Apparent diffusion coefficient of the superior cerebellar peduncle differentiates progressive supranuclear palsy from Parkinson's disease

Planimetric and Volumetric Brainstem MRI Markers in Progressive Supranuclear Palsy, Multiple System Atrophy, and Corticobasal Syndrome. A Systematic Review and Meta-Analysis

BACKGROUND

Various MRI markers-including midbrain and pons areas (Marea, Parea) and volumes (Mvol, Pvol), ratios (M/Parea, M/Pvol), and composite markers (magnetic resonance imaging Parkinsonism Indices 1,2; MRPI 1,2)-have been proposed as imaging markers of Richardson's syndrome (RS) and multiple system atrophy-Parkinsonism (MSA-P). A systematic review/meta-analysis of relevant studies aiming to compare the diagnostic accuracy of these imaging markers is lacking.

METHODS

Pubmed and Scopus were searched for studies with >10 patients (RS, MSA-P or CBS) and >10 controls with data on Marea, Parea, Mvol, Pvol, M/Parea, M/Pvol, MRPI 1, and MRPI 2. Cohen's d, as a measure of effect size, was calculated for all markers in RS, MSA-P, and CBS.

RESULTS

Twenty-five studies on RS, five studies on MSA-P, and four studies on CBS were included. Midbrain area provided the greatest effect size for differentiating RS from controls (Cohen's d = -3.10; p < 0.001), followed by M/Parea and MRPI 1. MSA-P had decreased midbrain and pontine areas. Included studies exhibited high heterogeneity, whereas publication bias was low.

CONCLUSIONS

Midbrain area is the optimal MRI marker for RS, and pons area is optimal for MSA-P. M/Parea and MRPIs produce smaller effect sizes for differentiating RS from controls.

Differentiating between common PSP phenotypes using structural MRI: a machine learning study

BACKGROUND

Differentiating Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data.

METHODS

Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes.

RESULTS

MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients.

CONCLUSION

The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials.

The Role of Clinical Assessment in the Era of Biomarkers

Hippocratic Medicine revolved around the three main principles of patient, disease, and physician and promoted the systematic observation of patients, rational reasoning, and interpretation of collected information. Although these remain the cardinal features of clinical assessment today, Medicine has evolved from a more physician-centered to a more patient-centered approach. Clinical assessment allows physicians to encounter, observe, evaluate, and connect with patients. This establishes the patient-physician relationship and facilitates a better understanding of the patient-disease relationship, as the ultimate goal is to diagnose, prognosticate, and treat. Biomarkers are at the core of the more disease-centered approach that is currently revolutionizing Medicine as they provide insight into the underlying disease pathomechanisms and biological changes. Genetic, biochemical, radiographic, and clinical biomarkers are currently used. Here, we define a seven-level theoretical construct for the utility of biomarkers in neurodegenerative diseases. Level 1-3 biomarkers are considered supportive of clinical assessment, capable of detecting susceptibility or risk factors, non-specific neurodegeneration or dysfunction, and/or changes at the individual level which help increase clinical diagnostic accuracy and confidence. Level 4-7 biomarkers have the potential to surpass the utility of clinical assessment through detection of early disease stages and prediction of underlying pathology. In neurodegenerative diseases, biomarkers can potentiate, but cannot substitute, clinical assessment. In this current era, aside from adding to the discovery, evaluation/validation, and implementation of more biomarkers, clinical assessment remains crucial to maintaining the personal, humanistic, and sociocultural aspects of patient care. We would argue that clinical assessment is a custom that should never go obsolete.

Structural and Molecular Imaging for Clinically Uncertain Parkinsonism

Neuroimaging is an important adjunct to the clinical assessment of Parkinson disease (PD). Parkinsonism can be challenging to differentiate, especially in early disease stages, when it mimics other movement disorders or when there is a poor response to dopaminergic therapies. There is also a discrepancy between the phenotypic presentation of degenerative parkinsonism and the pathological outcome. The emergence of more sophisticated and accessible neuroimaging can identify molecular mechanisms of PD, the variation between clinical phenotypes, and the compensatory mechanisms that occur with disease progression. Ultra-high-field imaging techniques have improved spatial resolution and contrast that can detect microstructural changes, disruptions in neural pathways, and metabolic and blood flow alterations. We highlight the imaging modalities that can be accessed in clinical practice and recommend an approach to the diagnosis of clinically uncertain parkinsonism.

Combination of Quantitative Susceptibility Mapping and Diffusion Kurtosis Imaging Provides Potential Biomarkers for Early-Stage Parkinson's Disease

Purpose: This study aimed to detect changes in iron deposition and neural microstructure in the substantia nigra (SN), red nucleus (RN), and basal ganglia of Parkinson's disease (PD) patients at different stages using quantitative susceptibility mapping and diffusion kurtosis imaging to identify potential indicators of early-stage PD. Methods: We enrolled 20 early-stage and 15 late-stage PD patients, as well as 20 age- and sex-matched controls. All participants underwent quantitative susceptibility mapping and diffusion kurtosis imaging to determine magnetic susceptibility (MS), fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) in several brain regions. Results: Compared with the control group, MS and MK values in the SN were significantly increased in the early- and late-stage PD group, whereas MS values in the red nucleus (RN), globus pallidus (GP), and caudate nucleus (CN), FA value in the CN and GP, and MK value in the CN and putamen (PU) were significantly increased in the late-stage PD group. There were positive correlations between MS and MK values in the CN and MS and FA values in the GP. Furthermore, the combination of MS and MK values in the SN provided high accuracy for distinguishing early-stage PD patients from controls. Conclusions: This study identified MS and MK in the SN as potential indicators of early-stage PD.

Diffusion tractography of superior cerebellar peduncle and dentatorubrothalamic tracts in two autopsy confirmed progressive supranuclear palsy variants: Richardson syndrome and the speech-language variant

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Different changes in DTI metrics in SCP and DRTT can be seen across PSP subtypes.

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DRTT tractography reconstructions demonstrated specific changes in PSP-RS.

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DTI and clinical PSP scores are specifically linked across each PSP variant.

Background

Progressive supranuclear palsy (PSP) is a 4-repeat tauopathy with neurodegeneration typically observed in the superior cerebellar peduncle (SCP) and dentatorubrothalamic tracts (DRTT). However, it is unclear how these tracts are differentially affected in different clinical variants of PSP.

