Increased iron-deposition in lateral-ventral substantia nigra pars compacta: A promising neuroimaging marker for Parkinson's disease

NOX1 triggers ferroptosis and ferritinophagy, contributes to Parkinson's disease

The progressive loss of dopaminergic neurons in the midbrain is the hallmark of Parkinson's disease (PD). A newly emerging form of lytic cell death, ferroptosis, has been implicated in PD. However, it remains unclear in terms of PD-associated ferroptosis underlying causative genes and effective therapeutic approaches. This research explored the underlying mechanism of ferroptosis-related genes in PD. Here, Firstly, we found NOX1 associated with ferroptosis differently in PD patients by bioinformatics analysis. In vitro and in vivo models of PD were constructed to explore the underlying mechanism. qPCR, Western blot analysis, immunohistochemistry, immunofluorescence, Ferro orange, and BODIPY C11 were utilized to analyze the levels of ferroptosis. Transcriptomics sequencing was to investigate the downstream pathway and the analysis of immunoprecipitation to validate the upstream factor. In conclusion, NOX1 upregulation and activation of ferroptosis-related neurodegeneration, therefore, might be useful as a clinical therapeutic agent.

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.

Zeolite and Neurodegenerative Diseases

Neurodegenerative diseases (NDs), characterized by progressive degeneration and death of neurons, are strongly related to aging, and the number of people with NDs will continue to rise. Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common NDs, and the current treatments offer no cure. A growing body of research shows that AD and especially PD are intricately related to intestinal health and the gut microbiome and that both diseases can spread retrogradely from the gut to the brain. Zeolites are a large family of minerals built by [SiO4]4- and [AlO4]5- tetrahedrons joined by shared oxygen atoms and forming a three-dimensional microporous structure holding water molecules and ions. The most widespread and used zeolite is clinoptilolite, and additionally, mechanically activated clinoptilolites offer further improved beneficial effects. The current review describes and discusses the numerous positive effects of clinoptilolite and its forms on gut health and the gut microbiome, as well as their detoxifying, antioxidative, immunostimulatory, and anti-inflammatory effects, relevant to the treatment of NDs and especially AD and PD. The direct effects of clinoptilolite and its activated forms on AD pathology in vitro and in vivo are also reviewed, as well as the use of zeolites as biosensors and delivery systems related to PD.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

REGISTRISION

PROSPERO; CRD42022344413.

The Irony of Iron: The Element with Diverse Influence on Neurodegenerative Diseases

Iron accumulation in the brain is a common feature of many neurodegenerative diseases. Its involvement spans across the main proteinopathies involving tau, amyloid-beta, alpha-synuclein, and TDP-43. Accumulating evidence supports the contribution of iron in disease pathologies, but the delineation of its pathogenic role is yet challenged by the complex involvement of iron in multiple neurotoxicity mechanisms and evidence supporting a reciprocal influence between accumulation of iron and protein pathology. Here, we review the major proteinopathy-specific observations supporting four distinct hypotheses: (1) iron deposition is a consequence of protein pathology; (2) iron promotes protein pathology; (3) iron protects from or hinders protein pathology; and (4) deposition of iron and protein pathology contribute parallelly to pathogenesis. Iron is an essential element for physiological brain function, requiring a fine balance of its levels. Understanding of disease-related iron accumulation at a more intricate and systemic level is critical for advancements in iron chelation therapies.

Inhibition of ferroptosis underlies EGCG mediated protection against Parkinson's disease in a Drosophila model

