Emerging Neuroimaging Biomarkers Across Disease Stage in Parkinson Disease: A Review

Characterizing microstructural patterns within the cortico-striato-thalamo-cortical circuit in Parkinson's disease

PURPOSE

Parkinson's disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI).

METHODS

The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity.

RESULTS

Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus.

CONCLUSION

These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Bibliometric analysis of global research trends in magnetic resonance imaging studies of substantia nigra in Parkinson's disease (2001-2024)

Background

Parkinson's disease (PD) is a globally prevalent neurodegenerative disorder, primarily characterized by muscle rigidity, resting tremor, and bradykinesia. The incidence of PD is rapidly escalating worldwide. Numerous studies have been conducted on the application of magnetic resonance imaging (MRI) in investigating the substantia nigra (SN) in PD patients. However, to date, no bibliometric analysis has been performed on this specific research area. Therefore, this study aimed to provide a comprehensive analysis of the current status in MRI research on the SN in PD patients.

Materials and methods

MRI study records related to the SN in PD patients from 2001 to 2024 were searched by using the Web of Science Core Collection (WOSCC) database and then the CiteSpace and VOSviewer were used to conduct bibliometric analysis.

Results

Our analysis found that the number of published articles related studies on MRI of the SN in PD showed an overall upward trend over the past decade, in which Lehericy, Stephane, Du, Guangwei, and Huang, Xuemei are the top three authors with the most articles. Additionally, United States, China and Germany are the main contributors to MRI studies of SN in PD. And Shanghai Jiao Tong University, University of Florida and Seoul National University are the leading institutions in the field. Finally, the keyword analysis showed that the hotspots and trends of research in this field are mainly concentrated in quantitative susceptibility mapping, neuroimaging, and neuromelanin-sensitive MRI.

Conclusion

These analysis identified the most influential authors, institutions, countries and research hotspots in the field of SN-MRI research in PD, which has reference significance for the research interest in this field and provides a new idea for PD prevention.

Analysis of diffusion changes in cerebral tissues of Parki̇nson's patients who underwent subthalamic nucleus deep brain stimulation: Correlation of improvements in motor and neuropsychiatric symptoms

OBJECTIVE

Parkinson's disease (PD) as a neurodegenerative disorder characterized by a reduction in both the quantity and functionality of dopaminergic neurons. This succinctly highlights the central pathological feature of PD and its association with dopaminergic neuron degeneration, which underlies the motor and non-motor symptoms of the disease. This study aims to elucidate the nuances of apparent diffusion coefficient (ADC) changes in different cerebral regions by after the bilateral subthalamic nucleus (STN) deep brain stimulation (DBS) surgery of PD, as well as to investigate their potential interactions with the motor and neuropsychiatric spectrum.

METHODS

Patients who underwent STN-DBS surgery for PD between 2017 and 2019 were included in this study. The results of diffusion magnetic resonance imaging (MRI), Unified Parkinson Disease Rating Scale (UPDRS) III scores, Beck and Hamilton depression tests were recorded before and at the 3rd month of postoperative stimulation. The data obtained were evaluated with the Wilcoxon signed rank test. Result of the statistical tests were within the 95 % confidence interval and p values were significant below 0.05.

RESULTS

Our study was conducted with a total of 13 patients, 8 men and 5 women. As a result of measurements made in a total of 32 different regions, especially in the motor and neuropsychiatric areas of the brain, an increase in ADC values was found in all areas. ADC changes of eight localizations such as left corpus callosum, right corona radiata, left corona radiata, hippocampus, right insula, left superior cerebellar peduncle, left caudate nucleus and left putamen were statistically significant. UPDRS III scores improved by 57 % (p <0.05), and Beck and Hamilton depression scores by 25 % and 33 %, respectively (p> 0.05).

CONCLUSIONS

This article implicate that bilateral STN-DBS surgery potentially exerts beneficial effects on both motor and neuropsychiatric symptomatology in individuals with PD. We believe that this therapeutic mechanism is hypothesized to involve modulation of diffusion alterations within distinct cerebral tissues.

Evaluation of damage discrimination in dopaminergic neurons using dopamine transporter PET tracer [18F]FECNT-d4

BACKGROUND

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, diagnosed based on classic symptoms like motor dysfunction and cognitive impairments. With the development of various radioactive ligands, positron emission tomography (PET) imaging combined with specific radiolabelling probes has proven to be effective in aiding clinical PD diagnosis. Among these probes, 2β-Carbomethoxy-3β-(4-chlorophenyl)-8-(2-[18F]-fluoroethyl) nortropane ([18F]FECNT) has been utilized as a PET tracer to image dopamine transporter (DAT) integrity in striatal presynaptic dopaminergic terminals. However, the presence of brain-penetrant radioactive metabolites produced by [18F]FECNT may impact the accuracy of PET imaging. In previous research, we developed 2β-Carbomethoxy-3β-(4-chlorophenyl)-8-(2-[18F]-fluoroethyl-1,1,2,2-d4) nortropane ([18F]FECNT-d4), a deuterated derivative with enhanced stability in plasma and the striatum, along with a slower washout rate. In this study, we further investigated the potential of [18F]FECNT-d4 to detect dopaminergic neuron degeneration in Parkinson's disease. This involved PET imaging in unilaterally-lesioned PD model rats and in vitro autoradiography conducted on postmortem brain sections.

RESULTS

PET images revealed reduced specific uptake in the ipsilateral striatum of rats stereotactically injected with 6-hydroxydopamine hydrochloride (6-OHDA). Compared to the sham group, the ratio of standardized uptake value (SUV) in the ipsilateral to contralateral striatum decreased by 13%, 23%, and 63% in the mild, moderate, and severe lesioned groups, respectively. Dopaminergic denervation observed in PET imaging was further supported by behavioral assessments, immunostaining, and monoamine concentration tests. Moreover, the microPET results exhibited positive correlations with these measurements, except for the apomorphine-induced rotational behavior test, which showed a negative correlation. Additionally, [18F]FECNT-d4 uptake was approximately 40% lower in the postmortem striatal sections of a PD patient compared to a healthy subject. Furthermore, estimated human dosimetry (effective dose equivalent: 5.06 E-03 mSv/MBq), extrapolated from rat biodistribution data, remained below the current Food and Drug Administration limit for radiation exposure.

CONCLUSION

Our findings demonstrate that [18F]FECNT-d4 accurately estimates levels of dopaminergic neuron degeneration in the 6-OHDA-induced PD rat model and effectively distinguishes between PD patients and healthy individuals. This highly sensitive and safe PET probe holds promising potential for clinical application in the diagnosis and monitoring of Parkinson's disease.

Accuracy of 18F-FDG PET Imaging in Differentiating Parkinson's Disease from Atypical Parkinsonian Syndromes: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVE

To quantitatively assess the accuracy of 18F-FDG PET in differentiating Parkinson's Disease (PD) from Atypical Parkinsonian Syndromes (APSs).

METHODS

PubMed, Embase, and Web of Science databases were searched to identify studies published from the inception of the databases up to June 2024 that used 18F-FDG PET imaging for the differential diagnosis of PD and APSs. The risk of bias in the included studies was assessed using the QUADAS-2 or QUADAS-AI tool. Bivariate random-effects models were used to calculate the pooled sensitivity, specificity, and the area under the curves (AUC) of summary receiver operating characteristic (SROC).

RESULTS

24 studies met the inclusion criteria, involving a total of 1508 PD patients and 1370 APSs patients. 12 studies relied on visual interpretation by radiologists, of which the pooled sensitivity, specificity, and SROC-AUC for direct visual interpretation in diagnosing PD were 96% (95%CI: 91%, 98%), 90% (95%CI: 83%, 95%), and 0.98 (95%CI: 0.96, 0.99), respectively; the pooled sensitivity, specificity, and SROC-AUC for visual interpretation supported by univariate algorithms in diagnosing PD were 93% (95%CI: 90%, 95%), 90% (95%CI: 85%, 94%), and 0.96 (95%CI: 0.94, 0.97), respectively. 12 studies relied on artificial intelligence (AI) to analyze 18F-FDG PET imaging data. The pooled sensitivity, specificity, and SROC-AUC of machine learning (ML) for diagnosing PD were 87% (95%CI: 82%, 91%), 91% (95%CI: 86%, 94%), and 0.95 (95%CI: 0.93, 0.96), respectively. The pooled sensitivity, specificity, and SROC-AUC of deep learning (DL) for diagnosing PD were 97% (95%CI: 95%, 98%), 95% (95%CI: 89%, 98%), and 0.98 (95%CI: 0.96, 0.99), respectively.