Objectives

To determine whether diffusion tractography of the SCP and DRTT can differentiate autopsy-confirmed PSP with Richardson’s syndrome (PSP-RS) and PSP with predominant speech/language disorder (PSP-SL).

Methods

We studied 22 autopsy-confirmed PSP patients that included 12 with PSP-RS and 10 with PSP-SL. We compared these two groups to 11 patients with autopsy-confirmed Alzheimer’s disease with SL problems, i.e., logopenic progressive aphasia (AD-LPA) (disease controls) and 10 healthy controls. Whole brain tractography was performed to identify the SCP and DRTT, as well as the frontal aslant tract and superior longitudinal fasciculus. We assessed fractional anisotropy and mean diffusivity for each tract. Hierarchical linear modeling was used for statistical comparisons, and correlations were assessed with clinical disease severity, ocular motor impairment, and parkinsonism. DRTT connectomics matrix analysis was also performed across groups.

Results

The SCP showed decreased fractional anisotropy for PSP-RS and PSP-SL and increased mean diffusivity in PSP-RS, compared to controls and AD-LPA. Right DRTT fibers showed lower fractional anisotropy in PSP-RS and PSP-SL compared to controls and AD-LPA, with PSP-RS also showing lower values compared to PSP-SL. Reductions in connectivity were observed in infratentorial DRTT regions in PSP-RS vs cortical regions in PSP-SL. PSP-SL showed greater abnormalities in the frontal aslant tract and superior longitudinal fasciculus compared to controls, PSP-RS, and AD-LPA. Significant correlations were observed between ocular motor impairment and SCP in PSP-RS (p = 0.042), and DRTT in PSP-SL (p = 0.022). In PSP-SL, the PSP Rating Scale correlated with the SCP (p = 0.045) and DRTT (p = 0.008), and the Unified Parkinson’s Disease Rating Scale correlated with the DRTT (p = 0.014).

Conclusions

Degeneration of the SCP and DRTT are diagnostic features of both PSP-RS and PSP-SL and associations with clinical metrics validate the role of these tracts in PSP-related clinical features, particularly in PSP-SL.

Progressive supranuclear palsy and corticobasal degeneration: novel clinical concepts and advances in biomarkers

BACKGROUND

Progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) are sporadic adult-onset primary tauopathies clinically classified among the atypical parkinsonian syndromes. They are intrinsically related with regard to their clinical features, pathology, biochemistry, and genetic risk factors.

OBJECTIVES

This review highlights the current knowledge on PSP and CBD, focusing on evolving clinical concepts, new diagnostic criteria, and advances in biomarkers.

METHODS

We performed a non-systematic literature review through the PubMed database. The search was restricted to articles written in English, published from 1964 to date.

RESULTS

Clinicopathologic and in vivo biomarkers studies have broadened PSP and CBD clinical phenotypes. They are now recognized as a range of motor and behavioral syndromes associated with underlying 4R-tauopathy neuropathology. The Movement Disorders Society PSP diagnostic criteria included clinical variants apart from the classical description, increasing diagnostic sensitivity. Meanwhile, imaging biomarkers have explored the complexity of symptoms and pathological processes related to corticobasal syndrome and CBD.

CONCLUSIONS

In recent years, several prospective or clinicopathologic studies have assessed clinical, radiological, and fluid biomarkers that have helped us gain a better understanding of the complexity of the 4R-tauopathies, mainly PSP and CBD.

Investigating the Anatomy and Microstructure of the Dentato-rubro-thalamic and Subthalamo-ponto-cerebellar Tracts in Parkinson's Disease

Cerebellar-thalamic connections play a central role in deep brain stimulation-based treatment of tremor syndromes. Here, we used diffusion Magnetic Resonance Imaging (MRI) tractography to delineate the main cerebellar peduncles as well as two main white matter tracts that connect the cerebellum with the thalamus, the dentato-rubro-thalamic tract (DRTT) and the subthalamo-ponto-cerebellar tract (SPCT). We first developed a reconstruction protocol in young healthy adults with high-resolution diffusion imaging data and then demonstrate feasibility of transferring this protocol to clinical studies using standard diffusion MRI data from a cohort of patients with Parkinson's disease (PD) and their matched healthy controls. The tracts obtained closely corresponded to the previously described anatomical pathways and features of the DRTT and the SPCT. Second, we investigated the microstructure of these tracts with fractional anisotropy (FA), radial diffusivity (RD), and hindrance modulated orientational anisotropy (HMOA) in patients with PD and healthy controls. By reducing dimensionality of both the microstructural metrics and the investigated cerebellar and cerebellar-thalamic tracts using principal component analyses, we found global differences between patients with PD and controls, suggestive of higher fractional anisotropy, lower radial diffusivity, and higher hindrance modulated orientational anisotropy in patients. However, separate analyses for each of the tracts did not yield any significant differences. Our findings contribute to the characterization of the distinct anatomical connections between the cerebellum and the diencephalon. Microstructural differences between patients and controls in the cerebellar pathways suggest involvement of these structures in PD, complementing previous functional and diffusion imaging studies.

Pragmatic Approach on Neuroimaging Techniques for the Differential Diagnosis of Parkinsonisms

Background

Rapid advances in neuroimaging technologies in the exploration of the living human brain also apply to movement disorders. However, the accurate diagnosis of Parkinson's disease (PD) and atypical parkinsonian disorders (APDs) still remains a challenge in daily practice.

Methods

We review the literature and our own experience as the Movement Disorder Society-Neuroimaging Study Group in Movement Disorders with the aim of providing a practical approach to the use of imaging technologies in the clinical setting.

Results

The enormous amount of articles published so far and our increasing recognition of imaging technologies contrast with a lack of imaging protocols and updated algorithms for differential diagnosis. The distinctive pathological involvement in different brain structures and the correlation with imaging findings obtained with magnetic resonance, positron emission tomography, or single-photon emission computed tomography illustrate what qualitative and quantitative measures may be useful in the clinical setting.

Conclusion

We delineate a pragmatic approach to discuss imaging technologies, updated imaging algorithms, and their implications for differential diagnoses in PD and APDs.

