Novel decision algorithm to discriminate parkinsonism with combined blood and imaging biomarkers

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study

INTRODUCTION

Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases.

METHODS

Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC.

RESULTS

PSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85-0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04.

CONCLUSION

Our findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.

Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor

Introduction

Distinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data.

Methods

We enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients.

Results

Both structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase.

Conclusion

Machine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

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.

Midbrain and pons MRI shape analysis and its clinical and CSF correlates in degenerative parkinsonisms: a pilot study

OBJECTIVES

To conduct brainstem MRI shape analysis across neurodegenerative parkinsonisms and control subjects (CS), along with its association with clinical and cerebrospinal fluid (CSF) correlates.

METHODOLOGY

We collected demographic and clinical variables, performed planimetric and shape MRI analyses, and determined CSF neurofilament-light chain (NfL) levels in 84 participants: 11 CS, 12 with Parkinson's disease (PD), 26 with multiple system atrophy (MSA), 21 with progressive supranuclear palsy (PSP), and 14 with corticobasal degeneration (CBD).

RESULTS

MSA featured the most extensive and significant brainstem shape narrowing (that is, atrophy), mostly in the pons. CBD presented local atrophy in several small areas in the pons and midbrain compared to PD and CS. PSP presented local atrophy in small areas in the posterior and upper midbrain as well as the rostral pons compared to MSA. Our findings of planimetric MRI measurements and CSF NfL levels replicated those from previous literature. Brainstem shape atrophy correlated with worse motor state in all parkinsonisms and with higher NfL levels in MSA, PSP, and PD.

CONCLUSION

Atypical parkinsonisms present different brainstem shape patterns which correlate with clinical severity and neuronal degeneration. In MSA, shape analysis could be further explored as a potential diagnostic biomarker. By contrast, shape analysis appears to have a rather limited discriminant value in PSP.

KEY POINTS

• Atypical parkinsonisms present different brainstem shape patterns. • Shape patterns correlate with clinical severity and neuronal degeneration. • In MSA, shape analysis could be further explored as a potential diagnostic biomarker.

Neurofilament light chain: a promising diagnostic biomarker for functional motor disorders

OBJECTIVE

Functional motor disorders (FMDs) are disabling neurological conditions characterized by abnormal movements which are inconsistent and incongruent with recognized neurological diseases. Aim of this study is to investigate whether FMDs are related to structural axonal damage.

METHODS

Consecutive patients with a definite diagnosis of FMD with no other neurological/psychiatric comorbidities (pure FMDs) and age-matched healthy controls (HCs) were recruited in a tertiary center and demographic/clinical data were collected. Serum neurofilament light chain (NfL) assessment was performed with ultrasensitive paramagnetic bead-based enzyme-linked immunosorbent assay.

RESULTS

34 patients with FMDs and 34 HCs were included. NfL levels were similar (p = 0.135) in FMDs (median 8.3 pg/mL, range 2-33.7) and HCs (median 6.1 pg/mL, range 2.7-15.6). The area under curve (0.606, 95% CI 0.468-0.743) confirmed that NfL concentration was not different in the two groups. NfL values were similar in patients with paroxysmal vs persistent disease course (p = 0.301), and isolated vs combined symptoms (p = 0.537). NfL levels were associated with age (p < 0.0001), but not with disease duration (p = 0.425), number of CNS acting drugs (p = 0.850), or clinical features (p = 0.983).

DISCUSSION

Our preliminary data show that NfL levels are similar in patients with FMDs and HCs, indicating the lack of neuroaxonal damage. These results have relevant pathogenic and clinical implications and suggest that serum NfL may be a promising diagnostic biomarker, potentially useful to differentiate functional vs structural neurological disorders.

