Imaging analysis of Parkinson's disease patients using SPECT and tractography

Mapping brain morphology to cognitive deficits: a study on PD-CRS scores in Parkinson's disease with mild cognitive impairment

Background

The Parkinson's Disease-Cognitive Rating Scale (PD-CRS) is a widely used tool for detecting mild cognitive impairment (MCI) in Parkinson's Disease (PD) patients, however, the neuroanatomical underpinnings of this test's outcomes require clarification. This study aims to: (a) investigate cortical volume (CVol) and cortical thickness (CTh) disparities between PD patients exhibiting mild cognitive impairment (PD-MCI) and those with preserved cognitive abilities (PD-IC); and (b) identify the structural correlates in magnetic resonance imaging (MRI) of overall PD-CRS performance, including its subtest scores, within a non-demented PD cohort.

Materials and methods

This study involved 51 PD patients with Hoehn & Yahr stages I-II, categorized into two groups: PD-IC (n = 36) and PD-MCI (n = 15). Cognitive screening evaluations utilized the PD-CRS and the Montreal Cognitive Assessment (MoCA). PD-MCI classification adhered to the Movement Disorder Society Task Force criteria, incorporating extensive neuropsychological assessments. The interrelation between brain morphology and cognitive performance was determined using FreeSurfer.

Results

Vertex-wise analysis of the entire brain demonstrated a notable reduction in CVol within a 2,934 mm2 cluster, encompassing parietal and temporal regions, in the PD-MCI group relative to the PD-IC group. Lower PD-CRS total scores correlated with decreased CVol in the middle frontal, superior temporal, inferior parietal, and cingulate cortices. The PD-CRS subtests for Sustained Attention and Clock Drawing were associated with cortical thinning in distinct regions: the Clock Drawing subtest correlated with changes in the parietal lobe, insula, and superior temporal cortex morphology; while the PD-CRS frontal-subcortical scores presented positive correlations with CTh in the transverse temporal, medial orbitofrontal, superior temporal, precuneus, fusiform, and supramarginal regions. Additionally, PD-CRS subtests for Semantic and Alternating verbal fluency were linked to CTh changes in orbitofrontal, temporal, fusiform, insula, and precentral regions.

Conclusion

PD-CRS performance mirrors neuroanatomical changes across extensive fronto-temporo-parietal areas, covering both lateral and medial cortical surfaces, in PD patients without dementia. The observed changes in CVol and CTh associated with this cognitive screening tool suggest their potential as surrogate markers for cognitive decline in PD. These findings warrant further exploration and validation in multicenter studies involving independent patient cohorts.

Microstructural alterations in white matter and related neurobiology based on the new clinical subtypes of Parkinson's disease

Background and objectives

The advent of new clinical subtyping systems for Parkinson's disease (PD) has led to the classification of patients into distinct groups: mild motor predominant (PD-MMP), intermediate (PD-IM), and diffuse malignant (PD-DM). Our goal was to evaluate the efficacy of diffusion tensor imaging (DTI) in the early diagnosis, assessment of clinical progression, and prediction of prognosis of these PD subtypes. Additionally, we attempted to understand the pathological mechanisms behind white matter damage using single-photon emission computed tomography (SPECT) and cerebrospinal fluid (CSF) analyses.

Methods

We classified 135 de novo PD patients based on new clinical criteria and followed them up after 1 year, along with 45 healthy controls (HCs). We utilized tract-based spatial statistics to assess the microstructural changes of white matter at baseline and employed multiple linear regression to examine the associations between DTI metrics and clinical data at baseline and after follow-up.

