Imaging of Substantia Nigra in Parkinson's Disease: A Narrative Review

Aggregated Pattern Classification Method for improving neural disorder stage detection

BACKGROUND

Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages.

METHOD

The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data.

RESULTS

The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm.

CONCLUSION

The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

Parkinson's image detection and classification based on deep learning

OBJECTIVE

There are two major issues in the MRI image diagnosis task for Parkinson's disease. Firstly, there are slight differences in MRI images between healthy individuals and Parkinson's patients, and the medical field has not yet established precise lesion localization standards, which poses a huge challenge for the effective prediction of Parkinson's disease through MRI images. Secondly, the early diagnosis of Parkinson's disease traditionally relies on the subjective judgment of doctors, which leads to insufficient accuracy and consistency. This article proposes an improved YOLOv5 detection algorithm based on deep learning for predicting and classifying Parkinson's images.

METHODS

This article improves the YOLOv5s network as the basic framework. Firstly, the CA attention mechanism was introduced to enable the model to dynamically adjust attention based on local features of the image, significantly enhancing the sensitivity of the model to PD related small pathological features; Secondly, replace the dynamic full dimensional convolution module to optimize the multi-level extraction of image features; Finally, the coupling head strategy is adopted to improve the execution efficiency of classification and localization tasks separately.

RESULTS

We validated the effectiveness of the proposed method using a dataset of 582 MRI images from 108 patients. The results show that the proposed method achieves 0.961, 0.974, and 0.986 in Precision, Recall, and mAP, respectively, and the experimental results are superior to other algorithms.

CONSLUSION

The improved model has achieved high accuracy and detection accuracy, and can accurately detect and recognize complex Parkinson's MRI images.

SIGNIFICANCE

This algorithm has shown good performance in the early diagnosis of Parkinson's disease and can provide clinical assistance for doctors in early diagnosis. It compensates for the limitations of traditional methods.

Disease-Modifying Effects of Vincamine Supplementation in Drosophila and Human Cell Models of Parkinson's Disease Based on DJ-1 Deficiency

Parkinson's disease (PD) is an incurable neurodegenerative disorder caused by the selective loss of dopaminergic neurons in the substantia nigra pars compacta. Current therapies are only symptomatic and are not able to stop or delay its progression. In order to search for new and more effective therapies, our group carried out a high-throughput screening assay, identifying several candidate compounds that are able to improve locomotor ability in DJ-1β mutant flies (a Drosophila model of familial PD) and reduce oxidative stress (OS)-induced lethality in DJ-1-deficient SH-SY5Y human cells. One of them was vincamine (VIN), a natural alkaloid obtained from the leaves of Vinca minor. Our results showed that VIN is able to suppress PD-related phenotypes in both Drosophila and human cell PD models. Specifically, VIN reduced OS levels in PD model flies. Besides, VIN diminished OS-induced lethality by decreasing apoptosis, increased mitochondrial viability, and reduced OS levels in DJ-1-deficient human cells. In addition, our results show that VIN might be exerting its beneficial role, at least partially, by the inhibition of voltage-gated sodium channels. Therefore, we propose that these channels might be a promising target in the search for new compounds to treat PD and that VIN represents a potential therapeutic treatment for the disease.

Distinguishing Parkinson's Disease with GLCM Features from the Hankelization of EEG Signals

This study proposes a novel method that uses electroencephalography (EEG) signals to classify Parkinson's Disease (PD) and demographically matched healthy control groups. The method utilizes the reduced beta activity and amplitude decrease in EEG signals that are associated with PD. The study involved 61 PD patients and 61 demographically matched controls groups, and EEG signals were recorded in various conditions (eyes closed, eyes open, eyes both open and closed, on-drug, off-drug) from three publicly available EEG data sources (New Mexico, Iowa, and Turku). The preprocessed EEG signals were classified using features obtained from gray-level co-occurrence matrix (GLCM) features through the Hankelization of EEG signals. The performance of classifiers with these novel features was evaluated using extensive cross-validations (CV) and leave-one-out cross-validation (LOOCV) schemes. This method under 10 × 10 fold CV, the method was able to differentiate PD groups from healthy control groups using a support vector machine (SVM) with an accuracy of 92.4 ± 0.01, 85.7 ± 0.02, and 77.1 ± 0.06 for New Mexico, Iowa, and Turku datasets, respectively. After a head-to-head comparison with state-of-the-art methods, this study showed an increase in the classification of PD and controls.