CONCLUSION

18F-FDG PET has a high accuracy in differentiating PD from APS, among which AI-assisted automatic classification performs well, with a diagnostic accuracy comparable to that of radiologists, and is expected to become an important auxiliary means of clinical diagnosis in the future.

Exploring Analysis Approaches for Using the Dopamine Transporter Striatal Binding Ratio in Early- to Mid-Stage Parkinson's Disease Modification Trials

BACKGROUND

The dopamine transporter striatal binding ratio (DAT SBR) has been used as an outcome measure in Parkinson's disease (PD) trials of potential disease-modifying therapies; however, both patient characteristics and analysis approach potentially complicate its interpretation.

OBJECTIVE

The aim was to explore how well DAT SBR reflects PD motor severity across different striatal subregions and the relationship to disease duration, and side of onset.

METHODS

DAT SBR for the anterior and posterior putamen and caudate in both hemispheres was obtained using validated automated quantitative software on baseline scans of 132 patients recruited for the Exenatide PD2 and PD3 trials. Associations between mean and lateralized SBR subregions (posterior and anterior putamen and caudate) and summed and lateralized motor characteristics were explored using regression analysis. Analyses were repeated considering disease duration and limiting analysis to the less-affected hemisphere.

RESULTS

Lateralized bradykinesia was most consistently associated with the loss of DAT uptake in the contralateral anterior putamen. There was much higher variance in the posterior putamen, and in all regions in those with longer duration disease, although bradykinesia remained robustly associated with anterior putaminal DAT uptake even in longer-duration patients. Restricting analyses to the less-affected side did not usefully reduce the variance compared to the overall cohort.

CONCLUSION

These data suggest that DAT SBR could be a useful biomarker in disease-modifying trials, but a focus on anterior striatal subregions and incorporating disease duration into analyses may improve its utility.

The association of vagal atrophy with parameters of autonomic function in multiple system atrophy and progressive supranuclear palsy

Background

Vagal atrophy is a hallmark of Parkinson's disease (PD) and has been found to be associated with autonomic dysfunction, while analyses of the vagus nerve (VN) in atypical Parkinsonian syndromes (APS) have not yet been performed. We here investigate the characteristics of the VN in multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) and, in a second step, its potential as a possible biomarker for orthostatic dysregulation.

Objectives

The aim was to compare the VN pathology in MSA and PSP with healthy individuals and patients with PD as a differentiating factor and to further analyse the correlation of the VN with clinical parameters and cardiovascular response.

Design

We conducted a monocentric, cross-sectional cohort study in 41 APS patients and compared nerve ultrasound (NUS) parameters with 90 PD patients and 39 healthy controls.

Methods

In addition to a detailed neurological history and examination, several clinical severity and motor scores were obtained. Autonomic symptoms were reported in the Scales for Outcomes in Parkinson's Disease - Autonomic questionnaire. Further scores were used to detect other non-motor symptoms, quality of life and cognition. Additionally, we performed a head up tilt test (HUTT) and NUS of the VN. We conducted correlation analyses of the VN cross-sectional area (CSA) with clinical scores and the heart rate and blood pressure variability parameters of the HUTT.

Results

The examination demonstrated a high prevalence of abnormal autonomic response in both MSA (90%) and PSP (80%). The VN CSA correlated with spectral parameters of the HUTT, which are associated with sympatho-vagal imbalance. In addition, the CSA of the VN in patients with PD and PSP were significantly smaller than in healthy controls. In MSA, however, there was no marked vagal atrophy in comparison.

Conclusion

The occurrence of autonomic dysfunction was high in MSA and PSP, which underlines its impact on these syndromes. Our findings indicate a connection between vagal pathology and autonomic dysfunction and might contribute to a better comprehension of APS. To further evaluate the clinical relevance and the VN as a possible marker of autonomic dysfunction in APS, prospective longitudinal observations are necessary.

Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression

Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 PD patients and 366 controls, aiming to disentangle PD-specific neuroanatomical alterations. The results reveal that these neuroanatomical alterations in PD are correlated with individual differences in dopamine transporter binding deficit, neurodegeneration biomarkers, and clinical severity and progression. The correlation with clinical severity is verified in an external cohort. Notably, certain proteins in the cerebrospinal fluid are strongly associated with PD-specific features, particularly those involved in the immune function. The most notable neuroanatomical alterations are observed in both subcortical and temporal regions. Our findings provide deeper insights into the patterns of brain atrophy in PD and potential underlying molecular mechanisms, paving the way for earlier patient stratification and the development of treatments to slow down neurodegeneration.

Considerations on How to Prevent Parkinson's Disease Through Exercise

The increasing prevalence of people with Parkinson's disease (PD) necessitates a high priority for finding interventions to delay or even prevent the onset of PD. There is converging evidence that exercise may exert disease-modifying effects in people with clinically manifest PD, but whether exercise also has a preventive effect or is able to modify the progression of the pathology in the prodromal phase of PD is unclear. Here we provide some considerations on the design of trials that aim to prevent PD through exercise. First, we discuss the who could benefit from exercise, and potential exercise-related risks. Second, we discuss what specific components of exercise mediate the putative disease-modifying effects. Third, we address how methodological challenges such as blinding, adherence and remote monitoring could be handled and how we can measure the efficacy of exercise as modifier of the course of prodromal PD. We hope that these considerations help in designing exercise prevention trials for persons at risk of developing PD.

Combined MR quantitative susceptibility mapping and multi-shell diffusion in Parkinson's disease

BACKGROUND AND PURPOSE

Quantitative susceptibility mapping (QSM), neurite orientation dispersion and density imaging (NODDI), and the g-ratio have separately shown differences between Parkinson's disease (PD) and healthy controls. The g-ratio has, however, not been studied in PD in the substantia nigra (SN) and the putamen. A combination of these methods could also potentially be a complementary imaging biomarker for PD. This study aimed to assess the diagnostic performance of QSM, NODDI, the g-ratio, and a combined QSM-NODDI imaging marker in the SN and putamen of PD patients.

METHODS

In this prospective study, the diagnostic performance of median region of interest values was compared in a cohort of 15 participants with PD and 14 healthy controls after manual segmentation. The diagnostic performance was assessed using the area under curve (AUC) for the receiving operator characteristic.

RESULTS

Median QSM in the contralateral SN identified PD with AUC 0.77, and median isotropic volume fraction identified PD in the ipsilateral SN with AUC 0.68. A combined NODDI-QSM marker improved diagnostic performance (AUC 0.80). No significant differences were found in the g-ratio.

CONCLUSION

A combination of median QSM and median isotropic volume fraction improves the differentiation of PD from healthy controls and is a potential biomarker in the diagnostics of PD. This confirms previously reported results indicating that combining QSM and NODDI modestly improves differentiation of PD.