Results

Compared to HCs, patients with the PD-DM subtype demonstrated reduced fractional anisotropy (FA), increased axial diffusivity (AD), and elevated radial diffusivity (RD) at baseline. The FA and RD values correlated with the severity of motor symptoms, with RD also linked to cognitive performance. Changes in FA over time were found to be in sync with changes in motor scores and global composite outcome measures. Furthermore, baseline AD values and their rate of change were related to alterations in semantic verbal fluency. We also discovered the relationship between FA values and the levels of α-synuclein and β-amyloid. Reduced dopamine transporter uptake in the left putamen correlated with RD values in superficial white matter, motor symptoms, and autonomic dysfunction at baseline as well as cognitive impairments after 1 year.

Conclusions

The PD-DM subtype is characterized by severe clinical symptoms and a faster progression when compared to the other subtypes. DTI, a well-established technique, facilitates the early identification of white matter damage, elucidates the pathophysiological mechanisms of disease progression, and predicts cognitively related outcomes. The results of SPECT and CSF analyses can be used to explain the specific pattern of white matter damage in patients with the PD-DM subtype.

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI-the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

Longitudinal prognosis of Parkinson's outcomes using causal connectivity

Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

An Update on Parkinson's Disease and its Neurodegenerative Counterparts

INTRODUCTION

Neurodegenerative disorders are a group of diseases that cause nerve cell degeneration in the brain, resulting in a variety of symptoms and are not treatable with drugs. Parkinson's disease (PD), prion disease, motor neuron disease (MND), Huntington's disease (HD), spinal cerebral dyskinesia (SCA), spinal muscle atrophy (SMA), multiple system atrophy, Alzheimer's disease (AD), spinocerebellar ataxia (SCA) (ALS), pantothenate kinase-related neurodegeneration, and TDP-43 protein disorder are examples of neurodegenerative diseases. Dementia is caused by the loss of brain and spinal cord nerve cells in neurodegenerative diseases.

BACKGROUND

Even though environmental and genetic predispositions have also been involved in the process, redox metal abuse plays a crucial role in neurodegeneration since the preponderance of symptoms originates from abnormal metal metabolism.

METHOD

Hence, this review investigates several neurodegenerative diseases that may occur symptoms similar to Parkinson's disease to understand the differences and similarities between Parkinson's disease and other neurodegenerative disorders based on reviewing previously published papers.

RESULTS

Based on the findings, the aggregation of alpha-synuclein occurs in Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies. Other neurodegenerative diseases occur with different protein aggregation or mutations.

CONCLUSION

We can conclude that Parkinson's disease, Multiple system atrophy, and Dementia with Lewy bodies are closely related. Therefore, researchers must distinguish among the three diseases to avoid misdiagnosis of Multiple System Atrophy and Dementia with Lewy bodies with Parkinson's disease symptoms.

Dopaminergic damage pattern predicts phenoconversion time in isolated rapid eye movement sleep behavior disorder

PURPOSE

The exact phenoconversion time from isolated rapid eye movement (REM) sleep behavior disorder (iRBD) to synucleinopathies remains unpredictable. This study investigated whole-brain dopaminergic damage pattern (DDP) with disease progression and predicted phenoconversion time in individual patients.

METHODS

Age-matched 33 iRBD patients and 20 healthy controls with 11C-CFT-PET scans were enrolled. The patients were followed up 2-10 (6.7 ± 2.0) years. The phenoconversion year was defined as the base year, and every 2 years before conversion was defined as a stage. Support vector machine with leave-one-out cross-validation strategy was used to perform prediction.

RESULTS

Dopaminergic degeneration of iRBD was found to occur about 6 years before conversion and then abnormal brain regions gradually expanded. Using DDP, area under curve (AUC) was 0.879 (90% sensitivity and 88.3% specificity) for predicting conversion in 0-2 years, 0.807 (72.7% sensitivity and 83.3% specificity) in 2-4 years, 0.940 (100% sensitivity and 84.6% specificity) in 4-6 years, and 0.879 (100% sensitivity and 80.7% specificity) over 6 years. In individual patients, predicted stages correlated with whole-brain dopaminergic levels (r =  - 0.740, p < 0.001).

