Network imaging biomarkers: insights and clinical applications in Parkinson's disease

Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder

Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal syndrome for Parkinson's disease (PD) and related 𝛼-synucleinopathies. We conducted a longitudinal imaging study of network changes in iRBD and their relationship to phenoconversion. Expression levels for the PD-related motor and cognitive networks (PDRP and PDCP) were measured at baseline, 2 and 4 years, along with dopamine transporter (DAT) binding. PDRP and PDCP expression increased over time, with higher values in the former network. While abnormal functional connections were identified initially within the PDRP, others bridging the two networks appeared later. A model based on the rates of PDRP progression and putamen dopamine loss predicted phenoconversion within 1.2 years in individuals with iRBD. In aggregate, the data suggest that maladaptive reorganization of brain networks takes place in iRBD years before phenoconversion. Network expression and DAT binding measures can be used together to assess phenoconversion risk in these individuals.

Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response

Subthalamic nucleus deep brain stimulation (STN-DBS) alleviates motor symptoms of Parkinson's disease (PD), thereby improving quality of life. However, quantitative brain markers to evaluate DBS responses and select suitable patients for surgery are lacking. Here, we used metabolic brain imaging to identify a reproducible STN-DBS network for which individual expression levels increased with stimulation in proportion to motor benefit. Of note, measurements of network expression from metabolic and BOLD imaging obtained preoperatively predicted motor outcomes determined after DBS surgery. Based on these findings, we computed network expression in 175 PD patients, with time from diagnosis ranging from 0 to 21 years, and used the resulting data to predict the outcome of a potential STN-DBS procedure. While minimal benefit was predicted for patients with early disease, the proportion of potential responders increased after 4 years. Clinically meaningful improvement with stimulation was predicted in 18.9 - 27.3% of patients depending on disease duration.

Large-scale functional network connectivity mediates the association between nigral neuromelanin hypopigmentation and motor impairment in Parkinson's disease

Neuromelanin hypopigmentation within substantia nigra pars compacta (SNc) reflects the loss of pigmented neurons, which in turn contributes to the dysfunction of the nigrostriatal and striato-cortical pathways in Parkinson's disease (PD). Our study aims to investigate the relationships between SN degeneration manifested by neuromelanin reduction, functional connectivity (FC) among large-scale brain networks, and motor impairment in PD. This study included 68 idiopathic PD patients and 32 age-, sex- and education level-matched healthy controls who underwent neuromelanin-sensitive magnetic resonance imaging (MRI), functional MRI, and motor assessments. SN integrity was measured using the subregional contrast-to-noise ratio calculated from neuromelanin-sensitive MRI. Resting-state FC maps were obtained based on the independent component analysis. Subsequently, we performed partial correlation and mediation analyses in SN degeneration, network disruption, and motor impairment for PD patients. We found significantly decreased neuromelanin within SN and widely altered inter-network FCs, mainly involved in the basal ganglia (BG), sensorimotor and frontoparietal networks in PD. In addition, decreased neuromelanin content was negatively correlated with the dorsal sensorimotor network (dSMN)-medial visual network connection (P = 0.012) and dSMN-BG connection (P = 0.004). Importantly, the effect of SN neuromelanin hypopigmentation on motor symptom severity in PD is partially mediated by the increased connectivity strength between BG and dSMN (indirect effect =  - 1.358, 95% CI: - 2.997, - 0.147). Our results advanced our understanding of the interactions between neuromelanin hypopigmentation in SN and altered FCs of functional networks in PD and suggested the potential of multimodal metrics for early diagnosis and monitoring the response to therapies.

Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson's disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure-function relationships both at the micro-and mesoscale. Taken together, the findings of our study points toward an overall heightened metabolic demand in networks carrying the LRRK2 G2019S mutation, as well as a resilience to change in response to perturbation, compared to healthy isogenic controls.

Parkinson's disease cerebrovascular reactivity pattern: A feasibility study

A mounting body of research points to cerebrovascular dysfunction as a fundamental element in the pathophysiology of Parkinson's disease (PD). In the current feasibility study, blood-oxygen-level-dependent (BOLD) MRI was used to measure cerebrovascular reactivity (CVR) in response to hypercapnia in 26 PD patients and 16 healthy controls (HC), and aimed to find a multivariate pattern specific to PD. Whole-brain maps of CVR amplitude (i.e., magnitude of response to CO2) and latency (i.e., time to reach maximum amplitude) were computed, which were further analyzed using scaled sub-profile model principal component analysis (SSM-PCA) with leave-one-out cross-validation. A meaningful pattern based on CVR latency was identified, which was named the PD CVR pattern (PD-CVRP). This pattern was characterized by relatively increased latency in basal ganglia, sensorimotor cortex, supplementary motor area, thalamus and visual cortex, as well as decreased latency in the cerebral white matter, relative to HC. There were no significant associations with clinical measures, though sample size may have limited our ability to detect significant associations. In summary, the PD-CVRP highlights the importance of cerebrovascular dysfunction in PD, and may be a potential biomarker for future clinical research and practice.

Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems

BACKGROUND

Disease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems.

METHODS

In this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state.

RESULTS

The proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called "dark genes" that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis.

CONCLUSIONS

The proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Investigating the aspect of asymmetry in brain-first versus body-first Parkinson's disease

Parkinson's disease (PD) is characterized by a progressive loss of dopaminergic neurons in the substantia nigra. Recent literature has proposed two subgroups of PD. The "body-first subtype" is associated with a prodrome of isolated REM-sleep Behavior Disorder (iRBD) and a relatively symmetric brain degeneration. The "brain-first subtype" is suggested to have a more asymmetric degeneration and a prodromal stage without RBD. This study aims to investigate the proposed difference in symmetry of the degeneration pattern in the presumed body and brain-first PD subtypes. We analyzed 123I-FP-CIT (DAT SPECT) and 18F-FDG PET brain imaging in three groups of patients (iRBD, n = 20, de novo PD with prodromal RBD, n = 22, and de novo PD without RBD, n = 16) and evaluated dopaminergic and glucose metabolic symmetry. The RBD status of all patients was confirmed with video-polysomnography. The PD groups did not differ from each other with regard to the relative or absolute asymmetry of DAT uptake in the putamen (p = 1.0 and p = 0.4, respectively). The patient groups also did not differ from each other with regard to the symmetry of expression of the PD-related metabolic pattern (PDRP) in each hemisphere. The PD groups had no difference in symmetry considering mean FDG uptake in left and right regions of interest and generally had the same degree of symmetry as controls, while the iRBD patients had nine regions with abnormal left-right differences (p < 0.001). Our findings do not support the asymmetry aspect of the "body-first" versus "brain-first" hypothesis.

