Neuroimaging Advances in Parkinson's Disease and Atypical Parkinsonian Syndromes

Complemental Value of Microstructural and Macrostructural MRI in the Discrimination of Neurodegenerative Parkinson Syndromes

PURPOSE

Various MRI-based techniques were tested for the differentiation of neurodegenerative Parkinson syndromes (NPS); the value of these techniques in direct comparison and combination is uncertain. We thus compared the diagnostic performance of macrostructural, single compartmental, and multicompartmental MRI in the differentiation of NPS.

METHODS

We retrospectively included patients with NPS, including 136 Parkinson's disease (PD), 41 multiple system atrophy (MSA) and 32 progressive supranuclear palsy (PSP) and 27 healthy controls (HC). Macrostructural tissue probability values (TPV) were obtained by CAT12. The microstructure was assessed using a mesoscopic approach by diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI). After an atlas-based read-out, a linear support vector machine (SVM) was trained on a training set (n = 196) and validated in an independent test cohort (n = 40). The diagnostic performance of the SVM was compared for different inputs individually and in combination.

RESULTS

Regarding the inputs separately, we observed the best diagnostic performance for DMI. Overall, the combination of DMI and TPV performed best and correctly classified 88% of the patients. The corresponding area under the receiver operating characteristic curve was 0.87 for HC, 0.97 for PD, 1.0 for MSA, and 0.99 for PSP.

CONCLUSION

We were able to demonstrate that (1) MRI parameters that approximate the microstructure provided substantial added value over conventional macrostructural imaging, (2) multicompartmental biophysically motivated models performed better than the single compartmental DTI and (3) combining macrostructural and microstructural information classified NPS and HC with satisfactory performance, thus suggesting a complementary value of both approaches.

Differentiating Prodromal Dementia with Lewy Bodies from Prodromal Alzheimer's Disease: A Pragmatic Review for Clinicians

This pragmatic review synthesises the current understanding of prodromal dementia with Lewy bodies (pDLB) and prodromal Alzheimer's disease (pAD), including clinical presentations, neuropsychological profiles, neuropsychiatric symptoms, biomarkers, and indications for disease management. The core clinical features of dementia with Lewy bodies (DLB)-parkinsonism, complex visual hallucinations, cognitive fluctuations, and REM sleep behaviour disorder are common prodromal symptoms. Supportive clinical features of pDLB include severe neuroleptic sensitivity, as well as autonomic and neuropsychiatric symptoms. The neuropsychological profile in mild cognitive impairment attributable to Lewy body pathology (MCI-LB) tends to include impairment in visuospatial skills and executive functioning, distinguishing it from MCI due to AD, which typically presents with impairment in memory. pDLB may present with cognitive impairment, psychiatric symptoms, and/or recurrent episodes of delirium, indicating that it is not necessarily synonymous with MCI-LB. Imaging, fluid and other biomarkers may play a crucial role in differentiating pDLB from pAD. The current MCI-LB criteria recognise low dopamine transporter uptake using positron emission tomography or single photon emission computed tomography (SPECT), loss of REM atonia on polysomnography, and sympathetic cardiac denervation using meta-iodobenzylguanidine SPECT as indicative biomarkers with slowing of dominant frequency on EEG among others as supportive biomarkers. This review also highlights the emergence of fluid and skin-based biomarkers. There is little research evidence for the treatment of pDLB, but pharmacological and non-pharmacological treatments for DLB may be discussed with patients. Non-pharmacological interventions such as diet, exercise, and cognitive stimulation may provide benefit, while evaluation and management of contributing factors like medications and sleep disturbances are vital. There is a need to expand research across diverse patient populations to address existing disparities in clinical trial participation. In conclusion, an early and accurate diagnosis of pDLB or pAD presents an opportunity for tailored interventions, improved healthcare outcomes, and enhanced quality of life for patients and care partners.

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

Fluid and tissue biomarkers in Parkinson's disease: Immunodetection or seed amplification? Central or peripheral?

Over the last two decades there have been meaningful developments on biomarkers of neurodegenerative diseases, extensively (but not solely) focusing on their proteinopathic nature. Accordingly, in Alzheimer's disease determination of levels of total and phosphorylated tau (τ and p-τ, usually p-τ181) along with amyloid-beta1-42 (Aβ1-42) by immunodetection in cerebrospinal fluid (CSF) and currently even in peripheral blood, have been widely accepted and introduced to routine diagnosis. In the case of Parkinson's disease, α-synuclein as a potential biomarker (both for diagnosis and progression tracking) has proved more elusive under the immunodetection approach. In recent years, the emergence of the so-called seed amplification assays is proving to be a game-changer, with mounting evidence under different technical approaches and using a variety of biofluids or tissues, yielding promising diagnostic accuracies. Currently the least invasive but at once more reliable source of biosamples and techniques are being sought. Here we overview these advances.

Polypathologic Associations with Gray Matter Atrophy in Neurodegenerative Disease

Mixed pathologies are common in neurodegenerative disease; however, antemortem imaging rarely captures copathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer's pathologies. We leveraged a dataset comprising antemortem MRI and postmortem histopathology to assess polypathologic associations with atrophy in a clinically heterogeneous sample of 125 human dementia patients (41 female, 84 male) with T1-weighted MRI ≤ 5 years before death and postmortem ordinal ratings of amyloid-[Formula: see text], tau, TDP-43, and [Formula: see text]-synuclein. Regional volumes were related to pathology using linear mixed-effects models; approximately 25% of data were held out for testing. We contrasted a polypathologic model comprising independent factors for each proteinopathy with two alternatives: a model that attributed atrophy entirely to the protein(s) associated with the patient's primary diagnosis and a protein-agnostic model based on the sum of ordinal scores for all pathology types. Model fits were evaluated using log-likelihood and correlations between observed and fitted volume scores. Additionally, we performed exploratory analyses relating atrophy to gliosis, neuronal loss, and angiopathy. The polypathologic model provided superior fits in the training and testing datasets. Tau, TDP-43, and [Formula: see text]-synuclein burden were inversely associated with regional volumes, but amyloid-[Formula: see text] was not. Gliosis and neuronal loss explained residual variance in and mediated the effects of tau, TDP-43, and [Formula: see text]-synuclein on atrophy. Regional brain atrophy reflects not only the primary molecular pathology but also co-occurring proteinopathies; inflammatory immune responses may independently contribute to degeneration. Our findings underscore the importance of antemortem biomarkers for detecting mixed pathology.