Ferroptosis, a new type of cell death accompanied by iron accumulation and lipid peroxidation, is implicated in the pathology of Parkinson's disease (PD), which is a prevalent neurodegenerative disorder that primarily occurred in the elderly population. Epigallocatechin-3-gallate (EGCG) is the major polyphenol in green tea with known neuroprotective effects in PD patients. But whether EGCG-mediated neuroprotection against PD involves regulation of ferroptosis has not been elucidated. In this study, we established a PD model using PINK1 mutant Drosophila. Iron accumulation, lipid peroxidation and decreased activity of GPX, were detected in the brains of PD flies. Additionally, phenotypes of PD, including behavioral defects and dopaminergic neurons loss, were ameliorated by ferroptosis inhibitor ferrostatin-1 (Fer-1). Notably, the increased iron level, lipid peroxidation and decreased GPX activity in the brains of PD flies were relieved by EGCG. We found that EGCG exerted neuroprotection mainly by restoring iron homeostasis in the PD flies. EGCG inhibited iron influx by suppressing Malvolio (Mvl) expression and simultaneously promoted the upregulation of ferritin, the intracellular iron storage protein, leading to a reduction in free iron ions. Additionally, EGCG downregulated the expression of Duox and Nox, two NADPH oxidases that produce reactive oxygen species (ROS) and increased SOD enzyme activity. Finally, modulation of intracellular iron levels or regulation of oxidative stress by genetic means exerted great influence on PD phenotypes. As such, the results demonstrated that ferroptosis has a role in the established PD model. Altogether, EGCG has therapeutic potentials for treating PD by targeting the ferroptosis pathway, providing new strategies for the prevention and treatment of PD and other neurodegenerative diseases.

The Construction and Validation of a Novel Ferroptosis-Related Gene Signature in Parkinson's Disease

As a newly discovered regulated cell death mode, ferroptosis is associated with the development of Parkinson's disease (PD) and has attracted much attention. Nonetheless, the relationship between ferroptosis and PD pathogenesis remains unclear. The GSE8397 dataset includes GPL96 and GPL97 platforms. The differential genes were analyzed by immune infiltration and Gene Set Enrichment Analysis (GSEA) (p < 0.05), and differential multiple |logFC| > 1 and weighted gene coexpression network analysis (WGCNA) were used to screen differential expression genes (DEGs). The intersection with 368 ferroptosis-related genes (FRGs) was conducted for gene ontology/Kyoto encyclopedia of gene and genome (GO/KEGG) enrichment analysis, gene expression analysis, correlation analysis, single-cell sequencing analysis, and prognosis analysis (area under the curve, AUC) and to predict relevant miRNAs and construct network diagrams using Cytoscape. The intersection genes of differentially expressed ferroptosis-related genes (DEFRGs) and mitochondrial dysfunction genes were validated in the substantia nigra of MPTP-induced PD mice models by Western blotting and immunohistochemistry, and the protein-binding pocket was predicted using the DoGSiteScorer database. According to the results, the estimated scores were positively correlated with the stromal scores or immune scores in the GPL96 and GPL97 platforms. In the GPL96 platform, the GSEA showed that differential genes were mainly involved in the GnRH signaling pathway, B cell receptor signaling pathway, inositol phosphate metabolism, etc. In the GPL97 platform, the GSEA showed that differential genes were mainly involved in the ubiquitin-mediated proteolysis, axon guidance, Wnt signaling pathway, MAPK signaling pathway, etc. We obtained 26 DEFRGs, including 12 up-regulated genes and 14 down-regulated genes, with good correlation. The area under the prognostic analysis curve (AUC > 0.700) showed a good prognostic ability. We found that they were enriched in different neuronal cells, oligodendrocytes, astrocytes, oligodendrocyte precursor cells, and microglial cells, and their expression scores were positively correlated, and selected genes with an AUC curve ≥0.9 were used to predict miRNA, including miR-214/761/3619-5p, miR-203, miR-204/204b/211, miR-128/128ab, miR-199ab-5p, etc. For the differentially expressed ferroptosis-mitochondrial dysfunction-related genes (DEF-MDRGs) (AR, ISCU, SNCA, and PDK4), in the substantia nigra of mice, compared with the Saline group, the expression of AR and ISCU was decreased (p < 0.05), and the expression of α-Syn and PDK4 was increased (p < 0.05) in the MPTP group. Therapeutic drugs that target SNCA include ABBV-0805, Prasinezumab, Cinpanemab, and Gardenin A. The results of this study suggest that cellular DEF-MDRGs might play an important role in PD. AR, ISCU, SNCA, and PDK4 have the potential to be specific biomarkers for the early diagnosis of PD.