Slow-wave sleep and REM sleep without atonia predict motor progression in Parkinson's disease

BACKGROUND

Growing evidence supports the potential role of sleep in the motor progression of Parkinson's disease (PD). Slow-wave sleep (SWS) and rapid eye movement (REM) sleep without atonia (RWA) are important sleep parameters. The association between SWS and RWA with PD motor progression and their predictive value have not yet been elucidated.

METHODS

We retro-prospectively analyzed clinical and polysomnographic data of 136 patients with PD. The motor symptoms were assessed using Unified Parkinson's Disease Rating Scale Part III (UPDRS III) at baseline and follow-up to determine its progression. Partial correlation analysis was used to explore the cross-sectional associations between slow-wave energy (SWE), RWA and clinical symptoms. Longitudinal analyses were performed using Cox regression and linear mixed-effects models.

RESULTS

Among 136 PD participants, cross-sectional partial correlation analysis showed SWE decreased with the prolongation of the disease course (P = 0.046), RWA density was positively correlated with Hoehn & Yahr (H-Y) stage (tonic RWA, P < 0.001; phasic RWA, P = 0.002). Cox regression analysis confirmed that low SWE (HR = 1.739, 95% CI = 1.038-2.914; P = 0.036; FDR-P = 0.036) and high tonic RWA (HR = 0.575, 95% CI = 0.343-0.963; P = 0.032; FDR-P = 0.036) were predictors of motor symptom progression. Furthermore, we found that lower SWE predicted faster rate of axial motor progression (P < 0.001; FDR-P < 0.001) while higher tonic RWA density was associated with faster rate of rigidity progression (P = 0.006; FDR-P = 0.024) using linear mixed-effects models.

CONCLUSIONS

These findings suggest that SWS and RWA might represent markers of different motor subtypes progression in PD.

Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission

PURPOSE

Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA).

METHODS

FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level.

RESULTS

Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912).

CONCLUSION

Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.

99mTc-HMPAO SPECT Perfusion Signatures Associated With Clinical Progression in Patients With Isolated REM Sleep Behavior Disorder

BACKGROUND AND OBJECTIVES

Idiopathic/isolated REM sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies and Parkinson disease. Despite evidence of abnormal cerebral perfusion in iRBD, there is currently no pattern that can predict whether an individual will develop dementia with Lewy bodies or Parkinson disease. The objective was to identify a perfusion signature associated with conversion to dementia with Lewy bodies in iRBD.

METHODS

Patients with iRBD underwent video-polysomnography, neurologic and neuropsychological assessments, and baseline 99mTc-HMPAO SPECT to assess relative cerebral blood flow. Partial least squares correlation was used to identify latent variables that maximized covariance between 27 clinical features and relative gray matter perfusion. Patient-specific scores on the latent variables were used to test the association with conversion to dementia with Lewy bodies compared with that with Parkinson disease. The signature's expression was also assessed in 24 patients with iRBD who underwent a second perfusion scan, 22 healthy controls, and 19 individuals with Parkinson disease.

RESULTS

Of the 137 participants, 93 underwent SPECT processing, namely 52 patients with iRBD (67.9 years, 73% men), 19 patients with Parkinson disease (67.3 years, 37% men), and 22 controls (67.0 years, 73% men). Of the 47 patients with iRBD followed up longitudinally (4.5 years), 12 (26%) developed a manifest synucleinopathy (4 dementia with Lewy bodies and 8 Parkinson disease). Analysis revealed 2 latent variables between relative blood flow and clinical features: the first was associated with a broad set of features that included motor, cognitive, and perceptual variables, age, and sex; the second was mostly associated with cognitive features and RBD duration. When brought back into the patient's space, the expression of the first variable was associated with conversion to a manifest synucleinopathy, whereas the second was associated with conversion to dementia with Lewy bodies. The expression of the patterns changed over time and was associated with worse motor features.

DISCUSSION

This study identified a brain perfusion signature associated with cognitive impairment in iRBD and transition to dementia with Lewy bodies. This signature, which can be derived from individual scans, has the potential to be developed into a biomarker that predicts dementia with Lewy bodies in at-risk individuals.

Effects of PRRT2 mutation on brain gray matter networks in paroxysmal kinesigenic dyskinesia

Although proline-rich transmembrane protein 2 is the primary causative gene of paroxysmal kinesigenic dyskinesia, its effects on the brain structure of paroxysmal kinesigenic dyskinesia patients are not yet clear. Here, we explored the influence of proline-rich transmembrane protein 2 mutations on similarity-based gray matter morphological networks in individuals with paroxysmal kinesigenic dyskinesia. A total of 51 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations, 55 paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, and 80 healthy controls participated in the study. We analyzed the structural connectome characteristics across groups by graph theory approaches. Relative to paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation and healthy controls, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations exhibited a notable increase in characteristic path length and a reduction in both global and local efficiency. Relative to healthy controls, both patient groups showed reduced nodal metrics in right postcentral gyrus, right angular, and bilateral thalamus; Relative to healthy controls and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations showed almost all reduced nodal centralities and structural connections in cortico-basal ganglia-thalamo-cortical circuit including bilateral supplementary motor area, bilateral pallidum, and right caudate nucleus. Finally, we used support vector machine by gray matter network matrices to classify paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 mutations and paroxysmal kinesigenic dyskinesia patients possessing proline-rich transmembrane protein 2 non-mutation, achieving an accuracy of 73%. These results show that proline-rich transmembrane protein 2 related gray matter network deficits may contribute to paroxysmal kinesigenic dyskinesia, offering new insights into its pathophysiological mechanisms.

Association between gene expression and functional-metabolic architecture in Parkinson's disease

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR  < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR  < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.

A Network Imaging Biomarker of X-Linked Dystonia-Parkinsonism

OBJECTIVE

The purpose of this study was to characterize a metabolic brain network associated with X-linked dystonia-parkinsonism (XDP).

METHODS

Thirty right-handed Filipino men with XDP (age = 44.4 ± 8.5 years) and 30 XDP-causing mutation negative healthy men from the same population (age = 37.4 ± 10.5 years) underwent [18 F]-fluorodeoxyglucose positron emission tomography. Scans were analyzed using spatial covariance mapping to identify a significant XDP-related metabolic pattern (XDPRP). Patients were rated clinically at the time of imaging according to the XDP-Movement Disorder Society of the Philippines (MDSP) scale.