New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review

Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.

How should we be using biomarkers in trials of disease modification in Parkinson's disease?

The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.

Using 18F-AV-133 VMAT2 PET Imaging to Monitor Progressive Nigrostriatal Degeneration in Parkinson Disease

BACKGROUND AND OBJECTIVES

There are limited validated biomarkers in Parkinson disease (PD) which substantially hinders the ability to monitor disease progression and consequently measure the efficacy of disease-modifying treatments. Imaging biomarkers, such as vesicular monoamine transporter type 2 (VMAT2) PET, enable enhanced diagnostic accuracy and detect early neurodegenerative changes associated with prodromal PD. This study sought to assess whether 18F-AV-133 VMAT2 PET is sensitive enough to monitor and quantify disease progression over a 2-year window.

METHODS

18F-AV-133 PET scans were performed on participants with PD and REM sleep behavior disorder (RBD) and neurologic controls (NC). All participants were scanned twice ∼26 months apart. Regional tracer retention was calculated with a primary visual cortex reference region and expressed as the standard uptake volume ratio. Regions of interest included caudate, anterior, and posterior putamen. At the time of scanning, participants underwent clinical evaluation including UPDRSMOTOR test, Sniffin' Sticks, and Hospital Anxiety and Depression Score.

RESULTS

Over the 26-month interval, a significant decline in PET signal was observed in all 3 regions in participants with PD (N = 26) compared with NC (N = 12), consistent with a decrease in VMAT2 level and ongoing neurodegeneration. Imaging trajectory calculations suggest that the neurodegeneration in PD occurs over ∼33 years [CI: 27.2-39.5], with ∼10.5 years [CI: 9.1-11.3] of degeneration in the posterior putamen before it becomes detectable on a VMAT2 PET scan, a further ∼6.5 years [CI: 1.6-12.7] until symptom onset, and a further ∼3 years [CI: 0.3-8.7] until clinical diagnosis.

DISCUSSION

Over a 2-year period, 18F-AV-133 VMAT2 PET was able to detect progression of nigrostriatal degeneration in participants with PD, and it represents a sensitive tool to identify individuals at risk of progression to PD, which are currently lacking using clinical readouts. Trajectory models propose that there is nigrostriatal degeneration occurring for 20 years before clinical diagnosis. These data demonstrate that VMAT2 PET provides a sensitive measure to monitor neurodegenerative progression of PD which has implications for PD diagnostics and subsequently clinical trial patient stratification and monitoring.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that VMAT2 PET can detect patients with Parkinson disease and quantify progression over a 2-year window.

Comparative study of bilateral putamen for patients with severe Parkinson's disease detected by 1H magnetic resonance spectroscopy

Background

The diagnosis of Parkinson's disease (PD) is challenging because the clinical symptoms overlap with other neurodegenerative diseases. The discovery of reliable biomarkers is highly expected to facilitate clinical diagnosis. Through the analysis of the 1H magnetic resonance spectroscopy (1H-MRS) in the putamen, the purpose of the study was to discuss the possibility of the difference in metabolite concentrations between the left and right putamen as biomarkers for patients with severe PD.

Methods

We collected 1H-MRS of unilateral or bilateral putamen from 41 patients and used the independent sample t-test and paired t-test to analyze 4 metabolite concentrations, including choline (Cho), total N-acetyl aspartate (tNAA), total creatine (tCr), and combined glutamate and glutamine; Bonferroni correction was used to correct P values for multiple comparisons. We designed 4 controlled experiments as follows: (I) PD patients versus healthy controls (HCs) in the left putamen; (II) PD patients versus HCs in the right putamen; (III) the left putamen versus the right putamen for PD patients; and (IV) the left putamen versus the right putamen for HCs.

Results

No statistically significant differences (P>0.05) were detected among 4 metabolites in the ipsilateral and bilateral putamen for the PD and HCs groups, except for tCr in the left putamen (PD 6.426±0.557, HCs 6.026±0.460, P=0.046) for ipsilateral comparisons.

Conclusions

In the bilateral putamen of severe PD patients, there was no statistically significant difference in the 4 metabolites. The difference (P<0.05) in tCr in the left putamen might be a potential biomarker to distinguish HCs from severe patients in clinic. This might provide a reference for the clinical diagnosis and acquisition strategy of 1H-MRS in severe PD.

Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy

BACKGROUND

To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS).

OBJECTIVES

To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice.

METHODS

This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern.

RESULTS

Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample.

CONCLUSIONS

Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fluorescence-tagged salivary small extracellular vesicles as a nanotool in early diagnosis of Parkinson's disease

BACKGROUND

Parkinson's disease is generally asymptomatic at earlier stages. At an early stage, there is an extensive progression in the neuropathological hallmarks, although, at this stage, diagnosis is not possible with currently available diagnostic methods. Therefore, the pressing need is for susceptibility risk biomarkers that can aid in better diagnosis and therapeutics as well can objectively serve to measure the endpoint of disease progression. The role of small extracellular vesicles (sEV) in the progression of neurodegenerative diseases could be potent in playing a revolutionary role in biomarker discovery.

METHODS

In our study, the salivary sEV were efficiently isolated by chemical precipitation combined with ultrafiltration from subjects (PD = 70, healthy controls = 26, and prodromal PD = 08), followed by antibody-based validation with CD63, CD9, GAPDH, Flotillin-1, and L1CAM. Morphological characterization of the isolated sEV through transmission electron microscopy. The quantification of sEV was achieved by fluorescence (lipid-binding dye-labeled) nanoparticle tracking analysis and antibody-based (CD63 Alexa fluor 488 tagged sEV) nanoparticle tracking analysis. The total alpha-synuclein (α-synTotal) in salivary sEVs cargo was quantified by ELISA. The disease severity staging confirmation for n = 18 clinically diagnosed Parkinson's disease patients was done by 99mTc-TRODAT-single-photon emission computed tomography.