Imaging modalities in differential diagnosis of Parkinson’s disease: opportunities and challenges

Background

Parkinson’s disease (PD) diagnosis is yet largely based on the related clinical aspects. However, genetics, biomarkers, and neuroimaging studies have demonstrated a confirming role in the diagnosis, and future developments might be used in a pre-symptomatic phase of the disease.

Main text

This review provides an update on the current applications of neuroimaging modalities for PD diagnosis. A literature search was performed to find published studies that were involved on the application of different imaging modalities for PD diagnosis. An organized search of PubMed/MEDLINE, Embase, ProQuest, Scopus, Cochrane, and Google Scholar was performed based on MeSH keywords and suitable synonyms. Two researchers (TM and JPI) independently and separately performed the literature search. Our search strategy in each database was done by the following terms: ((Parkinson [Title/Abstract]) AND ((“Parkinsonian syndromes ”[Mesh]) OR Parkinsonism [Title/Abstract])) AND ((PET [Title/Abstract]) OR “SPECT”[Mesh]) OR ((Functional imaging, Transcranial sonography [Title/Abstract]) OR “Magnetic resonance spectroscopy ”[Mesh]). Database search had no limitation in time, and our last update of search was in February 2021. To have a comprehensive search and to find possible relevant articles, a manual search was conducted on the reference list of the articles and limited to those published in English.

Conclusion

Early diagnosis of PD could be vital for early management and adequate neuroprotection. Recent neuroimaging modalities such as SPECT and PET imaging using radiolabeled tracers, MRI, and CT are used to discover the disease. By the modalities, it is possible to early diagnose dopaminergic degeneration and also to differentiate PD from others parkinsonian syndromes, to monitor the natural progression of the disease and the effect of neuroprotective treatments on the progression. In this regard, functional imaging techniques have provided critical insights and roles on PD.

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.

Diffusion tensor imaging: a smart move to olfactory pathway imaging; comparative study of chronic sinonasal polyposis patients and normal control

Background

Olfaction is critically important for a good quality of life and incorporated in many physiological domains such as attention, emotion, memory, and airflow motor control. Olfactory researches have been expanded in the last decade due to close relation between the olfactory disorders and different brain diseases. Diagnosis of anosmia and hyposmia are based on history, smell tests, and physical examination which rely on the patient’s response without an objective measure of impairment. This study assessed the value of volumetry and DTI parameters as objective measurements for olfactory dysfunction.

Fourteen patients with chronic sinonasal polyposis for at least 6 months were included in this study; all of them underwent tailored MRI examination including volumetry and DTI for olfactory bulbs and tracts. The results were compared to the same number of age and sex-matched healthy control group.

Results

The study results showed that olfactory bulb and tract (OB/T) volume, FA and ADC could distinguish between patients and healthy controls. Statistically significant differences were noticed between the FA & ADC values of patient and control groups (p < 0.05) and a highly significant one was noticed as regarding the OT volume (p < 0.001).

Conclusion

MR volumetry and DTI parameters can be used as objective measurements for the olfactory dysfunction for patients with chronic sinonasal polyposis.

Frontrunner in Translation: Progressive Supranuclear Palsy

Progressive supranuclear palsy (PSP) is a four-repeat tau proteinopathy. Abnormal tau deposition is not unique for PSP and is the basic pathologic finding in some other neurodegenerative disorders such as Alzheimer's disease (AD), age-related tauopathy, frontotemporal degeneration, corticobasal degeneration, and chronic traumatic encephalopathy. While AD research has mostly been focused on amyloid beta pathology until recently, PSP as a prototype of a primary tauopathy with high clinical-pathologic correlation and a rapid course is a crucial candidate for tau therapeutic research. Several novel approaches to slow disease progression are being developed. It is expected that the benefits of translational research in this disease will extend beyond the PSP population. This article reviews advances in the diagnosis, epidemiology, pathology, hypothesized etiopathogenesis, and biomarkers and disease-modifying therapeutic approaches of PSP that is leading it to become a frontrunner in translation.

Use of Magnetic Resonance Imaging and Artificial Intelligence in Studies of Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder. It has a delitescent onset and a slow progress. The clinical manifestations of PD in patients are highly heterogeneous. Thus, PD diagnosis process is complex and mainly depends on the professional knowledge and experience of the physician. Magnetic resonance imaging (MRI) could detect the small changes in the brain of PD patients, and quantitative analysis of brain MRI may improve the clinical diagnosis efficiency. However, due to the complexity of clinical courses in PD and the high dimensionality in multimodal MRI data, traditional mathematical analysis could not effectively extract the huge information in them. Up to now, the accuracy of PD diagnosis in large sample size is still unsatisfying. As artificial intelligence (AI) is becoming more mature, varieties of statistical models and machine learning (ML) algorithms have been used for quantitative imaging data analysis to explore a diagnostic result. This review aims to state an overview of existing research recently that used statistical ML/AI methods to perform quantitative analysis of MR image data for the study of PD diagnosis. First we review the recent research in three subareas: diagnosis, differential diagnosis, and subtyping of PD. Then we described the overall workflow from MR image to classification result. Finally, we summarized a critical assessment of the current research and provide some recommendations for likely future research developments and trends.

Cortical, subcortical and brain stem connections of the cerebellum via the superior and middle cerebellar peduncle in the rat

The role of cerebellum in coordination of somatic motor activity has been studied in detailed in various species. However, experimental and clinical studies have shown the involvement of the cerebellum with various visceral and cognitive functions via its vast connections with the central nervous system. The present study aims to define the cortical and subcortical and brain stem connections of the cerebellum via the superior (SCP) and middle (MCP) cerebellar peduncle using biotinylated dextran amine (BDA) and Fluoro-Gold (FG) tracer in Wistar albino rats. 14 male albino rats received 20-50-nl pressure injections of either FG or BDA tracer into the SCP and MCP. Following 7-10 days of survival period, the animals were processed according to the related protocol for two tracers. Labelled cells and axons were documented using light and fluorescence microscope. The SCP connects cerebellum to the insular and infralimbic cortices whereas, MCP addition to the insular cortex, it also connects cerebellum to the rhinal, primary sensory, piriform and auditory cortices. Both SCP and MCP connected the cerebellum to the ventral, lateral, posterior and central, thalamic nuclei. Additionally, SCP also connects parafasicular thalamic nucleus to the cerebellum. The SCP connects cerebellum to basal ganglia (ventral pallidum and clastrum) and limbic structures (amygdaloidal nuclei and bed nucleus of stria terminalis), however, the MCP have no connections with basal ganglia or limbic structures. Both the SCP and MCP densely connects cerebellum to various brainstem structures. Attaining the knowledge of the connections of the SCP and MCP is important for the diagnosis of lesions in the MCP and SCP and would deepen current understanding of the neuronal circuit of various diseases or lesions involving the SCP and MCP.

