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

Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty

BACKGROUND

International clinical criteria are the reference for the diagnosis of degenerative parkinsonism in clinical research, but they may lack sensitivity and specificity in the early stages.

OBJECTIVES

To determine whether magnetic resonance imaging (MRI) analysis, through visual reading or machine-learning approaches, improves diagnostic accuracy compared with clinical diagnosis at an early stage in patients referred for suspected degenerative parkinsonism.

MATERIALS

Patients with initial diagnostic uncertainty between Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA), with brain MRI performed at the initial visit (V1) and available 2-year follow-up (V2), were included. We evaluated the accuracy of the diagnosis established based on: (1) the international clinical diagnostic criteria for PD, PSP, and MSA at V1 ("Clin1"); (2) MRI visual reading blinded to the clinical diagnosis ("MRI"); (3) both MRI visual reading and clinical criteria at V1 ("MRI and Clin1"), and (4) a machine-learning algorithm ("Algorithm"). The gold standard diagnosis was established by expert consensus after a 2-year follow-up.

RESULTS

We recruited 113 patients (53 with PD, 31 with PSP, and 29 with MSA). Considering the whole population, compared with clinical criteria at the initial visit ("Clin1": balanced accuracy, 66.2%), MRI visual reading showed a diagnostic gain of 14.3% ("MRI": 80.5%; P = 0.01), increasing to 19.2% when combined with the clinical diagnosis at the initial visit ("MRI and Clin1": 85.4%; P < 0.0001). The algorithm achieved a diagnostic gain of 9.9% ("Algorithm": 76.1%; P = 0.08).

CONCLUSION

Our study shows the use of MRI analysis, whether by visual reading or machine-learning methods, for early differentiation of parkinsonism. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A neoteric annotation on the advances in combination therapy for Parkinson's disease: nanocarrier-based combination approach and future anticipation. Part I: exploring theoretical insights and pharmacological advances

INTRODUCTION

Parkinson's disease (PD) is a neurological condition defined by a substantial reduction in dopamine-containing cells in the substantia nigra. Levodopa (L-Dopa) is considered the gold standard in treatment. Recent research has clearly shown that resistance to existing therapies can develop. Moreover, the involvement of multiple pathways in the nigrostriatal dopaminergic neuronal loss suggests that modifying the treatment strategy could effectively reduce this degeneration.

AREAS COVERED

This review summarizes the key concerns with treating PD patients and the combinations, aimed at effectively managing PD. Part I focuses on the clinical diagnosis at every stage of the disease as well as the pharmacological treatment strategies that are applied throughout its course. It methodically elucidates the potency of multifactorial interventions in attenuating the disease trajectory, substantiating the rationale for co-administration of dual or multiple therapeutic agents. Significant emphasis is laid on evidence-based pharmacological combinations for PD management.

EXPERT OPINION

By utilizing multiple drugs in a combination fashion, this approach can leverage the additive or synergistic effects of these agents, amplify the spectrum of treatment, and curtail the risk of side effects by reducing the dose of each drug, demonstrating significantly greater efficacy.

A neoteric annotation on the advances in combination therapy for Parkinson's disease: nanocarrier-based combination approach and future anticipation. Part II: nanocarrier design and development in focus

INTRODUCTION

The current treatment modalities available for Parkinson's disease (PD) prove inadequate due to the inherent constraints in effectively transporting bioactive compounds across the blood-brain barrier. The utilization of synergistic combinations of multiple drugs in conjunction with advanced nanotechnology, emerges as a promising avenue for the treatment of PD, offering potential breakthroughs in treatment efficacy, targeted therapy, and personalized medicine.

AREAS COVERED

This review provides a comprehensive analysis of the efficacy of multifactorial interventions for PD, simultaneously addressing the primary challenges of conventional therapies and highlighting how advanced technologies can help overcome these limitations. Part II focuses on the effectiveness of nanotechnology for improving pharmacokinetics of conventional therapies, through the synergistic use of dual or multiple therapeutic agents into a single nanoformulation. Significant emphasis is laid on the advancements toward innovative integrations, such as CRISPR/Cas9 with neuroprotective agents and stem cells, all effectively synergized with nanocarriers.

EXPERT OPINION

By using drug combinations, we can leverage their combined effects to enhance treatment efficacy and mitigate side effects through lower dosages. This article is meant to give nanocarrier-mediated co-delivery of drugs and the strategic incorporation of CRISPR/Cas9, either as an independent intervention or synergized with a neuroprotective agent.

Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here?

Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.

Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design

Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.

Case 317: Adult-Onset Leukoencephalopathy with Axonal Spheroids and Pigmented Glia

HISTORY

A 44-year-old previously healthy man with a 9-month history of progressive cognitive decline, depression, urinary incontinence, and inability to perform tasks of daily living presented to the emergency department with worsening cognitive and neuropsychiatric symptoms. He had become more distressed, and his family noticed him departing the house without closing doors, leaving water faucets running, and sending his children to school on Sundays. History taken from the patient's wife revealed that his brother had passed away in his late 30s after a slowly progressing functional and cognitive decline over the course of 5 years. No further detailed family history could be obtained. The review of systems was negative; he had no prior medical, psychiatric, or surgical history; and he denied any history of recent travel, camping, hiking, or vaccination. The patient was not taking any dietary supplements, nor was he taking any over-the-counter or prescription medication. Examination revealed vital signs were within normal limits. Neurocognitive assessment revealed a conscious, coherent, and alert patient with impaired memory and concentration. He showed poor attention, depressed mood, and restricted affect. He was unable to spell the word world forward, nor was he able to understand a request to spell it backward. The rest of the physical and neurologic examination revealed no abnormalities. Extensive laboratory work-up was conducted and included the following: toxicology screening; screening for HIV-1, HIV-2, and syphilis treponemal antibodies; COVID-19 polymerase chain reaction; and measurement of B1 and B12 levels. The results of screening were negative. Cerebrospinal fluid (CSF) assays, including CSF oligoclonal bands and CSF flow cytometry, revealed values within normal limits. CT of the brain without intravenous contrast material was performed in the emergency department to rule out acute intracranial abnormality. Multiplanar multisequence MRI of the brain without and with intravenous contrast material was ordered for further assessment. CT images of chest, abdomen, and pelvis were unremarkable (images not shown).

Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

Altered Cerebral Vasoreactivity on Transcranial Color-Coded Sonography Related to Akinetic-Rigid Phenotype of Parkinson's Disease: Interim Analysis of a Cross-Sectional Study

BACKGROUND

A correlation between worse functional outcomes in Parkinson's disease (PD) patients with cerebrovascular disease (CVD) or the Akinetic-rigid phenotype has been argued in recent studies. We aimed to evaluate the association of cerebral hemodynamics impairments, assessed by Transcranial Color-coded Doppler sonography (TCCS), on PD patients with different phenotypes of the disease and with risk factors for CVD.

METHODOLOGY

Idiopathic PD patients (n = 51) were divided into motor subtypes: Akinetic-rigid (AR) (n = 27) and Tremor-dominant (TD) (n = 24) and into two groups regarding vascular risk factors: when ≥2 were present (PDvasc) (n = 18) and <2 (PDnvasc) (n = 33). In a parallel analysis, the Fazekas scale on brain magnetic resonance imaging (MRI) was applied to a sample to assess the degree of leukoaraiosis. TCCS examinations were prospectively performed obtaining middle cerebral artery Mean Flow Velocities (Vm), Resistance Index (RI), and Pulsatility Index (PI). The Breath-Holding Index (BHI) was calculated to assess cerebrovascular reactivity (cVR). Standardized functional scales were administered (UPDRS III and Hoehn&Yahr).

RESULTS

The phenotype groups were similar in age, disease duration and demographic parameters, but there were significantly higher H&Y scores than TD group. cVR was impaired in 66.7% of AR vs. 37.5% of TD. AR group exhibited lower BHI (0.53 ± 0.31 vs. 0.91 ± 0.62; p = 0.000), lower Vm after apnea (44.3 ± 9.0 cm/s vs. 53.4 ± 11.4 cm/s; p = 0.003), higher PI (0.91 ± 0.26 vs. 0.76 ± 0.12; p = 0.000) and RI (0.58 ± 0.11 vs. 0.52 ± 0.06; p = 0.021). PDvasc group showed higher PI (0.98 vs. 0.76; p = 0.001) and higher frequency of altered cVR (72.2% vs. 42.2%; p = 0.004). There was a significant predominance of higher values on Fazekas scale in the PDvasc group. We found no difference between the Fazekas scale when comparing motor subtypes groups but there was a trend toward higher scores in the AR phenotype.

CONCLUSIONS

TCCS, a cost-effective method, displayed impaired cVR in Parkinsonian patients with risk factors for CVD with higher degree of MRI leukoaraiosis. PD patients with the AR disease phenotype also presented impaired cVR on TCCS and greater functional impairment, although with just a trend to higher scores on MRI Fazekas.

Morphometric imaging and quantitative susceptibility mapping as complementary tools in the diagnosis of parkinsonisms

Background and purpose

In the quest for in vivo diagnostic biomarkers to discriminate Parkinson's disease (PD) from progressive supranuclear palsy (PSP) and multiple system atrophy (MSA, mainly p phenotype), many advanced magnetic resonance imaging (MRI) techniques have been studied. Morphometric indices, such as the Magnetic Resonance Parkinsonism Index (MRPI), demonstrated high diagnostic value in the comparison between PD and PSP. The potential of quantitative susceptibility mapping (QSM) was hypothesized, as increased magnetic susceptibility (Δχ) was reported in the red nucleus (RN) and medial part of the substantia nigra (SNImed) of PSP patients and in the putamen of MSA patients. However, disease‐specific susceptibility values for relevant regions of interest are yet to be identified. The aims of the study were to evaluate the diagnostic potential of a multimodal MRI protocol combining morphometric and QSM imaging in patients with determined parkinsonisms and to explore its value in a population of undetermined cases.

Method

Patients with suspected degenerative parkinsonism underwent clinical evaluation, 3 T brain MRI and clinical follow‐up. The MRPI was manually calculated on T1‐weighted images. QSM maps were generated from 3D multi‐echo T2*‐weighted sequences.

Results

In determined cases the morphometric evaluation confirmed optimal diagnostic accuracy in the comparison between PD and PSP but failed to discriminate PD from MSA‐p. Significant nigral and extranigral differences were found with QSM. RN Δχ showed excellent diagnostic accuracy in the comparison between PD and PSP and good accuracy in the comparison of PD and MSA‐p. Optimal susceptibility cut‐off values of RN and SNImed were tested in undetermined cases in addition to MRPI.

Conclusions

A combined use of morphometric imaging and QSM could improve the diagnostic phase of degenerative parkinsonisms.

Clinical Aspects of the Differential Diagnosis of Parkinson's Disease and Parkinsonism

Parkinsonism is a clinical syndrome presenting with bradykinesia, tremor, rigidity, and postural instability. Nonmotor symptoms have recently been included in the parkinsonian syndrome, which was traditionally associated with motor symptoms only. Various pathologically distinct and unrelated diseases have the same clinical manifestations as parkinsonism or parkinsonian syndrome. The etiologies of parkinsonism are classified as neurodegenerative diseases related to the accumulation of toxic protein molecules or diseases that are not neurodegenerative. The former class includes Parkinson's disease (PD), multiple-system atrophy, progressive supranuclear palsy, and corticobasal degeneration. Over the past decade, clinical diagnostic criteria have been validated and updated to improve the accuracy of diagnosing these diseases. The latter class of disorders unrelated to neurodegenerative diseases are classified as secondary parkinsonism, and include drug-induced parkinsonism (DIP), vascular parkinsonism, and idiopathic normal-pressure hydrocephalus (iNPH). DIP and iNPH are regarded as reversible and treatable forms of parkinsonism. However, studies have suggested that the absence of protein accumulation in the nervous system as well as managing the underlying causes do not guarantee recovery. Here we review the differential diagnosis of PD and parkinsonism, mainly focusing on the clinical aspects. In addition, we describe recent updates to the clinical criteria of various disorders sharing clinical symptoms with parkinsonism.

Normative Data for Brainstem Structures, the Midbrain-to-Pons Ratio, and the Magnetic Resonance Parkinsonism Index

BACKGROUND AND PURPOSE

Imaging biomarkers derived from different brainstem structures are suggested to differentiate among parkinsonian disorders, but clinical implementation requires normative data. The main objective was to establish high-quality, sex-specific data for relevant brainstem structures derived from MR imaging in healthy subjects from the general population in their sixth and seventh decades of life.

MATERIALS AND METHODS

3D T1WI acquired on the same 1.5T scanner of 996 individuals (527 women) between 50 and 66 years of age from a prospective population study was used. The area of the midbrain and pons and the widths of the middle cerebellar peduncles and superior cerebellar peduncles were measured, from which the midbrain-to-pons ratio and Magnetic Resonance Parkinsonism Index [MRPI = (Pons Area / Midbrain Area) × (Middle Cerebellar Peduncles / Superior Cerebellar Peduncles)] were calculated. Sex differences in brainstem measures and correlations to age, height, weight, and body mass index were investigated.

RESULTS

Inter- and intrareliability for measuring the different brainstem structures showed good-to-excellent reliability (intraclass correlation coefficient = 0.785-0.988). There were significant sex differences for the pons area, width of the middle cerebellar peduncles and superior cerebellar peduncles, midbrain-to-pons ratio, and MRPI (all, P <  .001; Cohen D = 0.44-0.98), but not for the midbrain area (P = .985). There were significant very weak-to-weak correlations between several of the brainstem measures and age, height, weight, and body mass index in both sexes. However, no systematic difference in distribution caused by these variables was found, and because age had the highest and most consistent correlations, age-/sex-specific percentiles for the brainstem measures were created.

CONCLUSIONS

We present high-quality, sex-specific data and age-/sex-specific percentiles for the mentioned brainstem measures. These normative data can be implemented in the neuroradiologic work-up of patients with suspected brainstem atrophy to avoid the risk of misdiagnosis.

Regional Selectivity of Neuromelanin Changes in the Substantia Nigra in Atypical Parkinsonism

BACKGROUND

Neurodegeneration in the substantia nigra pars compacta (SNc) in parkinsonian syndromes may affect the nigral territories differently.

OBJECTIVE

The objective of this study was to investigate the regional selectivity of neurodegenerative changes in the SNc in patients with Parkinson's disease (PD) and atypical parkinsonism using neuromelanin-sensitive magnetic resonance imaging (MRI).

METHODS

A total of 22 healthy controls (HC), 38 patients with PD, 22 patients with progressive supranuclear palsy (PSP), 20 patients with multiple system atrophy (MSA, 13 with the parkinsonian variant, 7 with the cerebellar variant), 7 patients with dementia with Lewy body (DLB), and 4 patients with corticobasal syndrome were analyzed. volume and signal-to-noise ratio (SNR) values of the SNc were derived from neuromelanin-sensitive MRI in the whole SNc. Analysis of signal changes was performed in the sensorimotor, associative, and limbic territories of the SNc.

RESULTS

SNc volume and corrected volume were significantly reduced in PD, PSP, and MSA versus HC. Patients with PSP had lower volume, corrected volume, SNR, and contrast-to-noise ratio than HC and patients with PD and MSA. Patients with PSP had greater SNR reduction in the associative region than HC and patients with PD and MSA. Patients with PD had reduced SNR in the sensorimotor territory, unlike patients with PSP. Patients with MSA did not differ from patients with PD.

CONCLUSIONS

This study provides the first MRI comparison of the topography of neuromelanin changes in parkinsonism. The spatial pattern of changes differed between PSP and synucleinopathies. These nigral topographical differences are consistent with the topography of the extranigral involvement in parkinsonian syndromes. © 2022 International Parkinson and Movement Disorder Society.

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.

Imaging of Substantia Nigra in Parkinson's Disease: A Narrative Review

Parkinson's disease (PD) is a progressive neurodegenerative disorder, characterized by motor and non-motor symptoms due to the degeneration of the pars compacta of the substantia nigra (SNc) with dopaminergic denervation of the striatum. Although the diagnosis of PD is principally based on a clinical assessment, great efforts have been expended over the past two decades to evaluate reliable biomarkers for PD. Among these biomarkers, magnetic resonance imaging (MRI)-based biomarkers may play a key role. Conventional MRI sequences are considered by many in the field to have low sensitivity, while advanced pulse sequences and ultra-high-field MRI techniques have brought many advantages, particularly regarding the study of brainstem and subcortical structures. Nowadays, nigrosome imaging, neuromelanine-sensitive sequences, iron-sensitive sequences, and advanced diffusion weighted imaging techniques afford new insights to the non-invasive study of the SNc. The use of these imaging methods, alone or in combination, may also help to discriminate PD patients from control patients, in addition to discriminating atypical parkinsonian syndromes (PS). A total of 92 articles were identified from an extensive review of the literature on PubMed in order to ascertain the-state-of-the-art of MRI techniques, as applied to the study of SNc in PD patients, as well as their potential future applications as imaging biomarkers of disease. Whilst none of these MRI-imaging biomarkers could be successfully validated for routine clinical practice, in achieving high levels of accuracy and reproducibility in the diagnosis of PD, a multimodal MRI-PD protocol may assist neuroradiologists and clinicians in the early and differential diagnosis of a wide spectrum of neurodegenerative disorders.

MRI Signs of Parkinson's Disease and Atypical Parkinsonism

BACKGROUND

 Diagnosis of Parkinson's disease and atypical parkinsonism is based on clinical evaluation of the patient's symptoms and on magnetic resonance imaging (MRI) of the brain, which can be supplemented by nuclear medicine techniques. MRI plays a leading role in the differentiation between Parkinson's disease and atypical parkinsonism. While atypical parkinsonism is characterized by relatively specific MRI signs, imaging of Parkinson's disease previously lacked such signs. However, high-field MRI and new optimized MRI sequences now make it possible to define specific MRI signs of Parkinson's disease and have significant potential regarding differentiated imaging, early diagnosis, and imaging of disease progression.

METHODS

 PubMed was selectively searched for literature regarding the definition and discussion of specific MRI signs of Parkinson's disease, as well as the most common types of atypical parkinsonism with a leading motor component. No time frame was set, but the search was particularly focused on current literature.

RESULTS

 This review article discusses the different MRI signs of Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. The pathogenesis of the MRI signs is described, and imaging examples are given. The technical aspects of image acquisition are briefly defined, and the different signs are discussed and compared with regard to their diagnostic significance according to current literature.

CONCLUSION

 The MRI signs of Parkinson's disease, which can be defined with high-field MRI and new optimized MRI sequences, enable differentiated structural image interpretation and consecutive diagnostic workup. Despite the fact that the signs are in need of further validation by bigger studies, they have the potential to achieve significant diagnostic relevance regarding the imaging of Parkinson's disease and atypical parkinsonism.

KEY POINTS

  · High-field MRI and specialized sequences make it possible to define specific MRI signs for neurodegenerative disorders. · Cerebral alterations can be detected in prodromal stages of Parkinson's disease. · The combination of specific MRI signs makes it possible to differentiate between Parkinson's disease and atypical parkinsonism.

CITATION FORMAT

· Aludin S, Schmill LA. MRI Signs of Parkinson's Disease and Atypical Parkinsonism. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1460-8795.

Update on neuroimaging for categorization of Parkinson's disease and atypical parkinsonism

PURPOSE OF REVIEW

Differential diagnosis of Parkinsonism may be difficult. The objective of this review is to present the work of the last three years in the field of imaging for diagnostic categorization of parkinsonian syndromes focusing on progressive supranuclear palsy (PSP) and multiple system atrophy (MSA).

RECENT FINDINGS

Two main complementary approaches are being pursued. The first seeks to develop and validate manual qualitative or semi-quantitative imaging markers that can be easily used in clinical practice. The second is based on quantitative measurements of magnetic resonance imaging abnormalities integrated in a multimodal approach and in automatic categorization machine learning tools.

SUMMARY

These two complementary approaches obtained high diagnostic around 90% and above in the classical Richardson form of PSP and probable MSA. Future work will determine if these techniques can improve diagnosis in other PSP variants and early forms of the diseases when all clinical criteria are not fully met.

Radiomics on routine T1-weighted MRI can delineate Parkinson's disease from multiple system atrophy and progressive supranuclear palsy

OBJECTIVES

This study aimed to explore the feasibility of radiomics features extracted from T1-weighted MRI images to differentiate Parkinson's disease (PD) from atypical parkinsonian syndromes (APS).

METHODS

Radiomics features were computed from T1 images of 65 patients with PD, 61 patients with APS (31: progressive supranuclear palsy and 30: multiple system atrophy), and 75 healthy controls (HC). These features were extracted from 19 regions of interest primarily from subcortical structures, cerebellum, and brainstem. Separate random forest classifiers were applied to classify different groups based on a reduced set of most important radiomics features for each classification as determined by the random forest-based recursive feature elimination by cross-validation method.

RESULTS

The PD vs HC classifier illustrated an accuracy of 70%, while the PD vs APS classifier demonstrated a superior test accuracy of 92%. Moreover, a 3-way PD/MSA/PSP classifier performed with 96% accuracy. While first-order and texture-based differences like Gray Level Co-occurrence Matrix (GLCM) and Gray Level Difference Matrix for the substantia nigra pars compacta and thalamus were highly discriminative for PD vs HC, textural features mainly GLCM of the ventral diencephalon were highlighted for APS vs HC, and features extracted from the ventral diencephalon and nucleus accumbens were highlighted for the classification of PD and APS.

CONCLUSIONS

This study establishes the utility of radiomics to differentiate PD from APS using routine T1-weighted images. This may aid in the clinical diagnosis of PD and APS which may often be indistinguishable in early stages of disease.

KEY POINTS

• Radiomics features were extracted from T1-weighted MRI images. • Parkinson's disease and atypical parkinsonian syndromes were classified at an accuracy of 92%. • This study establishes the utility of radiomics to differentiate Parkinson's disease and atypical parkinsonian syndromes using routine T1-weighted images.

Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.

Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

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