RESULTS

We observed a significant increase in total sEVs concentration in PD patients than in the healthy control (HC), where fluorescence lipid-binding dye-tagged sEV were observed to be higher in PD (p = 0.0001) than in the HC using NTA with a sensitivity of 94.34%. In the prodromal PD cases, the fluorescence lipid-binding dye-tagged sEV concentration was found to be higher (p = 0.008) than in HC. This result was validated through anti-CD63 tagged sEV (p = 0.0006) with similar sensitivity of 94.12%. We further validated our findings with the ELISA based on α-synTotal concentration in sEV, where it was observed to be higher in PD (p = 0.004) with a sensitivity of 88.24%. The caudate binding ratios in 99mTc-TRODAT-SPECT represent a positive correlation with sEV concentration (r = 0.8117 with p = 0.0112).

CONCLUSIONS

In this study, for the first time, we have found that the fluorescence-tagged sEV has the potential to screen the progression of disease with clinically acceptable sensitivity and can be a potent early detection method for PD.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

The Role of the Evans Index and the Maximal Width of the Frontal Horns of the Lateral Ventricles in the Diagnostic Imaging of Progressive Supranuclear Palsy and Multiple-System Atrophy

Progressive Supranuclear Palsy and Multiple-System Atrophy are entities within the spectrum of atypical parkinsonism. The role of imaging methods in the diagnosis and differentiation between PSP and MSA is limited and Magnetic Resonance Imaging (MRI) is currently used as a reference modality. In this study, the authors examined a group of patients with atypical parkinsonism using a 1.5 T MRI system and aimed to find simple and repeatable measurements that may be useful to distinguish between these diseases. The results of the study indicate that the maximal width of the frontal horns of the lateral ventricles and Evans' Index may, to some extent, be useful as basic and simple measurements in the diagnostic imaging of patients with atypical parkinsonism.

The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis

Background

In recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD.

Methods

We systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson's disease and various atypical parkinsonism syndromes (APS).

Results

Twenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833-0.891), 0.91 (95% CI: 0.86-0.94), and 0.93 (95% CI: 0.87-0.96) in the training set, and 0.871 (95% CI: 0.853-0.890), 0.86 (95% CI: 0.81-0.89), and 0.87 (95% CI: 0.83-0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843-0.889), 0.86 (95% CI: 0.84-0.88), and 0.80 (95% CI: 0.75-0.84) in the training set, and 0.879 (95% CI: 0.854-0.903), 0.87 (95% CI: 0.85-0.89), and 0.82 (95% CI: 0.77-0.86) in the validation set, respectively.

Conclusion

Radiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson's disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson's disease and related fields.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197.

Structural magnetic resonance imaging in Parkinson disease with freezing of gait: A systematic review of literature

Objective

This review aims to the existing structural neuroimaging literature in Parkinson disease presenting with freezing of gait. The summary of this article provides an opportunity for a better understanding of the structural findings of freezing of gait in Parkinson disease based on MRI.

Methods

This systematic review of literature follows the procedures as described by the guideline of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

Results

Initial searches yielded 545 documents. After exclusions, 11 articles were included into our study. Current findings of structural MRI on freezing of gait in Parkinson disease are associated with structural damage between sensorimotor-related cortical grey matter structures and thalamus, but not cerebellum and smaller systems, as well as extensive injuries on white matter connecting between those structures.

Conclusion

Current findings of structural MRI on freezing of gait in Parkinson disease are associated with structural damage between sensorimotor-related cortical grey matter structures and thalamus, but not cerebellum and smaller systems, as well as extensive injuries on white matter connecting between those structures.

Imaging Criteria for the Diagnosis of Progressive Supranuclear Palsy: Supportive or Mandatory?

We present the case of a 54-year-old male, without any significant medical history, who insidiously developed speech disturbances and walking difficulties, accompanied by backward falls. The symptoms progressively worsened over time. The patient was initially diagnosed with Parkinson's disease; however, he failed to respond to standard therapy with Levodopa. He came to our attention for worsening postural instability and binocular diplopia. A neurological exam was highly suggestive of a Parkinson-plus disease, most likely progressive supranuclear gaze palsy. Brain MRI was performed and revealed moderate midbrain atrophy with the characteristic "hummingbird" and "Mickey mouse" signs. An increased MR parkinsonism index was also noted. Based on all clinical and paraclinical data, a diagnosis of probable progressive supranuclear palsy was established. We review the main imaging features of this disease and their current role in diagnosis.

Large-scale activation likelihood estimation meta-analysis of parkinsonian disorders

Parkinsonism is a feature of several neurodegenerative disorders, including Parkinson's disease, progressive supranuclear palsy, corticobasal syndrome and multiple system atrophy. Neuroimaging studies have yielded insights into parkinsonian disorders; however, due to variability in results, the brain regions consistently implicated in these disorders remain to be characterized. The aim of this meta-analysis was to identify consistent brain abnormalities in individual parkinsonian disorders (Parkinson's disease, progressive supranuclear palsy, corticobasal syndrome and multiple system atrophy) and to investigate any shared abnormalities across disorders. A total of 44 591 studies were systematically screened following searches of two databases. A series of whole-brain activation likelihood estimation meta-analyses were performed on 132 neuroimaging studies (69 Parkinson's disease; 23 progressive supranuclear palsy; 17 corticobasal syndrome; and 23 multiple system atrophy) utilizing anatomical MRI, perfusion or metabolism PET and single-photon emission computed tomography. Meta-analyses were performed in each parkinsonian disorder within each imaging modality, as well as across all included disorders. Results in progressive supranuclear palsy and multiple system atrophy aligned with current imaging markers for diagnosis, encompassing the midbrain, and brainstem and putamen, respectively. PET imaging studies of patients with Parkinson's disease most consistently reported abnormality of the middle temporal gyrus. No significant clusters were identified in corticobasal syndrome. When examining abnormalities shared across all four disorders, the caudate was consistently reported in MRI studies, whilst the thalamus, inferior frontal gyrus and middle temporal gyri were commonly implicated by PET. To our knowledge, this is the largest meta-analysis of neuroimaging studies in parkinsonian disorders and the first to characterize brain regions implicated across parkinsonian disorders.

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

The Future of Functional Neuroimaging in Parkinsonism: Prove It!

This Viewpoint discusses the current lack of evidence of functional neuroimaging affecting patient outcomes in parkinsonian conditions.

Dopaminergic imaging in degenerative parkinsonisms, an established clinical diagnostic tool

Parkinson's disease (PD) and other neurodegenerative parkinsonisms are characterised by loss of striatal dopaminergic neurons. Dopamine functional deficits can be measured in vivo using positron emission tomography (PET) and single-photon emission computed tomography (SPECT) ligands assessing either presynaptic (e.g. dopamine synthesis and storage, transporter density) or postsynaptic terminals (i.e. D2 receptors availability). Nuclear medicine imaging thus helps the clinician to separate degenerative forms of parkinsonism with other neurological conditions, e.g. essential tremor or drug-induced parkinsonism. With the present study, we aimed at summarizing the current evidence about dopaminergic molecular imaging in the diagnostic evaluation of PD, atypical parkinsonian syndromes and dementia with Lewy bodies (DLB), as well as its potential to distinguish these conditions and to estimate disease progression. In fact, PET/SPECT methods are clinically validated and have been increasingly integrated into diagnostic guidelines (e.g. for PD and DLB). In addition, there is novel evidence on the classification properties of extrastriatal signal. Finally, dopamine imaging has an outstanding potential to detect neurodegeneration at the premotor stage, including REM-sleep behavior disorder and olfactory loss. Therefore, inclusion of subjects at an early stage for clinical trials can largely benefit from a validated in vivo biomarker such as presynaptic dopamine pathways PET/SPECT assessment.

Disrupted topological organization of resting-state functional brain networks in Parkinson's disease patients with glucocerebrosidase gene mutations

PURPOSE

The mutations of the glucocerebrosidase (GBA) gene are the greatest genetic risk factor for Parkinson's disease (PD). The mechanism underlying the association between GBA mutations and PD has not been fully elucidated.

METHODS

Using resting-state functional magnetic resonance imaging and graph theory analysis to investigate the disrupted topological organization in PD patients with GBA mutation (GBA-PD). Eleven GBA-PD patients, 11 noncarriers with PD, and 18 healthy controls (HCs) with a similar age and sex distribution were recruited. Individual whole-brain functional connectome was constructed, and the global and nodal topological disruptions were calculated among groups. Partial correlation analyses between the clinical features of patients with PD and topological alterations were performed.

RESULTS

The GBA-PD group showed prominently decreased characteristic path length (L_p) and increased global efficiency (E_g) compared to HCs at the global level; a significantly increased nodal betweenness centrality in the medial prefrontal cortex (mPFC) and precuneus within the default mode network, and precentral gyrus within the sensorimotor network, while a significantly decreased betweenness centrality in nodes within the cingulo-opercular network compared to the noncarrier group at the regional level. The altered nodal betweenness centrality of mPFC was positively correlated with fatigue severity scale scores in all patients with PD.

CONCLUSION

The preliminary pilot study found that GBA-PD patients had a higher functional integration at the global level. The nodal result of the mPFC is congruent with the potential fatigue pathology in PD and is suggestive of a profound effect of GBA mutations on the clinical fatigue in patients with PD.

Outcome Measures for Disease-Modifying Trials in Parkinson's Disease: Consensus Paper by the EJS ACT-PD Multi-Arm Multi-Stage Trial Initiative

BACKGROUND

Multi-arm, multi-stage (MAMS) platform trials can accelerate the identification of disease-modifying treatments for Parkinson's disease (PD) but there is no current consensus on the optimal outcome measures (OM) for this approach.

OBJECTIVE

To provide an up-to-date inventory of OM for disease-modifying PD trials, and a framework for future selection of OM for such trials.

METHODS

As part of the Edmond J Safra Accelerating Clinical Trials in Parkinson Disease (EJS ACT-PD) initiative, an expert group with Patient and Public Involvement and Engagement (PPIE) representatives' input reviewed and evaluated available evidence on OM for potential use in trials to delay progression of PD. Each OM was ranked based on aspects such as validity, sensitivity to change, participant burden and practicality for a multi-site trial. Review of evidence and expert opinion led to the present inventory.

RESULTS

An extensive inventory of OM was created, divided into: general, motor and non-motor scales, diaries and fluctuation questionnaires, cognitive, disability and health-related quality of life, capability, quantitative motor, wearable and digital, combined, resource use, imaging and wet biomarkers, and milestone-based. A framework for evaluation of OM is presented to update the inventory in the future. PPIE input highlighted the need for OM which reflect their experience of disease progression and are applicable to diverse populations and disease stages.

CONCLUSION

We present a range of OM, classified according to a transparent framework, to aid selection of OM for disease-modifying PD trials, whilst allowing for inclusion or re-classification of relevant OM as new evidence emerges.

Magnetic resonance imaging modalities aid in the differential diagnosis of atypical parkinsonian syndromes

Accurate and timely diagnosis of atypical parkinsonian syndromes (APS) remains a challenge. Especially early in the disease course, the clinical manifestations of the APS overlap with each other and with those of idiopathic Parkinson's disease (PD). Recent advances in magnetic resonance imaging (MRI) technology have introduced promising imaging modalities to aid in the diagnosis of APS. Some of these MRI modalities are also included in the updated diagnostic criteria of APS. Importantly, MRI is safe for repeated use and more affordable and accessible compared to nuclear imaging. These advantages make MRI tools more appealing for diagnostic purposes. As the MRI field continues to advance, the diagnostic use of these techniques in APS, alone or in combination, are expected to become commonplace in clinical practice.

Synucleinopathies

The α-synucleinopathies include pure autonomic failure, multiple system atrophy, dementia with Lewy bodies, and Parkinson disease. The past two decades have witnessed significant advances in the diagnostic strategies and symptomatic treatment of motor and nonmotor symptoms of the synucleinopathies. This chapter provides an in-depth review of the pathophysiology, pathology, genetic, epidemiology, and clinical and laboratory autonomic features that distinguish the different synucleinopathies with an emphasis on autonomic failure as a common feature. The treatment of the different synucleinopathies is discussed along with the proposal for multidisciplinary, individualized care models that optimally address the various symptoms. There is an urgent need for clinical scientific studies addressing patients at risk of developing synucleinopathies and the investigation of disease mechanisms, biomarkers, potential disease-modifying therapies, and further advancement of symptomatic treatments for motor and nonmotor symptoms.

Future Prospects in Parkinson's Disease Diagnosis and Treatment

Parkinson's disease (PD) is a neurodegenerative disease with a rapidly increasing incidence and prevalence. Although it affects more than 6 million people worldwide, it is predicted to be doubled by 2040. Current criteria used in the diagnosis of PD include the presence of bradykinesia as well as the presence of rest tremor and/or rigidity, but the clinic is multifaceted and includes many non-motor symptoms. Non-motor symptoms may occur in the prodromal period, years before clinically evident Parkinson's disease. During this period, diagnosing the disease will likely be even more important when disease-modifying treatments are available. Currently, there is no single biomarker that can be used in the diagnosis of PD and no disease-modifying treatment is available. Identification of biomarkers in early diagnosis will enable the most effective use of disease-modifying therapies and will shed light on possible underlying pathologies, studies in this area have gained momentum in recent years. Molecular imaging methods, genetic studies, salivary gland and skin biopsies, metabolomics, lysosomal pathway are some of them. In this article, besides the current diagnosis and treatment methods of the disease, biomarkers and treatments that are expected to be better understood in the near future will be mentioned.

Investigating neurological symptoms of infectious diseases like COVID-19 leading to a deeper understanding of neurodegenerative disorders such as Parkinson's disease

Apart from common respiratory symptoms, neurological symptoms are prevalent among patients with COVID-19. Research has shown that infection with SARS-CoV-2 accelerated alpha-synuclein aggregation, induced Lewy-body-like pathology, caused dopaminergic neuron senescence, and worsened symptoms in patients with Parkinson's disease (PD). In addition, SARS-CoV-2 infection can induce neuroinflammation and facilitate subsequent neurodegeneration in long COVID, and increase individual vulnerability to PD or parkinsonism. These findings suggest that a post-COVID-19 parkinsonism might follow the COVID-19 pandemic. In order to prevent a possible post-COVID-19 parkinsonism, this paper reviewed neurological symptoms and related findings of COVID-19 and related infectious diseases (influenza and prion disease) and neurodegenerative disorders (Alzheimer's disease, PD and amyotrophic lateral sclerosis), and discussed potential mechanisms underlying the neurological symptoms and the relationship between the infectious diseases and the neurodegenerative disorders, as well as the therapeutic and preventive implications in the neurodegenerative disorders. Infections with a relay of microbes (SARS-CoV-2, influenza A viruses, gut bacteria, etc.) and prion-like alpha-synuclein proteins over time may synergize to induce PD. Therefore, a systematic approach that targets these pathogens and the pathogen-induced neuroinflammation and neurodegeneration may provide cures for neurodegenerative disorders. Further, antiviral/antimicrobial drugs, vaccines, immunotherapies and new therapies (e.g., stem cell therapy) need to work together to treat, manage or prevent these disorders. As medical science and technology advances, it is anticipated that better vaccines for SARS-CoV-2 variants, new antiviral/antimicrobial drugs, effective immunotherapies (alpha-synuclein antibodies, vaccines for PD or parkinsonism, etc.), as well as new therapies will be developed and made available in the near future, which will help prevent a possible post-COVID-19 parkinsonism in the 21st century.

Rhythmic auditory cueing in atypical parkinsonism: A pilot study

Rhythmic auditory cueing (RAC) can improve gait parameters in neurological disorders such as Parkinson's disease and stroke. However, there is a lack of research on the effects of RAC in patients with atypical parkinsonian disorders (APD). Using a smartphone metronome application, we aimed to investigate the immediate effects of RAC in patients with clinically diagnosed APD, namely Progressive Supranuclear Palsy (PSP-Richardson Syndrome and other variants, PSP-nonRS), Corticobasal Syndrome (CBS), Multiple System Atrophy (MSA), and Dementia with Lewy Bodies (DLB). A total of 46 APD participants (25 PSP, 9 CBS, 8 MSA and 4 DLB; age: mean = 70.17, standard deviation = 7.15) walked at their preferred pace for 2 min without any rhythmic auditory cueing (RAC). Participants then walked the same path for another 2 min with RAC set at a tempo 10% faster than the baseline cadence of each participant. After a 10–15-min break, participants walked the same path for another 2 min without RAC to observe for carryover effects. Gait parameters [cadence (steps/minute), gait velocity (meters/minute), and stride length (centimeters)] were collected at baseline, during RAC, and post-RAC. There was a significant improvement in cadence in all participants from baseline to during RAC and post-RAC (corrected p-values = 0.009 for both). Gait velocity also improved from baseline to during RAC and post-RAC in all participants, although this improvement was not significant after correcting for multiple comparisons. The changes in cadence and gait velocity were most pronounced in PSP. In addition, our exploratory analysis showed that the cadence in the suspected TAU group (PSP+CBS) showed a significant improvement from baseline to during RAC and post-RAC (corr. p-value = 0.004 for both). This pilot study using short-term RAC in APD patients demonstrated improvements in cadence and velocity. There is an urgent need for effective gait rehabilitation modalities for patients with APD, and rhythmic cueing can be a practical and useful intervention to improve their gait pattern.

Multiple system atrophy

Multiple system atrophy (MSA) is a rare neurodegenerative disease that is characterized by neuronal loss and gliosis in multiple areas of the central nervous system including striatonigral, olivopontocerebellar and central autonomic structures. Oligodendroglial cytoplasmic inclusions containing misfolded and aggregated α-synuclein are the histopathological hallmark of MSA. A firm clinical diagnosis requires the presence of autonomic dysfunction in combination with parkinsonism that responds poorly to levodopa and/or cerebellar ataxia. Clinical diagnostic accuracy is suboptimal in early disease because of phenotypic overlaps with Parkinson disease or other types of degenerative parkinsonism as well as with other cerebellar disorders. The symptomatic management of MSA requires a complex multimodal approach to compensate for autonomic failure, alleviate parkinsonism and cerebellar ataxia and associated disabilities. None of the available treatments significantly slows the aggressive course of MSA. Despite several failed trials in the past, a robust pipeline of putative disease-modifying agents, along with progress towards early diagnosis and the development of sensitive diagnostic and progression biomarkers for MSA, offer new hope for patients.

Comparison of 18 F-DOPA and 18 F-DTBZ for PET/CT Imaging of Idiopathic Parkinson Disease

OBJECTIVE

The aim of this study was to compare 2 imaging tracers, 18 F-DOPA and 18 F-DTBZ, for PET/CT imaging in idiopathic Parkinson disease (PD).

METHODS

We recruited 32 PD patients and 12 healthy controls in this study. All subjects underwent both 18 F-DOPA and 18 F-DTBZ PET/CT, and the results were interpreted by visual analysis and semiquantitative analysis (specific uptake ratios [SURs]). A 1-way analysis of variance was used to compare the clinical data and the SURs among the patients at different stages. Regression analysis was performed to analyze the correlation between the SURs and the clinical data.

RESULTS

Among the PD patients, there were 7 patients in Hoehn and Yahr stage I, 14 patients in stage II, and 11 patients in stage III. Linear correlation was found in striatal SURs between the 2 tracers ( P < 0.05). In patients of early stages, the striatal SUR decrease percent of 2 tracers had no statistical difference (paired t test, P > 0.05). By initial visual analysis, all the patients were interpreted as positive with 18 F-DBTZ (6 unilaterally, 26 bilaterally), and 31 cases were regarded as positive with 18 F-DOPA (8 unilaterally, 23 bilaterally). After setting the upper limit of SUR images with the putamen SURs of healthy controls (SUR T ), all patients were interpreted as positive with both tracers ( 18 F-DTBZ: 5 unilaterally, 27 bilaterally; 18 F-DOPA: 4 unilaterally, 28 bilaterally).

CONCLUSION

18 F-DTBZ and 18 F-DOPA could reflect the same level of dopaminergic neuron degeneration for PD in early stages, and they have the consistent visual analysis results.

Recent advances in establishing fluid biomarkers for the diagnosis and differentiation of alpha-synucleinopathies - a mini review

The clinical differentiation between multiple system atrophy (MSA), Parkinson's disease (PD), dementia with Lewy bodies (DLB), as well as the distinction between these synucleinopathies from other neurodegenerative disorders can be challenging, particularly at early disease stages or when the presentation is atypical. That is also true for predicting the fate of patients with limited or prodromal forms of synucleinopathies such as pure autonomic failure (PAF) or idiopathic REM-sleep behavior disorder (iRBD) which are known to be at risk of developing MSA, PD, or DLB. After discussing current classification concepts of the synucleinopathies, this invited mini-review reflects on two recently described and validated spinal fluid biomarkers, namely neurofilament light chain (NfL) and α-synuclein oligomers detected by protein aggregation assays, that have shown great promise not only as markers differentiating MSA from the Lewy-body synucleinopathies but also as markers that predict future phenoconversion to MSA among patients with PAF. Discussed are the strengths and limitations of these markers, and how they appear to complement each other nicely as a biomarker panel, enhancing the specificity of one of these markers, yet adding further robustness and simplicity to a marker that is technically rather challenging. The review concludes with thoughts on potential next steps in the development of fluid biomarkers in this rapidly emerging field.

[Atypical Parkinson's syndrome in old age]

Atypical Parkinson syndromes represent a neuropathologically heterogeneous group and include the clinical entities dementia with Lewy bodies (DLB), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). The DLB and MSA are characterized by deposition of the protein alpha-synuclein (synucleinopathy), PSP and CBD are characterized by deposition of tau protein, often in the form of neurofibrillary tangles in nerve and glial cells (tauopathy). Misfolding and aggregation of the aforementioned proteins causes degeneration of the affected cell populations but the disease also spreads to anatomically neighboring brain regions, thus contributing to disease progression. The clinical characteristics (poor response to dopaminergic treatment, ataxia, apraxia, vertical gaze palsy and rapid progression) enable a differentiation from idiopathic Parkinson's disease.

Baseline Cerebrospinal Fluid α-Synuclein in Parkinson's Disease Is Associated with Disease Progression and Cognitive Decline

Biomarkers are increasingly recognized as tools in the diagnosis and prognosis of neurodegenerative diseases. No fluid biomarker for Parkinson’s disease (PD) has been established to date, but α-synuclein, a major component of Lewy bodies in PD and dementia with Lewy bodies (DLB), has become a promising candidate. Here, we investigated CSF α-synuclein in patients with PD (n = 28), PDD (n = 8), and DLB (n = 5), applying an electrochemiluminescence immunoassay. Median values were non-significantly (p = 0.430) higher in patients with PDD and DLB (287 pg/mL) than in PD (236 pg/mL). A group of n = 36 primarily non-demented patients with PD and PDD was clinically followed for up to two years. A higher baseline α-synuclein was associated with increases in Hoehn and Yahr classifications (p = 0.019) and Beck Depression Inventory scores (p < 0.001) as well as worse performance in Trail Making Test A (p = 0.017), Trail Making Test B (p = 0.043), and the Boston Naming Test (p = 0.002) at follow-up. Surprisingly, higher levels were associated with a better performance in semantic verbal fluency tests (p = 0.046). In summary, CSF α-synuclein may be a potential prognostic marker for disease progression, affective symptoms, and executive cognitive function in PD. Larger-scaled studies have to validate these findings and the discordant results for single cognitive tests in this exploratory investigation.