Cholinergic changes in Lewy body disease: implications for presentation, progression and subtypes

Cholinergic degeneration is significant in Lewy body disease, including Parkinson's disease, dementia with Lewy bodies, and isolated REM sleep behaviour disorder. Extensive research has demonstrated cholinergic alterations in the CNS of these disorders. More recently, studies have revealed cholinergic denervation in organs that receive parasympathetic denervation. This enables a comprehensive review of cholinergic changes in Lewy body disease, encompassing both central and peripheral regions, various disease stages and diagnostic categories. Across studies, brain regions affected in Lewy body dementia show equal or greater levels of cholinergic impairment compared to the brain regions affected in Lewy body disease without dementia. This observation suggests a continuum of cholinergic alterations between these disorders. Patients without dementia exhibit relative sparing of limbic regions, whereas occipital and superior temporal regions appear to be affected to a similar extent in patients with and without dementia. This implies that posterior cholinergic cell groups in the basal forebrain are affected in the early stages of Lewy body disorders, while more anterior regions are typically affected later in the disease progression. The topographical changes observed in patients affected by comorbid Alzheimer pathology may reflect a combination of changes seen in pure forms of Lewy body disease and those seen in Alzheimer's disease. This suggests that Alzheimer co-pathology is important to understand cholinergic degeneration in Lewy body disease. Thalamic cholinergic innervation is more affected in Lewy body patients with dementia compared to those without dementia, and this may contribute to the distinct clinical presentations observed in these groups. In patients with Alzheimer's disease, the thalamus is variably affected, suggesting a different sequential involvement of cholinergic cell groups in Alzheimer's disease compared to Lewy body disease. Patients with isolated REM sleep behaviour disorder demonstrate cholinergic denervation in abdominal organs that receive parasympathetic innervation from the dorsal motor nucleus of the vagus, similar to patients who experienced this sleep disorder in their prodrome. This implies that REM sleep behaviour disorder is important for understanding peripheral cholinergic changes in both prodromal and manifest phases of Lewy body disease. In conclusion, cholinergic changes in Lewy body disease carry implications for understanding phenotypes and the influence of Alzheimer co-pathology, delineating subtypes and pathological spreading routes, and for developing tailored treatments targeting the cholinergic system.

Dysfunction of motor cortices in Parkinson's disease

The cerebral cortex has long been thought to be involved in the pathophysiology of motor symptoms of Parkinson's disease. The impaired cortical function is believed to be a direct and immediate effect of pathologically patterned basal ganglia output, mediated to the cerebral cortex by way of the ventral motor thalamus. However, recent studies in humans with Parkinson's disease and in animal models of the disease have provided strong evidence suggesting that the involvement of the cerebral cortex is much broader than merely serving as a passive conduit for subcortical disturbances. In the present review, we discuss Parkinson's disease-related changes in frontal cortical motor regions, focusing on neuropathology, plasticity, changes in neurotransmission, and altered network interactions. We will also examine recent studies exploring the cortical circuits as potential targets for neuromodulation to treat Parkinson's disease.

Gray matter volume alterations in de novo Parkinson's disease: A mediational role in the interplay between sleep quality and anxiety

OBJECTIVE

Parkinson's disease (PD) is increasingly recognized for its non-motor symptoms, among which emotional disturbances and sleep disorders frequently co-occur. The commonality of neuroanatomical underpinnings for these symptoms is not fully understood. This study is intended to investigate the differences in gray matter volume (GMV) between PD patients with anxiety (A-PD) and those without anxiety (NA-PD). Additionally, it seeks to uncover the interplay between GMV variations and the manifestations of anxiety and sleep quality.

METHODS

A total of 37 A-PD patients, 43 NA-PD patients, and 36 healthy controls (HCs) were recruited, all of whom underwent voxel-based morphometry (VBM) analysis. Group differences in GMV were assessed using analysis of covariance (ANCOVA). Partial correlation between GMV, anxiety symptom, and sleep quality were analyzed. Mediation analysis explored the mediating role of the volume of GMV-distinct brain regions on the relationship between sleep quality and anxiety within the PD patient cohort.

RESULTS

A-PD patients showed significantly lower GMV in the fusiform gyrus (FG) and right inferior temporal gyrus (ITG) compared to HCs and NA-PD patients. GMV in these regions correlated negatively with Hamilton Anxiety Rating Scale (HAMA) scores (right ITG: r = -0.690, p < 0.001; left FG: r = -0.509, p < 0.001; right FG: r = -0.576, p < 0.001) and positively with sleep quality in PD patients (right ITG: r = 0.592, p < 0.001; left FG: r = 0.356, p = 0.001; right FG: r = 0.470, p < 0.001). Mediation analysis revealed that GMV in the FG and right ITG mediated the relationship between sleep quality and anxiety symptoms, with substantial effect sizes accounted for by the right ITG (25.74%) and FG (left: 11.90%, right: 15.59%).

CONCLUSION

This study has shed further light on the relationship between sleep disturbances and anxiety symptoms in PD patients. Given the pivotal roles of the FG and the ITG in facial recognition and the recognition of emotion-related facial expressions, our findings indicate that compromised sleep quality, under the pathological conditions of PD, may exacerbate the reduction in GMV within these regions, impairing the recognition of emotional facial expressions and thereby intensifying anxiety symptoms.

Cortical changes in the brain of patients with hemifacial spasm

OBJECTIVE

Hemifacial spasm (HFS) is a movement disorder characterized by involuntary muscle contractions on one side of the face. It is associated with disturbances in the brain's functional architecture. Despite this, the structural alterations in the brain related to HFS remain poorly understood. In this study, we investigated the cortical morphology changes in patients with HFS compared to healthy controls (HCs).

METHODS

We analyzed 3D T1-weighted MRI images from 33 patients with left-sided primary HFS and 33 age- and sex-matched HCs. Measurements of cortical thickness (CTh), sulcal depth, local gyrification index (lGI), and fractal dimension were taken using a computational anatomy toolbox. A general linear model, accounting for age, gender, and total brain volume, was applied for statistical analyses. Significant clusters were then assessed for correlations with clinical parameters.

RESULTS

The HFS patients displayed several cortical abnormalities when compared to HCs, including reduced CTh in the contralateral precentral gyrus and left orbitofrontal cortex, decreased sulcal depth in the left orbitofrontal cortex, and increased lGI in the right insula and superior temporal cortex. However, fractal dimension did not differ significantly between the groups. Additionally, in HFS patients, a notable negative correlation was found between the sulcal depth in the left orbitofrontal cortex and the Beck Depression Inventory-II scores.

CONCLUSIONS

Our findings reveal that HFS is associated with specific surface-based morphological changes in the brain. These alterations contribute to a deeper understanding of the neurophysiological mechanisms involved in HFS and may have implications for future research and treatment strategies.

Intracerebral hemodynamic abnormalities in patients with Parkinson's disease: Comparison between multi-delay arterial spin labelling and conventional single-delay arterial spin labelling

PURPOSE

The purpose of this study was to analyze the intracerebral abnormalities of hemodynamics in patients with Parkinson's disease (PD) through arterial spin labelling (ASL) technique with multi-delay ASL (MDASL) and conventional single-delay ASL (SDASL) protocols and to verify the potential clinical application of these features for the diagnosis of PD.

MATERIALS AND METHODS

Perfusion data of the brain obtained using MDASL and SDASL in patients with PD were compared to those obtained in healthy control (HC) subjects. Intergroup comparisons of z-scored cerebral blood flow (zCBF), arterial transit time (zATT) and cerebral blood volume (zCBV) were performed via voxel-based analysis. Performance of these perfusion metrics were estimated using area under the receiver operating characteristic curve (AUC) and compared using Delong test.

RESULTS

A total of 47 patients with PD (29 men; 18 women; mean age, 69.0 ± 7.6 (standard deviation, [SD]) years; range: 50.0-84.0 years) and 50 HC subjects (28 men; 22 women; mean age, 70.1 ± 6.2 [SD] years; range: 50.0-93.0 years) were included. Relative to the uncorrected-zCBF map, the corrected-zCBF map further refined the distributed brain regions in the PD group versus the HC group, manifested as the extension of motor-related regions (PFWE < 0.001). Compared to the HC subjects, patients with PD had elevated zATT and zCBV in the right putamen, a shortened zATT in the superior frontal gyrus, and specific zCBV variations in the left precuneus and the right supplementary motor area (PFWE < 0.001). The corrected-zCBF (AUC, 0.90; 95% confidence interval [CI]: 0.84-0.96) showed better classification performance than uncorrected-zCBF (AUC, 0.84; 95% CI: 0.75-0.92) (P = 0.035). zCBV achieved an AUC of 0.89 (95% CI: 0.82-0.96) and zATT achieved an AUC of 0.66 (95% CI: 0.55-0.77). The integration model of hemodynamic features from MDASL provided improved performance (AUC, 0.97; 95% CI: 0.95-0.98) for the diagnosis of PD by comparison with each perfusion model (P < 0.001).