CONCLUSION

Our findings suggest that DDP could accurately predict phenoconversion time of individual iRBD patients, which may help to screen patients for early intervention.

Nigral Pathology Contributes to Microstructural Integrity of Striatal and Frontal Tracts in Parkinson's Disease

BACKGROUND

Motor and cognitive impairment in Parkinson's disease (PD) is associated with dopaminergic dysfunction that stems from substantia nigra (SN) degeneration and concomitant α-synuclein accumulation. Diffusion magnetic resonance imaging (MRI) can detect microstructural alterations of the SN and its tracts to (sub)cortical regions, but their pathological sensitivity is still poorly understood.

OBJECTIVE

To unravel the pathological substrate(s) underlying microstructural alterations of SN, and its tracts to the dorsal striatum and dorsolateral prefrontal cortex (DLPFC) in PD.

METHODS

Combining post-mortem in situ MRI and histopathology, T1-weighted and diffusion MRI, and neuropathological samples of nine PD, six PD with dementia (PDD), five dementia with Lewy bodies (DLB), and 10 control donors were collected. From diffusion MRI, mean diffusivity (MD) and fractional anisotropy (FA) were derived from the SN, and tracts between the SN and caudate nucleus, putamen, and DLPFC. Phosphorylated-Ser129-α-synuclein and tyrosine hydroxylase immunohistochemistry was included to quantify nigral Lewy pathology and dopaminergic degeneration, respectively.

RESULTS

Compared to controls, PD and PDD/DLB showed increased MD of the SN and SN-DLPFC tract, as well as increased FA of the SN-caudate nucleus tract. Both PD and PDD/DLB showed nigral Lewy pathology and dopaminergic loss compared to controls. Increased MD of the SN and FA of SN-caudate nucleus tract were associated with SN dopaminergic loss. Whereas increased MD of the SN-DLPFC tract was associated with increased SN Lewy neurite load.

CONCLUSIONS

In PD and PDD/DLB, diffusion MRI captures microstructural alterations of the SN and tracts to the dorsal striatum and DLPFC, which differentially associates with SN dopaminergic degeneration and Lewy neurite pathology. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A tissue-fraction estimation-based segmentation method for quantitative dopamine transporter SPECT

BACKGROUND

Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus (GP) derived from dopamine transporter-single-photon emission computed tomography (DaT-SPECT) images have potential as biomarkers for measuring the severity of Parkinson's disease. Reliable quantification of this uptake requires accurate segmentation of the considered regions. However, segmentation of these regions from DaT-SPECT images is challenging, a major reason being partial-volume effects (PVEs) in SPECT. The PVEs arise from two sources, namely the limited system resolution and reconstruction of images over finite-sized voxel grids. The limited system resolution results in blurred boundaries of the different regions. The finite voxel size leads to TFEs, that is, voxels contain a mixture of regions. Thus, there is an important need for methods that can account for the PVEs, including the TFEs, and accurately segment the caudate, putamen, and GP, from DaT-SPECT images.

PURPOSE

Design and objectively evaluate a fully automated tissue-fraction estimation-based segmentation method that segments the caudate, putamen, and GP from DaT-SPECT images.

METHODS

The proposed method estimates the posterior mean of the fractional volumes occupied by the caudate, putamen, and GP within each voxel of a three-dimensional DaT-SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross-entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of true fractional volumes is obtained from existing populations of clinical magnetic resonance images. The method is implemented using a supervised deep-learning-based approach.

RESULTS

Evaluations using clinically guided highly realistic simulation studies show that the proposed method accurately segmented the caudate, putamen, and GP with high mean Dice similarity coefficients of ∼ 0.80 and significantly outperformed (p < 0.01 $p < 0.01$ ) all other considered segmentation methods. Further, an objective evaluation of the proposed method on the task of quantifying regional uptake shows that the method yielded reliable quantification with low ensemble normalized root mean square error (NRMSE) < 20% for all the considered regions. In particular, the method yielded an even lower ensemble NRMSE of ∼ 10% for the caudate and putamen.