The critical role of gut-brain axis microbiome in mental disorders

The Gut-brain axis is a bidirectional neural and humoral signaling that plays an important role in mental disorders and intestinal health and connects them as well. Over the past decades, the gut microbiota has been explored as an important part of the gastrointestinal tract that plays a crucial role in the regulation of most functions of various human organs. The evidence shows several mediators such as short-chain fatty acids, peptides, and neurotransmitters that are produced by the gut may affect the brain's function directly or indirectly. Thus, dysregulation in this microbiome community can give rise to several diseases such as Parkinson's disease, depression, irritable bowel syndrome, and Alzheimer's disease. So, the interactions between the gut and the brain are significantly considered, and also it provides a prominent subject to investigate the causes of some diseases. In this article, we reviewed and focused on the role of the largest and most repetitive bacterial community and their relevance with some diseases that they have mentioned previously.

Meta-analysis of iron metabolism markers levels of Parkinson's disease patients determined by fluid and MRI measurements

BACKGROUND

Parkinson' s disease (PD) is a progressive neurodegenerative disease featured neuropathologically by the loss of dopaminergic neurons of the substantia nigra (SN). Iron overload in the SN is mainly relative to the pathology and pathogenesis of PD. Postmortem samples of PD has indicated the increased levels of brain iron. However, there is no consensus on iron content through iron-sensitive magnetic resonance imaging (MRI) techniques and the alteration of iron and iron related metabolism markers levels in blood and cerebrospinal fluids (CSF) are still unclear based on the current studies. In this study, we performed a meta-analysis to explore the iron concentration and iron metabolism markers levels through iron-sensitive MRI quantification and body fluid.

METHODS

A comprehensive literature search was performed in PubMed, EMBASE and Cochrane Library databases for relevant published studies that analyzed iron load in the SN of PD patients using quantitative susceptibility mapping (QSM) or susceptibility weighting imaging (SWI), and iron metabolism markers, iron, ferritin, transferrin, total iron-binding capacity（TIBC）in CSF sample or serum/plasma sample (from Jan 2010 to Sep 2022 to filter these inaccurate researches attributed to unadvanced equipment, inaccurate analytical methods). Standardized mean differences (SMD) or mean differences (MD) and 95% confidence intervals (CI) with random or fixed effect model was used to estimate the results.

RESULTS

Forty-two articles fulfilled the inclusion criteria including 19 for QSM, 6 for SWI, and 17 for serum/plasma/CSF sample including 2874 PD patients and 2821 healthy controls (HCs). Our meta-analysis results founded a notable difference for QSM values increase (19.67, 95% CI=18.69-20.64) and for SWI measurements (-1.99, 95% CI= -3.52 to -0.46) in the SN in PD patients. However, the serum/plasma/CSF iron levels and serum/plasma ferritin, transferrin, total iron-binding capacity (TIBC) did not differ significantly between PD patients and HCs.

CONCLUSIONS

Our meta-analysis showed the consistent increase in the SN in PD patients using QSM and SWI techniques of iron-sensitive MRI measures while no significant differences were observed in other iron metabolism markers levels.

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.

Diagnosing Parkinson's disease by combining neuromelanin and iron imaging features using an automated midbrain template approach

BACKGROUND AND PURPOSE

Early diagnosis of Parkinson's disease (PD) is still a clinical challenge. Most previous studies using manual or semi-automated methods for segmenting the substantia nigra (SN) are time-consuming and, despite raters being well-trained, individual variation can be significant. In this study, we used a template-based, automatic, SN subregion segmentation pipeline to detect the neuromelanin (NM) and iron features in the SN and SN pars compacta (SNpc) derived from a single 3D magnetization transfer contrast (MTC) gradient echo (GRE) sequence in an attempt to develop a comprehensive imaging biomarker that could be used to diagnose PD.