CONCLUSION

ASL identifies impaired hemodynamics in patients with PD including regional abnormalities of CBF, CBV and ATT, which can better be mapped with MDASL compared to SDASL. These findings provide complementary depictions of perfusion abnormalities in patients with PD and highlight the clinical feasibility of MDASL.

Increased mean diffusivity of the caudal motor SNc identifies patients with REM sleep behaviour disorder and Parkinson's disease

Idiopathic rapid eye movement sleep behaviour disorder (iRBD)-a Parkinson's disease (PD) prodrome-might exhibit neural changes similar to those in PD. Substantia nigra pars compacta (SNc) degeneration underlies motor symptoms of PD. In iRBD and early PD (ePD), we measured diffusion MRI (dMRI) in the caudal motor SNc, which overlaps the nigrosome-1-the earliest-degenerating dopaminergic neurons in PD-and in the striatum. Nineteen iRBD, 26 ePD (1.7 ± 0.03 years), and 46 age-matched healthy controls (HCs) were scanned at Western University, and 47 iRBD, 115 ePD (0.9 ± 0.01 years), and 56 HCs were scanned through the Parkinson's Progression Markers Initiative, using 3T MRI. We segmented the SNc and striatum into subregions using automated probabilistic tractography to the cortex. We measured mean diffusivity (MD) and fractional anisotropy (FA) along white-matter bundles and subregional surfaces. We performed group-level and classification analyses. Increased caudal motor SNc surface MD was the only iRBD-HCs and ePD-HCs difference replicating across datasets (padj < 0.05). No iRBD-ePD differences emerged. Caudal motor SNc surface MD classified patient groups from HCs at the single-subject level with good-to-excellent balanced accuracy in an independent sample (0.91 iRBD and 0.86 iRBD and ePD combined), compared to fair performance for total SNc surface MD (0.72 iRBD and ePD). Caudal motor SNc surface MD correlated significantly with MDS-UPDRS-III scores in ePD patients. Using dMRI and automated segmentation, we detected changes suggesting altered microstructural integrity in iRBD and ePD in the nigrostriatal subregion known to degenerate first in PD. Surface MD of the caudal motor SNc presents a potential measure for inclusion in neuroimaging biomarkers of iRBD and PD.

Left corticospinal tract could be a biomarker to identify the dual prodromal LRRK2/GBA mutated Parkinson's disease

INTRODUCTION

Prodromal Parkinson's disease (PD) carriers of dual leucine-rich repeat kinase 2 (LRRK2) and glucosylceramidase β (GBA) variants are rare, and their biomarkers are less well developed.

OBJECTIVE

This study aimed to investigate the biomarkers for diagnosing the prodromal phase of LRRK2-GBA-PD (LRRK2-GBA-prodromal).

METHODS

We assessed the clinical and whole-brain white matter microstructural characteristics of 54 prodromal PD carriers of dual LRRK2 (100% M239T) and GBA (95% N409S) variants, along with 76 healthy controls (HCs) from the Parkinson's Progression Markers Initiative (PPMI) cohort.

RESULTS

By analyzing the four values of 100 nodes on 20 fiber bundles, totaling 8000 data points, we identified the smallest p value in the fractional anisotropy (FA) value of the 38th segment of left corticospinal tract (L-CST) with differences between LRRK2-GBA-prodromal and HCs (p = 8.94 × 10-9). The FA value of the 38th node of the L-CST was significantly lower in LRRK2-GBA-prodromal (FA value, 0.65) compared with HCs (FA value, 0.71). The receiver-operating characteristic curve showed a cut-off value of 0.218 for the FA value of L-CST, providing sufficient sensitivity (79.2%) and specificity (72.2%) to distinguish double mutation prodromal PD from the healthy population.

CONCLUSION

L-CST, especially the 38th node, may potentially serve as a biomarker for distinguishing individuals with double mutation prodromal PD from the healthy population.

Basic Science in Movement Disorders: Fueling the Engine of Translation into Clinical Practice

Basic Science is crucial for the advancement of clinical care for Movement Disorders. Here, we provide brief updates on how basic science is important for understanding disease mechanisms, disease prevention, disease diagnosis, development of novel therapies and to establish the basis for personalized medicine. We conclude the viewpoint by a call to action to further improve interactions between clinician and basic scientists. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study

OBJECTIVE

To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD).

METHODS

This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model.

RESULTS

The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups.

CONCLUSIONS

The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

White Matter Microstructural Underpinnings of Mild Behavioral Impairment in Parkinson's Disease

BACKGROUND

Patients with Parkinson's disease (PD) experience changes in behavior, personality, and cognition that can manifest even in the initial stages of the disease. Previous studies have suggested that mild behavioral impairment (MBI) should be considered an early marker of cognitive decline. However, the precise neurostructural underpinnings of MBI in early- to mid-stage PD remain poorly understood.

OBJECTIVE

The aim was to explore the changes in white matter microstructure linked to MBI and mild cognitive impairment (MCI) in early- to mid-stage PD using diffusion magnetic resonance imaging (dMRI).

METHODS

A total of 91 PD patients and 36 healthy participants were recruited and underwent anatomical MRI and dMRI, a comprehensive neuropsychological battery, and the completion of the Mild Behavioral Impairment-Checklist. Metrics of white matter integrity included tissue fractional anisotropy (FAt) and radial diffusivity (RDt), free water (FW), and fixel-based apparent fiber density (AFD).

RESULTS

The connection between the left amygdala and the putamen was disrupted when comparing PD patients with MBI (PD-MBI) to PD-non-MBI, as evidenced by increased RDt (η2 = 0.09, P = 0.004) and both decreased AFD (η2 = 0.05, P = 0.048) and FAt (η2 = 0.12, P = 0.014). Compared to controls, PD patients with both MBI and MCI demonstrated increased FW for the connection between the left orbitofrontal gyrus (OrG) and the hippocampus (η2 = 0.22, P = 0.008), augmented RDt between the right OrG and the amygdala (η2 = 0.14, P = 0.008), and increased RDt (η2 = 0.25, P = 0.028) with decreased AFD (η2 = 0.10, P = 0.046) between the right OrG and the caudate nucleus.

CONCLUSION

MBI is associated with abnormal microstructure of connections involving the orbitofrontal cortex, putamen, and amygdala. To our knowledge, this is the first assessment of the white matter microstructure in PD-MBI using dMRI. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Iron deposition in subcortical nuclei of Parkinson's disease: A meta-analysis of quantitative iron-sensitive magnetic resonance imaging studies

BACKGROUND

Iron deposition plays a crucial role in the pathophysiology of Parkinson's disease (PD), yet the distribution pattern of iron deposition in the subcortical nuclei has been inconsistent across previous studies. We aimed to assess the difference patterns of iron deposition detected by quantitative iron-sensitive magnetic resonance imaging (MRI) between patients with PD and patients with atypical parkinsonian syndromes (APSs), and between patients with PD and healthy controls (HCs).

METHODS

A systematic literature search was conducted on PubMed, Embase, and Web of Science databases to identify studies investigating the iron content in PD patients using the iron-sensitive MRI techniques (R2* and quantitative susceptibility mapping [QSM]), up until May 1, 2023. The quality assessment of case-control and cohort studies was performed using the Newcastle-Ottawa Scale, whereas diagnostic studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Standardized mean differences and summary estimates of sensitivity, specificity, and area under the curve (AUC) were calculated for iron content, using a random effects model. We also conducted the subgroup-analysis based on the MRI sequence and meta-regression.