RESULTS

We identified a significant XDPRP topography from 15 randomly selected subjects with XDP and 15 control subjects. This pattern was characterized by bilateral metabolic reductions in caudate/putamen, frontal operculum, and cingulate cortex, with relative increases in the bilateral somatosensory cortex and cerebellar vermis. Age-corrected expression of XDPRP was significantly elevated (p < 0.0001) in XDP compared to controls in the derivation set and in the remaining 15 patients (testing set). We validated the XDPRP topography by identifying a similar pattern in the original testing set (r = 0.90, p < 0.0001; voxel-wise correlation between both patterns). Significant correlations between XDPRP expression and clinical ratings for parkinsonism-but not dystonia-were observed in both XDP groups. Further network analysis revealed abnormalities of information transfer through the XDPRP space, with loss of normal connectivity and gain of abnormal functional connections linking network nodes with outside brain regions.

INTERPRETATION

XDP is associated with a characteristic metabolic network associated with abnormal functional connectivity among the basal ganglia, thalamus, motor regions, and cerebellum. Clinical signs may relate to faulty information transfer through the network to outside brain regions. ANN NEUROL 2023;94:684-695.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

Occipital hypometabolism is a risk factor for conversion to Parkinson's disease in isolated REM sleep behaviour disorder

PURPOSE

Isolated REM sleep behaviour disorder (iRBD) patients are at high risk of developing clinical syndromes of the α-synuclein spectrum. Progression markers are needed to determine the neurodegenerative changes and to predict their conversion. Brain imaging with 18F-FDG PET in iRBD is promising, but longitudinal studies are scarce. We investigated the regional brain changes in iRBD over time, related to phenoconversion.

METHODS

Twenty iRBD patients underwent two consecutive 18F-FDG PET brain scans and clinical assessments (3.7 ± 0.6 years apart). Seventeen patients also underwent 123I-MIBG and 123I-FP-CIT SPECT scans at baseline. Four subjects phenoconverted to Parkinson's disease (PD) during follow-up. 18F-FDG PET scans were compared to controls with a voxel-wise single-subject procedure. The relationship between regional brain changes in metabolism and PD-related pattern scores (PDRP) was investigated.

RESULTS

Individual hypometabolism t-maps revealed three scenarios: (1) normal 18F-FDG PET scans at baseline and follow-up (N = 10); (2) normal scans at baseline but occipital or occipito-parietal hypometabolism at follow-up (N = 4); (3) occipital hypometabolism at baseline and follow-up (N = 6). All patients in the last group had pathological 123I-MIBG and 123I-FP-CIT SPECT. iRBD converters (N = 4) showed occipital hypometabolism at baseline (third scenario). At the group level, hypometabolism in the frontal and occipito-parietal regions and hypermetabolism in the cerebellum and limbic regions were progressive over time. PDRP z-scores increased over time (0.54 ± 0.36 per year). PDRP expression was driven by occipital hypometabolism and cerebellar hypermetabolism.

CONCLUSIONS

Our results suggest that occipital hypometabolism at baseline in iRBD implies a short-term conversion to PD. This might help in stratification strategies for disease-modifying trials.

[Application of Polysomnography in Common Neurodegenerative Diseases]

At present, the etiology and pathogenesis of most neurodegenerative diseases are still not fully understood, which poses challenges for the prevention, diagnosis, and treatment of these diseases. Sleep disorders are one of the common chief complaints of neurodegenerative diseases. When patients suffer from comorbid sleep disorder and neurodegenerative diseases, the severity of their condition increases, the quality of their life drops further, and the difficulty of treatment increases. A large number of studies have been conducted to monitor the sleep of patients with neurodegenerative diseases, and it has been found that there are significant changes in their polysomnography (PSG) results compared to those of healthy control populations. In addition, there are also significant differences between the PSG findings of patients with different neurodegenerative diseases and the differences are closely associated with the pathogenesis and development of the disease. Herein, we discussed the characteristics of the sleep structure of patients with Parkinson's disease, Alzheimer's disease, Huntington's disease, and dementia with Lewy bodies and provided a brief review of the sleep disorders and the PSG characteristics of these patients. The paper will help improve the understanding of the pathogenesis and pathological changes of neurodegenerative diseases, clarify the relationship between sleep disorders and these diseases, improve clinicians' further understanding of these diseases, and provide a basis for future research.

Validation of the REM behaviour disorder phenoconversion-related pattern in an independent cohort

BACKGROUND

A brain glucose metabolism pattern related to phenoconversion in patients with idiopathic/isolated REM sleep behaviour disorder (iRBDconvRP) was recently identified. However, the validation of the iRBDconvRP in an external, independent group of iRBD patients is needed to verify the reproducibility of such pattern, so to increase its importance in clinical and research settings. The aim of this work was to validate the iRBDconvRP in an independent group of iRBD patients.

METHODS

Forty iRBD patients (70 ± 5.59 years, 19 females) underwent brain [18F]FDG-PET in Seoul National University. Thirteen patients phenoconverted at follow-up (7 Parkinson disease, 5 Dementia with Lewy bodies, 1 Multiple system atrophy; follow-up time 35 ± 20.56 months) and 27 patients were still free from parkinsonism/dementia after 62 ± 29.49 months from baseline. We applied the previously identified iRBDconvRP to validate its phenoconversion prediction power.

RESULTS

The iRBDconvRP significantly discriminated converters from non-converters iRBD patients (p = 0.016; Area under the Curve 0.74, Sensitivity 0.69, Specificity 0.78), and it significantly predicted phenoconversion (Hazard ratio 4.26, C.I.95%: 1.18-15.39).

CONCLUSIONS

The iRBDconvRP confirmed its robustness in predicting phenoconversion in an independent group of iRBD patients, suggesting its potential role as a stratification biomarker for disease-modifying trials.

Parkinson's disease progression: Increasing expression of an invariant common core subnetwork

Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies

BACKGROUND

Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies.

METHODS

We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA).

RESULTS

The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941).

CONCLUSIONS

SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Distinct cortical and subcortical predictors of Purdue Pegboard decline in Parkinson's disease and atypical parkinsonism

Objective measures of disease progression are critically needed in research on Parkinson's disease (PD) and atypical Parkinsonism but may be hindered by both practicality and cost. The Purdue Pegboard Test (PPT) is objective, has high test-retest reliability, and has a low cost. The goals of this study were to determine: (1) longitudinal changes in PPT in a multisite cohort of patients with PD, atypical Parkinsonism, and healthy controls; (2) whether PPT performance reflects brain pathology revealed by neuroimaging; (3) quantify kinematic deficits shown by PD patients during PPT. Parkinsonian patients showed a decline in PPT performance that correlated with motor symptom progression, which was not seen in controls. Neuroimaging measures from basal ganglia were significant predictors of PPT performance in PD, whereas cortical, basal ganglia, and cerebellar regions were predictors for atypical Parkinsonism. Accelerometry in a subset of PD patients showed a diminished range of acceleration and irregular patterns of acceleration, which correlated with PPT scores.

