Differentiation of typical and atypical Parkinson syndromes by quantitative MR imaging

Comparison of mean diffusivity, R2* relaxation rate and morphometric biomarkers for the clinical differentiation of parkinsonism

INTRODUCTION

Quantitative biomarkers for clinical differentiation of parkinsonian syndromes are still lacking. Our aim was to evaluate the value of combining clinically feasible manual measurements of R2* relaxation rates and mean diffusivity (MD) in subcortical regions and brainstem morphometric measurements to improve the discrimination of parkinsonian syndromes.

METHODS

Twenty-two healthy controls (HC), 25 patients with Parkinson's disease (PD), 19 with progressive supranuclear palsy (PSP) and 27 with multiple system atrophy (MSA, 21 with the parkinsonian variant -MSAp, 6 with the cerebellar variant -MSAc) were recruited. R2*, MD measurements and morphometric biomarkers including the midbrain to pons area ratio and the Magnetic Resonance Parkinsonism Index (MRPI) were compared between groups and their diagnostic performances were assessed.

RESULTS

Morphometric biomarkers discriminated better patients with PSP (ratio: AUC 0.89, MRPI: AUC 0.89) and MSAc (ratio: AUC 0.82, MRPI: AUC 0.75) from other groups. R2* and MD measurements in the posterior putamen performed better in separating patients with MSAp from PD (R2*: AUC 0.89; MD: AUC 0.89). For the three-class classification "MSA vs PD vs PSP", the combination of MD and R2* measurements in the posterior putamen with morphometric biomarkers (AUC: 0.841) outperformed each marker separately. At the individual-level, there were seven discordances between imaging-based prediction and clinical diagnosis involving MSA. Using the new Movement Disorder Society criteria for the diagnosis of MSA, three of these seven patients were clinically reclassified as predicted by quantitative imaging.

CONCLUSION

Combining R2* and MD measurements in the posterior putamen with morphometric biomarkers improves the discrimination of parkinsonism.

Nigral diffusivity, but not free water, correlates with iron content in Parkinson's disease

The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinson's disease. Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and reductions in fractional anisotropy as well as increases in the free water compartment in metrics derived from bi-compartment models. However, studies examining nigral diffusivity changes from Parkinson's disease with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. We aimed to examine Parkinson's disease-related changes in nigral iron and diffusion measures from single- and bi-compartment models as well as assess the effect of iron on these diffusion measures in two separate Parkinson's cohorts. Iron-sensitive data and diffusion data were analysed in two cohorts: First, a discovery cohort consisting of 71 participants (32 control participants and 39 Parkinson's disease participants) was examined. Second, an external validation cohort, obtained from the Parkinson's Progression Marker's Initiative, consisting of 110 participants (58 control participants and 52 Parkinson's disease participants) was examined. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment (discovery cohort: P = 0.006; external cohort: P = 0.01) and iron content (discovery cohort: P < 0.001; validation cohort: P = 0.02) were found to increase in substantia nigra of the Parkinson's disease group in both cohorts. However, diffusion markers derived from the single-compartment model (i.e. mean diffusivity and fractional anisotropy) were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort (iron-mean diffusivity: r = -0.400, P = 0.006) and validation cohort (iron-mean diffusivity: r = -0.387, P = 0.003) but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. In conclusion, the variability of nigral diffusion metrics derived from the single-compartment model in Parkinson's disease may be attributed to competing influences of increased iron content, which tends to drive diffusivity down, and increases in the free water compartment, which tends to drive diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.

Investigating the 1-year decline in midbrain-to-pons ratio in the differential diagnosis of PSP and IPD

Background

A reliable measure of PSP-specific midbrain atrophy, the midbrain-to-pons ratio (MTPR) has been reported to support the differential diagnosis of progressive supranuclear palsy (PSP) from idiopathic Parkinson’s disease (IPD). Since longitudinal analyses are lacking so far, the present study aimed to evaluate the diagnostic value of the relative change of MTPR (relΔt_MTPR) over a 1-year period in patients with PSP, IPD, and healthy controls (HC).

Methods

Midsagittal individual MRIs of patients with PSP (n = 15), IPD (n = 15), and healthy controls (HC; n = 15) were assessed and the MTPR at baseline and after 1 year were defined. The diagnostic accuracy of the MTPR and its relative change were evaluated using ROC curve analyses.

Results

PSP-patients had a significantly lower MTPR at baseline (M = 0.45 ± 0.06), compared to both non-PSP groups (F (2, 41) = 62.82, p < 0.001), with an overall predictive accuracy of 95.6% for an MTPR ≤ 0.54. PSP-patients also presented a significantly stronger 1-year decline in MTPR compared to IPD (p < 0.001). Though predictive accuracy of relΔ_t_MTPR for PSP (M = − 4.74% ± 4.48) from IPD (M =  + 1.29 ± 3.77) was good (76.6%), ROC analysis did not reveal a significant improvement of diagnostic accuracy by combining the MTPR and relΔ_t_MTPR (p = 0.670). Still, specificity for PSP increased, though not significantly (p = 0.500).

Conclusion

The present results indicate that the relΔ_t_MTPR is a potentially useful tool to support the differential diagnosis of PSP from IPD. For its relative 1-year change, still, more evaluation is needed.

A pilot study of magnetic resonance fingerprinting in Parkinson's disease

Parkinson's disease (PD) affects more than six million people, but reliable MRI biomarkers with which to diagnose patients have not been established. Magnetic resonance fingerprinting (MRF) is a recent quantitative technique that can provide relaxometric maps from a single sequence. The purpose of this study is to assess the potential of MRF to identify PD in patients and their disease severity, as well as to evaluate comfort during MRF. Twenty-five PD patients and 25 matching controls underwent 3 T MRI, including an axial 2D spoiled gradient echo MRF sequence. T1 and T2 maps were generated by voxel-wise matching the measured MRF signal to a precomputed dictionary. All participants also received standard inversion recovery T1 and multi-echo T2 mapping. An ROI-based analysis of relaxation times was performed. Differences between patients and controls as well as techniques were determined by logistic regression, Spearman correlation and t-test. Patients were asked to estimate the subjective comfort of the MRF sequence. Both MRF-based T1 and T2 mapping discriminated patients from controls: T1 relaxation times differed most in cortical grey matter (PD 1337 ± 38 vs. control 1386 ± 37 ms; mean ± SD; P = .0001) and, in combination with normal-appearing white matter, enabled correct discrimination in 85.7% of cases (sensitivity 83.3%; specificity 88.0%; receiver-operating characteristic [ROC]) area under the curve [AUC] 0.87), while for T2 mapping the left putamen was the strongest classifier (40.54 ± 6.28 vs. 34.17 ± 4.96 ms; P = .0001), enabling differentiation of groups in 84.0% of all cases (sensitivity 80.0%; specificity 88.0%; ROC AUC 0.87). Relaxation time differences were not associated with disease severity. Standard mapping techniques generated significantly different relaxation time values and identified other structures as different between groups other than MRF. Twenty-three out of 25 PD patients preferred the MRF examination instead of a standard MRI. MRF-based mapping can identify PD patients with good comfort but needs further assessment regarding disease severity identification and its potential for comparability with standard mapping technique results.

Three-dimensional whole-brain simultaneous T1, T2, and T1ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis

PURPOSE

To develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9 min.

