Milestones in magnetic resonance imaging and transcranial sonography of movement disorders

Investigating the discrimination ability of 3D convolutional neural networks applied to altered brain MRI parametric maps

Convolutional neural networks (CNNs) are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. Despite their outstanding performances, some aspects of CNN functioning are still not fully understood by human operators. We postulated that the interpretability of CNNs applied to neuroimaging data could be improved by investigating their behavior when they are fed data with known characteristics. We analyzed the ability of 3D CNNs to discriminate between original and altered whole-brain parametric maps derived from diffusion-weighted magnetic resonance imaging. The alteration consisted in linearly changing the voxel intensity of either one (monoregion) or two (biregion) anatomical regions in each brain volume, but without mimicking any neuropathology. Performing ten-fold cross-validation and using a hold-out set for testing, we assessed the CNNs' discrimination ability according to the intensity of the altered regions, comparing the latter's size and relative position. Monoregion CNNs showed that the larger the modified region, the smaller the intensity increase needed to achieve good performances. Biregion CNNs systematically outperformed monoregion CNNs, but could only detect one of the two target regions when tested on the corresponding monoregion images. Exploiting prior information on training data allowed for a better understanding of CNN behavior, especially when altered regions were combined. This can inform about the complexity of CNN pattern retrieval and elucidate misclassified examples, particularly relevant for pathological data. The proposed analytical approach may serve to gain insights into CNN behavior and guide the design of enhanced detection systems exploiting our prior knowledge.

Clinical study of transcranial sonography image characteristics in patients with obstructive sleep apnea

OBJECTIVE

This study aimed to explore the relationship between patients with obstructive sleep apnea (OSA) from subgroups of varying severity and substantia nigra (SN) hyperechogenicity as well as cerebral blood flow detected by transcranial sonography (TCS). The study also explored if there were differences in damage of the SN and in the cerebral blood flow between the bilateral sides.

METHODS

Right-handed men diagnosed with OSA by polysomnography were recruited from August 2018 to August 2020. The included patients were divided into 3 subgroups (mild, moderate, and severe OSA), and all patients underwent TCS.

RESULTS

Among the 157 study patients (30 with mild OSA, 25 moderate, and 102 severe), the overall prevalence of SN hyperechogenicity was 15% (23/157). The hyperechogenicity detection rates were 3% (4/157) in the right SN subgroup and 13% (20/157) in the left SN subgroup, which were significantly different. The left side always had reduced blood flow on TCS (P < 0.05). No correlation was observed between the severity of OSA and the detection rates of SN hyperechogenicity (P > 0.05).

CONCLUSION

Patients with OSA showed a higher detection rate of SN hyperechogenicity on the left compared with the right side. The left middle cerebral arteries had reduced blood flow, which was consistent with the more severe damage of the left SN. No relationship was observed between the severity of OSA and the detection rate of SN hyperechogenicity or hemodynamic parameters.

Imaging the Functional Neuroanatomy of Parkinson's Disease: Clinical Applications and Future Directions

The neurobiology of Parkinson’s disease and its progression has been investigated during the last few decades. Braak et al. proposed neuropathological stages of this disease based on the recognizable topographical extent of Lewy body lesions. This pathological process involves specific brain areas with an ascending course from the brain stem to the cortex. Post-mortem studies are of importance to better understand not only the progression of motor symptoms, but also the involvement of other domains, including cognition and behavior. The correlation between the neuropathological expansion of the disease and the clinical phases remains demanding. Neuroimaging, including magnetic resonance imaging (MRI), positron emission tomography (PET), and single photon emission computed tomography (SPECT), could help to bridge this existing gap by providing in vivo evidence of the extension of the disorders. In the last decade, we observed an overabundance of reports regarding the sensitivity of neuroimaging techniques. All these studies were aimed at improving the accuracy of Parkinson’s disease (PD) diagnosis and discriminating it from other causes of parkinsonism. In this review, we look at the recent literature concerning PD and address the new frontier of diagnostic accuracy in terms of identification of early stages of the disease and conventional neuroimaging techniques that, in vivo, are capable of photographing the basal ganglia network and its cerebral connections.