Potential role of acupuncture in the treatment of Parkinson's disease: A narrative review

Background

The prevalence of Parkinson's disease (PD) has grown rapidly compared to that of other neurological disorders. Acupuncture has been used to address the complex symptoms of PD. Recently, similarities in the mechanisms of action between acupuncture and neuromodulation have received considerable attention. This review aims to summarize the evidence regarding these similarities to suggest potential role of acupuncture in the treatment of PD.

Methods

The literature from two electronic databases, PubMed and Google Scholar, was searched using the search terms 'Acupuncture', 'Parkinson's disease', 'Vagus nerve stimulation', and 'Brain functional connectivity'. We then explored the evidence for the effectiveness of acupuncture in PD and evaluated the evidence for similarities in the mechanisms of action between acupuncture and neuromodulation.

Results

Data suggests that acupuncture treatment is effective for PD symptoms by modulating inflammation and brain functional connectivity (BFC). These acupuncture effects have been shown to be similar to neuromodulation in controlling inflammation and BFC. Based on the shared mechanisms of action, potential acupuncture mechanisms that may ameliorate a wide range of PD symptoms include but are not limited to (1) vagal activation of the anti-inflammatory pathway and (2) BFC enhancement.

Conclusion

The development of acupuncture strategies based on shared mechanisms with neuromodulation will provide new treatment options for patients with PD as personalized neuromodulating therapies. Further studies are needed to gather scientific evidence for optimizing parameters in PD patients.

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.

Abnormal dynamic functional connectivity changes correlated with non-motor symptoms of Parkinson’s disease

Background

Non-motor symptoms are common in Parkinson’s disease (PD) patients, decreasing quality of life and having no specific treatments. This research investigates dynamic functional connectivity (FC) changes during PD duration and its correlations with non-motor symptoms.

Methods

Twenty PD patients and 19 healthy controls (HC) from PPMI dataset were collected and used in this study. Independent component analysis (ICA) was performed to select significant components from the entire brain. Components were grouped into seven resting-state intrinsic networks. Static and dynamic FC changes during resting-state functional magnetic resonance imaging (fMRI) were calculated based on selected components and resting state networks (RSN).

Results

Static FC analysis results showed that there was no difference between PD-baseline (PD-BL) and HC group. Network averaged connection between frontoparietal network and sensorimotor network (SMN) of PD-follow up (PD-FU) was lower than PD-BL. Dynamic FC analysis results suggested four distinct states, and each state’s temporal characteristics, such as fractional windows and mean dwell time, were calculated. The state 2 of our study showed positive coupling within and between SMN and visual network, while the state 3 showed hypo-coupling through all RSN. The fractional windows and mean dwell time of PD-FU state 2 (positive coupling state) were statistically lower than PD-BL. Fractional windows and mean dwell time of PD-FU state 3 (hypo-coupling state) were statistically higher than PD-BL. Outcome scales in Parkinson’s disease–autonomic dysfunction scores of PD-FU positively correlated with mean dwell time of state 3 of PD-FU.

Conclusion

Overall, our finding indicated that PD-FU patients spent more time in hypo-coupling state than PD-BL. The increase of hypo-coupling state and decrease of positive coupling state might correlate with the worsening of non-motor symptoms in PD patients. Dynamic FC analysis of resting-state fMRI can be used as monitoring tool for PD progression.

Adaptive and pathological connectivity responses in Parkinson's disease brain networks

Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Severity-dependent functional connectome and the association with glucose metabolism in the sensorimotor cortex of Parkinson's disease

Functional MRI studies have achieved promising outcomes in revealing abnormal functional connectivity in Parkinson's disease (PD). The primary sensorimotor area (PSMA) received a large amount of attention because it closely correlates with motor deficits. While functional connectivity represents signaling between PSMA and other brain regions, the metabolic mechanism behind PSMA connectivity has rarely been well established. By introducing hybrid PET/MRI scanning, the current study enrolled 33 advanced PD patients during medication-off condition and 25 age-and-sex-matched healthy controls (HCs), aiming to not only identify the abnormal functional connectome pattern of the PSMA, but also to simultaneously investigate how PSMA functional connectome correlates with glucose metabolism. We calculated degree centrality (DC) and the ratio of standard uptake value (SUVr) using resting state fMRI and ¹⁸F-FDG-PET data. A two-sample t-test revealed significantly decreased PSMA DC (P_FWE < 0.014) in PD patients. The PSMA DC also correlated negatively with H-Y stage (P = 0.031). We found a widespread reduction of H-Y stage associated (P-values < 0.041) functional connectivity between PSMA and the visual network, attention network, somatomotor network, limbic network, frontoparietal network as well as the default mode network. The PSMA DC correlated positively with FDG-uptake in the HCs (P = 0.039) but not in the PD patients (P > 0.44). In summary, we identified disease severity-dependent PSMA functional connectome which in addition uncoupled with glucose metabolism in PD patients. The current study highlighted the critical role of simultaneous PET/fMRI in revealing the functional-metabolic mechanism in the PSMA of PD patients.

Derivation and Validation of a Phenoconversion-Related Pattern in Idiopathic Rapid Eye Movement Behavior Disorder

BACKGROUND

Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents the prodromal stage of α-synucleinopathies. Reliable biomarkers are needed to predict phenoconversion.

OBJECTIVE

The aim was to derive and validate a brain glucose metabolism pattern related to phenoconversion in iRBD (iRBDconvRP) using spatial covariance analysis (Scaled Subprofile Model and Principal Component Analysis [SSM-PCA]).

METHODS

Seventy-six consecutive iRBD patients (70 ± 6 years, 15 women) were enrolled in two centers and prospectively evaluated to assess phenoconversion (30 converters, 73 ± 6 years, 14 Parkinson's disease and 16 dementia with Lewy bodies, follow-up time: 21 ± 14 months; 46 nonconverters, 69 ± 6 years, follow-up time: 33 ± 19 months). All patients underwent [¹⁸ F]FDG-PET (¹⁸ F-fluorodeoxyglucose positron emitting tomography) to investigate brain glucose metabolism at baseline. SSM-PCA was applied to obtain the iRBDconvRP; nonconverter patients were considered as the reference group. Survival analysis and Cox regression were applied to explore prediction power.