MATERIALS AND METHODS

A total of 100 PD patients and 100 age- and sex-matched healthy controls (HCs) were imaged on a 3T scanner. NM-based SN (SN_NM) boundaries and iron-based SN (SN_QSM) boundaries and their overlap region (representing the SNpc) were delineated automatically using a template-based SN subregion segmentation approach based on quantitative susceptibility mapping (QSM) and NM images derived from the same MTC-GRE sequence. All PD and HC subjects were evaluated for the nigrosome-1 (N1) sign by two raters independently. Receiver Operating Characteristic (ROC) analyses were performed to evaluate the utility of SN_NM volume, SN_QSM volume, SNpc volume and iron content with a variety of thresholds as well as the N1 sign in diagnosing PD. Correlation analyses were performed to study the relationship between these imaging measures and the clinical scales in PD.

RESULTS

In this study, we verified the value of the fully automatic template based midbrain deep gray matter mapping approach in differentiating PD patients from HCs. The automatic segmentation of the SN in PD patients led to satisfactory DICE similarity coefficients and volume ratio (VR) values of 0.81 and 1.17 for the SN_NM, and 0.87 and 1.05 for the SN_QSM, respectively. For the HC group, the average DICE similarity coefficients and VR values were 0.85 and 0.94 for the SN_NM, and 0.87 and 0.96 for the SN_QSM, respectively. The SN_QSM volume tended to decrease with age for both the PD and HC groups but was more severe for the PD group. For diagnosing PD, the N1 sign performed reasonably well by itself (Area Under the Curve (AUC) = 0.783). However, combining the N1 sign with the other quantitative measures (SN_NM volume, SN_QSM volume, SNpc volume and iron content) resulted in an improved diagnosis of PD with an AUC as high as 0.947 (using an SN threshold of 50ppb and an NM threshold of 0.15). Finally, the SN_QSM volume showed a negative correlation with the MDS-UPDRS III (R² = 0.1, p = 0.036) and the Hoehn and Yahr scale (R² = 0.04, p = 0.013) in PD patients.

CONCLUSION

In summary, this fully automatic template based deep gray matter mapping approach performs well in the segmentation of the SN and its subregions for not only HCs but also PD patients with SN degeneration. The combination of the N1 sign with other quantitative measures (SN_NM volume, SN_QSM volume, SNpc volume and iron content) resulted in an AUC of 0.947 and provided a comprehensive set of imaging biomarkers that, potentially, could be used to diagnose PD clinically.

Regional age-related changes of neuromelanin and iron in the substantia nigra based on neuromelanin accumulation and iron deposition

OBJECTIVES

To investigate age-related neuromelanin signal variation and iron content changes in the subregions of substantia nigra (SN) using magnetization transfer contrast neuromelanin-sensitive multi-echo fast field echo sequence in a normal population.

METHODS

In this prospective study, 115 healthy volunteers between 20 and 86 years of age were recruited and scanned using 3.0-T MRI. We manually delineated neuromelanin accumulation and iron deposition regions in neuromelanin image and quantitative susceptibility mapping, respectively. We calculated the overlap region using the two measurements mentioned above. Partial correlation analysis was used to evaluate the correlations between volume, contrast ratio (CR), susceptibility of three subregions of SN, and age. Curve estimation models were used to find the best regression model.

RESULTS

CR increased with age (r = 0.379, p < 0.001; r = 0.371, p < 0.001), while volume showed an age-related decline (r =  -0.559, p < 0.001; r =  -0.410, p < 0.001) in the neuromelanin accumulation and overlap regions. Cubic polynomial regression analysis found a small increase in neuromelanin accumulation volume with age until 34, followed by a significant decrease until the 80 s (R² = 0.358, p < 0.001). No significant correlations were found between susceptibility and age in any subregion. No correlation was found between CR and susceptibility in the overlap region.

CONCLUSIONS

Our results indicated that CR increased with age, while volume showed an age-related decline in the overlap region. We further found that the neuromelanin accumulation region volume increased until the 30 s and decreased into the 80 s. This study may provide a reference for future neurodegenerative elucidations of substantia nigra.