Conventional Magnetic Resonance Imaging in the Diagnosis of Parkinsonian Disorders: A Meta-Analysis

Background

Numerous conventional magnetic resonance imaging (cMRI) parameters were previously found to differentiate parkinsonian disorders with statistical significance, but effect size has not been considered.

Objectives

To quantify effect size of previously identified cMRI parameters that differentiated parkinsonian disorders with statistical significance.

Method

A PubMed search limited to studies assessing cMRI parameters in at least 2 of Parkinson's disease, progressive supranuclear palsy, multiple system atrophy, and corticobasal degeneration/syndrome were selected. Either Cohen's d or positive and negative likelihood (LR+/-) as well as diagnostic odds ratios (DORs) were calculated as appropriate. cMRI parameter was considered useful if Cohen's d > 1.94 (<20% overlap) or if LR+ > 10, LR- < 0.1, or DOR > 20.

Results

Literature search identified 8848 publications and 36 were included for analysis. Putaminal (Cohen's d 2.07; DOR 23-infinity), pontine (DOR 32-infinity), and middle cerebellar peduncle (Cohen's d 2.24; DOR infinity) abnormalities were most useful in differentiating multiple system atrophy while reduced midbrain (Cohen's d 2.33-8.69; DOR infinity) and superior cerebellar peduncle (Cohen's d 2.47; DOR 51-infinity) diameters separated progressive supranuclear palsy. Corticobasal degeneration/syndrome does not have any distinguishing cMRI features, but reduced midbrain diameter may help differentiate corticobasal degeneration/syndrome from Parkinson's disease (DOR infinity). When LR- was calculated, all of these features carried a value of <0.1.

Conclusion

A number of cMRI features consistently demonstrated large effect size in separating parkinsonian disorders. However, it is the presence and not absence of these cMRI features that is most useful in patients with low to moderate pretest probability.

Quantitative transcranial sonography of the substantia nigra as a predictor of therapeutic response to intravenous iron therapy in restless legs syndrome

OBJECTIVE

To analyze changes in substantia nigra (SN) iron deposits, assessed by quantitative transcranial sonography (TCS), to obtain and compare substantia nigra echogenicity indices (SNEI) at baseline and after intravenous (IV) iron therapy in patients with restless legs syndrome (RLS)/Willis-Ekbom disease (WED).

METHODS

A total of 30 consecutive subjects diagnosed with RLS/WED were recruited and underwent IV iron treatment. The SNEI, total daily dose of dopamine equivalents, and International Restless Legs Syndrome Rating Scale (IRLS) scores were obtained at baseline and following IV iron administration. Comparative statistics were performed by means of nonparametric testing.

RESULTS

The sample was stratified into two groups according to the median baseline SNEI and the grade of SN hypoechogenicity: severely hypoechogenic (HE) (n = 13) and moderately HE (n = 17). Following IV iron, the increase in SNEI among severely HE subjects was 19% (0.038 ± 0.046 cm²; P < 0.01), whereas in moderately HE subjects it was 10% (0.021 ± 0.069 cm²; P = 0.28). Among severely HE subjects, the average reduction in IRLS following IV iron was 10 ± 7.12 points (P < 0.01), in contrast to 1.85 ± 9.85 (not significant) among moderately HE subjects. Finally, we quantified the percentage of patients in each group who were able to reduce by ≥30% their total daily dopaminergic requirements (TDR) after IV iron, with a 57.14% reduction in the severely HE group vs 25% in the moderately HE group (P = 0.1). Three of 30 subjects (17%) were able to completely cease all dopaminergic treatment.

CONCLUSION

Intravenous iron caused changes in SNEI in both groups of patients, reflecting an increase in brain iron stores. However, the increase in SNEI was greater in patients previously defined as severely HE. Furthermore, RLS/WED symptoms also improved more in severely HE subjects, and there was a greater reduction in TDR. This study highlights the role of TCS in quantifying brain iron deposits and in predicting which patients will likely benefit from IV iron.