RESULTS

Seventy-seven studies with 3192 PD, 209 multiple system atrophy (MSA), 174 progressive supranuclear palsy (PSP), and 2447 HCs were included. Elevated iron content in substantia nigra (SN) pars reticulata (P <0.001) and compacta (P <0.001), SN (P <0.001), red nucleus (RN, P <0.001), globus pallidus (P <0.001), putamen (PUT, P = 0.009), and thalamus (P = 0.046) were found in PD patients compared with HCs. PD patients showed lower iron content in PUT (P <0.001), RN (P = 0.003), SN (P = 0.017), and caudate nucleus (P = 0.027) than MSA patients, and lower iron content in RN (P = 0.001), PUT (P <0.001), globus pallidus (P = 0.004), SN (P = 0.015), and caudate nucleus (P = 0.001) than PSP patients. The highest diagnostic accuracy distinguishing PD from HCs was observed in SN (AUC: 0.85), and that distinguishing PD from MSA was found in PUT (AUC: 0.90). In addition, the best diagnostic performance was achieved in the RN for distinguishing PD from PSP (AUC: 0.84).

CONCLUSION

Quantitative iron-sensitive MRI could quantitatively detect the iron content of subcortical nuclei in PD and APSs, while it may be insufficient to accurately diagnose PD. Future studies are needed to explore the role of multimodal MRI in the diagnosis of PD.

REGISTRISION

PROSPERO; CRD42022344413.

Neuromelanin-sensitive magnetic resonance imaging: Possibilities and promises as an imaging biomarker for Parkinson's disease

Parkinson's disease (PD) is an age-related progressive neurodegenerative disorder characterized by both motor and non-motor symptoms resulting from the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and noradrenergic neurons in the locus coeruleus (LC). The current diagnosis of PD primarily relies on motor symptoms, often leading to diagnoses in advanced stages, where a significant portion of SNpc dopamine neurons has already succumbed. Therefore, the identification of imaging biomarkers for early-stage PD diagnosis and disease progression monitoring is imperative. Recent studies propose that neuromelanin-sensitive magnetic resonance imaging (NM-MRI) holds promise as an imaging biomarker. In this review, we summarize the latest findings concerning NM-MRI characteristics at various stages in patients with PD and those with atypical parkinsonism. In conclusion, alterations in neuromelanin within the LC are associated with non-motor symptoms and prove to be a reliable imaging biomarker in the prodromal phase of PD. Furthermore, NM-MRI demonstrates efficacy in differentiating progressive supranuclear palsy (PSP) from PD and multiple system atrophy with predominant parkinsonism. The spatial patterns of changes in the SNpc can be indicative of PD progression and aid in distinguishing between PSP and synucleinopathies. We recommend that patients with PD and individuals at risk for PD undergo regular NM-MRI examinations. This technology holds the potential for widespread use in PD diagnosis.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease

BACKGROUNDS

Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context.

OBJECTIVES

Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD.

METHODS

This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter.

FINDINGS

This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD.

CONCLUSION

The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

How Lifetime Evolution of Parkinson's Disease Could Shape Clinical Trial Design: A Shared Patient-Clinician Viewpoint

Parkinson's disease (PD) has a long, heterogeneous, pre-diagnostic phase, during which pathology insidiously accumulates. Increasing evidence suggests that environmental and lifestyle factors in early life contribute to disease risk and progression. Thanks to the extensive study of this pre-diagnostic phase, the first prevention trials of PD are being designed. However, the highly heterogenous evolution of the disease across the life course is not yet sufficiently taken into account. This could hamper clinical trial success in the advent of biological disease definitions. In an interdisciplinary patient-clinician study group, we discussed how an approach that incorporates the lifetime evolution of PD may benefit the design of disease-modifying trials by impacting population, target and outcome selection. We argue that the timepoint of exposure to risk and protective factors plays a critical role in PD subtypes, influencing population selection. In addition, recent developments in differential disease mechanisms, aided by biological disease definitions, could impact optimal treatment targets. Finally, multimodal biomarker panels using this lifetime approach will likely be most sensitive as progression markers for more personalized trials. We believe that the lifetime evolution of PD should be considered in the design of clinical trials, and that such initiatives could benefit from more patient-clinician partnerships.

Parkinson's disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: a functional near-infrared spectroscopy study

Significance

People with Parkinson's disease (PD) experience changes in fine motor skills, which is viewed as one of the hallmark signs of this disease. Due to its non-invasive nature and portability, functional near-infrared spectroscopy (fNIRS) is a promising tool for assessing changes related to fine motor skills.

Aim

We aim to compare activation patterns in the primary motor cortex using fNIRS, comparing volunteers with PD and sex- and age-matched control participants during a fine motor task and walking. Moreover, inter and intrahemispheric functional connectivity (FC) was investigated during the resting state.

Approach

We used fNIRS to measure the hemodynamic changes in the primary motor cortex elicited by a finger-tapping task in 20 PD patients and 20 controls matched for age, sex, education, and body mass index. In addition, a two-minute walking task was carried out. Resting-state FC was also assessed.

Results

Patients with PD showed delayed hypoactivation in the motor cortex during the fine motor task with the dominant hand and delayed hyperactivation with the non-dominant hand. The findings also revealed significant correlations among various measures of hemodynamic activity in the motor cortex using fNIRS and different cognitive and clinical variables. There were no significant differences between patients with PD and controls during the walking task. However, there were significant differences in interhemispheric connectivity between PD patients and control participants, with a statistically significant decrease in PD patients compared with control participants.

Conclusions

Decreased interhemispheric FC and delayed activity in the primary motor cortex elicited by a fine motor task may one day serve as one of the many potential neuroimaging biomarkers for diagnosing PD.

Positive relation between dopamine neuron degeneration and metabolic connectivity disruption in the MPTP plus probenecid mouse model of Parkinson's disease

The clinical manifestation of Parkinson's disease (PD) appears when neurodegeneration is already advanced, compromising the efficacy of disease-modifying treatment approaches. Biomarkers to identify the early stages of PD are therefore of paramount importance for the advancement of the therapy of PD. In the present study, by using a mouse model of PD obtained by subchronic treatment with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and the clearance inhibitor probenecid (MPTPp), we identified prodromal markers of PD by combining in vivo positron emission tomography (PET) imaging and ex vivo immunohistochemistry. Longitudinal PET imaging of the dopamine transporter (DAT) by [18F]-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ([18F]-FP-CIT), and brain glucose metabolism by 2-deoxy-2-[18F]-fluoroglucose ([18F]-FDG) were performed before MPTPp treatment and after 1, 3, and 10 MPTPp administrations, in order to assess relation between dopamine neuron integrity and brain connectivity. The results show that in vivo [18F]-FP-CIT in the dorsal striatum was not modified after the first administration of MPTPp, tended to decrease after 3 administrations, and significantly decreased after 10 MPTPp administrations. Post-mortem immunohistochemical analyses of DAT and tyrosine hydroxylase (TH) in the striatum showed a positive correlation with [18F]-FP-CIT, confirming the validity of repeated MPTPp-treated mice as a model that can reproduce the progressive pathological changes in the early phases of PD. Analysis of [18F]-FDG uptake in several brain areas connected to the striatum showed that metabolic connectivity was progressively disrupted, starting from the first MPTPp administration, and that significant connections between cortical and subcortical regions were lost after 10 MPTPp administrations, suggesting an association between dopamine neuron degeneration and connectivity disruption in this PD model. The results of this study provide a relevant model, where new drugs that can alleviate neurodegeneration in PD could be evaluated preclinically.

Neuroimaging of Parkinson's disease by quantitative susceptibility mapping

Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.

Defining Parkinson's Disease: Past and Future

Parkinson's disease (PD) is the second most common still relentlessly progressive neurodegenerative disorder with a long period in which the pathophysiological process is already spreading but cardinal motor symptoms are not present. This review outlines the major developments and milestones in our understanding of PD that have shaped the way we define this disorder. Past criteria and definitions of PD have been based on clinical motor manifestations enabling diagnosis of the disease only in later symptomatic stages. Nevertheless, with advancing knowledge of disease pathophysiology and aim of early disease detection, a major shift of the diagnostic paradigm is being advocated towards a biological definition similar to other neurodegenerative disorders including Alzheimer's disease and Huntington's disease, with the ultimate goal of an earlier, disease course modifying therapy. We summarize the major pillars of this possible approach including in vivo detection of neuronal α-synuclein aggregation, neurodegeneration and genetics and outline their possible application in different contexts of use in the frame of biological PD definition.