METHODS

MR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in age-matched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined.

RESULTS

Our framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel "T1ρ-FLAIR" contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, compared to using any single parameter alone.

CONCLUSION

MR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases, such as MS.

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.

Can Autonomic Testing and Imaging Contribute to the Early Diagnosis of Multiple System Atrophy? A Systematic Review and Recommendations by the Movement Disorder Society Multiple System Atrophy Study Group

Background

In the current consensus diagnostic criteria, the diagnosis of probable multiple system atrophy (MSA) is based solely on clinical findings, whereas neuroimaging findings are listed as aid for the diagnosis of possible MSA. There are overlapping phenotypes between MSA-parkinsonian type and Parkinson's disease, progressive supranuclear palsy, and dementia with Lewy bodies, and between MSA-cerebellar type and sporadic adult-onset ataxia resulting in a significant diagnostic delay and misdiagnosis of MSA during life.

Objectives

In light of an ongoing effort to revise the current consensus criteria for MSA, the Movement Disorders Society Multiple System Atrophy Study Group performed a systematic review of original articles published before August 2019.

Methods

We included articles that studied at least 10 patients with MSA as well as participants with another disorder or control group for comparison purposes. MSA was defined by neuropathological confirmation, or as clinically probable, or clinically probable plus possible according to consensus diagnostic criteria.

Results

We discuss the pitfalls and benefits of each diagnostic test and provide specific recommendations on how to evaluate patients in whom MSA is suspected.

Conclusions

This systematic review of relevant studies indicates that imaging and autonomic function tests significantly contribute to increasing the accuracy of a diagnosis of MSA.

Increased iron-deposition in lateral-ventral substantia nigra pars compacta: A promising neuroimaging marker for Parkinson's disease

BACKGROUND

To date there are no validated MRI biomarkers to assist diagnosis of Parkinson's disease (PD). Our aim was to investigate PD related iron changes in the substantia nigra pars compacta (SNpc) as defined by neuromelanin-sensitive MR contrast.

METHODS

Thirty-nine PD participants and 33 healthy controls were scanned at 3.0-T using a 16-echo gradient echo sequence to create R2* maps for the evaluation of iron content to find the overlap with a neuromelanin based SNpc mask. The SNpc overlap percentage with the R2* map, and the R2* values in both the whole SNpc and the overlap volume were compared between PD and control groups, and correlated with clinical features for PD participants. Finally, the diagnostic performance of the SNpc overlap percentage was evaluated using ROC analysis.

RESULTS

PD related iron changes mostly occur in the lateral-ventral part of the neuromelanin SNpc. The R2* values in the whole SNpc and the SNpc overlap volume, and the SNpc overlap percentage were larger in PD participants than in controls. Furthermore, the SNpc overlap percentage was positively correlated with the disease duration in PD. The SNpc overlap percentage provided excellent diagnostic accuracy for discriminating PD participants from controls (AUC = 0.93), while the R2* values in the whole SNpc or the overlap volume were less effective.

CONCLUSION

The overlap between the iron content as determined by R2* mapping and neuromelanin in the substantia nigra pars compacta has the potential to be a neuroimaging biomarker for diagnosing Parkinson's disease.

The Role of Magnetic Resonance Imaging for the Diagnosis of Atypical Parkinsonism

The diagnosis of Parkinson's disease and atypical Parkinsonism remains clinically difficult, especially at the early stage of the disease, since there is a significant overlap of symptoms. Multimodal MRI has significantly improved diagnostic accuracy and understanding of the pathophysiology of Parkinsonian disorders. Structural and quantitative MRI sequences provide biomarkers sensitive to different tissue properties that detect abnormalities specific to each disease and contribute to the diagnosis. Machine learning techniques using these MRI biomarkers can effectively differentiate atypical Parkinsonian syndromes. Such approaches could be implemented in a clinical environment and improve the management of Parkinsonian patients. This review presents different structural and quantitative MRI techniques, their contribution to the differential diagnosis of atypical Parkinsonian disorders and their interest for individual-level diagnosis.

Iron Imaging as a Diagnostic Tool for Parkinson's Disease: A Systematic Review and Meta-Analysis

Background: Parkinson's disease (PD) is a progressive neurodegenerative disease whose main neuropathological feature is the loss of dopaminergic neurons of the substantia nigra (SN). There is also an increase in iron content in the SN in postmortem and imaging studies using iron-sensitive MRI techniques. However, MRI results are variable across studies. Objectives: We performed a systematic meta-analysis of SN iron imaging studies in PD to better understand the role of iron-sensitive MRI quantification to distinguish patients from healthy controls. We also studied the factors that may influence iron quantification and analyzed the correlations between demographic and clinical data and iron load. Methods: We searched PubMed and ScienceDirect databases (from January 1994 to December 2019) for studies that analyzed iron load in the SN of PD patients using T2^*, R2^*, susceptibility weighting imaging (SWI), or quantitative susceptibility mapping (QSM) and compared the values with healthy controls. Details for each study regarding participants, imaging methods, and results were extracted. The effect size and confidence interval (CI) of 95% were calculated for each study as well as the pooled weighted effect size for each marker over studies. Hence, the correlations between technical and clinical metrics with iron load were analyzed. Results: Forty-six articles fulfilled the inclusion criteria including 27 for T2^*/R2^* measures, 10 for SWI, and 17 for QSM (3,135 patients and 1,675 controls). Eight of the articles analyzed both R2^* and QSM. A notable effect size was found in the SN in PD for R2^* increase (effect size: 0.84, 95% CI: 0.60 to 1.08), for SWI measurements (1.14, 95% CI: 0.54 to 1.73), and for QSM increase (1.13, 95% CI: 0.86 to 1.39). Correlations between imaging measures and Unified Parkinson's Disease Rating Scale (UPDRS) scores were mostly observed for QSM. Conclusions: The consistent increase in MRI measures of iron content in PD across the literature using R2^*, SWI, or QSM techniques confirmed that these measurements provided reliable markers of iron content in PD. Several of these measurements correlated with the severity of motor symptoms. Lastly, QSM appeared more robust and reproducible than R2^* and more suited to multicenter studies.

MRI biomarkers of motor and non-motor symptoms in Parkinson's disease

Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.

Multiple system atrophy

Multiple system atrophy (MSA) is a sporadic, adult-onset, relentlessly progressive neurodegenerative disorder, clinically characterized by various combinations of autonomic failure, parkinsonism and ataxia. The neuropathological hallmark of MSA are glial cytoplasmic inclusions consisting of misfolded α-synuclein. Selective atrophy and neuronal loss in striatonigral and olivopontocerebellar systems underlie the division into two main motor phenotypes of MSA-parkinsonian type and MSA-cerebellar type. Isolated autonomic failure and REM sleep behavior disorder are common premotor features of MSA. Beyond the core clinical symptoms, MSA manifests with a number of non-motor and motor features. Red flags highly specific for MSA may provide clues for a correct diagnosis, but in general the diagnostic accuracy of the second consensus criteria is suboptimal, particularly in early disease stages. In this chapter, the authors discuss the historical milestones, etiopathogenesis, neuropathological findings, clinical features, red flags, differential diagnosis, diagnostic criteria, imaging and other biomarkers, current treatment, unmet needs and future treatments for MSA.