Radiomic and clinical nomogram for cognitive impairment prediction in Wilson's disease

Objective

To investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson's disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment.

Methods

Overall, 136 T1-weighted MR images were retrieved from the First Affiliated Hospital of Anhui University of Chinese Medicine, including 77 from patients with WD and 59 from patients with WD cognitive impairment. The images were divided into training and test groups at a ratio of 70:30. The radiomic features of each T1-weighted image were extracted using 3D Slicer software. R software was used to establish clinical and radiomic models based on clinical characteristics and radiomic features, respectively. The receiver operating characteristic profiles of the three models were evaluated to assess their diagnostic accuracy and reliability in distinguishing between WD and WD cognitive impairment. We combined relevant neuropsychological test scores of prospective memory to construct an integrated predictive model and visual nomogram to effectively assess the risk of cognitive decline in patients with WD.

Results

The area under the curve values for distinguishing WD and WD cognitive impairment for the clinical, radiomic, and integrated models were 0.863, 0.922, and 0.935 respectively, indicative of excellent performance. The nomogram based on the integrated model successfully differentiated between WD and WD cognitive impairment.

Conclusion

The nomogram developed in the current study may assist clinicians in the early identification of cognitive impairment in patients with WD. Early intervention following such identification may help improve long-term prognosis and quality of life of these patients.

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.

A brief history of brain iron accumulation in Parkinson disease and related disorders

Iron has a long and storied history in Parkinson disease and related disorders. This essential micronutrient is critical for normal brain function, but abnormal brain iron accumulation has been associated with extrapyramidal disease for a century. Precisely why, how, and when iron is implicated in neuronal death remains the subject of investigation. In this article, we review the history of iron in movement disorders, from the first observations in the early twentieth century to recent efforts that view extrapyramidal iron as a novel therapeutic target and diagnostic indicator.

A brief history of brain iron accumulation in Parkinson disease and related disorders

Iron has a long and storied history in Parkinson disease and related disorders. This essential micronutrient is critical for normal brain function, but abnormal brain iron accumulation has been associated with extrapyramidal disease for a century. Precisely why, how, and when iron is implicated in neuronal death remains the subject of investigation. In this article, we review the history of iron in movement disorders, from the first observations in the early twentieth century to recent efforts that view extrapyramidal iron as a novel therapeutic target and diagnostic indicator.

Magnetic resonance imaging of the dopamine system in schizophrenia - A scoping review

For decades, aberrant dopamine transmission has been proposed to play a central role in schizophrenia pathophysiology. These theories are supported by human in vivo molecular imaging studies of dopamine transmission, particularly positron emission tomography. However, there are several downsides to such approaches, for example limited spatial resolution or restriction of the measurement to synaptic processes of dopaminergic neurons. To overcome these limitations and to measure complementary aspects of dopamine transmission, magnetic resonance imaging (MRI)-based approaches investigating the macrostructure, metabolism, and connectivity of dopaminergic nuclei, i.e., substantia nigra pars compacta and ventral tegmental area, can be employed. In this scoping review, we focus on four dopamine MRI methods that have been employed in patients with schizophrenia so far: neuromelanin MRI, which is thought to measure long-term dopamine function in dopaminergic nuclei; morphometric MRI, which is assumed to measure the volume of dopaminergic nuclei; diffusion MRI, which is assumed to measure fiber-based structural connectivity of dopaminergic nuclei; and resting-state blood-oxygenation-level-dependent functional MRI, which is thought to measure functional connectivity of dopaminergic nuclei based on correlated blood oxygenation fluctuations. For each method, we describe the underlying signal, outcome measures, and downsides. We present the current state of research in schizophrenia and compare it to other disorders with either similar (psychotic) symptoms, i.e., bipolar disorder and major depressive disorder, or dopaminergic abnormalities, i.e., substance use disorder and Parkinson's disease. Finally, we discuss overarching issues and outline future research questions.