RESULTS

First, we derived and validated two distinct center-specific iRBDconvRP that were comparable and significantly able to predict phenoconversion. Then, SSM-PCA was applied to the whole set, identifying the iRBDconvRP. The iRBDconvRP included positive voxel weights in cerebellum; brainstem; anterior cingulate cortex; lentiform nucleus; and middle, mesial temporal, and postcentral areas. Negative voxel weights were found in posterior cingulate, precuneus, middle frontal gyrus, and parietal areas. Receiver operating characteristic analysis showed an area under the curve of 0.85 (sensitivity: 87%, specificity: 72%), discriminating converters from nonconverters. The iRBDconvRP significantly predicted phenoconversion (hazard ratio: 7.42, 95% confidence interval: 2.6-21.4).

CONCLUSIONS

We derived and validated an iRBDconvRP to efficiently discriminate converter from nonconverter iRBD patients. [¹⁸ F]FDG-PET pattern analysis has potential as a phenoconversion biomarker in iRBD patients. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The dilemma between milestones of progression versus clinical scales in Parkinson's disease

There are significant challenges in accurately documenting the progression of Parkinson's disease (PD). The disease course is highly heterogeneous, there are no validated biomarkers, and we are reliant on repeated clinical measures to assess disease state over time. Yet, the ability to chart disease progression accurately is vital in both observational and interventional study designs, where reliable measures are critical to determine whether an outcome has been met. In this chapter, we first discuss the natural history of PD, including the spectrum of clinical presentation and expected developments through the course of the disease. We then explore in detail the current strategies for measuring disease progression, which can be broadly divided into: (i) the use of quantitative clinical scales; and (ii) determination of the onset time of key milestones. We discuss the strengths and limitations of these approaches for use in clinical trials, with a particular focus on disease modification trials. The selection of outcome measures for a particular study will depend on multiple factors, but trial duration is an important determinant. Milestones are reached over a course of years rather than months, and hence clinical scales with sensitivity to change are needed for short-term studies. However, milestones represent important markers of disease stage which are not confounded by symptomatic therapies and are of critical relevance to the patient. Prolonged but low intensity follow-up beyond a limited period of treatment with a putative disease-modifying agent may allow milestones to be incorporated into evaluation of efficacy in a practical and cost-effective way.

Motor Memory Consolidation Deficits in Parkinson's Disease: A Systematic Review with Meta-Analysis

BACKGROUND

The ability to encode and consolidate motor memories is essential for persons with Parkinson's disease (PD), who usually experience a progressive loss of motor function. Deficits in memory encoding, usually expressed as poorer rates of skill improvement during motor practice, have been reported in these patients. Whether motor memory consolidation (i.e., motor skill retention) is also impaired is unknown.

OBJECTIVE

To determine whether motor memory consolidation is impaired in PD compared to neurologically intact individuals.

METHODS

We conducted a pre-registered systematic review (PROSPERO: CRD42020222433) following PRISMA guidelines that included 46 studies.

RESULTS

Meta-analyses revealed that persons with PD have deficits in retaining motor skills (SMD = -0.17; 95% CI = -0.32, -0.02; p = 0.0225). However, these deficits are task-specific, affecting sensory motor (SMD = -0.31; 95% CI -0.47, -0.15; p = 0.0002) and visuomotor adaptation (SMD = -1.55; 95% CI = -2.32, -0.79; p = 0.0001) tasks, but not sequential fine motor (SMD = 0.17; 95% CI = -0.05, 0.39; p = 0.1292) and gross motor tasks (SMD = 0.04; 95% CI = -0.25, 0.33; p = 0.7771). Importantly, deficits became non-significant when augmented feedback during practice was provided, and additional motor practice sessions reduced deficits in sensory motor tasks. Meta-regression analyses confirmed that deficits were independent of performance during encoding, as well as disease duration and severity.

CONCLUSION

Our results align with the neurodegenerative models of PD progression and motor learning frameworks and emphasize the importance of developing targeted interventions to enhance motor memory consolidation in PD.

Parkinson's Disease-Related Brain Metabolic Pattern Is Expressed in Schizophrenia Patients during Neuroleptic Drug-Induced Parkinsonism

Drug-induced parkinsonism (DIP) is a frequent parkinsonian syndrome that appears as a result of pharmacotherapy for the management of psychosis. It could substantially hamper treatment and therefore its diagnosis has a direct influence on treatment effectiveness. Although of such high importance, there is a lack of systematic research for developing neuroimaging-based criteria for DIP diagnostics for such patients. Therefore, the current study was aimed at applying a metabolic brain imaging approach using the 18F-FDG positron emission tomography and spatial covariance analysis to reveal possible candidates for DIP markers. As a result, we demonstrated, to our knowledge, the first attempt at the application of the Parkinson's Disease-Related Pattern (PDRP) as a metabolic signature of parkinsonism for the assessment of PDRP expression for schizophrenia patients with DIP. As a result, we observed significant differences in PDRP expression between the control group and the groups with PD and DIP patients. Similar differences in PDRP expression were also found when the non-DIP schizophrenia patients were compared with the PD group. Therefore, our findings made it possible to conclude that PDRP is a promising tool for the development of clinically relevant criteria for the estimation of the risk of developing DIP.

Elevated Monoamine Oxidase-A in Anterior Cingulate of Post-Mortem Human Parkinson's Disease: A Potential Surrogate Biomarker for Lewy Bodies?

Lewy bodies (LB) play a neuropathological role in Parkinson's disease (PD). Our goal was to evaluate LB using anti-ubiquitin immunohistochemistry (UIHC) and find correlations with monoamine oxidase-A (MAO-A) using imaging agent, [¹⁸F]FAZIN3. Human post-mortem anterior cingulate (AC) and corpus callosum (CC) from control subjects (CN), n = 6; age 81-90 LB = 0 and PD, n = 6, age 77-89, LB = III-IV were sectioned (10 μm slices). Brain slices were immunostained with anti-ubiquitin for LB (UIHC) and analyzed using QuPath for percent anti-ubiquitin per unit area (μm²). Adjacent brain slices were incubated with [¹⁸F]FAZIN3 and cortical layers I-III, IV-VI and CC (white matter) regions were quantified for the binding of [¹⁸F]FAZIN3. UIHC was correlated with [¹⁸F]FAZIN3 binding. All PD brains were positively UIHC stained and confirmed presence of LB. Outer cortical layers (I-III) of PD AC had 21% UIHC while inner layers (IV-VI) had >75% UIHC. In the CN brains LB were absent (<1% UIHC). Increased [¹⁸F]FAZIN3 binding to MAO-A in AC was observed in all PD subjects. [¹⁸F]FAZIN3 ratio in PD was AC/CC = 3.57 while in CN subjects it was AC/CC = 2.24. Increases in UIHC μm² correlated with [¹⁸F]FAZIN3 binding to MAO-A in DLU/mm². Increased [¹⁸F]FAZIN3 binding to MAO-A in PD is a potential novel "hot spot" PET imaging approach.

Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease

BACKGROUND

Idiopathic Parkinson's disease (iPD) is associated with two distinct brain networks, PD-related pattern (PDRP) and PD-related cognitive pattern (PDCP), which correlate respectively with motor and cognitive symptoms. The relationship between the two networks in individual patients is unclear.

OBJECTIVE

To determine whether a consistent relationship exists between these networks, we measured the difference between PDRP and PDCP expression, termed delta, on an individual basis in independent populations of patients with iPD (n = 356), patients with idiopathic REM sleep behavioral disorder (iRBD) (n = 21), patients with genotypic PD (gPD) carrying GBA1 variants (n = 12) or the LRRK2-G2019S mutation (n = 14), patients with atypical parkinsonian syndromes (n = 238), and healthy control subjects (n = 95) from the United States, Slovenia, India, and South Korea.

METHODS

We used [18 F]-fluorodeoxyglucose positron emission tomography and resting-state fMRI to quantify delta and to compare the measure across samples; changes in delta over time were likewise assessed in longitudinal patient samples. Lastly, we evaluated delta in prodromal individuals with iRBD and subjects with gPD.

RESULTS

Delta was abnormally elevated in each of the four iPD samples (P < 0.05), as well as in the at-risk iRBD group (P < 0.05), with increasing values over time (P < 0.001). PDRP predominance was also present in gPD, with higher values in patients with GBA1 variants compared with the less aggressive LRRK2-G2019S mutation (P = 0.005). This trend was not observed in patients with atypical parkinsonian syndromes, who were accurately discriminated from iPD based on PDRP expression and delta (area under the curve = 0.85; P < 0.0001).

CONCLUSIONS

PDRP predominance, quantified by delta, assays the spread of dysfunction from motor to cognitive networks in patients with PD. Delta may therefore aid in differential diagnosis and in tracking disease progression in individual patients. © 2022 International Parkinson and Movement Disorder Society.

Atlas-guided parcellation: Individualized functionally-homogenous parcellation in cerebral cortex

Resting-state Magnetic resonance imaging-based parcellation aims to group the voxels/vertices non-invasively based on their connectivity profiles, which has achieved great success in understanding the fundamental organizational principles of the human brain. Given the substantial inter-individual variability, the increasing number of studies focus on individual parcellation. However, current methods perform individual parcellations independently or are based on the group prior, requiring expensive computational costs, precise parcel alignment, and extra group information. In this work, an efficient and flexible parcellation framework of individual cerebral cortex was proposed based on a region growing algorithm by merging the unassigned and neighbor vertex with the highest-correlated parcel iteratively. It considered both consistency with prior atlases and individualized functional homogeneity of parcels, which can be applied to a single individual without parcel alignment and group information. The proposed framework was leveraged to 100 unrelated subjects for functional homogeneity comparison and individual identification, and 186 patients with Parkison's disease for symptom prediction. Results demonstrated our framework outperformed other methods in functional homogeneity, and the generated parcellations provided 100% individual identification accuracy. Moreover, the default mode network (DMN) exhibited higher functional homogeneity, intra-subject parcel reproducibility and fingerprinting accuracy, while the sensorimotor network did the opposite, reflecting that the DMN is the most representative, stable, and individual-identifiable network in the resting state. The correlation analysis showed that the severity of the disease symptoms was related negatively to the similarity of individual parcellation and the atlases of healthy populations. The disease severity can be correctly predicted using machine learning models based on individual topographic features such as parcel similarity and parcel size. In summary, the proposed framework not only significantly improves the functional homogeneity but also captures individualized and disease-related brain topography, serving as a potential tool to explore brain function and disease in the future.

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

BACKGROUND AND OBJECTIVES

¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson's disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory.

METHODS

We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories.

RESULTS

The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman's rho =0.62, P=0.004).

CONCLUSION

In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.

Cortical atrophy is associated with cognitive impairment in Parkinson's disease: a combined analysis of cortical thickness and functional connectivity

We aimed to perform a combined analysis of cortical thickness and functional connectivity to explore their association with cognitive impairment in Parkinson's disease (PD). A total of 53 PD and 15 healthy control subjects were enrolled. PD patients were divided into PD with normal cognition (PD-NC, n = 25), PD with mild cognitive impairment (PD-MCI, n = 11), and PD with dementia (PDD, n = 17). In some analyses, the PD-MCI and PDD groups were aggregated to represent "PD patients with cognitive impairment". Cognitive status was assessed with the Mini-Mental State Examination (MMSE). Anatomical magnetic resonance imaging and resting-state functional connectivity analysis were performed in all subjects. First, surface-based morphometry measurements of cortical thickness and voxels with cortical thickness reduction were detected. Then, regions showing reduced thickness were analyzed for changes in resting-state functional connectivity in PD involving cognitive impairment. Our results showed that, compared with PD-NC, patients with cognitive impairment showed decreased cortical thickness in the left superior temporal, left lingual, right insula, and right fusiform regions. PD-MCI patients showed these alterations in the right lingual region. Widespread cortical thinning was detected in PDD subjects, including the left superior temporal, left fusiform, right insula, and right fusiform areas. We found that cortical thinning in the left superior temporal, left fusiform, and right temporal pole regions positively correlated with MMSE score. In the resting-state functional connectivity analysis, we found a decrease in functional connectivity between the cortical atrophic brain areas mentioned above and cognition-related brain networks, as well as an increase in functional connectivity between those region and the cerebellum. Alterations in cortical thickness may result in a dysfunction of resting-state functional connectivity, contributing to cognitive decline in patients with PD. However, it is more probable that the relation between structure and FC would be bidirectional,and needs more research to explore in PD cognitve decline.

Molecular Imaging in Parkinsonian Disorders—What’s New and Hot?

Highlights

Abstract

Neurodegenerative parkinsonian disorders are characterized by a great diversity of clinical symptoms and underlying neuropathology, yet differential diagnosis during lifetime remains probabilistic. Molecular imaging is a powerful method to detect pathological changes in vivo on a cellular and molecular level with high specificity. Thereby, molecular imaging enables to investigate functional changes and pathological hallmarks in neurodegenerative disorders, thus allowing to better differentiate between different forms of degenerative parkinsonism, improve the accuracy of the clinical diagnosis and disentangle the pathophysiology of disease-related symptoms. The past decade led to significant progress in the field of molecular imaging, including the development of multiple new and promising radioactive tracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) as well as novel analytical methods. Here, we review the most recent advances in molecular imaging for the diagnosis, prognosis, and mechanistic understanding of parkinsonian disorders. First, advances in imaging of neurotransmission abnormalities, metabolism, synaptic density, inflammation, and pathological protein aggregation are reviewed, highlighting our renewed understanding regarding the multiplicity of neurodegenerative processes involved in parkinsonian disorders. Consequently, we review the role of molecular imaging in the context of disease-modifying interventions to follow neurodegeneration, ensure stratification, and target engagement in clinical trials.

Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase

Previous evidence suggests that the derangement of large-scale brain networks reflects structural, molecular, and functional mechanisms underlying neurodegenerative diseases. Although the alterations of multiple large-scale brain networks in Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are reported, a comprehensive study on connectivity reconfiguration starting from the preclinical phase is still lacking. We aimed to investigate shared and disease-specific changes in the large-scale networks across the Lewy Bodies (LB) disorders spectrum using a brain metabolic connectivity approach. We included 30 patients with isolated REM sleep behavior disorder (iRBD), 28 with stable PD, 30 with DLB, and 30 healthy controls for comparison. We applied seed-based interregional correlation analyses (IRCA) to evaluate the metabolic connectivity in the large-scale resting-state networks, as assessed by [18F]FDG-PET, in each clinical group compared to controls. We assessed metabolic connectivity changes by applying the IRCA and specific connectivity metrics, such as the weighted and unweighted Dice similarity coefficients (DC), for the topographical similarities. All the investigated large-scale brain resting-state networks showed metabolic connectivity alterations, supporting the widespread involvement of brain connectivity within the alpha-synuclein spectrum. Connectivity alterations were already evident in iRBD, severely affecting the posterior default mode, attentive and limbic networks. Strong similarities emerged in iRBD and DLB that showed comparable connectivity alterations in most large-scale networks, particularly in the posterior default mode and attentive networks. Contrarily, PD showed the main connectivity alterations limited to motor and somatosensory networks. The present findings reveal that metabolic connectivity alterations in the large-scale networks are already present in the early iRBD phase, resembling the DLB metabolic connectivity changes. This suggests and confirms iRBD as a risk condition for progression to the severe LB disease phenotype. Of note, the neurobiology of stable PD supports its more benign phenotype.

Consistent Abnormalities in Metabolic Patterns of Lewy Body Dementias

BACKGROUND

Whether dementia with Lewy bodies (DLB) and Parkinson's disease (PD) dementia (PDD) represent the same disease, distinct entities, or conditions within the same spectrum remains controversial.

OBJECTIVE

The objective of this study was to provide new insight into this debate by separately identifying disease-specific metabolic patterns and comparing them with each other and with previously established PD-related pattern (PDRP).

METHODS

Patients with DLB (n = 67), patients with PDD (n = 50), and healthy control subjects (HCs; n = 15) with brain ¹⁸ F-fluorodeoxyglucose positron emission tomography were enrolled as cohorts A and B for pattern identification and validation, respectively. Patients with PD (n = 30) were included for discrimination. Twenty-one participants had two scans. The principal component analysis was applied for pattern identification (DLB-related pattern [DLBRP], PDD-related pattern [PDDRP]). Similarities and differences among three patterns were assessed by pattern topography, pattern expression, clinical correlations cross-sectionally, and pattern expression changes longitudinally.

RESULTS

DLBRP and PDDRP shared highly similar topographies, with relative hypometabolism mainly in the middle temporal gyrus, middle occipital gyrus, lingual gyrus, precuneus, cuneus, angular gyrus, superior and inferior parietal gyrus, middle and inferior frontal gyrus, cingulate, and caudate, and relative hypermetabolism in the cerebellum, putamen, thalamus, precentral/postcentral gyrus, and paracentral lobule, which were more extensive than the PDRP. Patients with DLB and PDD could not be distinguished successfully by any pattern, but patients with PD were easily recognized, especially by DLBRP and PDDRP. The pattern expression of DLBRP and PDDRP showed similar efficacy in cross-sectional disease severity assessment and longitudinal progression monitoring.

CONCLUSIONS

The consistent abnormalities in metabolic patterns of DLB and PDD might underline the potential continuum across the clinical spectrum from PD to DLB. © 2022 International Parkinson and Movement Disorder Society.

Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients

Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer's disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer's disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients' clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76-0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

Electroretinography reveals retinal dysfunction in Parkinson's disease despite normal high-resolution optical coherence tomography findings

INTRODUCTION

Parkinson's disease (PD)-associated inner retinal abnormalities, particularly the retinal ganglion cells (RGC) layer, on optical coherence tomography (OCT) have recently gained importance as a biomarker of non-motor involvement of the disease but functional RGC evaluation using photopic negative response (PhNR) has not yet been determined. This study aims to compare structural and functional findings of the retina and optic nerve in PD with healthy controls (CT) including PhNR and OCT.

METHODS

Forty-one eyes of 21 PD patients and 38 eyes of 19 CT underwent ophthalmic examination including visual contrast sensitivity test (CS), OCT, light-adapted full-field electroretinography (ffERG), and PhNR. OCT was used to measure the peripapillary retinal nerve fiber layer, the segmented macular layers, and the choroid. For functional parameters, CS, ffERG (oscillatory potentials, photopic response, 30 Hz-flicker), and PhNR waves were used. Measurements were compared using generalized estimating equation and significance was set at P ≤ 0.05.

RESULTS

The PD group presented a significantly lower mono- and binocular CS, oscillatory potentials amplitude, b-wave amplitude on ffERG (152.3[45.4] vs 187.1[32.7]μV; P = 0.002), and PhNR amplitude (135.0[35.0] vs 156.3[34.1]μV; P = 0.025). There was no statistically significant difference in OCT measurements between groups. No correlation was found between statistically significant measurements and clinical data.

CONCLUSIONS

Functional abnormalities on CS, ffERG, and PhNR can be detected in PD even when structural damages are not observed on OCT. PhNR represents a new potential biomarker in PD. Our findings indicate dysfunction of bipolar, amacrine, and retinal ganglion cells in PD, probably with a cellular dysfunction overcoming morphological damage.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography

PURPOSE

Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value.

METHODS

A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[¹⁸F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored.

RESULTS

The newly identified DLBRP shared topographic similarities with ADRP (R² = 24%) and PDRP (R² = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time.

CONCLUSION

DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.

SERS based Y-shaped aptasensor for early diagnosis of acute kidney injury

Considering the pivotal role of biomarkers in plasma, the development of biomarker specific sensing platforms is of great significance to achieve accurate diagnosis and monitor the occurrence and progress in acute kidney injury (AKI). In this paper, we develop a promising surface-enhanced Raman scattering-based aptasensor for duplex detection of two protein biomarkers in AKI. Exploiting the base-pairing specificity of nucleic acids to form a Y-shaped self-assembled aptasensor, the MGITC labelled gold nanoparticles will be attached to the surface of magnetic beads. In the presence of specific AKI-related biomarkers, the gold nanoparticles will detach from magnetic beads into the supernatant, thus leading to a SERS signal increase, which can be used for the highly sensitive analysis of target biomarkers. In addition, the limit of detection calculated for each biomarker indicates that the SERS-based aptasensor can well meet the detection requirements in clinical applications. Finally, the generality of this sensor in the early diagnosis of AKI is confirmed by using a rat model and spiked plasma samples. This sensing platform provides a facile and general route for sensitive SERS detection of AKI-related biomarkers, which offers great promising utility for in vitro and accurate practical bioassay in AKI early diagnosis.

We develop a promising SERS-based aptasensor for duplex detection of protein biomarkers in AKI. The development of biomarker specific sensors is of great significance to achieving accurate diagnosis and monitoring the occurrence and progress of AKI.

A resting-state fMRI pattern of spinocerebellar ataxia type 3 and comparison with 18F-FDG PET

•

This is the first study identifying a resting-state fMRI pattern in SCA3.

•

This pattern was closely associated with a metabolic (¹⁸F-FDG PET) counterpart.

•

Pattern subject scores were highly correlated with ataxia severity.

Spinocerebellar ataxia type 3 (SCA3) is a rare genetic neurodegenerative disease. The neurobiological basis of SCA3 is still poorly understood, and up until now resting-state fMRI (rs-fMRI) has not been used to study this disease. In the current study we investigated (multi-echo) rs-fMRI data from patients with genetically confirmed SCA3 (n = 17) and matched healthy subjects (n = 16). Using independent component analysis (ICA) and subsequent regression with bootstrap resampling, we identified a pattern of differences between patients and healthy subjects, which we coined the fMRI SCA3 related pattern (fSCA3-RP) comprising cerebellum, anterior striatum and various cortical regions. Individual fSCA3-RP scores were highly correlated with a previously published ¹⁸F-FDG PET pattern found in the same sample (rho = 0.78, P = 0.0003). Also, a high correlation was found with the Scale for Assessment and Rating of Ataxia scores (r = 0.63, P = 0.007). No correlations were found with neuropsychological test scores, nor with levels of grey matter atrophy. Compared with the ¹⁸F-FDG PET pattern, the fSCA3-RP included a more extensive contribution of the mediofrontal cortex, putatively representing changes in default network activity. This rs-fMRI identification of additional regions is proposed to reflect a consequence of the nature of the BOLD technique, enabling measurement of dynamic network activity, compared to the more static ¹⁸F-FDG PET methodology. Altogether, our findings shed new light on the neural substrate of SCA3, and encourage further validation of the fSCA3-RP to assess its potential contribution as imaging biomarker for future research and clinical use.

Modulation effect of substantia nigra iron deposition and functional connectivity on putamen glucose metabolism in Parkinson's disease

Neurodegeneration of the substantia nigra affects putamen activity in Parkinson's disease (PD), yet in vivo evidence of how the substantia nigra modulates putamen glucose metabolism in humans is missing. We aimed to investigate how substantia nigra modulates the putamen glucose metabolism using a cross‐sectional design. Resting‐state fMRI, susceptibility‐weighted imaging, and [¹⁸F]‐fluorodeoxyglucose‐PET (FDG‐PET) data were acquired. Forty‐two PD patients and 25 healthy controls (HCs) were recruited for simultaneous PET/MRI scanning. The main measurements of the current study were R2* images representing iron deposition (28 PD and 25 HCs), standardized uptake value ratio (SUVr) images representing FDG‐uptake (33 PD and 25 HCs), and resting state functional connectivity maps from resting state fMRI (34 PD and 25 HCs). An interaction term based on the general linear model was used to investigate the joint modulation effect of nigral iron deposition and nigral‐putamen functional connectivity on putamen FDG‐uptake. Compared with HCs, we found increased iron deposition in the substantia nigra (p = .007), increased FDG‐uptake in the putamen (left: P_FWE < 0.001; right: P_FWE < 0.001), and decreased functional connectivity between the substantia nigra and the anterior putamen (left P_FWE < 0.001, right: P_FWE = 0.007). We then identified significant interaction effect of nigral iron deposition and nigral‐putamen connectivity on FDG‐uptake in the putamen (p = .004). The current study demonstrated joint modulation effect of the substantia nigra iron deposition and nigral‐putamen functional connectivity on putamen glucose metabolic distribution, thereby revealing in vivo pathological mechanism of nigrostriatal neurodegeneration of PD.

Gait‐Related Metabolic Covariance Networks at Rest in Parkinson's Disease

Background

Gait impairments are characteristic motor manifestations and significant predictors of poor quality of life in Parkinson's disease (PD). Neuroimaging biomarkers for gait impairments in PD could facilitate effective interventions to improve these symptoms and are highly warranted.

Objective

The aim of this study was to identify neural networks of discrete gait impairments in PD.

Methods

Fifty‐five participants with early‐stage PD and 20 age‐matched healthy volunteers underwent quantitative gait assessment deriving 12 discrete spatiotemporal gait characteristics and [¹⁸F]‐2‐fluoro‐2‐deoxyglucose‐positron emission tomography measuring resting cerebral glucose metabolism. A multivariate spatial covariance approach was used to identify metabolic brain networks that were related to discrete gait characteristics in PD.

Results

In PD, we identified two metabolic gait‐related covariance networks. The first correlated with mean step velocity and mean step length (pace gait network), which involved relatively increased and decreased metabolism in frontal cortices, including the dorsolateral prefrontal and orbital frontal, insula, supplementary motor area, ventrolateral thalamus, cerebellum, and cuneus. The second correlated with swing time variability and step time variability (temporal variability gait network), which included relatively increased and decreased metabolism in sensorimotor, superior parietal cortex, basal ganglia, insula, hippocampus, red nucleus, and mediodorsal thalamus. Expression of both networks was significantly elevated in participants with PD relative to healthy volunteers and were not related to levodopa dosage or motor severity.

Conclusions

We have identified two novel gait‐related brain networks of altered glucose metabolism at rest. These gait networks could serve as a potential neuroimaging biomarker of gait impairments in PD and facilitate development of therapeutic strategies for these disabling symptoms. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society