Disrupted structural connectivity of fronto-deep gray matter pathways in progressive supranuclear palsy

Background

Structural connectivity is a promising methodology to detect patterns of neural network dysfunction in neurodegenerative diseases. This approach has not been tested in progressive supranuclear palsy (PSP).

Objectives

The aim of this study is reconstructing the structural connectome to characterize and detect the pathways of degeneration in PSP patients compared with healthy controls and their correlation with clinical features. The second objective is to assess the potential of structural connectivity measures to distinguish between PSP patients and healthy controls at the single-subject level.

Methods

Twenty healthy controls and 19 PSP patients underwent diffusion-weighted MRI with a 3T scanner. Structural connectivity, represented by number of streamlines, was derived from probabilistic tractography. Global and local network metrics were calculated based on graph theory.

Results

Reduced numbers of streamlines were predominantly found in connections between frontal areas and deep gray matter (DGM) structures in PSP compared with controls. Significant changes in structural connectivity correlated with clinical features in PSP patients. An abnormal small-world architecture was detected in the subnetwork comprising the frontal lobe and DGM structures in PSP patients. The classification procedure achieved an overall accuracy of 82.23% with 94.74% sensitivity and 70% specificity.

Conclusion

Our findings suggest that modelling the brain as a structural connectome is a useful method to detect changes in the organization and topology of white matter tracts in PSP patients. Secondly, measures of structural connectivity have the potential to correctly discriminate between PSP patients and healthy controls.

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Reduced structural connectivity in PSP patients compared with healthy controls

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Connectivity reductions in fronto-DGM tracts correlate with PSPRS and FAB scores

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PSP patients present abnormal small-world architecture in the fronto-DGM network.

Reproducible detection of nigral iron deposition in 2 Parkinson's disease cohorts

BACKGROUND

Previous studies investigating nigral iron accumulation used T2 or T2 *-weighted contrasts to define the regions of interest (ROIs) in the substantia nigra with mixed results. Because these contrasts are not sensitive to neuromelanin, ROIs may have inadvertently missed the SNpc. An approach sensitive to neuromelanin should yield consistent results. We examine iron deposition in ROIs derived from neuromelanin-sensitive and T2 *-weighted contrasts, respectively.

METHODS

T1 -weighted and multiecho gradient echo imaging data were obtained in 2 cohorts. Multiecho gradient echo imaging data were analyzed using neuromelanin-sensitive SNpc ROIs as well as T2 *-weighted SNr ROIs.

RESULTS

When compared with controls, significantly larger R2 * values were seen in the SNpc of PD patients in both cohorts. Mean R2 * values in the SNr of PD patients showed no consistency, with 1 cohort showing a small, statistically significant increase, whereas the other cohort exhibited no statistical difference.

CONCLUSION

Mean R2 * in the SNpc defined by neuromelanin-sensitive MRI is significantly increased in PD. © 2018 International Parkinson and Movement Disorder Society.

Brain Iron Accumulation in Atypical Parkinsonian Syndromes: in vivo MRI Evidences for Distinctive Patterns

Recent data suggest mechanistic links among perturbed iron homeostasis, oxidative stress, and misfolded protein aggregation in neurodegenerative diseases. Iron overload and toxicity toward dopaminergic neurons have been established as playing a role in the pathogenesis of Parkinson's disease (PD). Brain iron accumulation has also been documented in atypical parkinsonian syndromes (APS), mainly comprising multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). Iron-sensitive magnetic resonance imaging (MRI) has been applied to identify iron-related signal changes for the diagnosis and differentiation of these disorders. Topographic patterns of widespread iron deposition in deep brain nuclei have been described as differing between patients with MSA and PSP and those with PD. A disease-specific increase of iron occurs in the brain regions mainly affected by underlying disease pathologies. However, whether iron changes are a primary pathogenic factor or an epiphenomenon of neuronal degeneration has not been fully elucidated. Moreover, the clinical implications of iron-related pathology in APS remain unclear. In this review study, we collected data from qualitative and quantitative MRI studies on brain iron accumulation in APS to identify disease-related patterns and the potential role of iron-sensitive MRI.

Diffusion Kurtosis Imaging for Detection of Early Brain Changes in Parkinson's Disease

We aimed to evaluate microscale changes in the bilateral red nucleus and substantia nigra of patients with Parkinson's disease (PD) using diffusion kurtosis imaging (DKI). Twenty-six patients with PD [mean age, 62.5 ± 8.7 years; Hoehn-Yahr stage, 0-4.0; Unified Parkinson's Disease Rating Scale (UPDRS) scores, 8-43] and 15 healthy controls (mean age, 59.5 ± 9.4 years) underwent DKI of the substantia nigra and red nucleus. Imaging was performed using a General Electric (GE) Signa 3.0-T MRI system. Patients with PD were divided into two groups consisting of 12 patients with UPDRS scores ≥ 30 and 14 patients with UPDRS scores < 30. All DKI data processing operations were performed with commercial workstations (GE, ADW 4.6) using Functool software to generate color-coded and parametric maps of mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD). MK values in the bilateral substantia nigra were significantly lower in patients with early- and advanced-stage PD than in controls. Moreover, MK values in the left substantia nigra were significantly lower in patients with advanced-stage PD than in those with early-stage PD. Patients with advanced-stage PD also exhibited significant decreases in MK values in the bilateral red nucleus relative to controls. No significant differences in FA or MD values were observed between the PD and control groups. There were no significant correlations between MK, FA, or MD values and UPDRS scores. Our findings suggest that decreased MK values in the substantia nigra may aid in determining the severity of PD and help provide early diagnoses.

Life span pigmentation changes of the substantia nigra detected by neuromelanin-sensitive MRI

Background: Neuromelanin is a pigment with strong iron‐chelating properties preferentially found in dopaminergic neurons of the substantia nigra pars compacta (SNpc). Parkinson's disease is characterized by pronounced, MRI‐detectable neuromelanin loss, but the neuroprotective or neurotoxic role of neuromelanin remains debated. Histological studies have demonstrated neuromelanin increases with age, but this has not been confirmed in vivo, and there is uncertainty whether neuromelanin declines, stabilizes, or increases from middle age.

Methods: This study aimed to establish physiological changes of pigmentation of the SNpc using a pooled data set of neuromelanin‐sensitive 3T MRI from 134 healthy individuals aged 5‐83 years. Neuromelanin‐related brightness (regional contrast to ratio) and calibrated hyperintense volumes were analyzed using linear and nonlinear regression models to characterize age effects. Laterality, sex, and subregional effects were also assessed.

Results: For brightness, age effects were best described as a quadratic trajectory explaining 81.5% of the observed variance in the SNpc showing a strong increase from childhood to adolescence, with plateauing in middle age and a decline in older age. Similar but less pronounced effects were seen in hyperintense volumes. We also show an anterior‐posterior gradient in SNpc contrast, larger normalized neuromelanin‐rich volume in women > 47 years old, but no laterality effect.