Track density imaging in progressive supranuclear palsy: A pilot study

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder characterized by white matter (WM) changes in different supra- and infratentorial brain structures. We used track density imaging (TDI) to characterize WM microstructural alterations in patients with PSP-Richardson's Syndrome (PSP-RS). Moreover, we investigated the diagnostic utility of TDI in distinguishing patients with PSP-RS from those with Parkinson's disease and healthy controls (HC). Twenty PSP-RS patients, 21 PD patients, and 23 HC underwent a 3 T MRI diffusion-weighted (DW) imaging. Then, we combined constrained spherical deconvolution and WM probabilistic tractography to reconstruct track density maps by calculating the number of WM streamlines traversing each voxel. Voxel-wise analysis was performed to assess group differences in track density maps. A support vector machine (SVM) approach was also used to evaluate the performance of TDI for discriminating between groups. Relative to PD patients, decreases in track density in PSP-RS patients were found in brainstem, cerebellum, thalamus, corpus callosum, and corticospinal tract. Similar findings were obtained between PSP-RS patients and HC. No differences in TDI were observed between PD and HC. SVM approach based on whole-brain analysis differentiated PD patients from PSP-RS with an area under the curve (AUC) of 0.82. The AUC reached a value of 0.98 considering only the voxels belonging to the superior cerebellar peduncle. This study shows that TDI may represent a useful approach for characterizing WM alterations in PSP-RS patients. Moreover, track density decrease in PSP could be considered a new feature for the differentiation of patients with PSP-RS from those with PD.

The Cerebello-Hypothalamic and Hypothalamo-Cerebellar Pathways via Superior and Middle Cerebellar Peduncle in the Rat

The connections between the cerebellum and the hypothalamus have been well documented. However, the specific cerebellar peduncle through which the hypothalamo-cerebellar and cerebello-hypothalamic connections pass has not been demonstrated. The present study aims to define the specific cerebellar peduncle through which connects the cerebellum to specific hypothalamic nuclei. Seventeen male albino rats received 20-50-nl pressure injections of either Fluoro-Gold (FG) or biotinylated dextran amine (BDA) tracer into the superior (SCP), middle (MCP), and inferior (ICP) cerebellar peduncle. Following 7-10 days of survival period, the animals were processed according to the appropriate protocol for the two tracers used. Labeled cells and axons were documented using light or fluorescence microscopy. The present study showed connections between the hypothalamus and the cerebellum via both the SCP and the MCP but not the ICP. The hypothalamo-cerebellar connections via the SCP were from the lateral, dorsomedial, paraventricular, and posterior hypothalamic nuclei, and cerebello-hypothalamic connections were to the preoptic and lateral hypothalamic nuclei. The hypothalamo-cerebellar connections via the MCP were from the lateral, dorsomedial, ventromedial, and mammillary hypothalamic nuclei; and cerebello-hypothalamic connections were to the posterior, arcuate, and ventromedial hypothalamic nuclei. The hypothlamo-cerebellar connections were denser compared to the cerebello-hypothlamic connections via both the SCP and the MCP. The connection between the cerebellum and the hypothalamus was more prominent via the SCP than MCP. Both the hypothlamo-cerebellar and cerebello-hypothalamic connections were bilateral, with ipsilateral preponderance. Reciprocal connections were with the lateral hypothalamic nucleus via the SCP and the ventromedial nucleus via the MCP were observed. Cerebellum takes part in the higher order brain functions via its extensive connections. The knowledge of hypothalamo-cerebellar and cerebello-hypothalamic connections conveyed within the SCP and MCP can be important for the lesions involving the MCP and SCP. These connections can also change the conceptual architecture of the cerebellar circuitry and deepen current understanding.

Widespread diffusion changes differentiate Parkinson's disease and progressive supranuclear palsy

BACKGROUND

Parkinson's disease (PD) and progressive supranuclear palsy - Richardson's syndrome (PSP-RS) are often represented by similar clinical symptoms, which may challenge diagnostic accuracy. The objective of this study was to investigate and compare regional cerebral diffusion properties in PD and PSP-RS subjects and evaluate the use of these metrics for an automatic classification framework.

MATERIAL AND METHODS

Diffusion-tensor MRI datasets from 52 PD and 21 PSP-RS subjects were employed for this study. Using an atlas-based approach, regional median values of mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were measured and employed for feature selection using RELIEFF and subsequent classification using a support vector machine.

RESULTS

According to RELIEFF, the top 17 diffusion values consisting of deep gray matter structures, the brainstem, and frontal cortex were found to be especially informative for an automatic classification. A MANCOVA analysis performed on these diffusion values as dependent variables revealed that PSP-RS and PD subjects differ significantly (p < .001). Generally, PSP-RS subjects exhibit reduced FA, and increased MD, RD, and AD values in nearly all brain structures analyzed compared to PD subjects. The leave-one-out cross-validation of the support vector machine classifier revealed that the classifier can differentiate PD and PSP-RS subjects with an accuracy of 87.7%. More precisely, six PD subjects were wrongly classified as PSP-RS and three PSP-RS subjects were wrongly classified as PD.

CONCLUSION

The results of this study demonstrate that PSP-RS subjects exhibit widespread and more severe diffusion alterations compared to PD patients, which appears valuable for an automatic computer-aided diagnosis approach.

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines

BACKGROUND AND PURPOSE

In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pattern recognition techniques to distinguish among several forms of parkinsonisms (multi-class classification), based on a set of binary classifiers that discriminate each disorder from all others.