Iron as the concert master in the pathogenic orchestra playing in sporadic Parkinson's disease

About 60 years ago, the discovery of a deficiency of dopamine in the nigro-striatal system led to a variety of symptomatic therapeutic strategies to supplement dopamine and to substantially improve the quality of life of patients with Parkinson's disease (PD). Since these seminal developments, neuropathological, neurochemical, molecular biological and genetic discoveries contributed to elucidate the pathology of PD. Oxidative stress, the consequences of reactive oxidative species, reduced antioxidative capacity including loss of glutathione, excitotoxicity, mitochondrial dysfunction, proteasomal dysfunction, apoptosis, lysosomal dysfunction, autophagy, suggested to be causal for ɑ-synuclein fibril formation and aggregation and contributing to neuroinflammation and neural cell death underlying this devastating disorder. However, there are no final conclusions about the triggered pathological mechanism(s) and the follow-up of pathological dysfunctions. Nevertheless, it is a fact, that iron, a major component of oxidative reactions, as well as neuromelanin, the major intraneuronal chelator of iron, undergo an age-dependent increase. And ageing is a major risk factor for PD. Iron is significantly increased in the substantia nigra pars compacta (SNpc) of PD. Reasons for this finding include disturbances in iron-related import and export mechanisms across the blood-brain barrier (BBB), localized opening of the BBB at the nigro-striatal tract including brain vessel pathology. Whether this pathology is of primary or secondary importance is not known. We assume that there is a better fit to the top-down hypotheses and pathogens entering the brain via the olfactory system, then to the bottom-up (gut-brain) hypothesis of PD pathology. Triggers for the bottom-up, the dual-hit and the top-down pathologies include chemicals, viruses and bacteria. If so, hepcidin, a regulator of iron absorption and its distribution into tissues, is suggested to play a major role in the pathogenesis of iron dyshomeostasis and risk for initiating and progressing ɑ-synuclein pathology. The role of glial components to the pathology of PD is still unknown. However, the dramatic loss of glutathione (GSH), which is mainly synthesized in glia, suggests dysfunction of this process, or GSH uptake into neurons. Loss of GSH and increase in SNpc iron concentration have been suggested to be early, may be even pre-symptomatic processes in the pathology of PD, despite the fact that they are progression factors. The role of glial ferritin isoforms has not been studied so far in detail in human post-mortem brain tissue and a close insight into their role in PD is called upon. In conclusion, "iron" is a major player in the pathology of PD. Selective chelation of excess iron at the site of the substantia nigra, where a dysfunction of the BBB is suggested, with peripherally acting iron chelators is suggested to contribute to the portfolio and therapeutic armamentarium of anti-Parkinson medications.

Mitofusin 2: The missing link between mtDNA maintenance defects and neurotransmitter disorders

Mitofusin (MFN) 2 belongs to the large family of mitochondrial transmembrane GTPases and has a role in dynamic mitochondrial remodeling process governed by fusion and fission. MFN2 pathogenic variants classically cause Charcot-Marie-Tooth disease type 2A (CMT2A), the most common axonal form of CMT, but patients with complex and unusual phenotypes involving the central and peripheral nervous system have been described, with mitochondrial dysfunction proposed as the underlying pathogenic mechanism. Here, we report the first description of a neurochemical pattern of secondary alterations in the metabolism of biogenic amines linked to the de novo presence of the hotspot MFN2 pathogenic variant p.Arg104Trp. The infant presented a very early onset choreic movement disorder associated with severe axial hypotonia and fluctuating dystonia of limbs. The relationship between mitochondrial DNA (mtDNA) maintenance defects and dopaminergic neurotransmitter disorders, governed by MFN2, is discussed.