Conclusions: Using optimized neuromelanin MRI in a life span sample, we demonstrate a strong age effect with inverted U‐shaped SNpc pigmentation‐related contrast from childhood to old age. This age trajectory of physiological SNpc pigmentation needs to be taken into account for diagnostic applications of depigmentation. The study also paves the way for systematic investigations of the mechanisms of neuromelanin in healthy and pathological brain development and aging. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Structural Imaging in Atypical Parkinsonism

Qualitative and quantitative structural magnetic resonance imaging offer objective measures of the underlying neurodegeneration in atypical parkinsonism. Regional changes in tissue volume, signal changes and increased deposition of iron as assessed with different structural MRI techniques are surrogate markers of underlying neurodegeneration and may reflect cell loss, microglial proliferation and astroglial activation. Structural MRI has been explored as a tool to enhance diagnostic accuracy in differentiating atypical parkinsonian disorders (APDs). Moreover, the longitudinal assessment of serial structural MRI-derived parameters offers the opportunity for robust inferences regarding the progression of APDs. This review summarizes recent research findings as (1) a diagnostic tool for APDs as well as (2) as a tool to assess longitudinal changes of serial MRI-derived parameters in the different APDs.

Susceptibility MRI captures nigral pathology in patients with parkinsonian syndromes

BACKGROUND

Parkinsonisms are neurodegenerative disorders characterized pathologically by α-synuclein-positive (e.g., PD, diffuse Lewy body disease, and MSA) and/or tau-positive (e.g., PSP, cortical basal degeneration) pathology. Using R2* and quantitative susceptibility mapping, susceptibility changes have been reported in the midbrain of living parkinsonian patients, although the exact underlying pathology of these alterations is unknown.

OBJECTIVE

The current study investigated the pathological correlates of these susceptibility MRI measures.

METHODS

In vivo MRIs (T1- and T2-weighted, and T2*) and pathology were obtained from 14 subjects enrolled in an NINDS PD Biomarker Program (PDBP). We assessed R2* and quantitative susceptibility mapping values in the SN, semiquantitative α-synuclein, tau, and iron values, as well as neuronal and glial counts. Data were analyzed using age-adjusted Spearman correlations.

RESULTS

R2* was associated significantly with nigral α-synuclein (r = 0.746; P = 0.003). Quantitative susceptibility mapping correlated significantly with Perls' (r = 0.758; P = 0.003), but not with other pathological measurements. Neither measurement correlated with tau or glial cell counts (r ≤ 0.11; P ≥ 0.129).

CONCLUSIONS

Susceptibility MRI measurements capture nigral pathologies associated with parkinsonian syndromes. Whereas quantitative susceptibility mapping is more sensitive to iron, R2* may reflect pathological aspects of the disorders beyond iron such as α-synuclein. They may be invaluable tools in diagnosing differential parkinsonian syndromes, and tracking in living patients the dynamic changes associated with the pathological progression of these disorders. © 2018 International Parkinson and Movement Disorder Society.

Cortical structural involvement and cognitive dysfunction in early Parkinson's disease

Magnetic resonance imaging (MRI) studies in early Parkinson's disease (PD) have shown promise in the detection of disease-related brain changes in the white and deep grey matter. We set out to establish whether intrinsic cortical involvement in early PD can be detected with quantitative MRI. We collected a rich, multi-modal dataset, including diffusion MRI, T₁ relaxometry and cortical morphometry, in 20 patients with early PD (disease duration, 1.9 ± 0.97 years, Hoehn & Yahr 1-2) and in 19 matched controls. The cortex was reconstructed using FreeSurfer. Data analysis employed linked independent component analysis (ICA), a novel data-driven technique that allows for data fusion and extraction of multi-modal components before further analysis. For comparison, we performed standard uni-modal analysis with a general linear model (GLM). Linked ICA detected multi-modal cortical changes in early PD (p = 0.015). These comprised fractional anisotropy reduction in dorsolateral prefrontal, cingulate and premotor cortex and the superior parietal lobule, mean diffusivity increase in the mesolimbic, somatosensory and superior parietal cortex, sparse diffusivity decrease in lateral parietal and right prefrontal cortex, and sparse changes to the cortex area. In PD, the amount of cortical dysintegrity correlated with diminished cognitive performance. Importantly, uni-modal analysis detected no significant group difference on any imaging modality. We detected microstructural cortical pathology in early PD using a data-driven, multi-modal approach. This pathology is correlated with diminished cognitive performance. Our results indicate that early degenerative processes leave an MRI signature in the cortex of patients with early PD. The cortical imaging findings are behaviourally meaningful and provide a link between cognitive status and microstructural cortical pathology in patients with early PD.

MRI supervised and unsupervised classification of Parkinson's disease and multiple system atrophy

BACKGROUND

Multimodal MRI approach is based on a combination of MRI parameters sensitive to different tissue characteristics (eg, volume atrophy, iron deposition, and microstructural damage). The main objective of the present study was to use a multimodal MRI approach to identify brain differences that could discriminate between matched groups of patients with multiple system atrophy, Parkinson's disease, and healthy controls. We assessed the 2 different MSA variants, namely, MSA-P, with predominant parkinsonism, and MSA-C, with more prominent cerebellar symptoms.

METHODS

Twenty-six PD patients, 29 MSA patients (16 MSA-P, 13 MSA-C), and 26 controls underwent 3-T MRI comprising T2*-weighted, T1-weighted, and diffusion tensor imaging scans. Using whole-brain voxel-based MRI, we combined gray-matter density, T2* relaxation rates, and diffusion tensor imaging scalars to compare and discriminate PD, MSA-P, MSA-C, and healthy controls.

RESULTS

Our main results showed that this approach reveals multiparametric modifications within the cerebellum and putamen in both MSA-C and MSA-P patients, compared with PD patients. Furthermore, our findings revealed that specific single multimodal MRI markers were sufficient to discriminate MSA-P and MSA-C patients from PD patients. Moreover, the unsupervised analysis based on multimodal MRI data could regroup individuals according to their clinical diagnosis, in most cases.

CONCLUSIONS

This study demonstrates that multimodal MRI is able to discriminate patients with PD from those with MSA with high accuracy. The combination of different MR biomarkers could be a great tool in early stage of disease to help diagnosis. © 2018 International Parkinson and Movement Disorder Society.

Multiple System Atrophy: An Oligodendroglioneural Synucleinopathy1

Multiple system atrophy (MSA) is an orphan, fatal, adult-onset neurodegenerative disorder of uncertain etiology that is clinically characterized by various combinations of parkinsonism, cerebellar, autonomic, and motor dysfunction. MSA is an α-synucleinopathy with specific glioneuronal degeneration involving striatonigral, olivopontocerebellar, and autonomic nervous systems but also other parts of the central and peripheral nervous systems. The major clinical variants correlate with the morphologic phenotypes of striatonigral degeneration (MSA-P) and olivopontocerebellar atrophy (MSA-C). While our knowledge of the molecular pathogenesis of this devastating disease is still incomplete, updated consensus criteria and combined fluid and imaging biomarkers have increased its diagnostic accuracy. The neuropathologic hallmark of this unique proteinopathy is the deposition of aberrant α-synuclein in both glia (mainly oligodendroglia) and neurons forming glial and neuronal cytoplasmic inclusions that cause cell dysfunction and demise. In addition, there is widespread demyelination, the pathogenesis of which is not fully understood. The pathogenesis of MSA is characterized by propagation of misfolded α-synuclein from neurons to oligodendroglia and cell-to-cell spreading in a "prion-like" manner, oxidative stress, proteasomal and mitochondrial dysfunction, dysregulation of myelin lipids, decreased neurotrophic factors, neuroinflammation, and energy failure. The combination of these mechanisms finally results in a system-specific pattern of neurodegeneration and a multisystem involvement that are specific for MSA. Despite several pharmacological approaches in MSA models, addressing these pathogenic mechanisms, no effective neuroprotective nor disease-modifying therapeutic strategies are currently available. Multidisciplinary research to elucidate the genetic and molecular background of the deleterious cycle of noxious processes, to develop reliable biomarkers and targets for effective treatment of this hitherto incurable disorder is urgently needed.

