter, white matter, or cerebrospinal fluid, this study explores the non-linear relation between different biomarkers (SPECT + biological) using deep learning and multivariate logistic regression. Striatal binding ratios are obtained using 123I-Ioflupane SPECT scans from four brain regions which are further integrated with five biological biomarkers to increase the diagnostic accuracy. Experimental results indicate that this investigated approach can differentiate subjects with 100% accuracy. The obtained results outperform the ones reported in the literature. Furthermore, logistic regression model has been developed for estimating the Parkinson’s disease onset probability. Such models may aid clinicians in diagnosing this disease.

Accumulation of Tau Protein, Metabolism and Perfusion-Application and Efficacy of Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) Imaging in the Examination of Progressive Supranuclear Palsy (PSP) and Corticobasal Syndrome (CBS)

Neuroimaging in the context of examining atypical parkinsonian tauopathies is an evolving matter. Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) bring tools, which may be reasonable in supplementary examination, however cannot be interpreted as a gold standard for correct diagnosis. The review presents advantages and limitations of tau radiotracers in PET, metabolic PET and perfusion SPECT. The aim of this paper is to highlight the possibilities and boundaries in the supplementary examination of tauopathic parkinsonian syndromes.

Advances in SPECT Methodology

The emerging role of molecular imaging has made possible to evaluate and quantify biochemical changes of molecular targets in specific neurochemical systems involved in movement disorders, providing neurochemical information of clinical changes before the pathological features occurred. In detail, radionuclides imaging techniques are frequently used for the in vivo study of neurotransmitter and receptor function, alterations in cerebral blood flow and metabolic activity, abnormal protein deposition, and inflammation, with a central role in molecular imaging for preclinical and clinical studies. The present chapter represents an overview of main use of single-photon emission computed tomography (SPECT) in movement disorders, with a focal attention on specific radiotracers used, recent advances in SPECT technology and reconstruction algorithm and added specific value of semiquantitative methods for images analysis. Finally, a brief paragraph is dedicated to description of SPECT/CT devices and advantages of using hybrid technology.

What a neurologist should know about PET and SPECT functional imaging for parkinsonism: A practical perspective

The diagnosis of a parkinsonian syndrome based on clinical criteria remains sometimes difficult, especially at disease onset. Brain or heart molecular imaging techniques (SPECT or PET) can provide a major help to improve and speed up diagnosis, influencing treatment strategies. Presynaptic dopaminergic imaging using either [¹⁸F]-Dopa PET or ¹²³I -2β-Carbomethoxy-3β-(4-Iodophenyl)- N-(3-Fluoropropyl) Nortropane ([¹²³I]-Ioflupane)SPECT demonstrates or rules out the presence of a dopaminergic degenerative process. This allows to distinguish Parkinson's disease, Parkinson "plus" syndromes and dementia with Lewy bodies (reduced radiotracers binding) from essential tremor, psychogenic, post-neuroleptic or vascular parkinsonisms, dopa-responsive dystonia and Alzheimer's disease (normal radiotracers binding). For differential diagnosis between Parkinson's disease and Parkinson "plus" syndromes, brain molecular imaging with [¹⁸F]-Fluorodeoxyglucose ([¹⁸F]-FDG) PET or ^99mTc-HMPAO SPECT can provide useful information, whereas [¹⁸F]-Dopa PET or [¹²³I]-Ioflupane does not separate these entities. Finally, sympathetic cardiac [¹²³I]-Metaiodobenzylguanidine ([¹²³I]-MIBG) scintigraphy or SPECT can help distinguishing Parkinson's disease and dementia with Lew bodies (decreased binding) from multiple system atrophy and progressive supranuclear palsy (normal binding). New radiotracers notably those targeting the pathological process itself such as Tau aggregates are under development and may provide interesting informations to delineate the different Parkinson "plus" syndromes.

Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial

BACKGROUND

Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers.

OBJECTIVES

The present study assessed the feasibility, reliability, and validity of smartphone-based digital biomarkers of PD in a clinical trial setting.

METHODS

During a 6-month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45-day study in 35 age-matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger-tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit-to-stand transitions by gyroscopic and accelerometer data.

RESULTS

Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor-based features showed moderate-to-excellent test-retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P < 0.005. All active test features except sustained phonation were significantly related to corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS clinical severity ratings. On passive monitoring, time spent walking had a significant (P = 0.005) relationship with average postural instability and gait disturbance scores. Of note, for all smartphone active and passive features except postural tremor, the monitoring procedure detected abnormalities even in those Parkinson participants scored as having no signs in the corresponding International Parkinson and Movement Disorder Society-Sponsored UPRDS items at the site visit.

CONCLUSIONS

These findings demonstrate the feasibility of smartphone-based digital biomarkers and indicate that smartphone-sensor technologies provide reliable, valid, clinically meaningful, and highly sensitive phenotypic data in Parkinson's disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Neuroimaging in Lewy body dementia

Lewy body dementia (DLB) is a common form of cognitive impairment, accounting for 30% of dementia cases in ages over 65 years. Early diagnosis of DLB has been challenging; particularly in the context of differentiation with Parkinson’s disease dementia and other forms of dementias, such as Alzheimer’s disease and rapidly progressive dementias. Current practice involves the use of [¹²³I]FP-CIT-SPECT, [¹⁸F]FDG PET and [¹²³I]MIBG molecular imaging to support diagnostic procedures. Structural imaging techniques have an essential role for excluding structural causes, which could lead to a DLB-like phenotype, as well as aiding differential diagnosis through illustrating disease-specific patterns of atrophy. Novel PET molecular imaging modalities, such as amyloid and tau imaging, may provide further insights into DLB pathophysiology and may aid in early diagnosis. A multimodal approach, through combining various established techniques and possibly using novel radioligands, might further aid towards an in-depth understanding of this highly disabling disease. In this review, we will provide an overview of neuroimaging applications in patients with DLB.