Integrated Clinical Features with Plasma and Multi-modal Neuroimaging Biomarkers to Diagnose Mild Cognitive Impairment in Early Drug-Naive Parkinson's Disease

The pathogenesis of cognitive impairment in Parkinson's disease (PD) patients remains unclear, and there is no ideal diagnostic tool available at present. We assessed integrated clinical features with plasma and multi-modal neuroimaging biomarkers to identify mild cognitive impairment (MCI) in early drug-naive PD patients. 49 early drug-naive PD patients, including 26 with MCI (PD-MCI) and 23 with normal cognition (PD-NC), and 20 controls were recruited. Plasma markers [α-synuclein, beta-amyloid 1-40 (Aβ40), beta-amyloid 1-42 (Aβ42), and phosphorylated Tau 181 (p-Tau181) levels], functional connectivity (FC) of the default mode network, and cortical thickness (CTh) were evaluated to identify PD-MCI. The PD-MCI group had significantly higher plasma p-Tau181 levels and p-Tau181/Aβ42 ratio and lower Aβ42/Aβ40 ratio compared to the PD-NC group. Compared to PD-NC, the PD-MCI group showed increased FC between left posterior cingulate cortex (pCC) and the left parahippocampal gyrus (PHG), and between the right hippocampal formation and the left anterior cingulate and paracingulate gyri, and the right middle temporal gyrus. Additionally, the PD-MCI group had thinner cortex thickness in the right lateral occipital and frontal pole compared to the PD-NC group. The final model combining clinical characteristics and several variables (age, sex, plasma p-Tau181 level, Aβ42/Aβ40 ratio, the right lateral occipital CTh, and the FC value between the left pCC and left PHG) had the highest diagnostic accuracy for PD-MCI (AUC = 0.987, 95% CI 0.903-1.000; p = 0.001 compared to age and sex alone). The combination of clinical features, plasma biomarkers, and multi-modal neuroimaging biomarkers can identify early cognitive decline in PD patients.

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.

Stacked Model-Based Classification of Parkinson’s Disease Patients Using Imaging Biomarker Data

Parkinson’s disease (PSD) is a neurological disorder of the brain where nigrostriatal integrity functions lead to motor and non-motor-based symptoms. Doctors can assess the patient based on the patient’s history and symptoms; however, the symptoms are similar in various neurodegenerative diseases, such as progressive supranuclear palsy (PSP), multiple system atrophy—parkinsonian type (MSA), essential tremor, and Parkinson’s tremor. Thus, sometimes it is difficult to identify a patient’s disease based on his or her symptoms. To address the issue, we have used neuroimaging biomarkers to analyze dopamine deficiency in the brains of subjects. We generated the different patterns of dopamine levels inside the brain, which identified the severity of the disease and helped us to measure the disease progression of the patients. For the classification of the subjects, we used machine learning (ML) algorithms for a multivariate classification of the subjects using neuroimaging biomarkers data. In this paper, we propose a stacked machine learning (ML)-based classification model to identify the HC and PSD subjects. In this stacked model, meta learners can learn and combine the predictions from various ML algorithms, such as K-nearest neighbor (KNN), random forest algorithm (RFA), and Gaussian naive Bayes (GANB) to achieve a high performance model. The proposed model showed 92.5% accuracy, outperforming traditional schemes.

Biofluid markers of blood-brain barrier disruption and neurodegeneration in Lewy body spectrum diseases: A systematic review and meta-analysis

BACKGROUND

Mixed evidence supports blood-brain barrier (BBB) dysfunction in Lewy body spectrum diseases.

METHODS

We compare biofluid markers in people with idiopathic Parkinson's disease (PD) and people with PD dementia (PDD) and/or dementia with Lewy bodies (DLB), compared with healthy controls (HC). Seven databases were searched up to May 10, 2021. Outcomes included cerebrospinal fluid to blood albumin ratio (Q_alb), and concentrations of 7 blood protein markers that also reflect BBB disruption and/or neurodegenerative co-pathology. We further explore differences between PD patients with and without evidence of dementia. Random-effects models were used to obtain standardized mean differences (SMD) with 95% confidence interval.