The role of diffusion magnetic resonance imaging in Parkinson's disease and in the differential diagnosis with atypical parkinsonism

Parkinson's disease is one of the most common neurodegenerative diseases. Clinically, it is characterized by motor symptoms. Parkinson's disease should be differentiated from atypical parkinsonism conditions. Conventional magnetic resonance imaging is the primary imaging method employed in order to facilitate the differential diagnosis, and its role has grown after the development of advanced techniques such as diffusion-weighted imaging. The purpose of this article was to review the role of magnetic resonance imaging in Parkinson's disease and in the differential diagnosis with atypical parkinsonism, emphasizing the diffusion technique.

Magnetic resonance imaging for the diagnosis of Parkinson's disease

The differential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology and error rates in the clinical diagnosis can be high even at specialized centres. Despite several limitations, magnetic resonance imaging (MRI) has undoubtedly enhanced the diagnostic accuracy in the differential diagnosis of neurodegenerative parkinsonism over the last three decades. This review aims to summarize research findings regarding the value of the different MRI techniques, including advanced sequences at high- and ultra-high-field MRI and modern image analysis algorithms, in the diagnostic work-up of Parkinson's disease. This includes not only the exclusion of alternative diagnoses for Parkinson's disease such as symptomatic parkinsonism and atypical parkinsonism, but also the diagnosis of early, new onset, and even prodromal Parkinson's disease.

Free water improves detection of changes in the substantia nigra in parkinsonism: A multisite study

BACKGROUND

Imaging markers that are sensitive to parkinsonism across multiple sites are critically needed for clinical trials. The objective of this study was to evaluate changes in the substantia nigra using single- and bi-tensor models of diffusion magnetic resonance imaging in PD, MSA, and PSP.

METHODS

The study cohort (n = 425) included 107 healthy controls and 184 PD, 63 MSA, and 71 PSP patients from 3 movement disorder centers. Bi-tensor free water, free-water-corrected fractional anisotropy, free-water-corrected mean diffusivity, single-tensor fractional anisotropy, and single-tensor mean diffusivity were computed for the anterior and posterior substantia nigra. Correlations were computed between diffusion MRI measures and clinical measures.

RESULTS

In the posterior substantia nigra, free water was greater for PSP than MSA and PD patients and controls. PD and MSA both had greater free water than controls. Free-water-corrected fractional anisotropy values were greater for PSP patents than for controls and PD patients. PSP and MSA patient single-tensor mean diffusivity values were greater than controls, and single-tensor fractional anisotropy values were lower for PSP patients than for healthy controls. The parkinsonism effect size for free water was 0.145 in the posterior substantia nigra and 0.072 for single-tensor mean diffusivity. The direction of correlations between single-tensor mean diffusivity and free-water values and clinical scores was similar at each site.

CONCLUSIONS

Free-water values in the posterior substantia nigra provide a consistent pattern of findings across patients with PD, MSA, and PSP in a large cohort across 3 sites. Free water in the posterior substantia nigra relates to clinical measures of motor and cognitive symptoms in a large cohort of parkinsonism. © 2017 International Parkinson and Movement Disorder Society.

Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging

Mapping gray matter (GM) pathology in Parkinson's disease (PD) with conventional MRI is challenging, and the need for more sensitive brain imaging techniques is essential to facilitate early diagnosis and assessment of disease severity. GM microstructure was assessed with GM-based spatial statistics applied to diffusion kurtosis imaging (DKI) and neurite orientation dispersion imaging (NODDI) in 30 participants with PD and 28 age- and gender-matched controls. These were compared with currently used assessment methods such as diffusion tensor imaging (DTI), voxel-based morphometry (VBM), and surface-based cortical thickness analysis. Linear discriminant analysis (LDA) was also used to test whether subject diagnosis could be predicted based on a linear combination of regional diffusion metrics. Significant differences in GM microstructure were observed in the striatum and the frontal, temporal, limbic, and paralimbic areas in PD patients using DKI and NODDI. Significant correlations between motor deficits and GM microstructure were also noted in these areas. Traditional VBM and surface-based cortical thickness analyses failed to detect any GM differences. LDA indicated that mean kurtosis (MK) and intra cellular volume fraction (ICVF) were the most accurate predictors of diagnostic status. In conclusion, DKI and NODDI can detect cerebral GM abnormalities in PD in a more sensitive manner when compared with conventional methods. Hence, these methods may be useful for the diagnosis of PD and assessment of motor deficits. Hum Brain Mapp 38:3704-3722, 2017. © 2017 Wiley Periodicals, Inc.

Combined Diffusion Tensor Imaging and Apparent Transverse Relaxation Rate Differentiate Parkinson Disease and Atypical Parkinsonism

BACKGROUND AND PURPOSE

Both diffusion tensor imaging and the apparent transverse relaxation rate have shown promise in differentiating Parkinson disease from atypical parkinsonism (particularly multiple system atrophy and progressive supranuclear palsy). The objective of the study was to assess the ability of DTI, the apparent transverse relaxation rate, and their combination for differentiating Parkinson disease, multiple system atrophy, progressive supranuclear palsy, and controls.

MATERIALS AND METHODS

A total of 106 subjects (36 controls, 35 patients with Parkinson disease, 16 with multiple system atrophy, and 19 with progressive supranuclear palsy) were included. DTI and the apparent transverse relaxation rate measures from the striatal, midbrain, limbic, and cerebellar regions were obtained and compared among groups. The discrimination performance of DTI and the apparent transverse relaxation rate among groups was assessed by using Elastic-Net machine learning and receiver operating characteristic curve analysis.

RESULTS

Compared with controls, patients with Parkinson disease showed significant apparent transverse relaxation rate differences in the red nucleus. Compared to those with Parkinson disease, patients with both multiple system atrophy and progressive supranuclear palsy showed more widespread changes, extending from the midbrain to striatal and cerebellar structures. The pattern of changes, however, was different between the 2 groups. For instance, patients with multiple system atrophy showed decreased fractional anisotropy and an increased apparent transverse relaxation rate in the subthalamic nucleus, whereas patients with progressive supranuclear palsy showed an increased mean diffusivity in the hippocampus. Combined, DTI and the apparent transverse relaxation rate were significantly better than DTI or the apparent transverse relaxation rate alone in separating controls from those with Parkinson disease/multiple system atrophy/progressive supranuclear palsy; controls from those with Parkinson disease; those with Parkinson disease from those with multiple system atrophy/progressive supranuclear palsy; and those with Parkinson disease from those with multiple system atrophy; but not those with Parkinson disease from those with progressive supranuclear palsy, or those with multiple system atrophy from those with progressive supranuclear palsy.

CONCLUSIONS

DTI and the apparent transverse relaxation rate provide different but complementary information for different parkinsonisms. Combined DTI and apparent transverse relaxation rate may be a superior marker for the differential diagnosis of parkinsonisms.