Comparative Performance of Susceptibility Map-Weighted MRI According to the Acquisition Planes in the Diagnosis of Neurodegenerative Parkinsonism

OBJECTIVE

To evaluate the diagnostic performance of susceptibility map-weighted imaging (SMwI) taken in different acquisition planes for discriminating patients with neurodegenerative parkinsonism from those without.

MATERIALS AND METHODS

This retrospective, observational, single-institution study enrolled consecutive patients who visited movement disorder clinics and underwent brain MRI and 18F-FP-CIT PET between September 2021 and December 2021. SMwI images were acquired in both the oblique (perpendicular to the midbrain) and the anterior commissure-posterior commissure (AC-PC) planes. Hyperintensity in the substantia nigra was determined by two neuroradiologists. 18F-FP-CIT PET was used as the reference standard. Inter-rater agreement was assessed using Cohen's kappa coefficient. The diagnostic performance of SMwI in the two planes was analyzed separately for the right and left substantia nigra. Multivariable logistic regression analysis with generalized estimating equations was applied to compare the diagnostic performance of the two planes.

RESULTS

In total, 194 patients were included, of whom 105 and 103 had positive results on 18F-FP-CIT PET in the left and right substantia nigra, respectively. Good inter-rater agreement in the oblique (κ = 0.772/0.658 for left/right) and AC-PC planes (0.730/0.741 for left/right) was confirmed. The pooled sensitivities for two readers were 86.4% (178/206, left) and 83.3% (175/210, right) in the oblique plane and 87.4% (180/206, left) and 87.6% (184/210, right) in the AC-PC plane. The pooled specificities for two readers were 83.5% (152/182, left) and 82.0% (146/178, right) in the oblique plane, and 83.5% (152/182, left) and 86.0% (153/178, right) in the AC-PC plane. There were no significant differences in the diagnostic performance between the two planes (P > 0.05).

CONCLUSION

There are no significant difference in the diagnostic performance of SMwI performed in the oblique and AC-PC plane in discriminating patients with parkinsonism from those without. This finding affirms that each institution may choose the imaging plane for SMwI according to their clinical settings.

MRI Assessment of Intrinsic Neural Timescale and Gray Matter Volume in Parkinson's Disease

BACKGROUND

Numerous studies have indicated altered temporal features of the brain function in Parkinson's disease (PD), and the autocorrelation magnitude of intrinsic neural signals, called intrinsic neural timescales, were often applied to estimate how long neural information stored in local brain areas. However, it is unclear whether PD patients at different disease stages exhibit abnormal timescales accompanied with abnormal gray matter volume (GMV).

PURPOSE

To assess the intrinsic timescale and GMV in PD.

STUDY TYPE

Prospective.

POPULATION

74 idiopathic PD patients (44 early stage (PD-ES) and 30 late stage (PD-LS), as determined by the Hoehn and Yahr (HY) severity classification scale), and 73 healthy controls (HC).

FIELD STRENGTH/SEQUENCE

3.0 T MRI scanner; magnetization prepared rapid acquisition gradient echo and echo planar imaging sequences.

ASSESSMENT

The timescales were estimated by using the autocorrelation magnitude of neural signals. Voxel-based morphometry was performed to calculate GMV in the whole brain. Severity of motor symptoms and cognitive impairments were assessed using the unified PD rating scale, the HY scale, the Montreal cognitive assessment, and the mini-mental state examination.

STATISTICAL TEST

Analysis of variance; two-sample t-test; Spearman rank correlation analysis; Mann-Whitney U test; Kruskal-Wallis' H test. A P value <0.05 was considered statistically significant.

RESULTS

The PD group had significantly abnormal intrinsic timescales in the sensorimotor, visual, and cognitive-related areas, which correlated with the symptom severity (ρ = -0.265, P = 0.022) and GMV (ρ = 0.254, P = 0.029). Compared to the HC group, the PD-ES group had significantly longer timescales in anterior cortical regions, whereas the PD-LS group had significantly shorter timescales in posterior cortical regions.

CONCLUSION

This study suggested that PD patients have abnormal timescales in multisystem and distinct patterns of timescales and GMV in cerebral cortex at different disease stages. This may provide new insights for the neural substrate of PD.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Clinical severity in Parkinson's disease is determined by decline in cortical compensation

Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.

Increased iron in the substantia nigra pars compacta identifies patients with early Parkinson'sdisease: A 3T and 7T MRI study

Degeneration in the substantia nigra (SN) pars compacta (SNc) underlies motor symptoms in Parkinson's disease (PD). Currently, there are no neuroimaging biomarkers that are sufficiently sensitive, specific, reproducible, and accessible for routine diagnosis or staging of PD. Although iron is essential for cellular processes, it also mediates neurodegeneration. MRI can localize and quantify brain iron using magnetic susceptibility, which could potentially provide biomarkers of PD. We measured iron in the SNc, SN pars reticulata (SNr), total SN, and ventral tegmental area (VTA), using quantitative susceptibility mapping (QSM) and R2* relaxometry, in PD patients and age-matched healthy controls (HCs). PD patients, diagnosed within five years of participation and HCs were scanned at 3T (22 PD and 23 HCs) and 7T (17 PD and 21 HCs) MRI. Midbrain nuclei were segmented using a probabilistic subcortical atlas. QSM and R2* values were measured in midbrain subregions. For each measure, groups were contrasted, with Age and Sex as covariates, and receiver operating characteristic (ROC) curve analyses were performed with repeated k-fold cross-validation to test the potential of our measures to classify PD patients and HCs. Statistical differences of area under the curves (AUCs) were compared using the Hanley-MacNeil method (QSM versus R2*; 3T versus 7T MRI). PD patients had higher QSM values in the SNc at both 3T (padj = 0.001) and 7T (padj = 0.01), but not in SNr, total SN, or VTA, at either field strength. No significant group differences were revealed using R2* in any midbrain region at 3T, though increased R2* values in SNc at 7T MRI were marginally significant in PDs compared to HCs (padj = 0.052). ROC curve analyses showed that SNc iron measured with QSM, distinguished early PD patients from HCs at the single-subject level with good diagnostic accuracy, using 3T (mean AUC = 0.83, 95 % CI = 0.82-0.84) and 7T (mean AUC = 0.80, 95 % CI = 0.79-0.81) MRI. Mean AUCs reported here are from averages of tests in the hold-out fold of cross-validated samples. The Hanley-MacNeil method demonstrated that QSM outperforms R2* in discriminating PD patients from HCs at 3T, but not 7T. There were no significant differences between 3T and 7T in diagnostic accuracy of QSM values in SNc. This study highlights the importance of segmenting midbrain subregions, performed here using a standardized atlas, and demonstrates high accuracy of SNc iron measured with QSM at 3T MRI in identifying early PD patients. QSM measures of SNc show potential for inclusion in neuroimaging diagnostic biomarkers of early PD. An MRI diagnostic biomarker of PD would represent a significant clinical advance.

Changes in iron load in specific brain areas lead to neurodegenerative diseases of the central nervous system

The causes of neurodegenerative diseases remain largely elusive, increasing their personal and societal impacts. To reveal the causal effects of iron load on Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis and multiple sclerosis, we used Mendelian randomisation and brain imaging data from a UK Biobank genome-wide association study of 39,691 brain imaging samples (predominantly of European origin). Using susceptibility-weighted images, which reflect iron load, we analysed genetically significant brain regions. Inverse variance weighting was used as the main estimate, while MR Egger and weighted median were used to detect heterogeneity and pleiotropy. Nine clear associations were obtained. For AD and PD, an increased iron load was causative: the right pallidum for AD and the right caudate, left caudate and right accumbens for PD. However, a reduced iron load was identified in the right and left caudate for multiple sclerosis, the bilateral hippocampus for mixed vascular dementia and the left thalamus and bilateral accumbens for subcortical vascular dementia. Thus, changes in iron load in different brain regions have causal effects on neurodegenerative diseases. Our results are crucial for understanding the pathogenesis and investigating the treatment of these diseases.