RESULTS

Of 13,949 unique records, 51 studies were meta-analyzed. Compared to HC, Q_alb was higher in PD (N_PD/N_HC = 224/563; SMD = 0.960 [0.227-1.694], p = 0.010; I² = 92.2%) and in PDD/DLB (N_PDD/DLB/N_HC = 265/670; SMD = 1.126 [0.358-1.893], p < 0.001; I² = 78.2%). Blood neurofilament light chain (NfL) was higher in PD (N_PD/N_HC = 1848/1130; SMD = 0.747 [0.442-1.052], p < 0.001; I² = 91.9%) and PDD/DLB (N_PDD/DLB/N_HC = 183/469; SMD = 1.051 [0.678-1.423], p = 0.004; I² = 92.7%) than in HC. p-tau 181 (N_PD/N_HC = 276/164; SMD = 0.698 [0.149-1.247], p = 0.013; I² = 82.7%) was also higher in PD compared to HC. In exploratory analyses, blood NfL was higher in PD without dementia (N_PDND/N_HC = 1005/740; SMD = 0.252 [0.042-0.462], p = 0.018; I² = 71.8%) and higher in PDD (N_PDD/N_HC = 100/111; SMD = 0.780 [0.347-1.214], p < 0.001; I² = 46.7%) compared to HC. Q_alb (N_PDD/N_PDND = 63/191; SMD = 0.482 [0.189-0.774], p = 0.010; I²<0.001%) and NfL (N_PDD/N_PDND = 100/223; SMD = 0.595 [0.346-0.844], p < 0.001; I² = 3.4%) were higher in PDD than in PD without dementia.

CONCLUSIONS

Biofluid markers suggest BBB disruption and neurodegenerative co-pathology involvement in common Lewy body diseases. Greater evidence of BBB breakdown was seen in Lewy body disease with cognitive impairment.

Serum and Cerebrospinal Fluid Biomarkers in Neuromyelitis Optica Spectrum Disorder and Myelin Oligodendrocyte Glycoprotein Associated Disease

The term neuromyelitis optica spectrum disorder (NMOSD) describes a group of clinical-MRI syndromes characterized by longitudinally extensive transverse myelitis, optic neuritis, brainstem dysfunction and/or, less commonly, encephalopathy. About 80% of patients harbor antibodies directed against the water channel aquaporin-4 (AQP4-IgG), expressed on astrocytes, which was found to be both a biomarker and a pathogenic cause of NMOSD. More recently, antibodies against myelin oligodendrocyte glycoprotein (MOG-IgG), have been found to be a biomarker of a different entity, termed MOG antibody-associated disease (MOGAD), which has overlapping, but different pathogenesis, clinical features, treatment response, and prognosis when compared to AQP4-IgG-positive NMOSD. Despite important refinements in the accuracy of AQP4-IgG and MOG-IgG testing assays, a small proportion of patients with NMOSD still remain negative for both antibodies and are called "seronegative" NMOSD. Whilst major advances have been made in the diagnosis and treatment of these conditions, biomarkers that could help predict the risk of relapses, disease activity, and prognosis are still lacking. In this context, a number of serum and/or cerebrospinal fluid biomarkers are emerging as potentially useful in clinical practice for diagnostic and treatment purposes. These include antibody titers, cytokine profiles, complement factors, and markers of neuronal (e.g., neurofilament light chain) or astroglial (e.g., glial fibrillary acidic protein) damage. The aim of this review is to summarize current evidence regarding the role of emerging diagnostic and prognostic biomarkers in patients with NMOSD and MOGAD.