Reproducibility of R2 * and quantitative susceptibility mapping (QSM) reconstruction methods in the basal ganglia of healthy subjects

The basal ganglia are key structures for motor, cognitive and behavioral functions. They undergo several changes with aging and disease, such as Parkinson's or Huntington's disease, for example. Iron accumulation in basal ganglia is often related to these diseases, which is conventionally monitored by the transverse relaxation rate (R₂ *). Quantitative susceptibility mapping (QSM) is a novel contrast mechanism in MRI produced by adding information taken from the phase of the MR signal to its magnitude. It has been shown to be more sensitive to subtle changes in Parkinson's disease. In order to be applied widely to various pathologies, its reproducibility must be evaluated in order to assess intra-subject variability and to disseminate into clinical and pharmaceutical studies. In this work, we studied the reproducibility and sensitivity of several QSM techniques. Fourteen subjects were scanned four times, and QSM and R₂ * images were reconstructed and registered. An atlas of the basal ganglia was used to automatically define regions of interest. We found that QSM measurements are indeed reproducible in the basal ganglia of healthy subjects and can be widely used as a replacement for R₂ * mapping in iron-rich regions. This reproducibility study could lead to several lines of research in relaxometry and susceptibility measurements, in vivo iron load evaluation as well as pharmacological assessment and biomarker development. Copyright © 2016 John Wiley & Sons, Ltd.

Imaging biomarkers in Parkinson's disease and Parkinsonian syndromes: current and emerging concepts

Two centuries ago in 1817, James Parkinson provided the first medical description of Parkinson’s disease, later refined by Jean-Martin Charcot in the mid-to-late 19th century to include the atypical parkinsonian variants (also termed, Parkinson-plus syndromes). Today, Parkinson’s disease represents the second most common neurodegenerative disorder with an estimated global prevalence of over 10 million. Conversely, atypical parkinsonian syndromes encompass a group of relatively heterogeneous disorders that may share some clinical features with Parkinson’s disease, but are uncommon distinct clinicopathological diseases. Decades of scientific advancements have vastly improved our understanding of these disorders, including improvements in in vivo imaging for biomarker identification. Multimodal imaging for the visualization of structural and functional brain changes is especially important, as it allows a ‘window’ into the underlying pathophysiological abnormalities. In this article, we first present an overview of the cardinal clinical and neuropathological features of, 1) synucleinopathies: Parkinson’s disease and other Lewy body spectrum disorders, as well as multiple system atrophy, and 2) tauopathies: progressive supranuclear palsy, and corticobasal degeneration. A comprehensive presentation of well-established and emerging imaging biomarkers for each disorder are then discussed. Biomarkers for the following imaging modalities are reviewed: 1) structural magnetic resonance imaging (MRI) using T1, T2, and susceptibility-weighted sequences for volumetric and voxel-based morphometric analyses, as well as MRI derived visual signatures, 2) diffusion tensor MRI for the assessment of white matter tract injury and microstructural integrity, 3) proton magnetic resonance spectroscopy for quantifying proton-containing brain metabolites, 4) single photon emission computed tomography for the evaluation of nigrostriatal integrity (as assessed by presynaptic dopamine transporters and postsynaptic dopamine D2 receptors), and cerebral perfusion, 5) positron emission tomography for gauging nigrostriatal functions, glucose metabolism, amyloid and tau molecular imaging, as well as neuroinflammation, 6) myocardial scintigraphy for dysautonomia, and 7) transcranial sonography for measuring substantia nigra and lentiform nucleus echogenicity. Imaging biomarkers, using the ‘multimodal approach’, may aid in making early, accurate and objective diagnostic decisions, highlight neuroanatomical and pathophysiological mechanisms, as well as assist in evaluating disease progression and therapeutic responses to drugs in clinical trials.

Brain MR Contribution to the Differential Diagnosis of Parkinsonian Syndromes: An Update

Brain magnetic resonance (MR) represents a useful and feasible tool for the differential diagnosis of Parkinson's disease. Conventional MR may reveal secondary forms of parkinsonism and may show peculiar brain alterations of atypical parkinsonian syndromes. Furthermore, advanced MR techniques, such as morphometric-volumetric analyses, diffusion-weighted imaging, diffusion tensor imaging, tractography, proton MR spectroscopy, and iron-content sensitive imaging, have been used to obtain quantitative parameters useful to increase the diagnostic accuracy. Currently, many MR studies have provided both qualitative and quantitative findings, reflecting the underlying neuropathological pattern of the different degenerative parkinsonian syndromes. Although the variability in the methods and results across the studies limits the conclusion about which technique is the best, specific radiologic phenotypes may be identified. Qualitative/quantitative MR changes in the substantia nigra do not discriminate between different parkinsonisms. In the absence of extranigral abnormalities, the diagnosis of PD is more probable, whereas basal ganglia changes (mainly in the putamen) suggest the diagnosis of an atypical parkinsonian syndrome. In this context, changes in pons, middle cerebellar peduncles, and cerebellum suggest the diagnosis of MSA, in midbrain and superior cerebellar peduncles the diagnosis of PSP, and in whole cerebral hemispheres (mainly in frontoparietal cortex with asymmetric distribution) the diagnosis of Corticobasal Syndrome.

Role of Magnetic Resonance Imaging in the Diagnosis of Multiple System Atrophy

Background

Multiple system atrophy (MSA) is a rapidly progressing neurodegenerative disorder without effective disease-modifying therapies. Because of a lack of reliable diagnostic biomarkers, there has been increasing interest in using magnetic resonance imaging (MRI) to improve the diagnostic accuracy of MSA.

Methods

This review summarizes recent literatures on the role of MRI in the diagnosis of MSA.

Results

Several MRI abnormalities on conventional MRI already are included in the current diagnostic criteria for MSA. Other features on conventional MRI are also used to make a diagnosis of MSA or to rule out alternative diagnoses. On the other hand, some of the MRI findings that were previously considered suggestive of a diagnosis of MSA are now being challenged, because it turned out that they were not as specific to MSA as previously thought. More advanced MRI modalities, including susceptibility-weighted imaging, diffusion-weighted imaging, diffusion tensor imaging, voxel-based morphometry, and cortical thickness analysis, are now used to study the changes in the brains of patients with MSA. Furthermore, studies have produced promising results demonstrating the use of MRI as a tool for monitoring and assessing disease progression in MSA.

Conclusions

MRI is useful and indispensable in the diagnosis of MSA and also possibly for monitoring disease progression. In this regard, well-designed, long-term, prospective studies on large numbers of patients are needed.

Combining Diffusion Tensor Imaging and Susceptibility Weighted Imaging on the Substantia Nigra of 1-Methyl-4-Phenyl-1, 2, 3, 6-Tetrahydropyridine (MPTP)-induced Rhesus Monkey Model of Parkinson's Disease

Objective

The aim of this study was to evaluate whether combining diffusion tensor magnetic resonance imaging (DTI) and susceptibility weighted imaging (SWI) techniques would provide a sensitive method for differentiating between 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced rhesus monkey model of Parkinson's disease (PD) and wild-type controls.

Subjects and Methods

Seventeen rhesus monkeys were divided into two groups. A series of intramuscular injections of either saline (control group, n = 8) or MPTP (0.2 mg/kg body weight; PD group, n = 9) were given to the monkeys, twice a week. Then, SWI and DTI scans were obtained from the monkeys with Siemens Magnetom Verio 3.0T superconductive MRI system. Region of interest analysis was performed on substantia nigra pars compacta (SNc) and substantia nigra pars reticulata (SNr). In addition, immunohistochemical staining of tyrosine hydroxylase was applied to assess degeneration of SN dopaminergic neurons.

Results

Monkeys in the PD group displayed mild to moderate motor symptoms assessed using Kurlan's scale. With SWI scans, decreased width of SNc but increased width of SNr was found in PD group monkeys compared to controls. Calculation of the ratios of widths of SNc and SNr to the anterior and posterior mesencephalic diameter also reflected narrower SNc but wider SNr than controls. Decreased SWI signal intensity of SNc and SNr suggested iron deposition in both subregions of SN. The DTI scans showed lower fractional anisotropy (FA) values in SNc of the PD group monkeys, while no change of FA values in SNr was detected. Immunohistochemical test displayed generalized loss of dopaminergic neurons in SN of PD group monkeys.

Conclusion

Combining the use of DTI and SWI can provide a sensitive method for differentiating between MPTP-induced rhesus monkey model of PD and wild-type controls. This effective imaging modality might provide additional information for characteristic identification of PD at early stages, thus enhancing the ability to make early diagnosis, and monitor progression of the natural history and treatment effects.

Imaging synucleinopathies

In this review the structural and functional imaging changes associated with the synucleinopathies PD, MSA, and dementias associated with Lewy bodies are reviewed. The role of imaging for supporting differential diagnosis, detecting subclinical disease, and following disease progression is discussed and its potential use for monitoring disease progression is debated. © 2016 International Parkinson and Movement Disorder Society.

Machine learning on Parkinson's disease? Let's translate into clinical practice

Machine learning techniques represent the third-generation of clinical neuroimaging studies where the principal interest is not related to describe anatomical changes of a neurological disorder, but to evaluate if a multivariate approach may use these abnormalities to predict the correct classification of previously unseen clinical cohort. In the next few years, Machine learning will revolutionize clinical practice of Parkinson's disease, but enthusiasm should be turned down before removing some important barriers.

Free-water imaging in Parkinson's disease and atypical parkinsonism

Conventional single tensor diffusion analysis models have provided mixed findings in the substantia nigra of Parkinson's disease, but recent work using a bi-tensor analysis model has shown more promising results. Using a bi-tensor model, free-water values were found to be increased in the posterior substantia nigra of Parkinson's disease compared with controls at a single site and in a multi-site cohort. Further, free-water increased longitudinally over 1 year in the posterior substantia nigra of Parkinson's disease. Here, we test the hypothesis that other parkinsonian disorders such as multiple system atrophy and progressive supranuclear palsy have elevated free-water in the substantia nigra. Equally important, however, is whether the bi-tensor diffusion model is able to detect alterations in other brain regions beyond the substantia nigra in Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy and to accurately distinguish between these diseases. Free-water and free-water-corrected fractional anisotropy maps were compared across 72 individuals in the basal ganglia, midbrain, thalamus, dentate nucleus, cerebellar peduncles, cerebellar vermis and lobules V and VI, and corpus callosum. Compared with controls, free-water was increased in the anterior and posterior substantia nigra of Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. Despite no other changes in Parkinson's disease, we observed elevated free-water in all regions except the dentate nucleus, subthalamic nucleus, and corpus callosum of multiple system atrophy, and in all regions examined for progressive supranuclear palsy. Compared with controls, free-water-corrected fractional anisotropy values were increased for multiple system atrophy in the putamen and caudate, and increased for progressive supranuclear palsy in the putamen, caudate, thalamus, and vermis, and decreased in the superior cerebellar peduncle and corpus callosum. For all disease group comparisons, the support vector machine 10-fold cross-validation area under the curve was between 0.93-1.00 and there was high sensitivity and specificity. The regions and diffusion measures selected by the model varied across comparisons and are consistent with pathological studies. In conclusion, the current study used a novel bi-tensor diffusion analysis model to indicate that all forms of parkinsonism had elevated free-water in the substantia nigra. Beyond the substantia nigra, both multiple system atrophy and progressive supranuclear palsy, but not Parkinson's disease, showed a broad network of elevated free-water and altered free-water corrected fractional anisotropy that included the basal ganglia, thalamus, and cerebellum. These findings may be helpful in the differential diagnosis of parkinsonian disorders, and thereby facilitate the development and assessment of targeted therapies.

Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease

OBJECTIVES

We used neurite orientation dispersion and density imaging (NODDI) to quantify changes in the substantia nigra pars compacta (SNpc) and striatum in Parkinson disease (PD).

METHODS

Diffusion-weighted magnetic resonance images were acquired from 58 PD patients and 36 age- and sex-matched controls. The intracellular volume fraction (Vic), orientation dispersion index (OD), and isotropic volume fraction (Viso) of the basal ganglia were compared between groups. Multivariate logistic regression analysis determined which diffusion parameters were independent predictors of PD. Receiver operating characteristic (ROC) analysis compared the diagnostic accuracies of the evaluated indices. Pearson coefficient analysis correlated each diffusional parameter with disease severity.

RESULTS

Vic in the contralateral SNpc and putamen were significantly lower in PD patients than in healthy controls (P < 0.00058). Vic and OD in the SNpc and putamen showed significant negative correlations (P < 0.05) with disease severity. Multivariate logistic analysis revealed that Vic (P = 0.0000046) and mean diffusivity (P = 0.019) in the contralateral SNpc were the independent predictors of PD. In the ROC analysis, Vic in the contralateral SNpc showed the best diagnostic performance (mean cutoff, 0.62; sensitivity, 0.88; specificity, 0.83).

CONCLUSION

NODDI is likely to be useful for diagnosing PD and assessing its progression.

KEY POINTS

• Neurite orientation dispersion and density imaging (NODDI) is a new diffusion MRI technique • NODDI estimates neurite microstructure more specifically than diffusion tensor imaging • By using NODDI, nigrostriatal alterations in PD can be evaluated in vivo • NOODI is useful for diagnosing PD and assessing its disease progression.

The role of neuroimaging in the diagnosis of the atypical parkinsonian syndromes in clinical practice

Atypical parkinsonian disorders (APD) are a heterogenous group of neurodegenerative diseases such as: progressive supranuclear palsy (PSP), multiple system atrophy (MSA), cortico-basal degeneration (CBD) and dementia with Lewy bodies (DLB). In all of them core symptoms of parkinsonian syndrome are accompanied by many additional clinical features not typical for idiopathic Parkinson's disease (PD) like rapid progression, gaze palsy, apraxia, ataxia, early cognitive decline, dysautonomia and usually poor response to levodopa therapy. In the absence of reliably validated biomarkers the diagnosis is still challenging and mainly based on clinical criteria. However, robust data emerging from routine magnetic resonance imaging (MRI) as well as from many advanced MRI techniques such as: diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), voxel-based morphometry (VBM), susceptibility-weighted imaging (SWI) may help in differential diagnosis. The main aim of this review is to summarize briefly the most important and acknowledged radiological findings of conventional MRI due to its availability in standard clinical settings. Nevertheless, we present shortly other methods of structural (like TCS - transcranial sonography) and functional imaging (like SPECT - single photon emission computed tomography or PET - positron emission tomography) as well as some selected advanced MRI techniques and their potential future applications in supportive role in distinguishing APD.

Chemical Exchange Saturation Transfer MR Imaging is Superior to Diffusion-Tensor Imaging in the Diagnosis and Severity Evaluation of Parkinson's Disease: A Study on Substantia Nigra and Striatum

Parkinson's disease (PD) is a neurodegenerative disorder characterized by nigrostriatal cell loss. To date, the diagnosis of PD is still based primarily on the clinical manifestations, which may be typical and obvious only in advanced-stage PD. Thus, it is crucial to find a reliable marker for the diagnosis of PD. We conducted this study to assess the diagnostic efficiency of chemical exchange saturation transfer (CEST) imaging and diffusion-tensor imaging (DTI) in PD at 3 T by evaluating changes on substantia nigra and striatum. Twenty-three PD patients and twenty-three age-matched normal controls were recruited. All patients and controls were imaged on a 3-T MR system, using an eight-channel head coil. CEST imaging was acquired in two transverse slices of the head, including substantia nigra and striatum. The magnetization transfer ratio asymmetry at 3.5 ppm, MTRasym(3.5 ppm), and the total CEST signal intensity between 0 and 4 ppm were calculated. Multi-slice DTI was acquired for all the patients and normal controls. Quantitative analysis was performed on the substantia nigra, globus pallidus, putamen, and caudate. The MTRasym(3.5 ppm) value, the total CEST signal intensity, and fractional anisotropy value of the substantia nigra were all significantly lower in PD patients than in normal controls (P = 0.003, P = 0.004, and P < 0.001, respectively). The MTRasym(3.5 ppm) values of the putamen and the caudate were significantly higher in PD patients than in normal controls (P = 0.010 and P = 0.009, respectively). There were no significant differences for the mean diffusivity in these four regions between PD patients and normal controls. In conclusion, CEST MR imaging provided multiple CEST image contrasts in the substantia nigra and the striatum in PD and may be superior to DTI in the diagnosis of PD.

Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease

BACKGROUND

The development of MRI based methods could prove extremely valuable for identification of reliable biomarkers to aid diagnosis of neurodegenerative diseases (NDs). A great deal of current research has been aimed at identification biomarkers for both diagnosis at early stage and evaluation of the progression of NDs.

NEW METHOD

We present here a novel synergetic paradigm integrating Kohonen self organizing map (KSOM) and least squares support vector machine (LS-SVM) for individual-level clinical diagnosis of NDs. Feature are extracted in an unsupervised manner using KSOM on preprocessed brain MRIs. Thereafter, these features are fed as input to LSSVM for subject classification.

RESULTS

The applicability of the proposed methodology has been demonstrated using 831 T1-weighted MRIs obtained from Parkinson's Progression Markers Initiative (PPMI) database. We have achieved classification accuracy of up to 99% for differential diagnosis of Parkinson disease with confidence interval of 99.9%.

COMPARISON WITH OTHER EXISTING METHODS

The potential for translation of similar research findings to clinical application is greatly dependent upon two factors (1) accuracy of subject classification achieved and (2) size of the dataset used. Here, we report very high accuracy achieved on one of the largest MRI datasets using multivariate analysis tools.

CONCLUSIONS

In this paper, we describe a methodology that has the potential to be translated into first-line diagnostic tool for NDs. We also demonstrate the applicability of this methodology for diagnosing PD subjects in early stages of the disease, i.e., subjects in age of 31-60 years.

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.

Magnetization transfer MRI in dementia disorders, Huntington's disease and parkinsonism

Magnetic resonance imaging is the most used technique of neuroimaging. Using recent advances in magnetic resonance application it is possible to investigate several changes in neurodegenerative disease. Among different techniques, magnetization-transfer imaging (MTI), a magnetic resonance acquisition protocol assessing the magnetization exchange between protons bound to water and those bound to macromolecules, is able to identify microstructural brain tissue changes peculiar of neurodegenerative diseases. This review provides a report on the MTI technique and its use in the dementia disorders, Huntington's disease and parkinsonisms, comprehensive of the predictive values of MTI in the identification of early-phase disease.

Frontal corpus callosum alterations in progressive supranuclear palsy but not in Parkinson's disease

BACKGROUND

Frontal lobe involvement is considered a clinical and magnetic resonance imaging (MRI) feature in later stages of progressive supranuclear palsy (PSP).

OBJECTIVE

Diffusion tensor imaging (DTI) was used to investigate the integrity of frontal pathways in PSP and Parkinson's disease (PD) patients.

METHODS

DTI and 3-D MRI were performed in 15 PSP patients (parkinsonism subtype: n = 8; Richardson subtype: n = 7), 15 PD patients, and 18 matched controls. DTI analysis was performed in order to identify differences along frontal white matter structures including the corpus callosum (CC) and was complemented by atlas-based volumetry and planimetry.

RESULTS

Significantly reduced regional fractional anisotropy was observed for PSP patients versus controls and PSP versus PD patients, respectively, in frontal areas including the area II of the CC and bilaterally in the callosal radiation. The DTI findings correlated with frontal lobe volumes. These differences were not observed between PD patients and controls.

CONCLUSION

DTI identified a PSP-associated microstructural alteration pattern in the frontal lobes and in the CC area II including the corresponding bilateral callosal radiation tracts that could not be identified in both control samples, supporting the prominent PSP-associated frontal involvement as a potential neuroimaging marker.

Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson's disease

PURPOSE

Parkinson's disease (PD) is a neurodegenerative disorder that impairs the motor functions. Both surgical treatment and study of PD require delineation of basal ganglia nuclei morphology. While many automatic volumetric segmentation methods have been proposed for the lentiform nucleus, few have attempted to identify the key brainstem substructures including the subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN) due to their small size and poor contrast in conventional T1W MRI.

METHODS

A dual-contrast patch-based label fusion method was developed to segment the SN, STN, and RN using multivariate cross-correlation. Two different MRI contrasts (T2*w and phase) are produced from a multi-contrast multi-echo FLASH MRI sequence, enabling visualization of these nuclei. T1-T2* fusion MRI was used to resolve the issue of poor nuclei (i.e., the STN, SN, and RN) contrast on T1w MRI, and to mitigate susceptibility artifacts that may hinder accurate nonlinear registration on T2*w MRI. Unbiased group-wise registration was used for anatomical normalization between the atlas library and the target subject. The performance of the proposed method was compared with a state-of-the-art single-contrast label fusion technique.

RESULTS

The proposed method outperformed a state-of-the-art single-contrast patch-based method in segmenting the STN, RN and SN, and the results were better than those reported in previous literature.

CONCLUSION

Our dual-contrast patch-based label fusion method was superior to a single-contrast method for segmenting brainstem nuclei using a multi-contrast multi-echo FLASH MRI sequence. The method is promising for the treatment and research of Parkinson's disease. This method can be extended for multiple alternative image contrasts and other fields of applications.

MRI reveals brain abnormalities in drug-naive Parkinson's disease

Most brain studies of Parkinson's disease (PD) focus on patients who are already taking antiparkinsonian medication. This makes it difficult to isolate the effects of disease from those of treatment. We review magnetic resonance imaging evidence supporting the hypothesis that early-stage untreated PD patients have structural and functional abnormalities in the brain, some of which are related to motor symptoms.