METHODS

We combine diffusion tensor imaging, proton spectroscopy and morphometric-volumetric data to obtain MR quantitative markers, which are provided to support vector machines with the aim of recognizing the different parkinsonian disorders. Feature selection is used to find the most important features for classification. We also exploit a graph-based technique on the set of quantitative markers to extract additional features from the dataset, and increase classification accuracy.

RESULTS

When graph-based features are not used, the MR markers that are most frequently automatically extracted by the feature selection procedure reflect alterations in brain regions that are also usually considered to discriminate parkinsonisms in routine clinical practice. Graph-derived features typically increase the diagnostic accuracy, and reduce the number of features required.

CONCLUSIONS

The results obtained in the work demonstrate that support vector machines applied to multimodal brain MR imaging and using graph-based features represent a novel and highly accurate approach to discriminate parkinsonisms, and a useful tool to assist the diagnosis.

White Matter Changes Correlates of Peripheral Neuroinflammation in Patients with Parkinson's Disease

Neuroinflammatory pathology has long been identified to contribute to the pathology of Parkinson disease. Early microstructural changes in white matter tracts might give a clue for earlier detection of PD. We investigated through diffusion MRI connectometry the structural correlates of white matter tracts of 81 patients with PD with whole blood neutrophil-to-lymphocyte ratio (NLR), controlling for age and sex. Diffusion data were reconstructed in the MNI space using q-space diffeomorphic reconstruction to obtain the spin distribution function. The spin distribution function (SDF) values were used in DMRI connectometry analysis. The connectometry analyses identified white matter QA of the following fibers to be correlated with NLR score after adjustment for age and sex: bilateral cingulum, body and left crus of fornix, bilateral corticospinal tract (CST), and body and splenium of corpus callosum (CC) and superior cerebellar peduncle with decreased connectivity related to NLR (FDR = 0.04542). Keeping with emerging evidence on the role of neuroinflammation in PD pathology, these results with functional relevance to prodromal Parkinson disease, bring new insights to pivotal role of peripheral inflammation in CNS neurodegeneration.

Merging Clinical and Imaging Biomarkers to Tackle Parkinson's Disease

Background

In Parkinson's disease, biomarkers represent tools that are potentially suitable for either clinical or research settings and are useful in predicting onset, confirming diagnosis, detecting progression, and evaluating response to potential disease-modifying treatments. The range of available biomarkers in Parkinson's disease is fast expanding and includes an increasing amount of laboratory, clinical, and imaging data. Indeed, the latter 2 represent the cornerstones of the diagnostic criteria for Parkinson's disease recently proposed by the International Parkinson and Movement Disorders Society Task Force on the definition of Parkinson's disease.

Methods and Results

In this review, we describe current knowledge and emerging findings on clinical (with emphasis on nonmotor symptoms) and imaging biomarkers for Parkinson's disease, with a focus on prodromal, diagnostic, and middle/advanced phases.

Conclusion

An increasing body of evidence suggests that merging clinical and imaging biomarkers through disease stages may be the best, fastest track to tackle Parkinson's disease.

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.

Radiological biomarkers for diagnosis in PSP: Where are we and where do we need to be?

PSP is a pathologically defined neurodegenerative tauopathy with a variety of clinical presentations including typical Richardson's syndrome and other variant PSP syndromes. A large body of neuroimaging research has been conducted over the past two decades, with many studies proposing different structural MRI and molecular PET/SPECT biomarkers for PSP. These include measures of brainstem, cortical and striatal atrophy, diffusion weighted and diffusion tensor imaging abnormalities, [18F] fluorodeoxyglucose PET hypometabolism, reductions in striatal dopamine imaging and, most recently, PET imaging with ligands that bind to tau. Our aim was to critically evaluate the degree to which structural and molecular neuroimaging metrics fulfill criteria for diagnostic biomarkers of PSP. We queried the PubMed, Cochrane, Medline, and PSYCInfo databases for original research articles published in English over the past 20 years using postmortem diagnosis or the NINDS-SPSP criteria as the diagnostic standard from 1996 to 2016. We define a five-level theoretical construct for the utility of neuroimaging biomarkers in PSP, with level 1 representing group-level findings, level 2 representing biomarkers with demonstrable individual-level diagnostic utility, level 3 representing biomarkers for early disease, level 4 representing surrogate biomarkers of PSP pathology, and level 5 representing definitive PSP biomarkers of PSP pathology. We discuss the degree to which each of the currently available biomarkers fit into this theoretical construct, consider the role of biomarkers in the diagnosis of Richardson's syndrome, variant PSP syndromes and autopsy confirmed PSP, and emphasize current shortfalls in the field. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Application of high-field magnetic resonance imaging in Parkinson's disease

The present study aimed to observe the structural changes of the extracorticospinal tract in Parkinson's disease (PD) using susceptibility-weighted imaging (SWI) and diffusion tensor imaging (DTI) magnetic resonance (MR) scans. The association of DTI parameters and brain-iron accumulation with PD was examined and imaging signs useful in the diagnosis of PD were explored. The study included 30 patients with PD and 30 age- and gender-matched healthy controls who underwent routine MR, SWI and DTI scans. The corrected phase (CP) values of the substantia nigra (SN), red nucleus (RN), globus pallidus (GP) and putamen (PUT) were measured, and fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were obtained. Significant differences were found in the CP values between the PD and control groups in the SN, RN and PUT, but there were no differences in other regions of interest (ROIs). The FA values of the SN and PUT in the PD group were significantly decreased compared with those of the control group, but there was no significant difference in the FA values of the GP. Furthermore, there was no significant inter-group difference in the ADC values of any ROIs. In conclusion, SWI is a method useful for evaluating brain-iron deposition in PD. Increasing iron storage levels have previously been shown to be associated with PD pathogenesis but not with the degree of PD severity. FA values may be useful for diagnosing PD, and DTI may offer some insight into PD pathomechanisms and clinical diagnosis.

Magnetic Resonance Parkinsonism Index: diagnostic accuracy of a fully automated algorithm in comparison with the manual measurement in a large Italian multicentre study in patients with progressive supranuclear palsy

OBJECTIVES

To investigate the reliability of a new in-house automatic algorithm for calculating the Magnetic Resonance Parkinsonism Index (MRPI), in a large multicentre study population of patients affected by progressive supranuclear palsy (PSP) or Parkinson's disease (PD), and healthy controls (HC), and to compare the diagnostic accuracy of the automatic and manual MRPI values.

METHODS

The study included 88 PSP patients, 234 PD patients and 117 controls. MRI was performed using both 3T and 1.5T scanners. Automatic and manual MRPI values were evaluated, and accuracy of both methods in distinguishing PSP from PD and controls was calculated.

RESULTS

No statistical differences were found between automated and manual MRPI values in all groups. The automatic MRPI values differentiated PSP from PD with an accuracy of 95 % (manual MRPI accuracy 96 %) and 97 % (manual MRPI accuracy 100 %) for 1.5T and 3T scanners, respectively.

CONCLUSION

Our study showed that the new in-house automated method for MRPI calculation was highly accurate in distinguishing PSP from PD. Our automatic approach allows a widespread use of MRPI in clinical practice and in longitudinal research studies.

KEY POINTS

• A new automatic method for calculating the MRPI is presented. • Automatic MRPI values are in good agreement with manual values. • Automatic MRPI can distinguish patients with PSP from patients with PD. • The automatic method overcomes MRPI application limitations in routine practice. • The automatic method may allow a more widespread use of MRPI.

Magnetic Resonance Parkinsonism Index and midbrain to pons ratio: Which index better distinguishes Progressive Supranuclear Palsy patients with a low degree of diagnostic certainty from patients with Parkinson Disease?

INTRODUCTION

Several studies have compared the performances of midbrain to pons area ratio (M/P) and the Magnetic Resonance Parkinsonism Index (MRPI) in distinguishing patients with Progressive Supranuclear Palsy (PSP) from those with Parkinson's disease (PD) with conflicting results. The current study aimed to compare the performance of these indexes in a well-characterized sample of PSP patients using either a manual or a fully automated approach to measure the brainstem structures involved in M/P and MRPI calculation.

METHODS

This study involved 179 patients affected by idiopathic PD, 35 patients affected by PSP (15 probable and 20 possible) and 87 healthy controls. Sensitivity, specificity, positive predictive value (PPV) and area under the curve (AUC) of MRPI and M/P in distinguishing possible and probable PSP from PD and controls were calculated.

RESULTS

No significant difference was found between manual and automated values for both MRPI and M/P. MRPI and M/P differentiated probable PSP from PD with similar performance. By contrast, MRPI showed higher sensitivity and specificity than M/P when patients with possible PSP were compared with PD (MRPI, sensitivity 100%, specificity 98.88%; M/P, sensitivity 85%, specificity 93.85%). A significant difference was also observed in AUC between MRPI and M/P in distinguishing possible PSP from PD.

CONCLUSION

Our study demonstrates that MRPI was more accurate than M/P, in differentiating patients with possible PSP from those with PD. In patients suspected of having PSP with a low level of clinic diagnostic accuracy, MRPI should be preferred to M/P for distinguishing these patients from PD.

A Fully Automated, Atlas-Based Approach for Superior Cerebellar Peduncle Evaluation in Progressive Supranuclear Palsy Phenotypes

BACKGROUND AND PURPOSE

The superior cerebellar peduncle is damaged in progressive supranuclear palsy. However, alterations differ between progressive supranuclear palsy with Richardson syndrome and progressive supranuclear palsy-parkinsonism. In this study, we propose an automated tool for superior cerebellar peduncle integrity assessment and test its performance in patients with progressive supranuclear palsy with Richardson syndrome, progressive supranuclear palsy-parkinsonism, Parkinson disease, and healthy controls.

MATERIALS AND METHODS

Structural and diffusion MRI was performed in 21 patients with progressive supranuclear palsy with Richardson syndrome, 9 with progressive supranuclear palsy-parkinsonism, 20 with Parkinson disease, and 30 healthy subjects. In a fully automated pipeline, the left and right superior cerebellar peduncles were first identified on MR imaging by using a tractography-based atlas of white matter tracts; subsequently, volume, mean diffusivity, and fractional anisotropy were extracted from superior cerebellar peduncles. These measures were compared across groups, and their discriminative power in differentiating patients was evaluated in a linear discriminant analysis.

RESULTS

Compared with those with Parkinson disease and controls, patients with progressive supranuclear palsy with Richardson syndrome showed alterations of all superior cerebellar peduncle metrics (decreased volume and fractional anisotropy, increased mean diffusivity). Patients with progressive supranuclear palsy-parkinsonism had smaller volumes than those with Parkinson disease and controls and lower fractional anisotropy than those with Parkinson disease. Patients with progressive supranuclear palsy with Richardson syndrome had significantly altered fractional anisotropy and mean diffusivity in the left superior cerebellar peduncle compared with those with progressive supranuclear palsy-parkinsonism. Discriminant analysis with the sole use of significant variables separated progressive supranuclear palsy-parkinsonism from progressive supranuclear palsy with Richardson syndrome with 70% accuracy and progressive supranuclear palsy-parkinsonism from Parkinson disease with 74% accuracy.

CONCLUSIONS

We demonstrate the feasibility of an automated approach for extracting multimodal MR imaging metrics from the superior cerebellar peduncle in healthy subjects and patients with parkinsonian. We provide evidence that structural and diffusion measures of the superior cerebellar peduncle might be valuable for computer-aided diagnosis of progressive supranuclear palsy subtypes and for differentiating patients with progressive supranuclear palsy-parkinsonism from with those with Parkinson disease.

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.

Accuracy of MR markers for differentiating Progressive Supranuclear Palsy from Parkinson's disease

Background

Advanced brain MR techniques are useful tools for differentiating Progressive Supranuclear Palsy from Parkinson's disease, although time-consuming and unlikely to be used all together in routine clinical work. We aimed to compare the diagnostic accuracy of quantitative morphometric, volumetric and DTI metrics for differentiating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease.

Methods

23 Progressive Supranuclear Palsy-Richardson's Syndrome and 42 Parkinson's disease patients underwent a standardized 1.5T brain MR protocol comprising high-resolution T1W1 and DTI sequences. Brainstem and cerebellar peduncles morphometry, automated volumetric analysis of brain deep gray matter and DTI metric analyses of specific brain structures were carried out. We determined diagnostic accuracy, sensitivity and specificity of MR-markers with respect to the clinical diagnosis by using univariate receiver operating characteristics curve analyses. Age-adjusted multivariate receiver operating characteristics analyses were then conducted including only MR-markers with a sensitivity and specificity exceeding 80%.

Results

Morphometric markers (midbrain area, pons to midbrain area ratio and MR Parkinsonism Index), DTI parameters (infratentorial structures) and volumetric analysis (thalamus, putamen and pallidus nuclei) presented moderate to high diagnostic accuracy in discriminating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease, with midbrain area showing the highest diagnostic accuracy (99%) (mean ± standard deviation: 75.87 ± 16.95 mm²vs 132.45 ± 20.94 mm², respectively; p < 0.001).

Conclusion

Although several quantitative brain MR markers provided high diagnostic accuracy in differentiating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease, the morphometric assessment of midbrain area is the best single diagnostic marker and should be routinely included in the neuroradiological work-up of parkinsonian patients.

•

We compared quantitative brain MR markers accuracy to differentiate advanced stages of PSP-RS from PD.

•

Quantitative morphometric, DTI and volumetric data showed moderate-high accuracies.

•

Midbrain area alone best discriminated advanced PSP-RS from PD (99%).

•

Midbrain area evaluation should be added to brain MR protocols for parkinsonisms.

A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis

Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations.

Computer-based magnetic resonance imaging as a tool in clinical diagnosis in neurodegenerative diseases

Magnetic resonance imaging (MRI) is one of the core elements within the differential diagnostic work-up of patients with neurodegenerative diseases such as dementia syndromes, Parkinsonian syndromes, and motor neuron diseases. Currently, computerized MRI analyses are not routinely used for individual diagnosis; however, they have improved the anatomical understanding of pathomorphological alterations in various neurodegenerative diseases by quantitative comparisons between patients and controls at the group level. For multiparametric MRI protocols, including T1-weighted MRI, diffusion-weighted imaging, and intrinsic functional connectivity MRI, the potential as a surrogate marker is a subject of investigation. The additional value of MRI with respect to diagnosis at the individual level and for future disease-modifying multicentre trials remains to be defined. Here, we give an overview of recent applications of multiparametric MRI to patients with various neurodegenerative diseases. Starting from applications at the group level, continuous progress of a transfer to individual diagnostic classification is ongoing.

Movement disorders

Movement disorders can be hypokinetic (e.g., parkinsonism), hyperkinetic, or dystonic in nature and commonly arise from altered function in nuclei of the basal ganglia or their connections. As obvious structural changes are often limited, standard imaging plays less of a role than in other neurologic disorders. However, structural imaging is indicated where clinical presentation is atypical, particularly if the disorder is abrupt in onset or remains strictly unilateral. More recent advances in magnetic resonance imaging (MRI) may allow for differentiation between Parkinson's disease and atypical forms of parkinsonism. Functional imaging can assess regional cerebral blood flow (functional MRI (fMRI), positron emission tomography (PET), or single-photon emission computed tomography (SPECT)), cerebral glucose metabolism (PET), neurochemical and neuroreceptor status (PET and SPECT), and pathologic processes such as inflammation or abnormal protein deposition (PET) (Table 49.1). Cerebral blood flow can be assessed at rest, during the performance of motor or cognitive tasks, or in response to a variety of stimuli. In appropriate situations, the correct imaging modality and/or combination of modalities can be used to detect early disease or even preclinical disease, and to monitor disease progression and the effects of disease-modifying interventions. Various approaches are reviewed here.

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.

The contribution of cerebellar proton magnetic resonance spectroscopy in the differential diagnosis among parkinsonian syndromes

INTRODUCTION

The in vivo differential diagnosis between idiopathic Parkinson's disease (PD) and atypical parkinsonian syndromes (PS), such as multiple system atrophy [MSA with a cerebellar (C) and parkinsonian (P) subtype] and progressive supranuclear palsy - Richardson's Syndrome (PSP-RS) is often challenging. Previous brain MR proton spectroscopy ((1)H-MRS) studies showed biochemical alterations in PS, despite results are conflicting. Cerebellum plays a central role in motor control and its alterations has been already demonstrated in atypical PS. The main aim of this study was to evaluate diagnostic accuracy of cerebellar (1)H-MRS in the differential diagnosis between PD and atypical PS.

METHODS

We obtained (1)H-MRS spectra from the left cerebellar hemisphere of 57 PS (21 PD, and 36 atypical PS) and 14 unaffected controls by using a 1.5 T GE scanner. N-acetyl-aspartate (NAA)/Creatine (Cr), choline-containing compounds (Cho)/Cr, myoinositol (mI)/Cr, and NAA/mI ratios were calculated.

RESULTS

NAA/Cr and NAA/mI ratios were significantly lower (p < 0.01) in atypical PS compared to PD and controls, and in MSA-C compared to PD, MSA-P, PSP-RS and controls. PSP-RS group showed reduced NAA/Cr ratios compared to PD (p < 0.05) and controls (p < 0.05), and reduced NAA/mI compared to controls (p < 0.01). NAA/Cr ratio values higher than 1.016 showed 100% sensitivity and negative predictive value, 62% positive predictive value and 64% specificity in discriminating PD.

CONCLUSION

Cerebellar biochemical alterations detected by using (1)H-MRS could represent an adjunctive diagnostic tool to improve the differential diagnosis of PS.

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.

Magnetic resonance imaging markers for early diagnosis of Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called “prodromal”, where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging (MRI) and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.

The binaural masking level difference: cortical correlates persist despite severe brain stem atrophy in progressive supranuclear palsy

Under binaural listening conditions, the detection of target signals within background masking noise is substantially improved when the interaural phase of the target differs from that of the masker. Neural correlates of this binaural masking level difference (BMLD) have been observed in the inferior colliculus and temporal cortex, but it is not known whether degeneration of the inferior colliculus would result in a reduction of the BMLD in humans. We used magnetoencephalography to examine the BMLD in 13 healthy adults and 13 patients with progressive supranuclear palsy (PSP). PSP is associated with severe atrophy of the upper brain stem, including the inferior colliculus, confirmed by voxel-based morphometry of structural MRI. Stimuli comprised in-phase sinusoidal tones presented to both ears at three levels (high, medium, and low) masked by in-phase noise, which rendered the low-level tone inaudible. Critically, the BMLD was measured using a low-level tone presented in opposite phase across ears, making it audible against the noise. The cortical waveforms from bilateral auditory sources revealed significantly larger N1m peaks for the out-of-phase low-level tone compared with the in-phase low-level tone, for both groups, indicating preservation of early cortical correlates of the BMLD in PSP. In PSP a significant delay was observed in the onset of the N1m deflection and the amplitude of the P2m was reduced, but these differences were not restricted to the BMLD condition. The results demonstrate that although PSP causes subtle auditory deficits, binaural processing can survive the presence of significant damage to the upper brain stem.

Diffusion tensor imaging parameters' changes of cerebellar hemispheres in Parkinson's disease

INTRODUCTION

Studies with diffusion tensor imaging (DTI) analysis have produced conflicting information about the involvement of the cerebellar hemispheres in Parkinson's disease (PD). We, thus, used a new approach for the analysis of DTI parameters in order to ascertain the involvement of the cerebellum in PD.

METHODS

We performed a fiber tract-based analysis of cerebellar peduncles and cerebellar hemispheres in 16 healthy subjects and in 16 PD patients with more than 5 years duration of disease, using a 3T MRI scanner and a constrained spherical deconvolution (CSD) approach for tractographic reconstructions. In addition, we performed statistical analysis of DTI parameters and fractional anisotropy (FA) XYZ direction samplings.

RESULTS

We found a statistically significant decrement of FA values in PD patients compared to controls (p < 0.05). In addition, extrapolating and analyzing FA XYZ direction samplings for each patient and each control, we found that this result was due to a stronger decrement of FA values along the Y axis (antero-posterior direction) (p < 0.01); FA changes along X and Z axes were not statistically significant (p > 0.05). We confirmed also no statistically significant differences of FA and apparent diffusion coefficient (ADC) for cerebellar peduncles in PD patients compared to healthy controls.

CONCLUSIONS

The DTI-based cerebellar abnormalities in PD could constitute an advance in the knowledge of this disease. We demonstrated a statistically significant reduction of FA in cerebellar hemispheres of PD patients compared to healthy controls. Our work also demonstrated that the use of more sophisticated approaches in the DTI parameter analysis could potentially have a clinical relevance.

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.

A review of the use of magnetic resonance imaging in Parkinson's disease

To date, the most frequently used Parkinson's disease (PD) biomarkers are the brain imaging measures of dopaminergic dysfunction using positron emission tomography and single photon emission computed tomography. However, major advances have occurred in the development of magnetic resonance imaging (MRI) biomarkers for PD in the past decade. Although conventional structural imaging remains normal in PD, advanced techniques have shown changes in the substantia nigra and the cortex. The most well-developed MRI markers in PD include diffusion imaging and iron load using T2/T2* relaxometry techniques. Other quantitative biomarkers such as susceptibility-weighted imaging for iron load, magnetization transfer and ultra-high-field MRI have shown great potential. More sophisticated techniques such as tractography and resting state functional connectivity give access to anatomical and functional connectivity changes in the brain, respectively. Brain perfusion can be assessed using non-contrast-agent techniques such as arterial spin labelling and spectroscopy gives access to metabolites concentrations. However, to date these techniques are not yet fully validated and standardized quantitative metrics for PD are still lacking. This review presents an overview of new structural, perfusion, metabolic and anatomo-functional connectivity biomarkers, their use in PD and their potential applications to improve the clinical diagnosis of Parkinsonian syndromes and the quality of clinical trials.

Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease

In Parkinson's disease (PD) the demonstration of neuropathological disturbances in nigrostriatal and extranigral brain pathways using magnetic resonance imaging remains a challenge. Here, we applied a novel diffusion-weighted imaging approach-track density imaging (TDI). Twenty-seven non-demented Parkinson's patients (mean disease duration: 5 years, mean score on the Hoehn & Yahr scale=1.5) were compared with 26 elderly controls matched for age, sex, and education level. Track density images were created by sampling each subject's spatially normalized fiber tracks in 1mm isotropic intervals and counting the fibers that passed through each voxel. Whole-brain voxel-based analysis was performed and significance was assessed with permutation testing. Statistically significant increases in track density were found in the Parkinson's patients, relative to controls. Clusters were distributed in disease-relevant areas including motor, cognitive, and limbic networks. From the lower medulla to the diencephalon and striatum, clusters encompassed the known location of the locus coeruleus and pedunculopontine nucleus in the pons, and from the substantia nigra up to medial aspects of the posterior putamen, bilaterally. The results identified in brainstem and nigrostriatal pathways show a large overlap with the known distribution of neuropathological changes in non-demented PD patients. Our results also support an early involvement of limbic and cognitive networks in Parkinson's disease.

Clinical, cognitive, and behavioural correlates of white matter damage in progressive supranuclear palsy

White matter (WM) tract alterations were assessed in patients with progressive supranuclear palsy (PSP) relative to healthy controls and patients with idiopathic Parkinson's disease (PD) to explore the relationship of WM tract damage with clinical disease severity, performance on cognitive tests, and apathy. 37 PSP patients, 41 PD patients, and 34 healthy controls underwent an MRI scan and clinical testing to evaluate physical disability, cognitive impairment, and apathy. In PSP, the contribution of WM tract damage to global disease severity and cognitive and behavioural disturbances was assessed using Random Forest analysis. Relative to controls, PSP patients showed diffusion tensor (DT) MRI abnormalities of the corpus callosum, superior cerebellar peduncle (SCP), cingulum and uncinate fasciculus bilaterally, and right inferior longitudinal fasciculus. Corpus callosum and SCP DT MRI measures distinguished PSP from PD patients with high accuracy (area under the curve ranging from 0.89 to 0.72). In PSP, DT MRI metrics of the corpus callosum and superior cerebellar peduncles were the best predictors of global disease severity scale scores. DT MRI metrics of the corpus callosum, right superior longitudinal and inferior longitudinal fasciculus, and left uncinate were the best predictors of executive dysfunction. In PSP, apathy severity was related to the damage to the corpus callosum, right superior longitudinal, and uncinate fasciculi. In conclusion, WM tract damage contributes to the motor, cognitive, and behavioural deficits in PSP. DT MRI offers markers for PSP diagnosis, assessment, and monitoring.