Longitudinal prognosis of Parkinson's outcomes using causal connectivity

Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

A Comprehensive Review of the Role of Biomarkers in Early Diagnosis of Parkinson's Disease

Parkinson's disease (PD) is a complex neurological, degenerative clinical condition depicted by the advancing loss of dopaminergic neurons in the substantia nigra pars compacta, which manifests itself as a myriad of sensorimotor and non-motor signs in patients. The disease occurs due to the reduced levels of the neurotransmitter dopamine in the brain, which is primarily associated with functional characteristics regarding mobility and cognition. The basal ganglion is mainly involved in the generation of cognitive functions and therefore is the most significantly associated area in PD. Since the classical diagnosis and assessment of PD depends majorly on the appearance of motor characteristics, which only arise when ~60-80% of the dopamine neuronal cell death has already occurred, it is imperative we focus on identifying biomarkers that can help us assess and diagnose PD in the earlier stages of disease progression, thus providing a better prognosis for the patients. This review article will focus on the different biomarkers that are currently available and in use, divided under the headings of clinical, biological, imaging, and genetic biomarkers, and assess their specificity and sensitivity toward providing an early assessment of Parkinson's for the patients and the future of preclinical diagnostics using molecular biomarkers. PD affects over 1% of the population worldwide and only ranks second to Alzheimer's disease in the context of its incidence and consequent socioeconomic burden. While recent breakthroughs in biomarkers have dramatically improved patients' odds of survival and prognosis, it still remains primarily a symptomatic diagnostic tool. It is an area of research that requires to focus on creating more advanced approaches toward diagnosing PD early, involving clinical diagnostics, neuroimaging technology, and molecular biology collaborations to provide the highest degree of care and quality of life that a Parkinson's patient deserves.

Cholinergic basal forebrain system degeneration underlies postural instability/gait difficulty and attention impairment in Parkinson's disease

BACKGROUND AND PURPOSE

The specific pathophysiological mechanisms underlying postural instability/gait difficulty (PIGD) and cognitive function in Parkinson's disease (PD) remain unclear. Both postural and gait control, as well as cognitive function, are associated with the cholinergic basal forebrain (cBF) system.

METHODS

A total of 84 PD patients and 82 normal controls were enrolled. Each participant underwent motor and cognitive assessments. Diffusion tensor imaging was used to detect structural abnormalities in the cBF system. The cBF was segmented using FreeSurfer, and its fiber tract was traced using probabilistic tractography. To provide information on extracellular water accumulation, free-water fraction (FWf) was quantified. FWf in the cBF and its fiber tract, as well as cortical projection density, were extracted for statistical analyses.

RESULTS

Patients had significantly higher FWf in the cBF (p < 0.001) and fiber tract (p = 0.021) than normal controls, as well as significantly lower cBF projection in the occipital (p < 0.001), parietal (p < 0.001) and prefrontal cortex (p = 0.005). In patients, a higher FWf in the cBF correlated with worse PIGD score (r = 0.306, p = 0.006) and longer Trail Making Test A time (r = 0.303, p = 0.007). Attentional function (Trail Making Test A) partially mediated the association between FWf in the cBF and PIGD score (indirect effect, a*b = 0.071; total effect, c = 0.256; p = 0.006).

CONCLUSIONS

Our findings suggest that degeneration of the cBF system in PD, from the cBF to its fiber tract and cortical projection, plays an important role in cognitive-motor interaction.

Neurovascular coupling alteration in drug-naïve Parkinson's disease: The underlying molecular mechanisms and levodopa's restoration effects

BACKGROUND

Parkinson's disease (PD) patients exhibit an imbalance between neuronal activity and perfusion, referred to as abnormal neurovascular coupling (NVC). Nevertheless, the underlying molecular mechanism and how levodopa, the standard treatment in PD, regulates NVC is largely unknown.

MATERIAL AND METHODS

A total of 52 drug-naïve PD patients and 49 normal controls (NCs) were enrolled. NVC was characterized in vivo by relating cerebral blood flow (CBF) and amplitude of low-frequency fluctuations (ALFF). Motor assessments and MRI scanning were conducted on drug-naïve patients before and after levodopa therapy (OFF/ON state). Regional NVC differences between patients and NCs were identified, followed by an assessment of the associated receptors/transporters. The influence of levodopa on NVC, CBF, and ALFF within these abnormal regions was analyzed.

RESULTS

Compared to NCs, OFF-state patients showed NVC dysfunction in significantly lower NVC in left precentral, postcentral, superior parietal cortex, and precuneus, along with higher NVC in left anterior cingulate cortex, right olfactory cortex, thalamus, caudate, and putamen (P-value <0.0006). The distribution of NVC differences correlated with the density of dopaminergic, serotonin, MU-opioid, and cholinergic receptors/transporters. Additionally, levodopa ameliorated abnormal NVC in most of these regions, where there were primarily ALFF changes with limited CBF modifications.

CONCLUSION

Patients exhibited NVC dysfunction primarily in the striato-thalamo-cortical circuit and motor control regions, which could be driven by dopaminergic and nondopaminergic systems, and levodopa therapy mainly restored abnormal NVC by modulating neuronal activity.

A biological classification of Parkinson's disease: the SynNeurGe research diagnostic criteria

With the hope that disease-modifying treatments could target the molecular basis of Parkinson's disease, even before the onset of symptoms, we propose a biologically based classification. Our classification acknowledges the complexity and heterogeneity of the disease by use of a three-component system (SynNeurGe): presence or absence of pathological α-synuclein (S) in tissues or CSF; evidence of underlying neurodegeneration (N) defined by neuroimaging procedures; and documentation of pathogenic gene variants (G) that cause or strongly predispose to Parkinson's disease. These three components are linked to a clinical component (C), defined either by a single high-specificity clinical feature or by multiple lower-specificity clinical features. The use of a biological classification will enable advances in both basic and clinical research, and move the field closer to the precision medicine required to develop disease-modifying therapies. We emphasise the initial application of these criteria exclusively for research. We acknowledge its ethical implications, its limitations, and the need for prospective validation in future studies.

A biological definition of neuronal α-synuclein disease: towards an integrated staging system for research

Parkinson's disease and dementia with Lewy bodies are currently defined by their clinical features, with α-synuclein pathology as the gold standard to establish the definitive diagnosis. We propose that, given biomarker advances enabling accurate detection of pathological α-synuclein (ie, misfolded and aggregated) in CSF using the seed amplification assay, it is time to redefine Parkinson's disease and dementia with Lewy bodies as neuronal α-synuclein disease rather than as clinical syndromes. This major shift from a clinical to a biological definition of Parkinson's disease and dementia with Lewy bodies takes advantage of the availability of tools to assess the gold standard for diagnosis of neuronal α-synuclein (n-αsyn) in human beings during life. Neuronal α-synuclein disease is defined by the presence of pathological n-αsyn species detected in vivo (S; the first biological anchor) regardless of the presence of any specific clinical syndrome. On the basis of this definition, we propose that individuals with pathological n-αsyn aggregates are at risk for dopaminergic neuronal dysfunction (D; the second biological anchor). Our biological definition establishes a staging system, the neuronal α-synuclein disease integrated staging system (NSD-ISS), rooted in the biological anchors (S and D) and the degree of functional impairment caused by clinical signs or symptoms. Stages 0-1 occur without signs or symptoms and are defined by the presence of pathogenic variants in the SNCA gene (stage 0), S alone (stage 1A), or S and D (stage 1B). The presence of clinical manifestations marks the transition to stage 2 and beyond. Stage 2 is characterised by subtle signs or symptoms but without functional impairment. Stages 2B-6 require both S and D and stage-specific increases in functional impairment. A biological definition of neuronal α-synuclein disease and an NSD-ISS research framework are essential to enable interventional trials at early disease stages. The NSD-ISS will evolve to include the incorporation of data-driven definitions of stage-specific functional anchors and additional biomarkers as they emerge and are validated. Presently, the NSD-ISS is intended for research use only; its application in the clinical setting is premature and inappropriate.