Diagnostic Performance of the Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson's Disease: An Updated Systematic Review and Meta-Analysis

Progressive supranuclear palsy (PSP) and Parkinson's disease (PD) are difficult to differentiate especially in the early stages. We aimed to investigate the diagnostic performance of the magnetic resonance parkinsonism index (MRPI) in differentiating PSP from PD. A systematic literature search of PubMed-MEDLINE and EMBASE was performed to identify original articles evaluating the diagnostic performance of the MRPI in differentiating PSP from PD published up to 20 February 2021. The pooled sensitivity, specificity, and 95% CI were calculated using the bivariate random-effects model. The area under the curve (AUC) was calculated using a hierarchical summary receiver operating characteristic (HSROC) model. Meta-regression was performed to explain the effects of heterogeneity. A total of 14 original articles involving 484 PSP patients and 1243 PD patients were included. In all studies, T1-weighted images were used to calculate the MRPI. Among the 14 studies, nine studies used 3D T1-weighted images. The pooled sensitivity and specificity for the diagnostic performance of the MRPI in differentiating PSP from PD were 96% (95% CI, 87-99%) and 98% (95% CI, 91-100%), respectively. The area under the HSROC curve was 0.99 (95% CI, 0.98-1.00). Heterogeneity was present (sensitivity: I² = 97.29%; specificity: I² = 98.82%). Meta-regression showed the association of the magnet field strength with heterogeneity. Studies using 3 T MRI showed significantly higher sensitivity (100%) and specificity (100%) than those of studies using 1.5 T MRI (sensitivity of 98% and specificity of 97%) (p < 0.01). Thus, the MRPI could accurately differentiate PSP from PD and support the implementation of appropriate management strategies for patients with PSP.

CSF and Circulating NfL as Biomarkers for the Discrimination of Parkinson Disease From Atypical Parkinsonian Syndromes: Meta-analysis

PURPOSE OF REVIEW

To evaluate whether CSF and circulating neurofilament light chain (NfL), a marker of axonal damage, could discriminate Parkinson disease (PD) from atypical parkinsonian syndromes (APSs).

RECENT FINDINGS

MEDLINE and Scopus were systematically searched, and 15 studies were included (1,035 patients with PD and 930 patients with APS). CSF NfL levels were 1.26 SDs higher in the APS group compared to the PD group (g = 1.26 [95% confidence interval 0.99-1.53]), and circulating NfL levels were 1.53 SDs higher in the APS group compared to the PD group (g = 1.53 [95% confidence interval 1.15-1.91]); 4 studies, 307 patients with PD, 197 patients with APS. Pooled areas under the curve were 0.941 (0.916-0.965) and 0.874 (0.802-0.946) for CSF and circulating NfL, corresponding to average sensitivities of 86% (79%-90%) and 91% (86%-95%), and specificity of 88% (82%-92%) and 76% (62%-85%), respectively.

SUMMARY

These results strongly support the high diagnostic accuracy of both CSF and circulating NfL in differentiating PD from APS, highlighting their usefulness as promising biomarkers.

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.

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.

"Hot cross bun" is a potential imaging marker for the severity of cerebellar ataxia in MSA-C

To evaluate the correlation between “hot cross bun” sign (HCBs) and disease severity in multiple system atrophy (MSA). We recruited patients with probable and possible MSA with parkinsonism (MSA-P) or the cerebellar ataxia (MSA-C) subtypes. Clinical and imaging characteristics were collected and comparison was performed between MSA-C and MSA-P cases. Spearman test was used to evaluate the correlation between HCBs and other variables. Curve estimate and general linear regression was performed to evaluate the relationship between HCBs and the Scale for Assessment and Rating of Ataxia (SARA). Unified Multiple System Atrophy Rating Scale (UMSARS) IV was used to assess the severity of disease. Multinomial ordered logistic regression was used to confirm the increased likelihood of disability for the disease. Eighty-one MSA with HCBs comprising of 50 MSA-C and 31 MSA-P were recruited. We demonstrated that the severity of HCBs showed a positive linear correlation with SARA scores in MSA-C. Multinomial ordered logistic regression test revealed that the increase in the HCBs grade may be associated with an increased likelihood of disability for the disease severity in MSA, especially in those with cerebellar ataxia subtype. We demonstrated that HCBs is a potential imaging marker for the severity of cerebellar ataxia. The increase in the HCBs grade may be associated with an increased likelihood of disability in MSA-C, but not MSA-P cases, suggesting that it may be a useful imaging indicator for disease progression in Chinese patients with MSA-C.

Multiple System Atrophy (MSA) and smoking: a meta-analysis and mechanistic insights

BACKGROUND

The association between cigarette smoking and multiple system atrophy (MSA) has been debated. We conducted a systematic review and a meta-analysis to investigate this link.

RESULTS

We identified 161 articles from database searching and bibliographic review. Five case-control studies satisfied the inclusion and exclusion criteria, and 435 and 352 healthy controls and MSA patients were examined. The prevalence of MSA amongst ever smokers was lower compared to never smokers (aOR=0.57; 95% CI, 0.29-1.14), although this result did not reach statistical significance. This was also observed for current and former smokers, with a stronger association for current smokers (aOR=0.63 vs aOR=0.96).

CONCLUSIONS

There is a suggestion that smoking protects against MSA. Prospective studies in larger patient cohorts are required to further evaluate the cause-effect relationship and functional studies in cellular and animal models will provide mechanistic insights on their potential etiologic links.

METHODS

PubMed and Cochrane Library were searched from inception to July 7, 2019 to identify case-control studies that analyzed smoking as an environmental risk or protective factor for MSA. Two authors independently extracted data and performed risk-of-bias and quality assessment. The random-effects model was assumed to account for between-study variance when pooling the crude and adjusted odds ratios.

Blood neurofilament light chain in Parkinson disease and atypical parkinsonisms: A protocol for systematic review and meta-analysis

BACKGROUND

Neurofilament light chain (NfL), an index of neuroaxonal injury, is a promising diagnostic and prognostic fluid biomarker with high translational value in many neurodegenerative disorders. Blood NfL measurement has been an exciting and active field of research in idiopathic Parkinson disease (PD) and atypical parkinsonisms. However, blood NfL levels in these parkinsonisms from existing literature were inconsistent. No comprehensive meta-analysis has ever been conducted.

METHODS

Three major biomedical electronic databases PubMed, Embase, and Web of Science were comprehensively searched from inception to July 10, 2020. This protocol will be prepared based on the guidelines recommended by the statement of Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Original observational studies that measured blood (serum/plasma) NfL concentrations in patients with parkinsonisms (multiple system atrophy [MSA], progressive supranuclear palsy [PSP], corticobasal syndrome [CBS], and dementia with Lewy bodies [DLB]), and healthy controls (HCs) will be included. Quality assessment of the included studies will be performed using the Newcastle Ottawa Scale (NOS). Meta-analyses will be conducted using the STATA software version 13.0. The standardized mean differences as the measure of effect size and 95% confidence intervals were calculated for each comparison of blood NfL levels. Heterogeneity analysis, sensitivity analysis, publication bias, subgroup analysis, and meta-regression analysis will be carried out to test the robustness of the results.

RESULTS

The meta-analysis will obtain the effect sizes of blood NfL levels in the following comparisons: PD versus HC, MSA versus HC, PSP versus HC, CBS versus HC, DLB versus HC, MSA versus PD, PSP versus PD, CBS versus PD, and DLB versus PD.

CONCLUSIONS

The present meta-analysis will provide the quantitative evidence of NfL levels in idiopathic PD and atypical parkinsonisms, hoping to facilitate differential diagnoses in clinical practice.

REGISTRATION NUMBER

INPLASY202070091.

Microglial Activation and Inflammation as a Factor in the Pathogenesis of Progressive Supranuclear Palsy (PSP)

Progressive supranuclear palsy (PSP) is a neurodegenerative disease based on four-repeat tauopathy pathology. Currently, this entity is not fully recognized in the context of pathogenesis or clinical examination. This review evaluates the association between neuroinflammation and microglial activation with the induction of pathological cascades that result in tauopathy pathology and the clinical manifestation of PSP. Multidimensional analysis was performed by evaluating genetic, biochemical, and neuroimaging biomarkers to determine whether neurodegeneration as an effect of neuroinflammation or neuroinflammation is a consequence of neurodegeneration in PSP. To the best of our knowledge, this review is the first to investigate PSP in this context.