CONCLUSIONS

The proposed tissue-fraction estimation-based segmentation method for DaT-SPECT images demonstrated the ability to accurately segment the caudate, putamen, and GP, and reliably quantify the uptake within these regions. The results motivate further evaluation of the method with physical-phantom and patient studies.

Clinical and Dopamine Transporter Imaging Trajectories in a Cohort of Parkinson's Disease Patients with GBA Mutations

BACKGROUND

Glucosylceramidase (GBA) mutations are considered the most common genetic risk factors for developing Parkinson's disease (PD).

OBJECTIVES

We aimed to assess, at different time points, the integrity of brain striatal and extra-striatal dopamine pathways and clinical phenotype of a group of PD subjects bearing heterozygous GBA mutations (GBA-PD), compared with a group of idiopathic PD patients (iPD) stratified by age at disease onset. A longitudinal approach was adopted to evaluate the progression over time for clinical and ¹²³ I-FP-CIT SPECT imaging features.

METHODS

We considered 46 GBA-PD patients and 339 iPD patients, subdivided into two groups according to age at PD onset (n = 58 < 50 years and n = 281 > 50 years). We measured differences in the occurrence/severity/progression of motor and non-motor features, ¹²³ I-FP-CIT standard uptake value ratios (SUVr) in striatal and extra-striatal regions, and global cognitive deterioration over time in a subset of 168 cases with available follow-up.

RESULTS

At baseline, the GBA-PD cohort showed more severe motor and cognitive deficits than the early-iPD cohort. The ¹²³ I-FP-CIT SUVr reduction in the striatal and the extra-striatal regions was more marked in the GBA-PD than the early- and late-iPD cohorts. Both GBA-PD and late-iPD patients had a significant annual deterioration in their global cognitive performance, while the early-iPD group showed global cognitive stability over time. At follow-up, the iPD cohorts became similar to the GBA-PD group in ¹²³ I-FP-CIT SUVr reduction.

CONCLUSION

These new findings support the hypothesis of a biological role of GBA mutations in accelerating the early neurodegenerative processes in PD, leading to the malignant clinical phenotype. © 2021 International Parkinson and Movement Disorder Society.

Ordinal classification of the affectation level of 3D-images in Parkinson diseases

Parkinson’s disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{123}$$\end{document}123I-ioflupane, considering a binary classification problem (absence or existence of Parkinson’s disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.

Large-Area SiPM Pixels (LASiPs): A cost-effective solution towards compact large SPECT cameras

Single Photon Emission Computed Tomography (SPECT) scanners based on photomultiplier tubes (PMTs) are still largely employed in the clinical environment. A standard camera for full-body SPECT employs ~50-100 PMTs of 4-8 cm diameter and is shielded by a thick layer of lead, becoming a heavy and bulky system that can weight a few hundred kilograms. The volume, weight and cost of a camera can be significantly reduced if the PMTs are replaced by silicon photomultipliers (SiPMs). The main obstacle to use SiPMs in full-body SPECT is the limited size of their sensitive area. A few thousand channels would be needed to fill a camera if using the largest commercially-available SiPMs of 6 × 6 mm². As a solution, we propose to use Large-Area SiPM Pixels (LASiPs), built by summing individual currents of several SiPMs into a single output. We developed a LASiP prototype that has a sensitive area 8 times larger than a 6 × 6 mm² SiPM. We built a proof-of-concept micro-camera consisting of a 40 × 40 × 8 mm³ NaI(Tl) crystal coupled to 4 LASiPs. We evaluated its performance in a central region of 15×15 mm², where we were able to reconstruct images of a ^99mTc capillary with an intrinsic spatial resolution of ~2 mm and an energy resolution of ~11.6% at 140 keV. We used these measurements to validate Geant4 simulations of the system. This can be extended to simulate a larger camera with more and larger pixels, which could be used to optimize the implementation of LASiPs in large SPECT cameras. We provide some guidelines towards this implementation.

A physics and learning-based transmission-less attenuation compensation method for SPECT

Attenuation compensation (AC) is a pre-requisite for reliable quantification and beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT). Typical AC methods require the availability of an attenuation map, which is obtained using a transmission scan, such as a CT scan. This has several disadvantages such as increased radiation dose, higher costs, and possible misalignment between SPECT and CT scans. Also, often a CT scan is unavailable. In this context, we and others are showing that scattered photons in SPECT contain information to estimate the attenuation distribution. To exploit this observation, we propose a physics and learning-based method that uses the SPECT emission data in the photopeak and scatter windows to perform transmission-less AC in SPECT. The proposed method uses data acquired in the scatter window to reconstruct an initial estimate of the attenuation map using a physics-based approach. A convolutional neural network is then trained to segment this initial estimate into different regions. Pre-defined attenuation coefficients are assigned to these regions, yielding the reconstructed attenuation map, which is then used to reconstruct the activity distribution using an ordered subsets expectation maximization (OSEM)-based reconstruction approach. We objectively evaluated the performance of this method using highly realistic simulation studies conducted on the clinically relevant task of detecting perfusion defects in myocardial perfusion SPECT. Our results showed no statistically significant differences between the performance achieved using the proposed method and that with the true attenuation maps. Visually, the images reconstructed using the proposed method looked similar to those with the true attenuation map. Overall, these results provide evidence of the capability of the proposed method to perform transmission-less AC and motivate further evaluation.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Extrastriatal SPECT-DAT uptake correlates with clinical and biological features of de novo Parkinson's disease

Striatal dopamine transporter (DAT) uptake assessment through I¹²³-Ioflupane Single-Pphoton Emission Computed Tomography (SPECT) provides valuable information about the dopaminergic denervation occurring in Parkinson's disease (PD). However, little is known about the clinical or biological relevance of extrastriatal DAT uptake in PD. Here, from the Parkinson's Progression Markers Initiative, we studied 623 participants (431 PD and 192 healthy controls) with available SPECT data. Even though striatal denervation was undoubtedly the imaging hallmark of PD, extrastriatal DAT uptake was also reduced in patients with PD. Topographically, widespread frontal but also temporal and posterior cortical regions showed lower DAT uptake in PD patients with respect to healthy controls. Importantly, a longitudinal voxelwise analysis confirmed an active one-year loss of extrastriatal DAT uptake within the PD group. Extrastriatal DAT uptake also correlated with the severity of motor symptoms, cognitive performance, and cerebrospinal fluid α-synuclein levels. In addition, we found an association between the Catechol-O-methyltransferase val¹⁵⁸met genotype and extrastriatal DAT uptake. These results highlight the clinical and biological relevance of extrastriatal SPECT-DAT uptake in PD.

Current Status of Stem Cell-Derived Therapies for Parkinson's Disease: From Cell Assessment and Imaging Modalities to Clinical Trials

Curative therapies or treatments reversing the progression of Parkinson’s disease (PD) have attracted considerable interest in the last few decades. PD is characterized by the gradual loss of dopaminergic (DA) neurons and decreased striatal dopamine levels. Current challenges include optimizing neuroprotective strategies, developing personalized drug therapy, and minimizing side effects from the long-term prescription of pharmacological drugs used to relieve short-term motor symptoms. Transplantation of DA cells into PD patients’ brains to replace degenerated DA has the potential to change the treatment paradigm. Herein, we provide updates on current progress in stem cell-derived DA neuron transplantation as a therapeutic alternative for PD. We briefly highlight cell sources for transplantation and focus on cell assessment methods such as identification of genetic markers, single-cell sequencing, and imaging modalities used to access cell survival and function. More importantly, we summarize clinical reports of patients who have undergone cell-derived transplantation in PD to better perceive lessons that can be drawn from past and present clinical outcomes. Modifying factors include (1) source of the stem cells, (2) quality of the stem cells, (3) age of the patient, (4) stage of disease progression at the time of cell therapy, (5) surgical technique/practices, and (6) the use of immunosuppression. We await the outcomes of joint efforts in clinical trials around the world such as NYSTEM and CiRA to further guide us in the selection of the most suitable parameters for cell-based neurotransplantation in PD.

Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review

Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.

Multimodal dopamine transporter (DAT) imaging and magnetic resonance imaging (MRI) to characterise early Parkinson's disease

Idiopathic Parkinson's disease (PD), the second most common neurodegenerative disorder, is characterised by the progressive loss of dopaminergic nigrostriatal terminals. Currently, in early idiopathic PD, dopamine transporter (DAT)-specific imaging assesses the extent of striatal dopaminergic deficits, and conventional magnetic resonance imaging (MRI) of the brain excludes the presence of significant ischaemic load in the basal ganglia as well as signs indicative of other forms of Parkinsonism. In this article, we discuss the use of multimodal DAT-specific and MRI protocols for insight into the early pathological features of idiopathic PD, including: structural MRI, diffusion tensor imaging, nigrosomal iron imaging and neuromelanin-sensitive MRI sequences. These measures may be acquired serially or simultaneously in a hybrid scanner. From current evidence, it appears that both nigrosomal iron imaging and neuromelanin-sensitive MRI combined with DAT-specific imaging are useful to assist clinicians in diagnosing PD, while conventional structural MRI and diffusion tensor imaging protocols are better suited to a research context focused on characterising early PD pathology. We believe that in the future multimodal imaging will be able to characterise prodromal PD and stratify the clinical stages of PD progression.

Enhanced Bidirectional Connectivity of the Subthalamo-pallidal Pathway in 6-OHDA-mouse Model of Parkinson's Disease Revealed by Probabilistic Tractography of Diffusion-weighted MRI at 9.4T

An important challenge in Parkinson’s disease (PD) based neuroscience and neuroimaging is mapping the neuronal connectivity of the basal ganglia to understand how the disease affects brain circuitry. However, a majority of diffusion tractography studies have shown difficulties in revealing connections between distant anatomic brain regions and visualizing basal ganglia connectome. In this current study, we investigated the differences in basal ganglia connectivity between 6-OHDA induced ex-vivo PD mouse model and normal ex-vivo mouse model by using diffusion tensor imaging tractography from diffusion-weighted images obtained with a high resolution 9.4 T MR scanner. Connectivity pattern of the basal ganglia were compared between five 6-OHDA and five control ex-vivo mouse brains using results of probabilistic tractography generated with PROBTRACKX. When compared with control mouse, 6-OHDA mouse showed significant enhancements to motor territory-related subthalamo-pallidal and pallido-subthalamic connectivity. Multi-fiber tractography combined with diffusion MRI data has the potential to help recognize the abnormalities found in connectivity of psychiatric and neurologic disease models.

Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

Recent studies combining diffusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson’s disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classification performance of subcortical FA and MD was also evaluated to compare the discriminant ability between diffusion tensor-derived metrics and NOS. Using diffusion-weighted images acquired in a 3 T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classification procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classified. NOS features outperformed the discrimination performance obtained with FA and MD. Our findings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than diffusion tensor-derived metrics for the detection of MSA.

Delusions in Parkinson's Disease: A Systematic Review of Published Cases

Delusions in Parkinson's disease (PD) are thought to be associated with disease progression and cognitive impairment. However, this symptom description is not consistent in the literature and there is a suggestion that different subgroups of psychotic patients occur in PD, which we aimed to clarify. Case reports were identified through a systematic search of databases (PUBMED, EMBASE, PsychInfo). Cases with isolated delusions were compared to those with both delusions and hallucinations. We identified 184 cases of delusions in PD. Delusions were primarily paranoid in nature (83%) and isolated in 50%. Those with isolated delusions had an earlier onset of PD (46 years vs 55 years), higher rates of impulse control disorders (40.2 vs 10.3%), dopamine dysregulation (29.9 vs 11.3%) and lower rates of cognitive impairment (8.0 vs 26.8%). There is unexpected heterogeneity amongst cases of delusional psychosis, that cannot adequately be explained by existing models of PD psychosis.

Dopamine transporter imaging predicts motor responsiveness to levodopa challenge in patients with Parkinson's disease: A pilot study of DATSCAN for subthalamic deep brain stimulation

Imaging studies are necessary prior to subthalamic deep brain stimulation (STN-DBS). Dopamine transporter (DAT) imaging is a powerful tool for visualizing dopamine terminals in the striatum, but its usefulness in STN-DBS is unclear. Here, we retrospectively investigated the relationship between motor symptoms and the specific binding ratio (SBR) on DAT imaging in patients with Parkinson's disease (PD). We included 23 consecutive patients (9 female; 14 male) who were evaluated for DBS eligibility between October 2013 and October 2014 and subsequently received bilateral STN-DBS. Correlation and simple regression analyses were performed on SBR values and clinical parameters before and after surgery. SBR value was negatively correlated with Unified Parkinson's Disease Rating Scale (UPDRS) motor score in the "ON" state before surgery (r_s=-0.637, p=0.001) and positively correlated with the reduction of the levodopa equivalent daily dose by surgery (r=0.422, p=0.045). A simple regression analysis revealed that SBR value was positively correlated with UPDRS motor score improvement after levodopa challenge before surgery (p=0.001, R²=0.423). DAT imaging may be useful in STN-DBS candidate selection and the identification of the therapeutic mechanism of STN-DBS in patients with advanced PD and motor symptom fluctuations.

Method for retrospective estimation of natural head movement during structural MRI

BACKGROUND

Head motion during brain structural MRI scans biases brain morphometry measurements but quantitative retrospective methods estimating head motion from structural MRI have not been evaluated.

PURPOSE

To verify the hypothesis that two metrics retrospectively computed from MR images: 1) average edge strength (AES, reduced with image blurring) and 2) entropy (ENT, increased with blurring and ringing artifacts) could be sensitive to in-scanner head motion during acquisition of T₁ -weighted MR images.

STUDY TYPE

Retrospective.

POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL

In all, 83 healthy control (HC) and 120 Parkinson's disease (PD) patients.

FIELD STRENGTH/SEQUENCE

3D magnetization-prepared rapid gradient-echo (MPRAGE) images at 3T.

ASSESSMENT

We 1) compared AES and ENT distribution between HC and PD; 2) evaluated the correlation between tremor score (TS) and AES (or ENT) in PD; and 3) investigated cortical regions showing an association between AES (or ENT) and local and network-level covariance measures of cortical thickness (CT), gray to white matter contrast (GWC) and gray matter density maps (GMx).

STATISTICAL TESTS

1) Student's t-test. 2) Spearman's rank correlation. 3) General linear model and partial least square analysis.

RESULTS

AES, but not ENT, differentiated HC and PD (P = 0.02, HC median AES = 39.8, interquartile range = 9.8, PD median AES = 37.6, interquartile range = 8.1). In PD, AES correlated negatively with TS (ρ = -0.21, P = 0.02) and showed a significant relationship (|Z| >3, P < 0.001) with structural covariance of CT and GWC in 54 out of 68 cortical regions.

DATA CONCLUSION

In clinical populations prone to head motion, AES can provide a reliable retrospective index of motion during structural scans, identifying brain areas whose morphometric measures covary with motion.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:927-937.

A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles

In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.