KEY POINTS

• Our results define the regional changes in neuromelanin and iron in the substantia nigra with age in the normal population, especially in the overlap region. • The contrast ratio increased with age in the neuromelanin accumulation and overlap regions, and volume showed an age-related decline, while contrast ratio and volume do not affect each other indirectly. • The contrast ratio of hyperintense neuromelanin in the overlap region was unaffected by iron content.

In vivo detection of substantia nigra and locus coeruleus volume loss in Parkinson's disease using neuromelanin-sensitive MRI: Replication in two cohorts

Patients with Parkinson's disease undergo a loss of melanized neurons in substantia nigra pars compacta and locus coeruleus. Very few studies have assessed substantia nigra pars compacta and locus coeruleus pathology in Parkinson's disease simultaneously with magnetic resonance imaging (MRI). Neuromelanin-sensitive MRI measures of substantia nigra pars compacta and locus coeruleus volume based on explicit magnetization transfer contrast have been shown to have high scan-rescan reproducibility in controls, but no study has replicated detection of Parkinson's disease-associated volume loss in substantia nigra pars compacta and locus coeruleus in multiple cohorts with the same methodology. Two separate cohorts of Parkinson's disease patients and controls were recruited from the Emory Movement Disorders Clinic and scanned on two different MRI scanners. In cohort 1, imaging data from 19 controls and 22 Parkinson's disease patients were acquired with a Siemens Trio 3 Tesla scanner using a 2D gradient echo sequence with magnetization transfer preparation pulse. Cohort 2 consisted of 33 controls and 39 Parkinson's disease patients who were scanned on a Siemens Prisma 3 Tesla scanner with a similar imaging protocol. Locus coeruleus and substantia nigra pars compacta volumes were segmented in both cohorts. Substantia nigra pars compacta volume (Cohort 1: p = 0.0148; Cohort 2: p = 0.0011) and locus coeruleus volume (Cohort 1: p = 0.0412; Cohort 2: p = 0.0056) were significantly reduced in the Parkinson's disease group as compared to controls in both cohorts. This imaging approach robustly detects Parkinson's disease effects on these structures, indicating that it is a promising marker for neurodegenerative neuromelanin loss.

Nigral volumetric and microstructural measures in individuals with scans without evidence of dopaminergic deficit

Introduction

Striatal dopamine transporter (DAT) imaging using ¹²³I-ioflupane single photon positron emitted computed tomography (SPECT) (DaTScan, GE) identifies 5-20% of newly diagnosed Parkinson's disease (PD) subjects enrolling in clinical studies to have scans without evidence of dopaminergic deficit (SWEDD). These individuals meet diagnostic criteria for PD, but do not clinically progress as expected, and they are not believed to have neurodegenerative Parkinsonism. Inclusion of SWEDD participants in PD biomarker studies or therapeutic trials may therefore cause them to fail. DaTScan can identify SWEDD individuals, but it is expensive and not widely available; an alternative imaging approach is needed. Here, we evaluate the use of neuromelanin-sensitive, iron-sensitive, and diffusion contrasts in substantia nigra pars compacta (SNpc) to differentiate SWEDD from PD individuals.

Methods

Neuromelanin-sensitive, iron-sensitive, and diffusion imaging data for SWEDD, PD, and control subjects were downloaded from the Parkinson's progression markers initiative (PPMI) database. SNpc volume, SNpc iron (R ₂), and SNpc free water (FW) were measured for each participant.

Results

Significantly smaller SNpc volume was seen in PD as compared to SWEDD (P < 10^-3) and control (P < 10^-3) subjects. SNpc FW was elevated in the PD group relative to controls (P = 0.017). No group difference was observed in SNpc R ₂.

Conclusion

In conclusion, nigral volume and FW in the SWEDD group were similar to that of controls, while a reduction in nigral volume and increased FW were observed in the PD group relative to SWEDD and control participants. These results suggest that these MRI measures should be explored as a cost-effective alternative to DaTScan for evaluation of the nigrostriatal system.

Role of heavy metals (copper (Cu), arsenic (As), cadmium (Cd), iron (Fe) and lithium (Li)) induced neurotoxicity

Parkinson's disease (PD) is a neurodegenerative condition characterized by the dopamine (DA) neuronal loss in the substantia nigra. PD impairs motor controls symptoms such as tremor, rigidity, bradykinesia and postural imbalance gradually along with non-motor problems such as olfactory dysfunction, constipation, sleeping disorder. Though surplus of factors and mechanisms have been recognized, the precise PD etiopathogenesis is not yet implied. Reports suggest that various environmental factors play a crucial role in the causality of the PD cases. Epidemiological studies have reported that heavy metals has a role in causing defects in substantia nigra region of brain in PD. Though the reason is unknown, exposure to heavy metals is reported to be an underlying factor in PD development. Metals are classified as either essential or non-essential, and they have a role in physiological processes such protein modification, electron transport, oxygen transport, redox reactions, and cell adhesion. Excessive metal levels cause oxidative stress, protein misfolding, mitochondrial malfunction, autophagy dysregulation, and apoptosis, among other things. In this review, we check out the link between heavy metals like copper (Cu), arsenic (As), cadmium (Cd), iron (Fe), and lithium (Li) in neurodegeneration, and how it impacts the pathological conditions of PD. In conclusion, increase or decrease in heavy metals involve in regulation of neuronal functions that have an impact on neurodegeneration process. Through this review, we suggest that more research is needed in this stream to bring more novel approaches for either disease modelling or therapeutics.

Rifaximin protects SH-SY5Y neuronal cells from iron overload-induced cytotoxicity via inhibiting STAT3/NF-κB signaling

Acute or chronic liver disease-caused liver failure is the cause of hepatic encephalopathy (HE), characterized by neuropsychiatric manifestations. Liver diseases potentially lead to peripheral iron metabolism dysfunction and surges of iron concentration in the brain, contributing to the pathophysiological process of degenerative disorders of the central nervous system. In this study, the mechanism of rifaximin treating hepatic encephalopathy was investigated. Ferric ammonium citrate (FAC)-induced iron overload significantly reduced the proliferation and boosted the apoptosis in SH-SY5Y cells through increasing reactive oxygen species (ROS) levels and inducing iron metabolism disorder. Rifaximin treatment could rectify the FAC-induced iron overload and lipopolysaccharide (LPS)-induced iron deposition, therefore effectively protecting SH-SY5Y cells from ROS-induced cell injury and apoptosis. Signal transducer and activator of transcription 3 (STAT3)/nuclear factor-kappaB (NF-κB) signaling is involved in the protective function of rifaximin against LPS-induced iron deposition. The therapeutic effect of rifaximin on HE associated with acute hepatic failure in mouse model was ascertained. In conclusion, Rifaximin could effectively protect SH-SY5Y cells against injury caused by iron overload through the rectification of the iron metabolism disorder via the STAT3/NF-κB signaling pathway. This article is protected by copyright. All rights reserved.

Automatic detection of neuromelanin and iron in the midbrain nuclei using a magnetic resonance imaging ‐based brain template

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei.

Low plasma serotonin linked to higher nigral iron in Parkinson's disease

A growing body of evidence suggests nigral iron accumulation plays an important role in the pathophysiology of Parkinson's disease (PD), contributing to dopaminergic neuron loss in the substantia nigra pars compacta (SNc). Converging evidence suggests this accumulation might be related to, or increased by, serotonergic dysfunction, a common, often early feature of the disease. We investigated whether lower plasma serotonin in PD is associated with higher nigral iron. We obtained plasma samples from 97 PD patients and 89 controls and MRI scans from a sub-cohort (62 PD, 70 controls). We measured serotonin concentrations using ultra-high performance liquid chromatography and regional iron content using MRI-based quantitative susceptibility mapping. PD patients had lower plasma serotonin (p < 0.0001) and higher nigral iron content (SNc: p < 0.001) overall. Exclusively in PD, lower plasma serotonin was correlated with higher nigral iron (SNc: r(58) =  - 0.501, p < 0.001). This correlation was significant even in patients newly diagnosed (< 1 year) and stronger in the SNc than any other region examined. This study reveals an early, linear association between low serotonin and higher nigral iron in PD patients, which is absent in controls. This is consistent with a serotonin-iron relationship in the disease process, warranting further studies to determine its cause and directionality.

Stability of AI-Enabled Diagnosis of Parkinson's Disease: A Study Targeting Substantia Nigra in Quantitative Susceptibility Mapping Imaging

Purpose: Parkinson's disease (PD) diagnosis algorithms based on quantitative susceptibility mapping (QSM) and image algorithms rely on substantia nigra (SN) labeling. However, the difference between SN labels from different experts (or segmentation algorithms) will have a negative impact on downstream diagnostic tasks, such as the decrease of the accuracy of the algorithm or different diagnostic results for the same sample. In this article, we quantify the accuracy of the algorithm on different label sets and then improve the convolutional neural network (CNN) model to obtain a high-precision and highly robust diagnosis algorithm. Methods: The logistic regression model and CNN model were first compared for classification between PD patients and healthy controls (HC), given different sets of SN labeling. Then, based on the CNN model with better performance, we further proposed a novel "gated pooling" operation and integrated it with deep learning to attain a joint framework for image segmentation and classification. Results: The experimental results show that, with different sets of SN labeling that mimic different experts, the CNN model can maintain a stable classification accuracy at around 86.4%, while the conventional logistic regression model yields a large fluctuation ranging from 78.9 to 67.9%. Furthermore, the "gated pooling" operation, after being integrated for joint image segmentation and classification, can improve the diagnosis accuracy to 86.9% consistently, which is statistically better than the baseline. Conclusion: The CNN model, compared with the conventional logistic regression model using radiomics features, has better stability in PD diagnosis. Furthermore, the joint end-to-end CNN model is shown to be suitable for PD diagnosis from the perspectives of accuracy, stability, and convenience in actual use.

Nigral diffusivity, but not free water, correlates with iron content in Parkinson's disease

The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinson's disease. Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and reductions in fractional anisotropy as well as increases in the free water compartment in metrics derived from bi-compartment models. However, studies examining nigral diffusivity changes from Parkinson's disease with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. We aimed to examine Parkinson's disease-related changes in nigral iron and diffusion measures from single- and bi-compartment models as well as assess the effect of iron on these diffusion measures in two separate Parkinson's cohorts. Iron-sensitive data and diffusion data were analysed in two cohorts: First, a discovery cohort consisting of 71 participants (32 control participants and 39 Parkinson's disease participants) was examined. Second, an external validation cohort, obtained from the Parkinson's Progression Marker's Initiative, consisting of 110 participants (58 control participants and 52 Parkinson's disease participants) was examined. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment (discovery cohort: P = 0.006; external cohort: P = 0.01) and iron content (discovery cohort: P < 0.001; validation cohort: P = 0.02) were found to increase in substantia nigra of the Parkinson's disease group in both cohorts. However, diffusion markers derived from the single-compartment model (i.e. mean diffusivity and fractional anisotropy) were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort (iron-mean diffusivity: r = -0.400, P = 0.006) and validation cohort (iron-mean diffusivity: r = -0.387, P = 0.003) but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. In conclusion, the variability of nigral diffusion metrics derived from the single-compartment model in Parkinson's disease may be attributed to competing influences of increased iron content, which tends to drive diffusivity down, and increases in the free water compartment, which tends to drive diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.

Predicting Motor Outcome of Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease Using Quantitative Susceptibility Mapping and Radiomics: A Pilot Study

Background

Emerging evidence indicates that iron distribution is heterogeneous within the substantia nigra (SN) and it may reflect patient-specific trait of Parkinson’s Disease (PD). We assume it could account for variability in motor outcome of subthalamic nucleus deep brain stimulation (STN-DBS) in PD.

Objective

To investigate whether SN susceptibility features derived from radiomics with machine learning (RA-ML) can predict motor outcome of STN-DBS in PD.

Methods

Thirty-three PD patients underwent bilateral STN-DBS were recruited. The bilateral SN were segmented based on preoperative quantitative susceptibility mapping to extract susceptibility features using RA-ML. MDS-UPDRS III scores were recorded 1–3 days before and 6 months after STN-DBS surgery. Finally, we constructed three predictive models using logistic regression analyses: (1) the RA-ML model based on radiomics features, (2) the RA-ML+LCT (levodopa challenge test) response model which combined radiomics features with preoperative LCT response, (3) the LCT response model alone.

Results

For the predictive performances of global motor outcome, the RA-ML model had 82% accuracy (AUC = 0.85), while the RA-ML+LCT response model had 74% accuracy (AUC = 0.83), and the LCT response model alone had 58% accuracy (AUC = 0.55). For the predictive performance of rigidity outcome, the accuracy of the RA-ML model was 80% (AUC = 0.85), superior to those of the RA-ML+LCT response model (76% accuracy, AUC = 0.82), and the LCT response model alone (58% accuracy, AUC = 0.42).

Conclusion

Our findings demonstrated that SN susceptibility features from radiomics could predict global motor and rigidity outcomes of STN-DBS in PD. This RA-ML predictive model might provide a novel approach to counsel candidates for STN-DBS.

Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.

Imaging iron and neuromelanin simultaneously using a single 3D gradient echo magnetization transfer sequence: Combining neuromelanin, iron and the nigrosome-1 sign as complementary imaging biomarkers in early stage Parkinson's disease

Diagnosing early stage Parkinson's disease (PD) is still a clinical challenge. Previous studies using iron, neuromelanin (NM) or the Nigrosome-1 (N1) sign in the substantia nigra (SN) by themselves have been unable to provide sufficiently high diagnostic performance for these methods to be adopted clinically. Our goal in this study was to extract the NM complex volume, iron content and volume representing the entire SN, and the N1 sign as potential complementary imaging biomarkers using a single 3D magnetization transfer contrast (MTC) gradient echo sequence and to evaluate their diagnostic performance and clinical correlations in early stage PD. A total of 40 early stage idiopathic PD subjects and 40 age- and sex-matched healthy controls (HCs) were imaged at 3T. NM boundaries (representing the SN pars compacta (SNpc) and parabrachial pigmented nucleus) and iron boundaries representing the total SN (SNpc and SN pars reticulata) were determined semi-automatically using a dynamic programming (DP) boundary detection algorithm. Receiver operating characteristic analyses were performed to evaluate the utility of these imaging biomarkers in diagnosing early stage PD. A correlation analysis was used to study the relationship between these imaging measures and the clinical scales. We also introduced the concept of NM and total iron overlap volumes to demonstrate the loss of NM relative to the iron containing SN. Furthermore, all 80 cases were evaluated for the N1 sign independently. The NM and SN volumes were lower while the iron content was higher in the SN for PD subjects compared to HCs. Interestingly, the PD subjects with bilateral loss of the N1 sign had the highest iron content. The area under the curve (AUC) values for the average of both hemispheres for single measures were: .960 for NM complex volume; .788 for total SN volume; .740 for SN iron content and .891 for the N1 sign. Combining NM complex volume with each of the following measures through binary logistic regression led to AUC values for the averaged right and left sides of: .976 for total iron content; .969 for total SN volume, .965 for overlap volume and .983 for the N1 sign. We found a negative correlation between SN volume and UPDRS-III (R² = .22, p = .002). While the N1 sign performed well, it does not contain any information about iron content or NM quantitatively, therefore, marrying this sign with the NM and iron measures provides a better physiological explanation of what is happening when the N1 sign disappears in PD subjects. In summary, the combination of NM complex volume, SN volume, iron content and the N1 sign as derived from a single MTC sequence provides complementary information for understanding and diagnosing early stage PD.