Neuroimaging in Glucocerebrosidase-Associated Parkinsonism: A Systematic Review

Background

Mutations in the GBA gene cause Gaucher's disease (GD) and constitute the most frequent genetic risk factor for idiopathic Parkinson's disease (iPD). Nonmanifesting carriers of GBA mutations/variants (GBA‐NMC) constitute a potential PD preclinical population, whereas PD patients carrying some GBA mutations/variants (GBA‐PD) have a higher risk of a more aggressive disease course. Different neuroimaging techniques are emerging as potential biomarkers in PD and have been used to study GBA‐associated parkinsonism.

Objective

The aim is to critically review studies applying neuroimaging to GBA‐associated parkinsonism.

Methods

Literature search was performed using PubMed and EMBASE databases (last search February 7, 2022). Studies reporting neuroimaging findings in GBA‐PD, GD with and without parkinsonism, and GBA‐NMC were included.

Results

Thirty‐five studies were included. In longitudinal studies, GBA‐PD patients show a more aggressive disease than iPD at both structural magnetic resonance imaging and 123‐fluoropropylcarbomethoxyiodophenylnortropane single‐photon emission computed tomography. Fluorodeoxyglucose‐positron emission tomography and brain perfusion studies reported a greater cortical involvement in GBA‐PD compared to iPD. Overall, contrasting evidence is available regarding GBA‐NMC for imaging and clinical findings, although subtle differences have been reported compared with healthy controls with no mutations.

Conclusions

Although results must be interpreted with caution due to limitations of the studies, in line with previous clinical observations, GBA‐PD showed a more aggressive disease progression in neuroimaging longitudinal studies compared to iPD. Cognitive impairment, a “clinical signature” of GBA‐PD, seems to find its neuroimaging correlate in the greater cortical burden displayed by these patients as compared to iPD. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Magnetic resonance and dopamine transporter imaging for the diagnosis of Parkinson´s disease: a narrative review

BACKGROUND

the diagnosis of Parkinson's disease (PD) can be challenging, especially in the early stages, albeit its updated and validated clinical criteria. Recent developments on neuroimaging in PD, altogether with its consolidated role of excluding secondary and other neurodegenerative causes of parkinsonism, provide more confidence in the diagnosis across the different stages of the disease. This review highlights current knowledge and major recent advances in magnetic resonance and dopamine transporter imaging in aiding PD diagnosis.

OBJECTIVE

This study aims to review current knowledge about the role of magnetic resonance imaging and neuroimaging of the dopamine transporter in diagnosing Parkinson's disease.

METHODS

We performed a non-systematic literature review through the PubMed database, using the keywords "Parkinson", "magnetic resonance imaging", "diffusion tensor", "diffusion-weighted", "neuromelanin", "nigrosome-1", "single-photon emission computed tomography", "dopamine transporter imaging". The search was restricted to articles written in English, published between January 2010 and February 2022.

RESULTS

The diagnosis of Parkinson's disease remains a clinical diagnosis. However, new neuroimaging biomarkers hold promise for increased diagnostic accuracy, especially in earlier stages of the disease.

CONCLUSION

Future validation of new imaging biomarkers bring the expectation of an increased neuroimaging role in the diagnosis of PD in the following years.

Automated Differentiation of Atypical Parkinsonian Syndromes Using Brain Iron Patterns in Susceptibility Weighted Imaging

In recent studies, iron overload has been reported in atypical parkinsonian syndromes. The topographic patterns of iron distribution in deep brain nuclei vary by each subtype of parkinsonian syndrome, which is affected by underlying disease pathologies. In this study, we developed a novel framework that automatically analyzes the disease-specific patterns of iron accumulation using susceptibility weighted imaging (SWI). We constructed various machine learning models that can classify diseases using radiomic features extracted from SWI, representing distinctive iron distribution patterns for each disorder. Since radiomic features are sensitive to the region of interest, we used a combination of T1-weighted MRI and SWI to improve the segmentation of deep brain nuclei. Radiomics was applied to SWI from 34 patients with a parkinsonian variant of multiple system atrophy, 21 patients with cerebellar variant multiple system atrophy, 17 patients with progressive supranuclear palsy, and 56 patients with Parkinson’s disease. The machine learning classifiers that learn the radiomic features extracted from iron-reflected segmentation results produced an average area under receiver operating characteristic curve (AUC) of 0.8607 on the training data and 0.8489 on the testing data, which is superior to the conventional classifier with segmentation using only T1-weighted images. Our radiomic model based on the hybrid images is a promising tool for automatically differentiating atypical parkinsonian syndromes.

Aberrant Structure MRI in Parkinson’s Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

Role of Apolipoprotein E in the Clinical Profile of Atypical Parkinsonian Syndromes

INTRODUCTION

Atypical Parkinsonian syndromes (APS) encompass a spectrum of neurodegenerative diseases including dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal syndrome (CBS). The effects of the Apolipoprotein E (APOE) gene on APS clinical features are controversial and understudied in several populations. We aimed to explore the influence of APOE genotype on clinical features in an APS Tunisian cohort.

METHODS

We included clinically diagnosed APS patients genotyped for APOE, and analyzed the clinical and APOE genotype associations.

RESULTS

A total of 328 APS patients were included, comprising 184 DLB, 58 PSP, 49 MSA, and 37 CBS. Significant differences in initial Mini-Mental State Examination and Frontal Assessment Battery scores according to APOE genotypes (P=0.05 and 0.0048) were found. Executive dysfunction (P=0.026) disorientation (P=0.025), and hallucinations (P<0.001) were more pronounced among APOE-ɛ4 carriers particularly in DLB. Memory disorders were also correlated to APOE-ɛ4 allele (P=0.048) and were more frequent among DLB and PSP carriers. Depression was associated to APOE-ε4 (P=0.042), more markedly in APOE-ε4-CBS and MSA carriers.

CONCLUSIONS

Our findings suggested a role of APOE-ε4 in defining a more altered cognitive phenotype with variable degrees across subgroups in APS patients, especially in DLB carriers. This effect mainly concerned executive, memory and orientation functions as well as hallucinations.

Short- and Long-Term Effect of Parkinson's Disease Multimodal Complex Treatment

Parkinson´s disease multimodal complex treatment (PD-MCT) is a multidisciplinary inpatient treatment option increasingly applied in Germany. However, data on its effectiveness are rare. Data were collected at the Department of Neurology of the University Hospital Jena, Germany. In 2019, 159 patients were admitted to our neurology ward for PD-MCT. Patients were followed for up to 12 months, and their data were retrospectively analyzed to assess the short- and long-term treatment effects. The treatment led to an improvement in motor function assessed by Movement Disorder Society sponsored revision of the unified Parkinson´s disease rating scale part III (MDS-UPDRS III) and motor performance (Tinetti test). Improvement of MDS-UPDRS III was associated with lower age, higher MDS-UPDRS III at admission, and less depression (assessed by Hospital Anxiety and Depression Scale and Beck-Depression Inventory II). One month after the hospital stay, 36.8% of the patients reported feeling better, while 32.6% reported feeling worse. If the patients were not depressed, they were more likely to have reported feeling better. PD-MCT is an effective inpatient treatment option. However, to improve patients’ satisfaction, screening and treatment for depression is essential. The effectiveness of different treatment durations has to be elucidated in further studies.

Self-Attention-Guided Recurrent Neural Network and Motion Perception for Intelligent Prediction of Chronic Diseases

Parkinson's disease is a common chronic disease that affects a large number of people. In the real world, however, Parkinson's disease can result in a loss of physical performance, which is classified as a movement disorder by clinicians. Parkinson's disease is currently diagnosed primarily through clinical symptoms, which are highly dependent on clinician experience. As a result, there is a need for effective early detection methods. Traditional machine learning algorithms filter out many inherently relevant features in the process of dimensionality reduction and feature classification, lowering the classification model's performance. To solve this problem and ensure high correlation between features while reducing dimensionality to achieve the goal of improving classification performance, this paper proposes a recurrent neural network classification model based on self attention and motion perception. Using a combination of self-attention mechanism and recurrent neural network, as well as wearable inertial sensors, the model classifies and trains the five brain area features extracted from MRI and DTI images (cerebral gray matter, white matter, cerebrospinal fluid density, and so on). Clinical and exercise data can be combined to produce characteristic parameters that can be used to describe movement sluggishness. The experimental results show that the model proposed in this paper improves the recognition performance of Parkinson's disease, which is better than the compared methods by 2.45% to 12.07%.

The role of neuroimaging in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Two hallmarks of PD are the accumulation of alpha-synuclein and the loss of dopaminergic neurons in the brain. There is no cure for PD, and all existing treatments focus on alleviating the symptoms. PD diagnosis is also based on the symptoms, such as abnormalities of movement, mood, and cognition observed in the patients. Molecular imaging methods such as magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) can detect objective alterations in the neurochemical machinery of the brain and help diagnose and study neurodegenerative diseases. This review addresses the application of functional MRI, PET, and SPECT in PD patients. We provide an overview of the imaging targets, discuss the rationale behind target selection, the agents (tracers) with which the imaging can be performed, and the main findings regarding each target's state in PD. Molecular imaging has proven itself effective in supporting clinical diagnosis of PD and has helped reveal that PD is a heterogeneous disorder, which has important implications for the development of future therapies. However, the application of molecular imaging for early diagnosis of PD or for differentiation between PD and atypical parkinsonisms has remained challenging. The final section of the review is dedicated to new imaging targets with which one can detect the PD-related pathological changes upstream from dopaminergic degeneration. The foremost of those targets is alpha-synuclein. We discuss the progress of tracer development achieved so far and challenges on the path toward alpha-synuclein imaging in humans.

The APOE ε4 variant and hippocampal atrophy in Alzheimer's disease and Lewy body dementia: a systematic review of magnetic resonance imaging studies and therapeutic relevance

Introduction: The apolipoprotein E ɛ4-allele (APOE-ɛ4) increases the risk not only for Alzheimer's disease (AD) but also for Parkinson's disease dementia and dementia with Lewy bodies (collectively, Lewy body dementia [LBD]). Hippocampal volume is an important neuroimaging biomarker for AD and LBD, although its association with APOE-ɛ4 is inconsistently reported. We investigated the association of APOE-ε4 with hippocampal atrophy quantified using magnetic resonance imaging in AD and LBD.Areas covered: Databases were searched for volumetric and voxel-based morphometric studies published up until December 31^st, 2020. Thirty-nine studies (25 cross-sectional, 14 longitudinal) were included. We observed that (1) APOE-ε4 was associated with greater rate of hippocampal atrophy in longitudinal studies in AD and in those who progressed from mild cognitive impairment to AD, (2) association of APOE-ε4 with hippocampal atrophy in cross-sectional studies was inconsistent, (3) APOE-ɛ4 may influence hippocampal atrophy in dementia with Lewy bodies, although longitudinal investigations are needed. We comprehensively discussed methodological aspects, APOE-based therapeutic approaches, and the association of APOE-ε4 with hippocampal sub-regions and cognitive performance.Expert opinion: The role of APOE-ɛ4 in modulating hippocampal phenotypes may be further clarified through more homogenous, well-powered, and pathology-proven, longitudinal investigations. Understanding the underlying mechanisms will facilitate the development of prevention strategies targeting APOE-ɛ4.