Longitudinal brain structure changes in Parkinson's disease: A replication study

CONTEXT

An existing major challenge in Parkinson's disease (PD) research is the identification of biomarkers of disease progression. While magnetic resonance imaging is a potential source of PD biomarkers, none of the magnetic resonance imaging measures of PD are robust enough to warrant their adoption in clinical research. This study is part of a project that aims to replicate 11 PD studies reviewed in a recent survey (JAMA neurology, 78(10) 2021) to investigate the robustness of PD neuroimaging findings to data and analytical variations.

OBJECTIVE

This study attempts to replicate the results in Hanganu et al. (Brain, 137(4) 2014) using data from the Parkinson's Progression Markers Initiative (PPMI).

METHODS

Using 25 PD subjects and 18 healthy controls, we analyzed the rate of change of cortical thickness and of the volume of subcortical structures, and we measured the relationship between structural changes and cognitive decline. We compared our findings to the results in the original study.

RESULTS

(1) Similarly to the original study, PD patients with mild cognitive impairment (MCI) exhibited increased cortical thinning over time compared to patients without MCI in the right middle temporal gyrus, insula, and precuneus. (2) The rate of cortical thinning in the left inferior temporal and precentral gyri in PD patients correlated with the change in cognitive performance. (3) There were no group differences in the change of subcortical volumes. (4) We did not find a relationship between the change in subcortical volumes and the change in cognitive performance.

CONCLUSION

Despite important differences in the dataset used in this replication study, and despite differences in sample size, we were able to partially replicate the original results. We produced a publicly available reproducible notebook allowing researchers to further investigate the reproducibility of the results in Hanganu et al. (2014) when more data is added to PPMI.

Alleviating Stress in Parkinson's Disease: Symptomatic Treatment, Disease Modification, or Both?

Psychological stress, a state of mental strain caused by mentally or physically threatening situations, plays a significant role in Parkinson's disease (PD). Motor symptoms worsen during acute stress and common non-motor symptoms in PD, such as anxiety and depression, are linked to chronic stress. Although evidence in humans is lacking, animal models of PD suggest that chronic stress can accelerate dopaminergic cell death. This suggests that stress-reducing interventions have not only symptomatic, but perhaps also disease-modifying effects. Our objective was to identify the most promising strategies for stress-reduction in PD and to analyze their potential value for disease-modification. An unstructured literature search was performed, primarily focusing on papers published between 2020-2023. Several large clinical trials have tested the efficacy of aerobic exercise and mindfulness-based interventions on PD symptoms. The evidence is promising, but not definitive yet: some exercise trials found a reduction in stress-related symptoms, whereas others did not or did not report it. In the majority of trials, biological measures of stress and of disease progression are missing. Furthermore, follow-up periods were generally too short to measure disease-modifying effects. Hence, mechanisms underlying the intervention effects remain largely unclear. These effects may consist of attenuating progressive neurodegeneration (measured with MRI-markers of substantia nigra integrity or cortical thickness), or a strengthening of compensatory cerebral mechanisms (measured with functional neuroimaging), or both. Lifestyle interventions are effective for alleviating stress-related symptoms in PD. They hold potential for exerting disease-modifying effects, but new evidence in humans is necessary to fulfill that promise.

Lifestyle Modulators of Neuroplasticity in Parkinson's Disease: Evidence in Human Neuroimaging Studies

Parkinson's disease (PD) is a neurodegenerative disease characterized by both motor and non-motor symptoms. A progressive neuronal loss and the consequent clinical impairment lead to deleterious effects on daily living and quality of life. Despite effective symptomatic therapeutic approaches, no disease-modifying therapies are currently available. Emerging evidence suggests that adopting a healthy lifestyle can improve the quality of life of PD patients. In addition, modulating lifestyle factors can positively affect the microstructural and macrostructural brain levels, corresponding to clinical improvement. Neuroimaging studies may help to identify the mechanisms through which physical exercise, dietary changes, cognitive enrichment, and exposure to substances modulate neuroprotection. All these factors have been associated with a modified risk of developing PD, with attenuation or exacerbation of motor and non-motor symptomatology, and possibly with structural and molecular changes. In the present work, we review the current knowledge on how lifestyle factors influence PD development and progression and the neuroimaging evidence for the brain structural, functional, and molecular changes induced by the adoption of positive or negative lifestyle behaviours.

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

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

Imaging the neurovascular unit in health and neurodegeneration: a scoping review of interdependencies between MRI measures

The neurovascular unit (NVU) is a complex structure that facilitates nutrient delivery and metabolic waste clearance, forms the blood-brain barrier (BBB), and supports fluid homeostasis in the brain. The integrity of NVU subcomponents can be measured in vivo using magnetic resonance imaging (MRI), including quantification of enlarged perivascular spaces (ePVS), BBB permeability, cerebral perfusion and extracellular free water. The breakdown of NVU subparts is individually associated with aging, pathology, and cognition. However, how these subcomponents interact as a system, and how interdependencies are impacted by pathology remains unclear. This systematic scoping review identified 26 studies that investigated the inter-relationships between multiple subcomponents of the NVU in nonclinical and neurodegenerative populations using MRI. A further 112 studies investigated associations between the NVU and white matter hyperintensities (WMH). We identify two putative clusters of NVU interdependencies: a 'vascular' cluster comprising BBB permeability, perfusion and basal ganglia ePVS; and a 'fluid' cluster comprising ePVS, free water and WMH. Emerging evidence suggests that subcomponent coupling within these clusters may be differentially related to aging, neurovascular injury or neurodegenerative pathology.

Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis

Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.

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.

Fibroblast growth factor 21 ameliorates behavior deficits in Parkinson's disease mouse model via modulating gut microbiota and metabolic homeostasis

AIMS

The effects of FGF21 on Parkinson's disease (PD) and its relationship with gut microbiota have not been elucidated. This study aimed to investigate whether FGF21 would attenuate behavioral impairment through microbiota-gut-brain metabolic axis in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced PD mice model.

METHODS

Male C57BL/6 mice were rendomized into 3 groups: vehicle (CON); MPTP 30 mg/kg/day i.p. injection (MPTP); FGF21 1.5 mg/kg/d i.p. injection plus MPTP 30 mg/kg/day i.p. injection (FGF21 + MPTP). The behavioral features, metabolimics profiling, and 16 s rRNA sequencing were performed after FGF21 treatment for 7 days.

RESULTS

MPTP-induced PD mice showed motor and cognitive deficits accompanied by gut microbiota dysbiosis and brain-region-specific metabolic abnormalities. FGF21 treatment dramatically attenuated motor and cognitive dysfunction in PD mice. FGF21 produced a region-specific alteration in the metabolic profile in the brain in ways indicative of greater ability in neurotransmitter metabolism and choline production. In addition, FGF21 also re-structured the gut microbiota profile and increased the relative abundance of Clostridiales, Ruminococcaceae, and Lachnospiraceae, thereby rescuing the PD-induced metabolic disorders in the colon.

CONCLUSION

These findings indicate that FGF21 could affect behavior and brain metabolic homeostasis in ways that promote a favorable colonic microbiota composition and through effects on the microbiota-gut-brain metabolic axis.

Applications of Machine Learning to Diagnosis of Parkinson's Disease

BACKGROUND

Accurate diagnosis of Parkinson's disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored.

OBJECTIVE

To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China.

METHODS

A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator-logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models.

RESULTS

SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908-0.947; accuracy: 0.844, 95% CI: 0.814-0.871; sensitivity: 0.826, 95% CI: 0.786-0.866; specificity: 0.861, 95% CI: 0.820-0.898; precision: 0.849, 95% CI: 0.807-0.891; F1 score: 0.837, 95% CI: 0.803-0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability.

CONCLUSION

We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients.