Neuromelanin-sensitive MRI for mechanistic research and biomarker development in psychiatry

Neuromelanin-sensitive MRI is a burgeoning non-invasive neuroimaging method with an increasing number of applications in psychiatric research. This MRI modality is sensitive to the concentration of neuromelanin, which is synthesized from intracellular catecholamines and accumulates in catecholaminergic nuclei including the dopaminergic substantia nigra and the noradrenergic locus coeruleus. Emerging data suggest the utility of neuromelanin-sensitive MRI as a proxy measure for variability in catecholamine metabolism and function, even in the absence of catecholaminergic cell loss. Given the importance of catecholamine function to several psychiatric disorders and their treatments, neuromelanin-sensitive MRI is ideally positioned as an informative and easy-to-acquire catecholaminergic index. In this review paper, we examine basic aspects of neuromelanin and neuromelanin-sensitive MRI and focus on its psychiatric applications in the contexts of mechanistic research and biomarker development. We discuss ongoing debates and state-of-the-art research into the mechanisms of the neuromelanin-sensitive MRI contrast, standardized protocols and optimized analytic approaches, and application of cutting-edge methods such as machine learning and artificial intelligence to enhance the feasibility and predictive power of neuromelanin-sensitive-MRI-based tools. We finally lay out important future directions to allow neuromelanin-sensitive-MRI to fulfill its potential as a key component of the research, and ultimately clinical, toolbox in psychiatry.

Validating new symptom emergence as a patient-centric outcome measure for PD clinical trials

INTRODUCTION

Tracking of emergent symptoms (ES) in de novo Parkinson Disease (PD) patients using Parts Ib and II of the MDS-UPDRS rating scale has been proposed as an outcome measure for PD clinical trials, based on observations in the Safety, Tolerability and Efficacy Assessment of Isradipine for PD (STEADY-PD3) clinical trial.

METHODS

Individual item-level data was extracted from the SURE-PD3 study (coded as "PD-1018" in the C-path pooled dataset). We sought to confirm the observations made in the STEADY-PD3 dataset by analyzing data from a different Phase 3 clinical trial, the Phase 3 Study of Urate Elevation in Parkinson Disease (SURE-PD3), in which MDS-UPDRS was assessed more frequently than the 12-month intervals in STEADY-PD3, using similar methodology.

RESULTS

We were able to broadly validate results that demonstrated the frequency of ES, lack of impact of the introduction of symptomatic medications, and in the reduction in sample size required to demonstrate slowing of disease progression at a group level compared with the traditional total MDS-UPDRS summed score scoring methods. Counts of ES generally correlated modestly with summed MDS-UPRDS scores, both for the various sub-parts and for the overall scale as well. However, instability of individual item responses, especially during the first 6 months of observation complicated the assessment of the temporal evolution and stability of ES over time in the course of the SURE-PD3 study.

CONCLUSION

Further validation using data sets with frequent administration of MDS-UPDRS is necessary to assess value of this approach as an outcome measure in PD clinical trials.

Research Progress on the Relationship between Parkinson's Disease and REM Sleep Behavior Disorder

An individual's quality of life is greatly affected by Parkinson's disease (PD), a prevalent neurological degenerative condition. Rapid eye movement (REM) sleep behavior disorder (RBD) is a prominent non-motor symptom commonly associated with PD. Previous studies have shown a close relationship between PD and RBD. In addition to being a prodromal symptom of PD, RBD has a major negative impact on the prognosis of PD patients. This intrinsic connection indicates that there is a bidirectional relationship between PD and RBD. This paper provides a comprehensive review of the pathological mechanism related to PD and RBD, including the α-synuclein pathological deposition, abnormal iron metabolism, neuroinflammation, glymphatic system dysfunction and dysbiosis of the gut microbiota. Increasing evidence has shown that RBD patients have the same pathogenic mechanisms that underlie PD, but relatively little research has been done on how RBD contributes to PD progression. Therefore, a more thorough investigation is warranted to characterise how RBD affects the course of PD, in order to prepare for future therapeutic trials.

Implication of regional selectivity of dopamine deficits in impaired suppressing of involuntary movements in Parkinson's disease

To improve the initiation and speed of intended action, one of the crucial mechanisms is suppressing unwanted movements that interfere with goal-directed behavior, which is observed relatively aberrant in Parkinson's disease patients. Recent research has highlighted that dopamine deficits in Parkinson's disease predominantly occur in the caudal lateral part of the substantia nigra pars compacta (SNc) in human patients. We previously found two parallel circuits within the basal ganglia, primarily divided into circuits mediated by the rostral medial part and caudal lateral part of the SNc dopamine neurons. We have further discovered that the indirect pathway in caudal basal ganglia circuits, facilitated by the caudal lateral part of the SNc dopamine neurons, plays a critical role in suppressing unnecessary involuntary movements when animals perform voluntary goal-directed actions. We thus explored recent research in humans and non-human primates focusing on the distinct functions and networks of the caudal lateral part of the SNc dopamine neurons to elucidate the mechanisms involved in the impairment of suppressing involuntary movements in Parkinson's disease patients.

Selective dopaminergic neurotoxicity modulated by inherent cell-type specific neurobiology

Parkinson's disease (PD) is a debilitating neurodegenerative disease affecting millions of individuals worldwide. Hallmark features of PD pathology are the formation of Lewy bodies in neuromelanin-containing dopaminergic (DAergic) neurons of the substantia nigra pars compacta (SNpc), and the subsequent irreversible death of these neurons. Although genetic risk factors have been identified, around 90 % of PD cases are sporadic and likely caused by environmental exposures and gene-environment interaction. Mechanistic studies have identified a variety of chemical PD risk factors. PD neuropathology occurs throughout the brain and peripheral nervous system, but it is the loss of DAergic neurons in the SNpc that produce many of the cardinal motor symptoms. Toxicology studies have found specifically the DAergic neuron population of the SNpc exhibit heightened sensitivity to highly variable chemical insults (both in terms of chemical structure and mechanism of neurotoxic action). Thus, it has become clear that the inherent neurobiology of nigral DAergic neurons likely underlies much of this neurotoxic response to broad insults. This review focuses on inherent neurobiology of nigral DAergic neurons and how such neurobiology impacts the primary mechanism of neurotoxicity. While interactions with a variety of other cell types are important in disease pathogenesis, understanding how inherent DAergic biology contributes to selective sensitivity and primary mechanisms of neurotoxicity is critical to advancing the field. Specifically, key biological features of DAergic neurons that increase neurotoxicant susceptibility.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

Objective

The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye.

Method

This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

Results

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature.

Conclusion

The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.

"Advanced" Parkinson's disease: A review

There is no consensus driven definition of "advanced" Parkinson's disease (APD) currently. APD has been described in terms of emergence of specific clinical features and clinical milestones of the disease e.g., motor fluctuations, time to increasing falls, emergence of cognitive decline, etc. The pathological burden of disease has been used to characterize various stages of the disease. Imaging markers have been associated with various motor and nonmotor symptoms of advancing disease. In this review, we present an overview of clinical, pathologic, and imaging markers of APD. We also propose a model of disease definition involving longitudinal assessments of these markers as well as quality of life metrics to better understand and predict disease progression in those with Parkinson's disease.

Regional nigral neuromelanin degeneration in asymptomatic leucine-rich repeat kinase 2 gene carrier using MRI

Asymptomatic Leucine-Rich Repeat Kinase 2 Gene (LRRK2) carriers are at risk for developing Parkinson's disease (PD). We studied presymptomatic substantia nigra pars compacta (SNc) regional neurodegeneration in asymptomatic LRRK2 carriers compared to idiopathic PD patients using neuromelanin-sensitive MRI technique (NM-MRI). Fifteen asymptomatic LRRK2 carriers, 22 idiopathic PD patients, and 30 healthy controls (HCs) were scanned using NM-MRI. We computed volume and contrast-to-noise ratio (CNR) derived from the whole SNc and the sensorimotor, associative, and limbic SNc regions. An analysis of covariance was performed to explore the differences of whole and regional NM-MRI values among the groups while controlling the effect of age and sex. In whole SNc, LRRK2 had significantly lower CNR than HCs but non-significantly higher volume and CNR than PD patients, and PD patients significantly lower volume and CNR compared to HCs. Inside SNc regions, there were significant group effects for CNR in all regions and for volumes in the associative region, with a trend in the sensorimotor region but no significant changes in the limbic region. PD had reduced volume and CNR in all regions compared to HCs. Asymptomatic LRRK2 carriers showed globally decreased SNc volume and CNR suggesting early nigral neurodegeneration in these subjects at risk of developing PD.

Levodopa Impairs the Energy Metabolism of the Basal Ganglia In Vivo

OBJECTIVE

One proposed mechanism of disease progression in Parkinson's disease includes the interplay of endogenous dopamine toxicity and mitochondrial dysfunction. However, the in-vivo effects of exogenous dopamine administration on cerebral bioenergetics are unknown.

METHODS

We performed a double-blinded, cross-over, placebo-controlled trial. Participants received either 200/50 mg levodopa/benserazide or a placebo and vice versa on the second study visit. Clinical assessments and multimodal neuroimaging were performed, including 31phosphorus magnetic resonance spectroscopy of the basal ganglia and the midbrain.

RESULTS

In total, 20 (6 female) patients with Parkinson's disease and 22 sex- and age-matched healthy controls (10 female) were enrolled. Treatment with levodopa/benserazide but not with placebo resulted in a substantial reduction of high-energy phosphorus-containing metabolites in the basal ganglia (patients with Parkinson's disease: -40%; healthy controls: -39%) but not in the midbrain. There were no differences in high-energy phosphorus-containing metabolites for patients with Parkinson's disease compared to healthy controls in the OFF state and treatment response.

INTERPRETATION

Exogenously administered levodopa/benserazide strongly interferes with basal ganglia high-energy phosphorus-containing metabolite levels in both groups. The lack of effects on midbrain levels suggests that the observed changes are limited to the site of dopamine action. ANN NEUROL 2024;95:849-857.

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.

Microstructural alterations of the hypothalamus in Parkinson's disease and probable REM sleep behavior disorder

BACKGROUND

Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial.

OBJECTIVES

We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels.

METHODS

This study included 186 PD, 67 pRBD, and 179 healthy controls. Multi-shell diffusion MRI were scanned and mean kurtosis (MK) in hypothalamic subunits were calculated. Participants were assessed using Unified Parkinson's Disease Rating Scale (UPDRS), RBD Questionnaire-Hong Kong (RBDQ-HK), Hamilton Depression Rating Scale (HAMD), and Activity of Daily Living (ADL) Scale. Additionally, a subgroup of PD (n = 31) underwent assessment of FT4.

RESULTS

PD showed significant decreases of MK in anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tubular (supTub), and inferior tubular hypothalamus when compared with healthy controls. Similarly, pRBD exhibited decreases of MK in a-iHyp and supTub. In PD group, MK in above four subunits were significantly correlated with UPDRS-I, HAMD, and ADL. Moreover, MK in a-iHyp and a-sHyp were significantly correlated with FT4 level. In pRBD group, correlations were observed between MK in a-iHyp and UPDRS-I.

CONCLUSIONS

Our study reveals that microstructural changes in the hypothalamus are already significant at the early neurodegenerative stage. These changes are associated with emotional alterations, daily activity levels, and thyroid hormone levels.

PENCIL imaging: A novel approach for neuromelanin sensitive MRI in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN.

METHODS

This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc.

RESULTS

PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard.

CONCLUSIONS

PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties.

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.

Aging pattern of the brainstem based on volumetric measurement and optimized surface shape analysis

The brainstem, a small and crucial structure, is connected to the cerebrum, spinal cord, and cerebellum, playing a vital role in regulating autonomic functions, transmitting motor and sensory information, and modulating cognitive processes, emotions, and consciousness. While previous research has indicated that changes in brainstem anatomy can serve as a biomarker for aging and neurodegenerative diseases, the structural changes that occur in the brainstem during normal aging remain unclear. This study aimed to examine the age- and sex-related differences in the global and local structural measures of the brainstem in 187 healthy adults (ranging in age from 18 to 70 years) using structural magnetic resonance imaging. The findings showed a significant negative age effect on the volume of the two major components of the brainstem: the medulla oblongata and midbrain. The shape analysis revealed that atrophy primarily occurs in specific structures, such as the pyramid, cerebral peduncle, superior and inferior colliculi. Surface area and shape analysis showed a trend of flattening in the aging brainstem. There were no significant differences between the sexes or sex-by-age interactions in brainstem structural measures. These findings provide a systematic description of age associations with brainstem structures in healthy adults and may provide a reference for future research on brain aging and neurodegenerative diseases.

Neuropsychological Changes in Isolated REM Sleep Behavior Disorder: A Systematic Review and Meta-analysis of Cross-sectional and Longitudinal Studies

The aim of this meta-analysis is twofold: (a) to assess cognitive impairments in isolated rapid eye movement (REM) sleep behavior disorder (iRBD) patients compared to healthy controls (HC); (b) to quantitatively estimate the risk of developing a neurodegenerative disease in iRBD patients according to baseline cognitive assessment. To address the first aim, cross-sectional studies including polysomnography-confirmed iRBD patients, HC, and reporting neuropsychological testing were included. To address the second aim, longitudinal studies including polysomnography-confirmed iRBD patients, reporting baseline neuropsychological testing for converted and still isolated patients separately were included. The literature search was conducted based on PRISMA guidelines and the protocol was registered at PROSPERO (CRD42021253427). Cross-sectional and longitudinal studies were searched from PubMed, Web of Science, Scopus, and Embase databases. Publication bias and statistical heterogeneity were assessed respectively by funnel plot asymmetry and using I2. Finally, a random-effect model was performed to pool the included studies. 75 cross-sectional (2,398 HC and 2,460 iRBD patients) and 11 longitudinal (495 iRBD patients) studies were selected. Cross-sectional studies showed that iRBD patients performed significantly worse in cognitive screening scores (random-effects (RE) model = -0.69), memory (RE model = -0.64), and executive function (RE model = -0.50) domains compared to HC. The survival analyses conducted for longitudinal studies revealed that lower executive function and language performance, as well as the presence of mild cognitive impairment (MCI), at baseline were associated with an increased risk of conversion at follow-up. Our study underlines the importance of a comprehensive neuropsychological assessment in the context of iRBD.

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.

Neuromelanin-sensitive MRI as a promising biomarker of catecholamine function

Disruptions to dopamine and noradrenergic neurotransmission are noted in several neurodegenerative and psychiatric disorders. Neuromelanin-sensitive (NM)-MRI offers a non-invasive approach to visualize and quantify the structural and functional integrity of the substantia nigra and locus coeruleus. This method may aid in the diagnosis and quantification of longitudinal changes of disease and could provide a stratification tool for predicting treatment success of pharmacological interventions targeting the dopaminergic and noradrenergic systems. Given the growing clinical interest in NM-MRI, understanding the contrast mechanisms that generate this signal is crucial for appropriate interpretation of NM-MRI outcomes and for the continued development of quantitative MRI biomarkers that assess disease severity and progression. To date, most studies associate NM-MRI measurements to the content of the neuromelanin pigment and/or density of neuromelanin-containing neurons, while recent studies suggest that the main source of the NM-MRI contrast is not the presence of neuromelanin but the high-water content in the dopaminergic and noradrenergic neurons. In this review, we consider the biological and physical basis for the NM-MRI contrast and discuss a wide range of interpretations of NM-MRI. We describe different acquisition and image processing approaches and discuss how these methods could be improved and standardized to facilitate large-scale multisite studies and translation into clinical use. We review the potential clinical applications in neurological and psychiatric disorders and the promise of NM-MRI as a biomarker of disease, and finally, we discuss the current limitations of NM-MRI that need to be addressed before this technique can be utilized as a biomarker and translated into clinical practice and offer suggestions for future research.

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.

Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease

BACKGROUND

Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers.

OBJECTIVE

To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD).

METHODS

Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow-up duration = 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers (dopaminergic denervation).

RESULTS

PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep-all before diagnosis-and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor symptoms.

CONCLUSIONS

Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.

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.

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.

Brain MRI Biomarkers in Isolated Rapid Eye Movement Sleep Behavior Disorder: Where Are We? A Systematic Review

The increasing number of MRI studies focused on prodromal Parkinson's Disease (PD) demonstrates a strong interest in identifying early biomarkers capable of monitoring neurodegeneration. In this systematic review, we present the latest information regarding the most promising MRI markers of neurodegeneration in relation to the most specific prodromal symptoms of PD, namely isolated rapid eye movement (REM) sleep behavior disorder (iRBD). We reviewed structural, diffusion, functional, iron-sensitive, neuro-melanin-sensitive MRI, and proton magnetic resonance spectroscopy studies conducted between 2000 and 2023, which yielded a total of 77 relevant papers. Among these markers, iron and neuromelanin emerged as the most robust and promising indicators for early neurodegenerative processes in iRBD. Atrophy was observed in several regions, including the frontal and temporal cortices, limbic cortices, and basal ganglia, suggesting that neurodegenerative processes had been underway for some time. Diffusion and functional MRI produced heterogeneous yet intriguing results. Additionally, reduced glymphatic clearance function was reported. Technological advancements, such as the development of ultra-high field MRI, have enabled the exploration of minute anatomical structures and the detection of previously undetectable anomalies. The race to achieve early detection of neurodegeneration is well underway.

The integrity of dopaminergic and noradrenergic brain regions is associated with different aspects of late-life memory performance

Changes in dopaminergic neuromodulation play a key role in adult memory decline. Recent research has also implicated noradrenaline in shaping late-life memory. However, it is unclear whether these two neuromodulators have distinct roles in age-related cognitive changes. Here, combining longitudinal MRI of the dopaminergic substantia nigra-ventral tegmental area (SN-VTA) and noradrenergic locus coeruleus (LC) in younger (n = 69) and older (n = 251) adults, we found that dopaminergic and noradrenergic integrity are differentially associated with memory performance. While LC integrity was related to better episodic memory across several tasks, SN-VTA integrity was linked to working memory. Longitudinally, we found that older age was associated with more negative change in SN-VTA and LC integrity. Notably, changes in LC integrity reliably predicted future episodic memory. These differential associations of dopaminergic and noradrenergic nuclei with late-life cognitive decline have potential clinical utility, given their degeneration in several age-associated diseases.

Recovery of motor function is associated with rescue of glutamate biomarkers in the striatum and motor cortex following treatment with Mucuna pruriens in a murine model of Parkinsons disease

There is growing interest in the use of natural products for the treatment of Parkinson's disease (PD). Mucuna pruriens has been used in the treatment of humans with PD. The goal of this study was to determine if daily oral treatment with an extract of Mucuna pruriens, starting after the MPTP-induced loss of nigrostriatal dopamine in male mice, would result in recovery/restoration of motor function, tyrosine hydroxylase (TH) protein expression in the nigrostriatal pathway, or glutamate biomarkers in both the striatum and motor cortex. Following MPTP administration, resulting in an 80 % loss of striatal TH, treatment with Mucuna pruriens failed to rescue either striatal TH or the dopamine transporter back to the control levels, but there was restoration of gait/motor function. There was an MPTP-induced loss of TH-labeled neurons in the substantia nigra pars compacta and in the number of striatal dendritic spines, both of which failed to be recovered following treatment with Mucuna pruriens. This Mucuna pruriens-induced locomotor recovery following MPTP was associated with restoration of two striatal glutamate transporter proteins, GLAST (EAAT1) and EAAC1 (EAAT3), and the vesicular glutamate transporter 2 (Vglut2) within the motor cortex. Post-MPTP treatment with Mucuna pruriens, results in locomotor improvement that is associated with recovery of striatal and motor cortex glutamate transporters but is independent of nigrostriatal TH restoration.

Imaging Markers in Genetic Forms of Parkinson's Disease

Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by motor symptoms such as bradykinesia, rigidity, and resting tremor. While the majority of PD cases are sporadic, approximately 15-20% of cases have a genetic component. Advances in neuroimaging techniques have provided valuable insights into the pathophysiology of PD, including the different genetic forms of the disease. This literature review aims to summarize the current state of knowledge regarding neuroimaging findings in genetic PD, focusing on the most prevalent known genetic forms: mutations in the GBA1, LRRK2, and Parkin genes. In this review, we will highlight the contributions of various neuroimaging modalities, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI), in elucidating the underlying pathophysiological mechanisms and potentially identifying candidate biomarkers for genetic forms of PD.

Neuromelanin related ultra-high field signal intensity of the locus coeruleus differs between Parkinson's disease and controls

INTRODUCTION

Neuromelanin related signal changes in catecholaminergic nuclei are considered as a promising MRI biomarker in Parkinson's disease (PD). Until now, most studies have investigated the substantia nigra (SN), while signal changes might be more prominent in the locus coeruleus (LC). Ultra-high field MRI improves the visualisation of these small brainstem regions and might support the development of imaging biomarkers in PD.

OBJECTIVES

To compare signal intensity of the SN and LC on Magnetization Transfer MRI between PD patients and healthy controls (HC) and to explore its association with cognitive performance in PD.

METHODS

This study was conducted using data from the TRACK-PD study, a longitudinal 7T MRI study. A total of 78 early-stage PD patients and 36 HC were included. A mask for the SN and LC was automatically segmented and manually corrected. Neuromelanin related signal intensity of the SN and LC was compared between PD and HC.

RESULTS

PD participants showed a lower contrast-to-noise ratio (CNR) in the right SN (p = 0.029) and left LC (p = 0.027). After adding age as a confounder, the CNR of the right SN did not significantly differ anymore between PD and HC (p = 0.055). Additionally, a significant positive correlation was found between the SN CNR and memory function.

DISCUSSION

This study confirms that neuromelanin related signal intensity of the LC differs between early-stage PD patients and HC. No significant difference was found in the SN. This supports the theory of bottom-up disease progression in PD. Furthermore, loss of SN integrity might influence working memory or learning capabilities in PD patients.

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

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

Substantia nigra nigrosome-1 imaging correlates with the severity of motor symptoms in Parkinson's disease

BACKGROUND

Nigrosome-1 imaging has been used for assisting the diagnosis of Parkinson's disease (PD). We aimed to examine the diagnostic performance of loss of nigrosome-1 in PD and the correlation between the size of the nigrosome-1 and motor severity of PD.

METHODS

We included 237 patients with PD and 165 controls. The motor severity of PD was assessed with the Unified Parkinson's Disease Rating Scale (UPDRS) part III score and Hoehn-Yahr staging. The 3 or 1.5 Tesla susceptibility-weighted imaging combined with a deep-learning algorithm was applied for detecting the loss and the size of nigrosome-1. Clinical correlations and diagnostic performance of size of nigrosome-1 were also investigated.

RESULTS

The mean nigrosome-1 size was significantly smaller in PD patients than in controls (0.06 ± 0.07 cm² vs. 0.20 ± 0.05 cm², P < 0.001). The area under the receiver operating characteristic curve (AUC) of the established model showed 0.94 accuracy (95% confidence interval [CI]: 0.87, 1.01, P < 0.01) in differentiating between the PD and control groups. Moreover, the partial loss of nigrosome-1 detected with SWI had an AUC of 0.96 in discriminating early-stage PD from controls (95% CI: 0.88, 1.02, P < 0.001). After adjusting for age, sex, disease duration, and levodopa equivalent daily dose, the estimated size of nigrosome-1 was negatively associated with the UPDRS part III motor score (ρ = -0.433, P < 0.001), but not with Mini-Mental State Examination scores (ρ = 0.006, P = 0.894).

CONCLUSIONS

The extent of loss and the size of nigrosome-1 may potentially assist in the diagnosis of PD. Nigrosome-1 size reflects the motor severity of PD.

Probing midbrain dopamine function in pediatric obsessive-compulsive disorder via neuromelanin-sensitive magnetic resonance imaging

Obsessive-compulsive disorder (OCD) is an impairing psychiatric condition, which often onsets in childhood. Growing research highlights dopaminergic alterations in adult OCD, yet pediatric studies are limited by methodological constraints. This is the first study to utilize neuromelanin-sensitive MRI as a proxy for dopaminergic function among children with OCD. N = 135 youth (6-14-year-olds) completed high-resolution neuromelanin-sensitive MRI across two sites; n = 64 had an OCD diagnosis. N = 47 children with OCD completed a second scan after cognitive-behavioral therapy. Voxel-wise analyses identified that neuromelanin-MRI signal was higher among children with OCD compared to those without (483 voxels, permutation-corrected p = 0.018). Effects were significant within both the substania nigra pars compacta (p = 0.004, Cohen's d = 0.51) and ventral tegmental area (p = 0.006, d = 0.50). Follow-up analyses indicated that more severe lifetime symptoms (t = -2.72, p = 0.009) and longer illness duration (t = -2.22, p = 0.03) related to lower neuromelanin-MRI signal. Despite significant symptom reduction with therapy (p < 0.001, d = 1.44), neither baseline nor change in neuromelanin-MRI signal associated with symptom improvement. Current results provide the first demonstration of the utility of neuromelanin-MRI in pediatric psychiatry, specifically highlighting in vivo evidence for midbrain dopamine alterations in treatment-seeking youth with OCD. Neuromelanin-MRI likely indexes accumulating alterations over time, herein, implicating dopamine hyperactivity in OCD. Given evidence of increased neuromelanin signal in pediatric OCD but negative association with symptom severity, additional work is needed to parse potential longitudinal or compensatory mechanisms. Future studies should explore the utility of neuromelanin-MRI biomarkers to identify early risk prior to onset, parse OCD subtypes or symptom heterogeneity, and explore prediction of pharmacotherapy response.

Parkinsonian Tremor Detection with Compact Convolutional Transformer from Bispectrum Representation of tri-Axial Accelerometer Signals

After the breakthroughs of Transformer networks in Natural Language Processing (NLP) tasks, they have led to exciting progress in visual tasks as well. Nonetheless, there has been a parallel growth in the number of parameters and the amount of training data, which led to the conclusion that Transformers are not suited for small datasets. This paper is the first to convey the feasibility of Compact Convolutional Transformers (CCT) for the prediction of Parkinsonian postural tremor based on the Bispectrum (BS) representation of IMU accelerometer time series. The dataset includes tri-axial accelerometer signals collected unobtrusively in-the-wild while subjects are on a phone call, and labelled by neurologists and signal processing experts. The BS is a noise-immune, higher-order representation that reflects a signal's deviation from Gaussianity and measures quadratic phase coupling. We performed comparative classification experiments using the CCT, pre-trained CNNs such as VGG-16 and ResNet-50, and the conventional Vision Transformer (ViT). Our model achieves competitive prediction accuracy and F1 score of 96% with only 1.016 M trainable parameters, compared to the ViT with 21.659 M trainable parameters, in a five-fold cross-validation scheme. Our model also outperforms pre-trained CNNs such as VGG-16 and ResNet-50. Furthermore, we show that the performance gains are maintained when training on a larger dataset of BS images. Our effort here is motivated by the hypothesis that data-efficient transformers outperform transfer learning using pre-trained CNNs, paving the way for promising deep learning architecture for small-scale, novel and noisy medical imaging datasets.Clinical relevance- Novel deep learning model for unobtrusive prediction of Parkinsonian Postural Tremor from Bispectrum image representation of tri-axial accelerometer signals collected in-the-wild.

Lewy bodies, iron, inflammation and neuromelanin: pathological aspects underlying Parkinson's disease

Since the description of some peculiar symptoms by James Parkinson in 1817, attempts have been made to define its cause or at least to enlighten the pathology of "Parkinson's disease (PD)." The vast majority of PD subtypes and most cases of sporadic PD share Lewy bodies (LBs) as a characteristic pathological hallmark. However, the processes underlying LBs generation and its causal triggers are still unknown. ɑ-Synuclein (ɑ-syn, encoded by the SNCA gene) is a major component of LBs, and SNCA missense mutations or duplications/triplications are causal for rare hereditary forms of PD. Thus, it is imperative to study ɑ-syn protein and its pathology, including oligomerization, fibril formation, aggregation, and spreading mechanisms. Furthermore, there are synergistic effects in the underlying pathogenic mechanisms of PD, and multiple factors-contributing with different ratios-appear to be causal pathological triggers and progression factors. For example, oxidative stress, reduced antioxidative capacity, mitochondrial dysfunction, and proteasomal disturbances have each been suggested to be causal for ɑ-syn fibril formation and aggregation and to contribute to neuroinflammation and neural cell death. Aging is also a major risk factor for PD. Iron, as well as neuromelanin (NM), show age-dependent increases, and iron is significantly increased in the Parkinsonian substantia nigra (SN). Iron-induced pathological mechanisms include changes of the molecular structure of ɑ-syn. However, more recent PD research demonstrates that (i) LBs are detected not only in dopaminergic neurons and glia but in various neurotransmitter systems, (ii) sympathetic nerve fibres degenerate first, and (iii) at least in "brain-first" cases dopaminergic deficiency is evident before pathology induced by iron and NM. These recent findings support that the ɑ-syn/LBs pathology as well as iron- and NM-induced pathology in "brain-first" cases are important facts of PD pathology and via their interaction potentiate the disease process in the SN. As such, multifactorial toxic processes posted on a personal genetic risk are assumed to be causal for the neurodegenerative processes underlying PD. Differences in ratios of multiple factors and their spatiotemporal development, and the fact that common triggers of PD are hard to identify, imply the existence of several phenotypical subtypes, which is supported by arguments from both the "bottom-up/dual-hit" and "brain-first" models. Therapeutic strategies are necessary to avoid single initiation triggers leading to PD.

Brainstem neuromelanin and iron MRI reveals a precise signature for idiopathic and LRRK2 Parkinson's disease

Neuromelanin (NM) loss in substantia nigra pars compacta (SNc) and locus coeruleus (LC) reflects neuronal death in Parkinson's disease (PD). Since genetically-determined PD shows varied clinical expressivity, we wanted to accurately quantify and locate brainstem NM and iron, to discover whether specific MRI patterns are linked to Leucine-rich repeat kinase 2 G2019S PD (LRRK2-PD) or idiopathic Parkinson's disease (iPD). A 3D automated MRI atlas-based segmentation pipeline (3D-ABSP) for NM/iron-sensitive MRI images topographically characterized the SNc, LC, and red nucleus (RN) neuronal loss and calculated NM/iron contrast ratio (CR) and normalized volume (nVol). Left-side NM nVol was larger in all groups. PD had lower NM CR and nVol in ventral-caudal SNc, whereas iron increased in lateral, medial-rostral, and caudal SNc. The SNc NM CR reduction was associated with psychiatric symptoms. LC CR and nVol discriminated better among subgroups: LRRK2-PD had similar LC NM CR and nVol as that of controls, and larger LC NM nVol and RN iron CR than iPD. PD showed higher iron SNc nVol than controls, especially among LRRK2-PD. ROC analyses showed an AUC > 0.92 for most pairwise subgroup comparisons, with SNc NM being the best discriminator between HC and PD. NM measures maintained their discriminator power considering the subgroup of PD patients with less than 5 years of disease duration. The SNc iron CR and nVol increase was associated with longer disease duration in PD patients. The 3D-ABSP sensitively identified NM and iron MRI patterns strongly correlated with phenotypic PD features.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Role of novel endpoints and evaluations of response in Parkinson disease

With progress in our understanding of Parkinson disease (PD) and other neurodegenerative disorders, from clinical features to imaging, genetic, and molecular characterization comes the opportunity to refine and revise how we measure these diseases and what outcome measures are used as endpoints in clinical trials. While several rater-, patient-, and milestone-based outcomes for PD exist that may serve as clinical trial endpoints, there remains an unmet need for endpoints that are clinically meaningful, patient centric while also being more objective and quantitative, less susceptible to effects of symptomatic therapy (for disease-modification trials), and that can be measured over a short period and yet accurately represent longer-term outcomes. Several novel outcomes that may be used as endpoints in PD clinical trials are in development, including digital measures of signs and symptoms, as well a growing array of imaging and biospecimen biomarkers. This chapter provides an overview of the state of PD outcome measures as of 2022, including considerations for selection of clinical trial endpoints in PD, advantages and limitations of existing measures, and emerging potential novel endpoints.

Application of Neuromelanin MR Imaging in Parkinson Disease

MRI has been used to develop biomarkers for movement disorders such as Parkinson disease (PD) and other neurodegenerative disorders with parkinsonism such as progressive supranuclear palsy and multiple system atrophy. One of these imaging biomarkers is neuromelanin (NM), whose integrity can be assessed from its contrast and volume. NM is found mainly in certain brain stem structures, namely, the substantia nigra pars compacta (SNpc), the ventral tegmental area, and the locus coeruleus. Another major biomarker is brain iron, which often increases in concert with NM degeneration. These biomarkers have the potential to improve diagnostic certainty in differentiating between PD and other neurodegenerative disorders similar to PD, as well as provide a better understanding of pathophysiology. Mapping NM in vivo has clinical importance for gauging the premotor phase of PD when there is a greater than 50% loss of dopaminergic SNpc melanized neurons. As a metal ion chelator, NM can absorb iron. When NM is released from neurons, it deposits iron into the intracellular tissues of the SNpc; the result is iron that can be imaged and measured using quantitative susceptibility mapping. An increase of iron also leads to the disappearance of the nigrosome-1 sign, another neuroimage biomarker for PD. Therefore, mapping NM and iron changes in the SNpc are a practical means for improving early diagnosis of PD and in monitoring disease progression. In this review, we discuss the functions and location of NM, how NM-MRI is performed, the automatic mapping of NM and iron content, how NM-related imaging biomarkers can be used to enhance PD diagnosis and differentiate it from other neurodegenerative disorders, and potential advances in NM imaging methods. With major advances currently evolving for rapid imaging and artificial intelligence, NM-related biomarkers are likely to have increasingly important roles for enhancing diagnostic capabilities in PD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Imaging in Movement Disorders

OBJECTIVE

This article reviews commonly used imaging modalities in movement disorders, particularly parkinsonism. The review includes the diagnostic utility, role in differential diagnosis, reflection of pathophysiology, and limitations of neuroimaging in the setting of movement disorders. It also introduces promising new imaging modalities and describes the current status of research.

LATEST DEVELOPMENTS

Iron-sensitive MRI sequences and neuromelanin-sensitive MRI can be used to directly assess the integrity of nigral dopaminergic neurons and thus may reflect disease pathology and progression throughout the full range of severity in Parkinson disease (PD). The striatal uptake of presynaptic radiotracers in their terminal axons as currently assessed using clinically approved positron emission tomography (PET) or single-photon emission computed tomography (SPECT) imaging correlates with nigral pathology and disease severity only in early PD. Cholinergic PET, using radiotracers that target the presynaptic vesicular acetylcholine transporter, constitutes a substantial advance and may provide crucial insights into the pathophysiology of clinical symptoms such as dementia, freezing, and falls.

ESSENTIAL POINTS

In the absence of valid, direct, objective biomarkers of intracellular misfolded α-synuclein, PD remains a clinical diagnosis. The clinical utility of PET- or SPECT-based striatal measures is currently limited given their lack of specificity and inability to reflect nigral pathology in moderate to severe PD. These scans may be more sensitive than clinical examination to detect nigrostriatal deficiency that occurs in multiple parkinsonian syndromes and may still be recommended for clinical use in the future to identify prodromal PD if and when disease-modifying treatments become available. Multimodal imaging to evaluate underlying nigral pathology and its functional consequences may hold the key to future advances.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Advances in the neuroimaging of motor disorders

Neuroimaging is a valuable adjunct to the history and examination in the evaluation of motor system disorders. Conventional imaging with computed tomography or magnetic resonance imaging depicts important anatomic information and helps to identify imaging patterns which may support diagnosis of a specific motor disorder. Advanced imaging techniques can provide further detail regarding volume, functional, or metabolic changes occurring in nervous system pathology. This chapter is an overview of the advances in neuroimaging with particular emphasis on both standard and less well-known advanced imaging techniques and findings, such as diffusion tensor imaging or volumetric studies, and their application to specific motor disorders. In addition, it provides reference to emerging imaging biomarkers in motor system disorders such as Parkinson disease, amyotrophic lateral sclerosis, and Huntington disease, and briefly reviews the neuroimaging findings in different causes of myelopathy and peripheral nerve disorders.

Iron- and Neuromelanin-Weighted Neuroimaging to Study Mitochondrial Dysfunction in Patients with Parkinson's Disease

The underlying causes of Parkinson's disease are complex, and besides recent advances in elucidating relevant disease mechanisms, no disease-modifying treatments are currently available. One proposed pathophysiological hallmark is mitochondrial dysfunction, and a plethora of evidence points toward the interconnected nature of mitochondria in neuronal homeostasis. This also extends to iron and neuromelanin metabolism, two biochemical processes highly relevant to individual disease manifestation and progression. Modern neuroimaging methods help to gain in vivo insights into these intertwined pathways and may pave the road to individualized medicine in this debilitating disorder. In this narrative review, we will highlight the biological rationale for studying these pathways, how distinct neuroimaging methods can be applied in patients, their respective limitations, and which challenges need to be overcome for successful implementation in clinical studies.

NigraNet: An automatic framework to assess nigral neuromelanin content in early Parkinson's disease using convolutional neural network

BACKGROUND

Parkinson's disease (PD) demonstrates neurodegenerative changes in the substantia nigra pars compacta (SNc) using neuromelanin-sensitive (NM)-MRI. As SNc manual segmentation is prone to substantial inter-individual variability across raters, development of a robust automatic segmentation framework is necessary to facilitate nigral neuromelanin quantification. Artificial intelligence (AI) is gaining traction in the neuroimaging community for automated brain region segmentation tasks using MRI.

OBJECTIVE

Developing and validating AI-based NigraNet, a fully automatic SNc segmentation framework allowing nigral neuromelanin quantification in patients with PD using NM-MRI.

METHODS

We prospectively included 199 participants comprising 144 early-stage idiopathic PD patients (disease duration = 1.5 ± 1.0 years) and 55 healthy volunteers (HV) scanned using a 3 Tesla MRI including whole brain T1-weighted anatomical imaging and NM-MRI. The regions of interest (ROI) were delineated in all participants automatically using NigraNet, a modified U-net, and compared to manual segmentations performed by two experienced raters. The SNc volumes (Vol), volumes corrected by total intracranial volume (Cvol), normalized signal intensity (NSI) and contrast-to-noise ratio (CNR) were computed. One-way GLM-ANCOVA was performed while adjusting for age and sex as covariates. Diagnostic performance measurement was assessed using the receiver operating characteristic (ROC) analysis. Inter and intra-observer variability were estimated using Dice similarity coefficient (DSC). The agreements between methods were tested using intraclass correlation coefficient (ICC) based on a mean-rating, two-way, mixed-effects model estimates for absolute agreement. Cronbach's alpha and Bland-Altman plots were estimated to assess inter-method consistency.

RESULTS

Using both methods, Vol, Cvol, NSI and CNR measurements differed between PD and HV with an effect of sex for Cvol and CNR. ICC values between the methods demonstrated optimal agreement for Cvol and CNR (ICC > 0.9) and high reproducibility (DSC: 0.80) was also obtained. The SNc measurements also showed good to excellent consistency values (Cronbach's alpha > 0.87). Bland-Altman plots of agreement demonstrated no association of SNc ROI measurement differences between the methods and ROI average measurements while confirming that 95 % of the data points were ranging between the limits of mean difference (d ± 1.96xSD). Percentage changes between PD and HV were -27.4 % and -17.7 % for Vol, -30.0 % and -22.2 % for Cvol, -15.8 % and -14.4 % for NSI, -17.1 % and -16.0 % for CNR for automatic and manual measurements respectively. Using automatic method, in the entire dataset, we obtained the areas under the ROC curve (AUC) of 0.83 for Vol, 0.85 for Cvol, 0.79 for NSI and 0.77 for CNR whereas in the training dataset of 0.96 for Vol, 0.95 for Cvol, 0.85 for NSI and 0.85 for CNR. Disease duration correlated negatively with NSI of the patients for both the automatic and manual measurements.

CONCLUSIONS

We presented an AI-based NigraNet framework that utilizes a small MRI training dataset to fully automatize the SNc segmentation procedure with an increased precision and more reproducible results. Considering the consistency, accuracy and speed of our approach, this study could be a crucial step towards the implementation of a time-saving non-rater dependent fully automatic method for studying neuromelanin changes in clinical settings and large-scale neuroimaging studies.

Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson's disease

MRI was suggested as a promising method for the diagnosis and assessment of Parkinson's Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T₂* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T₂* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T₂* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can't be performed. Combining neuromelanin and T₂*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.

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.

Dynamics of Nigral Iron Accumulation in Parkinson's Disease: From Diagnosis to Late Stage

BACKGROUND

Higher nigral iron has been reported in Parkinson's disease (PD).

OBJECTIVE

The aim is to understand the dynamics of nigral iron accumulation in PD and its association with drug treatment.

METHODS

Susceptibility magnetic resonance imaging data were obtained from 79 controls and 18 drug-naive (PD_DN ) and 87 drug-treated (PD_DT ) PD patients. Regional brain iron in basal ganglia and cerebellar structures was estimated using quantitative susceptibility mapping. Nigral iron was compared between PD_DN and PD_DT subgroups defined by disease duration (early [PDE, <2 years], middle [PDM, 2-6 years], and later [PDL, >6 years]). Associations with both disease duration and types of antiparkinson drugs were explored using regression analysis.

RESULTS

Compared to controls, PD_DN had lower iron in the substantia nigra (P = 0.018), caudate nucleus (P = 0.038), and globus pallidus (P = 0.01) but not in the putamen or red nucleus. In contrast, PD_DT had higher iron in the nigra (P < 0.001) but not in other regions, compared to either controls or PD_DN . Iron in the nigra increased with disease duration (PDE > PD_DN [P = 0.001], PDM > PDE [P = 0.045]) except for PDM versus PDL (P = 0.226). Levodopa usage was associated with higher (P = 0.013) nigral iron, whereas lower nigral iron was correlated with selegiline usage (P = 0.030).

CONCLUSION

Nigral iron is lower before the start of dopaminergic medication and then increases throughout the disease until it plateaus at late stages, suggesting increased iron may not be an etiological factor. Interestingly, PD medications may have differential associations with iron accumulation that need further investigation. © 2022 International Parkinson and Movement Disorder Society.

Cav1.3 calcium channels are full-range linear amplifiers of firing frequencies in lateral DA SN neurons

The low-threshold L-type calcium channel Cav1.3 accelerates the pacemaker rate in the heart, but its functional role for the extended dynamic range of neuronal firing is still unresolved. Here, we show that Cav1.3 calcium channels act as unexpectedly simple, full-range linear amplifiers of firing rates for lateral dopamine substantia nigra (DA SN) neurons in mice. This means that they boost in vitro or in vivo firing frequencies between 2 and 50 hertz by about 30%. Furthermore, we demonstrate that clinically relevant, low nanomolar concentrations of the L-type channel inhibitor isradipine selectively reduce the in vivo firing activity of these nigrostriatal DA SN neurons at therapeutic plasma concentrations. Thus, our study identifies the pacemaker function of neuronal Cav1.3 channels and provides direct evidence that repurposing dihydropyridines such as isradipine is feasible to selectively modulate the in vivo activity of highly vulnerable DA SN subpopulations in Parkinson's disease.

Functional Imaging for Neurodegenerative Diseases

Diagnosis and monitoring of neurodegenerative diseases has changed profoundly over the past twenty years. Biomarkers are now included in most diagnostic procedures as well as in clinical trials. Neuroimaging biomarkers provide access to brain structure and function over the course of neurodegenerative diseases. They have brought new insights into a wide range of neurodegenerative diseases and have made it possible to describe some of the imaging challenges in clinical populations. MRI mainly explores brain structure while molecular imaging, functional MRI and electro- and magnetoencephalography examine brain function. In this paper, we describe and analyse the current and potential contribution of MRI and molecular imaging in the field of neurodegenerative diseases.

Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis

The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease

Parkinson disease (PD) is a progressive disorder characterized by dopaminergic neurodegeneration in the brain. The development of parkinsonism is preceded by a long prodromal phase, and >50% of dopaminergic neurons can be lost from the substantia nigra by the time of the initial diagnosis. Therefore, validation of in vivo imaging biomarkers for early diagnosis and monitoring of disease progression is essential for future therapeutic developments. PET and single-photon emission CT targeting the presynaptic terminals of dopaminergic neurons can be used for early diagnosis by detecting axonal degeneration in the striatum. However, these techniques poorly differentiate atypical parkinsonian syndromes from PD, and their availability is limited in clinical settings. Advanced MRI in which pathological changes in the substantia nigra are visualized with diffusion, iron-sensitive susceptibility and neuromelanin-sensitive sequences potentially represents a more accessible imaging tool. Although these techniques can visualize the classic degenerative changes in PD, they might be insufficient for phenotyping or prognostication of heterogeneous aspects of PD resulting from extranigral pathologies. The retina is an emerging imaging target owing to its pathological involvement early in PD, which correlates with brain pathology. Retinal optical coherence tomography (OCT) is a non-invasive technique to visualize structural changes in the retina. Progressive parafoveal thinning and fovea avascular zone remodelling, as revealed by OCT, provide potential biomarkers for early diagnosis and prognostication in PD. As we discuss in this Review, multimodal imaging of the substantia nigra and retina is a promising tool to aid diagnosis and management of PD.