A totally data-driven whole-brain multimodal pipeline for the discrimination of Parkinson's disease, multiple system atrophy and healthy control

Parkinson's Disease (PD) and Multiple System Atrophy (MSA) are two parkinsonian syndromes that share many symptoms, albeit having very different prognosis. Although previous studies have proposed multimodal MRI protocols combined with multivariate analysis to discriminate between these two populations and healthy controls, studies combining all MRI indexes relevant for these disorders (i.e. grey matter volume, fractional anisotropy, mean diffusivity, iron deposition, brain activity at rest and brain connectivity) with a completely data-driven voxelwise analysis for discrimination are still lacking. In this study, we used such a complete MRI protocol and adapted a fully-data driven analysis pipeline to discriminate between these populations and a healthy controls (HC) group. The pipeline combined several feature selection and reduction steps to obtain interpretable models with a low number of discriminant features that can shed light onto the brain pathology of PD and MSA. Using this pipeline, we could discriminate between PD and HC (best accuracy = 0.78), MSA and HC (best accuracy = 0.94) and PD and MSA (best accuracy = 0.88). Moreover, we showed that indexes derived from resting-state fMRI alone could discriminate between PD and HC, while mean diffusivity in the cerebellum and the putamen alone could discriminate between MSA and HC. On the other hand, a more diverse set of indexes derived by multiple modalities was needed to discriminate between the two disorders. We showed that our pipeline was able to discriminate between distinct pathological populations while delivering sparse model that could be used to better understand the neural underpinning of the pathologies.

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Structuro-functional MRI can discriminate between parkinsonian syndromes

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Discriminant MRI modalities vary as a function of the discrimination task

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fMRI is crucial in discriminating between Parkinson's disease patients and controls

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Structural MRI discriminate between Multiple System Atrophy patients and controls

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.

Biomarkers for Parkinson's Disease: Recent Advancement

As a multi-factorial degenerative disease, Parkinson's disease (PD) leads to tremor, gait rigidity, and hypokinesia, thus hampering normal living. As this disease is usually detected in the later stages when neurons have degenerated completely, cure is on hold, ultimately leading to death due to the lack of early diagnostic techniques. Thus, biomarkers are required to detect the disease in the early stages when prevention is possible. Various biomarkers providing early diagnosis of the disease include those of imaging, cerebrospinal fluid, oxidative stress, neuroprotection, and inflammation. Also, biomarkers, alone or in combination, are used in the diagnosis and evolution of PD. This review encompasses various biomarkers available for PD and discusses recent advances in their development.

Usefulness of Cardiac MIBG Scintigraphy, Olfactory Testing and Substantia Nigra Hyperechogenicity as Additional Diagnostic Markers for Distinguishing between Parkinson's Disease and Atypical Parkinsonian Syndromes

Background

We aimed to evaluate the utility of the combined use of cardiac ¹²³I-metaiodobenzylguanidine (MIBG) scintigraphy, olfactory testing, and substantia nigra (SN) hyperechogenicity on transcranial sonography (TCS) in differentiating Parkinson’s disease (PD) from atypical parkinsonian syndromes (APSs), such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).

Methods

Cardiac MIBG scintigraphy, card-type odor identification testing (Open Essence (OE), Wako, Japan), and TCS were performed with 101 patients with PD and 38 patients with APSs (MSA and PSP). Receiver operating characteristic (ROC) curve analysis was used to assess the sensitivity and specificity of these batteries for diagnosing PD from APSs. The diagnostic accuracy of the three tests was also assessed among patients at the early disease stage (drug-naïve patients with a disease duration of 3 years or less).

Results

In differentiating PD from APSs, the area under the ROC curve was 0.74 (95% CI, 0.65–0.83), 0.8 (95% CI, 0.73–0.87), and 0.75 (95% CI, 0.67–0.82) for TCS, cardiac MIBG scintigraphy, and olfactory testing, respectively. The diagnostic sensitivity and specificity were 53.1% and 91.7%, respectively, for TCS, 70.3% and 86.8%, respectively, for cardiac MIBG scintigraphy, 58.4% and 76.3%, respectively, for OE. Among early-stage patients, sensitivity and specificity were 50.0% and 93.8%, respectively, for TCS, 57.1% and 87.5%, respectively, for cardiac MIBG scintigraphy, and 54.8% and 79.2%, respectively, for OE. At least one positive result from 3 tests improved sensitivity (86.1%) but decreased specificity (63.2%). In contrast, at least 2 positive results from 3 tests had good discrimination for both early-stage patients (50.0% sensitivity and 93.8% specificity) and patients overall (57.8% sensitivity and 95.8% specificity). Positive results for all 3 tests yielded 100% specificity but low sensitivity (25%).

Conclusions

At least 2 positive results from among TCS, cardiac MIBG scintigraphy, and olfactory testing can support clinical diagnosis in distinguishing PD from APSs.

Substantia Nigra Echogenicity Predicts Response to Drug Withdrawal in Suspected Drug-Induced Parkinsonism

Introduction

Response to drug withdrawal in patients with suspected drug-induced parkinsonism (DIP) is of prognostic and therapeutic importance, but cannot be predicted solely on clinical information. The aim of this study was to validate SN hyperechogenicity (SN+) assessed by transcranial sonography as a predictor of response to drug withdrawal in this group of patients.

Methods

Patients were diagnosed according to previously published criteria and prospectively included in the study. All patients were followed until complete recovery of parkinsonian symptoms or at least for 6 months after discontinuation of the offending drug and then diagnosed as DIP or parkinsonism following neuroleptic exposure (PFNE). Transcranial sonography (TCS) findings were compared with the clinical diagnosis.

Results

Sixty patients comprised the group for the final analysis. Sixteen patients were classified as PFNE and 44 as DIP. The area of SN echogenicity was significantly increased in the PFNE group (0.23 cm²; standard deviation [SD]: 0.04), compared to the DIP group (0.14 cm²; SD, 0.05; one-way analysis of variance; P < 0.001). Normal SN was significantly associated with complete recovery after withdrawal of the parkinsonism-inducing drug (P < 0.0005). Accuracy of SN+ to distinguish PFNE from DIP was: sensitivity 81.2%; specificity 84.1%; positive predictive value 47.4%; and negative predictive value 96.2%.

Conclusions

We believe that SN+ assessed with TCS is a valid prognostic marker in the setting of suspected DIP. It is a nonexpensive, feasible technique that can be implemented for proper counseling and guidance of treatment decisions.

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.

Patterns of gray matter atrophy in atypical parkinsonism syndromes: a VBM meta-analysis

BACKGROUND AND PURPOSE

Accurate diagnosis of Atypical Parkinsonian Syndromes (APS) is important due to differences in prognosis and management, but remains a challenge in the clinical setting. The purpose of our meta-analysis was to identify characteristic patterns of gray matter atrophy in Corticobasal Degeneration (CBD), Progressive Supranuclear Palsy (PSP), Multisystem-Atrophy Parkinsonian type (MSA-P), and Idiopathic Parkinson's Disease (IPD).

MATERIALS AND METHODS

Whole-brain meta-analysis was performed on 39 published voxel-based morphometry (VBM) articles (consisting of 404 IPD, 87 MSA-P, 165 CBD, and 176 PSP subjects) using the modified Anatomic Likelihood Estimation method. Based on these results, contrast analyses were then utilized to determine areas of atrophy shared by as well as unique to each disorder.

RESULTS

CBD was characterized by asymmetric gray matter atrophy in multiple cortical regions, while the thalamus-midbrain and insula were predominantly involved in PSP. The striatum and superior cerebellum were affected in MSA-P, while IPD demonstrated an anterior cerebral pattern. Although there was a mild overlap among PSP, CBD, and MSA-P, significant regions of atrophy unique to each disorder were identified, including (1) the superior parietal lobule in CBD (2) putamen in MSA-P (3) insula and medial dorsal nucleus in PSP.

CONCLUSION

Our results suggest that there are characteristic patterns of atrophy in APS. Guided by these findings, future studies on the individual subject level may lead to the development of robust imaging biomarkers.

Diffusion tensor imaging of nigral degeneration in Parkinson's disease: A region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis

There is increasing interest in developing a reliable, affordable and accessible disease biomarker of Parkinson's disease (PD) to facilitate disease modifying PD-trials. Imaging biomarkers using magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can describe parameters such as fractional anisotropy (FA), mean diffusivity (MD) or apparent diffusion coefficient (ADC). These parameters, when measured in the substantia nigra (SN), have not only shown promising but also varying and controversial results.

To clarify the potential diagnostic value of nigral DTI in PD and its dependency on selection of region-of-interest, we undertook a high resolution DTI study at 3 T. 59 subjects (32 PD patients, 27 age and sex matched healthy controls) were analysed using manual outlining of SN and substructures, and voxel-based analysis (VBA). We also performed a systematic literature review and meta-analysis to estimate the effect size (D_ES) of disease related nigral DTI changes.

We found a regional increase in nigral mean diffusivity in PD (mean ± SD, PD 0.80 ± 0.10 vs. controls 0.73 ± 0.06 · 10^− 3 mm²/s, p = 0.002), but no difference using a voxel based approach. No significant disease effect was seen using meta-analysis of nigral MD changes (10 studies, D_ES = + 0.26, p = 0.17, I² = 30%). None of the nigral regional or voxel based analyses of this study showed altered fractional anisotropy. Meta-analysis of 11 studies on nigral FA changes revealed a significant PD induced FA decrease. There was, however, a very large variation in results (I² = 86%) comparing all studies. After exclusion of five studies with unusual high values of nigral FA in the control group, an acceptable heterogeneity was reached, but there was non-significant disease effect (D_ES = − 0.5, p = 0.22, I² = 28%).

The small PD related nigral MD changes in conjunction with the negative findings on VBA and meta-analysis limit the usefulness of nigral MD measures as biomarker of Parkinson's disease. The negative results of nigral FA measurements at regional, sub-regional and voxel level in conjunction with the results of the meta-analysis of nigral FA changes question the stability and validity of this measure as a PD biomarker.

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Investigating diagnostic accuracy of nigral diffusion MRI to diagnose Parkinson's

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There is small, inconsistent increase of mean diffusivity of the substantia nigra.

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There is no change in nigral fractional anisotropy (FA) in the case–control study.

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Meta-analysis revealed nigral FA change is dependent on high FA in controls.

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This questions the usefulness of nigral diffusion MRI as biomarker in Parkinson's.

Ascertainment bias in dementias: a secondary to tertiary centre analysis in Central Italy and conceptual review

BACKGROUND AND AIMS

Ascertainment bias (AB) indicates a bias of an evaluation centre in estimating the prevalence/incidence of a disease due to the specific expertise of the centre. The aim of our study was to evaluate classification of different types of dementia in new cases appearing in secondary and tertiary centres, in order to evidence possible occurrence of AB in the various (secondary to tertiary) dementia centres.

METHODS

To assess the mechanism of AB, the rates of new cases of the different forms of dementia reported by different centres were compared. The centres involved in the study were 11 hospital-based centres including a tertiary centre, located in the University Department of Clinical Neurology. The tertiary centre is endowed with state-of-the-art diagnostic facilities and its scientific production is prominently focused on dementia with Lewy bodies (DLB) thus suggesting the possible occurrence of a bias. Four main categories of dementia were identified: Alzheimer's disease (AD), DLB, fronto-temporal dementia (FTD), vascular dementia (VaD), with other forms in a category apart. The classification rate of new cases of dementia in the tertiary centre was compared with rates reported by secondary centres and rates of recoding were calculated during a follow-up of 2 years.

RESULTS

The study classified 2,042 newly diagnosed cases of dementia in a population of 1,370,000 inhabitants of which 315,000 were older than 65. AD was categorized in 48-52 % of cases, DLB in 25-28 %, FTD in 2-4 % and VaD in 17-28 %. During the 2-year follow-up the diagnosis was re-classified in 40 patients (3 %). The rate of recoding was 5 % in the tertiary centre, 2-8 % in referrals from secondary to tertiary centre, 2-10 % in recodings performed in secondary centres and addressed to tertiary centre. Recoding or percentages of new cases of AD or DLB were not different in the comparison between secondary or between secondary and tertiary centres. FTD and VaD were instead significantly recoded.

CONCLUSION

The results of the study suggest that in a homogeneous area, AB is not interfering with diagnosis of AD or DLB.

The study of deep brain structures by transcranial duplex sonography and imaging resonance correlation

Transcranial duplex sonography (TCS) currently provides us with images of deep brain structures with sufficient resolution. We chose 2 sonographic quantitative parameters: the diameter of the third ventricle and a measurement not used by TCS to date, the midbrain area. Their reliability and reproducibility were assessed using magnetic resonance imaging (MRI) as the reference. A total of 99 patients free from neurodegenerative disorders were examined using TCS, and both parameters were measured by 2 independent explorers. Measurements of third-ventricle diameter by TCS showed very good correlation (r = 0.80) and agreement (ICC = 0.89) with measurements obtained by MRI. Measurements of the midbrain area by TCS also provided acceptable values with moderate correlation (r = 0.36) and agreement (ICC = 0.53). Interexplorer correlation values were good (ICC = 0.98 and 0.79 for the third ventricle and midbrain areas, respectively). Further studies will be required to determine the potential diagnostic usefulness of these parameters.

Diffusivity of cerebellar hemispheres enables discrimination of cerebellar or parkinsonian multiple system atrophy from progressive supranuclear palsy-Richardson syndrome and Parkinson disease

PURPOSE

To explore the usefulness of histogram analysis of mean diffusivity (MD) derived from diffusion-weighted imaging of large infratentorial structures to distinguish parkinsonian syndromes.

MATERIALS AND METHODS

Local research ethics committee approval and informed consent were obtained. Ten patients with Parkinson disease (PD), nine with the parkinsonian variant of multiple system atrophy (MSA-P), seven with the cerebellar variant of MSA (MSA-C), 17 with progressive supranuclear palsy-Richardson syndrome (PSP-RS), and 10 healthy subjects were recruited. Histograms of MD values were generated for all pixels in the whole infratentorial compartment and separately for the whole brainstem, vermis, and cerebellar hemispheres. To assess the differences in MD values among groups, the Kruskal-Wallis test was used, followed by the Mann-Whitney U test for pairwise comparisons. All P values resulting from pairwise comparisons were corrected with the Bonferroni method.

RESULTS

MSA-P and MSA-C groups had higher median MD values (P < .01) in the brainstem and cerebellum when compared with other groups; this finding was in line with the known consistent neurodegenerative damage in posterior cranial fossa structures in these diseases. Median MD values from cerebellar hemispheres were used to discriminate patients with MSA-C and those with MSA-P from patients with PD and those with PSP-RS (P < .01; sensitivity, specificity, and positive predictive value equaled 100%). Furthermore, patients with PSP-RS had significantly higher MD values in the vermis than did healthy subjects (P < .05) and patients with PD (P < .001).

CONCLUSION

These findings support the clinical usefulness of diffusion imaging in the differential diagnosis of parkinsonism, suggesting that the minimally operator-dependent histogram analysis of the infratentorial structures and particularly of the whole cerebellar hemispheres can be used to distinguish patients with MSA-P and those with MSA-C from patients with PSP-RS and those with PD.

Feature analysis for Parkinson's disease detection based on transcranial sonography image

Transcranial sonography (TCS) is a new tool for the diagnosis of Parkinson's disease (PD) according to a distinct hyperechogenic pattern in the substantia nigra (SN) region. However a procedure including rating scale of SN hyperechogenicity was required for a standard clinical setting with increased use. We applied the feature analysis method to a large TCS dataset that is relevant for clinical practice and includes the variability that is present under real conditions. In order to decrease the influence to the image properties from the different settings of ultrasound machine, we propose a local image analysis method using an invariant scale blob detection for the hyperechogenicity estimation. The local features are extracted from the detected blobs and the watershed regions in half of mesencephalon area. The performance of these features is evaluated by a feature-selection method. The cross validation results show that the local features could be used for PD detection.