Neuroimaging in Parkinson's disease: focus on substantia nigra and nigro-striatal projection

Prediction of Parkinson's disease by transcranial sonography-based deep learning

OBJECTIVES

Transcranial sonography has been used as a valid neuroimaging tool to diagnose Parkinson's disease (PD). This study aimed to develop a modified transcranial sonography (TCS) technique based on a deep convolutional neural network (DCNN) model to predict Parkinson's disease.

METHODS

This retrospective diagnostic study was conducted using 1529 transcranial sonography images collected from 854 patients with PD and 775 normal controls admitted to the Second Affiliated Hospital of Soochow University (Suzhou, Jiangsu, China) between September 2019 and May 2022. The data set was divided into training cohorts (570 PD patients and 541 normal controls), and the validation set (184 PD patients and 234 normal controls). Using these datasets, we developed four different DCNN models (ResNet18, ResNet50, ResNet152, and DenseNet121). We then assessed their diagnostic performance, including the area under the receiver operating characteristic (AUROC) curve, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F1 score and compared with traditional diagnostic criteria.

RESULTS

Among the 1529 TCS images, 570 PD patients and 541 normal controls from 4 of 6 sonographers of the TCS team were selected as the training cohort, and 184 PD patients and 234 normal controls from the other 2 sonographers were chosen as the validation cohort. There were no sex and age differences between PD patients and normal control subjects in the training and validation cohorts (P values > 0.05). All DCNN models achieved good performance in distinguishing PD patients from normal control subjects on the validation datasets, with diagnostic AUROCs and accuracy of 0.949 (95% CI 0.925, 0.965) and 86.60 for the RestNet18 model, 0.949 (95% CI 0.929, 0.971) and 87.56 for ResNet50, 0.945 (95% CI 0.931, 0.969) and 88.04 for ResNet152, 0.953 (95% CI 0.935, 0.971) and 87.80 for DenseNet121, respectively. On the other hand, the diagnostic accuracy of the traditional diagnostic method was 82.30. The accuracy of all DCNN models was higher than that of traditional diagnostic method. Moreover, the 5k-fold cross-validation results in train datasets showed that these DCNN models are robust.

CONCLUSION

The developed transcranial sonography-based DCNN models performed better than traditional diagnostic criteria, thus improving the sonographer's accuracy in diagnosing PD.

Parkinson's disease speech production network as determined by graph-theoretical network analysis

Parkinson's disease (PD) can affect speech as well as emotion processing. We employ whole-brain graph-theoretical network analysis to determine how the speech-processing network (SPN) changes in PD, and assess its susceptibility to emotional distraction. Functional magnetic resonance images of 14 patients (aged 59.6 ± 10.1 years, 5 female) and 23 healthy controls (aged 64.1 ± 6.5 years, 12 female) were obtained during a picture-naming task. Pictures were supraliminally primed by face pictures showing either a neutral or an emotional expression. PD network metrics were significantly decreased (mean nodal degree, p < 0.0001; mean nodal strength, p < 0.0001; global network efficiency, p < 0.002; mean clustering coefficient, p < 0.0001), indicating an impairment of network integration and segregation. There was an absence of connector hubs in PD. Controls exhibited key network hubs located in the associative cortices, of which most were insusceptible to emotional distraction. The PD SPN had more key network hubs, which were more disorganized and shifted into auditory, sensory, and motor cortices after emotional distraction. The whole-brain SPN in PD undergoes changes that result in (a) decreased network integration and segregation, (b) a modularization of information flow within the network, and (c) the inclusion of primary and secondary cortical areas after emotional distraction.

[Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status]

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD.

Dopamine Transporter Imaging, Current Status of a Potential Biomarker: A Comprehensive Review

A major goal of current clinical research in Parkinson's disease (PD) is the validation and standardization of biomarkers enabling early diagnosis, predicting outcomes, understanding PD pathophysiology, and demonstrating target engagement in clinical trials. Molecular imaging with specific dopamine-related tracers offers a practical indirect imaging biomarker of PD, serving as a powerful tool to assess the status of presynaptic nigrostriatal terminals. In this review we provide an update on the dopamine transporter (DAT) imaging in PD and translate recent findings to potentially valuable clinical practice applications. The role of DAT imaging as diagnostic, preclinical and predictive biomarker is discussed, especially in view of recent evidence questioning the incontrovertible correlation between striatal DAT binding and nigral cell or axon counts.

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.

A novel semi-supervised multi-view clustering framework for screening Parkinson's disease

In recent years, there are many research cases for the diagnosis of Parkinson's disease (PD) with the brain magnetic resonance imaging (MRI) by utilizing the traditional unsupervised machine learning methods and the supervised deep learning models. However, unsupervised learning methods are not good at extracting accurate features among MRIs and it is difficult to collect enough data in the field of PD to satisfy the need of training deep learning models. Moreover, most of the existing studies are based on single-view MRI data, of which data characteristics are not sufficient enough. In this paper, therefore, in order to tackle the drawbacks mentioned above, we propose a novel semi-supervised learning framework called Semi-supervised Multi-view learning Clustering architecture technology (SMC). The model firstly introduces the sliding window method to grasp different features, and then uses the dimensionality reduction algorithms of Linear Discriminant Analysis (LDA) to process the data with different features. Finally, the traditional single-view clustering and multi-view clustering methods are employed on multiple feature views to obtain the results. Experiments show that our proposed method is superior to the state-of-art unsupervised learning models on the clustering effect. As a result, it may be noted that, our work could contribute to improving the effectiveness of identifying PD by previous labeled and subsequent unlabeled medical MRI data in the realistic medical environment.

Multiple Kernel Learning Based Classification of Parkinson's Disease With Multi-Modal Transcranial Sonography

Parkinson's Disease (PD) is the most common motor neurodegenerative disease in elderly population. Transcranial sonography (TCS) has become a popular imaging tool for diagnosis of PD in clinical practice. Moreover, several pioneering work have developed the computer-aided diagnosis (CAD) for PD with the transcranial B-mode sonography (TBS). It is worth noting that TCS not only has the TBS modality, but also can image the blood flow of major cerebral arteries, which is named transcranial Doppler sonography (TDS). TDS also has been applied to evaluate PD patients with orthostatic hypotension. However, the TDS-based CAD for PD has not been investigated. Since TBS and TDS provide the complementary structural and functional information about brain, it is feasible to develop a multi-modal TCS-based CAD for PD by combining both TBS and TDS. Therefore, in this work, we propose a multiple kernel learning (MKL) based CAD for PD with multi-modal TCS imaging. Particularly, the statistical and texture features are extracted from the midbrain region from TBS images, and the features about blood flow are calculated from the spectrum curves in TDS. The multi-modal features are then fed to a MKL classifier for classification of PD. The experimental results show that the multi-modal TCS-based method outperforms both the single-modal TBS- and TDS-based algorithm, which suggests the feasibility and effectiveness of combining TBS and TDS for diagnosis of PD.

Multiple Empirical Kernel Mapping Based Broad Learning System for Classification of Parkinson's Disease With Transcranial Sonography

Transcranial sonography (TCS) has become more popular for diagnosis of Parkinson's disease (PD), and the TCS-based computer-aided diagnosis (CAD) for PD also attracts considerable attention, in which classifier is a critical component. Broad learning system (BLS) is a newly proposed single layer feedforward neural network for classification. In BLS, the original input features are mapped to several new feature representations to form the feature nodes, and then these mapped features are expanded to enhancement nodes by random mapping in a wide sense. However, random mapping performed for enhancement nodes is too simple and the generated features lack interpretability together with relative low representation. In this work, we propose a multiple empirical kernel mapping (MEKM) based BLS (MEKM-BLS) algorithm, which adopts MEKM to map the data of feature nodes to enhancement nodes. MEKM-BLS then has more meaningful enhancement layer in feedforward neural network. Moreover, the experiment for PD diagnosis with TCS shows that MEKM-BLS achieves superior performance to the original BLS algorithm.

Neuromelanin detection by magnetic resonance imaging (MRI) and its promise as a biomarker for Parkinson's disease

The diagnosis of Parkinson’s disease (PD) occurs after pathogenesis is advanced and many substantia nigra (SN) dopamine neurons have already died. Now that therapies to block this neuronal loss are under development, it is imperative that the disease be diagnosed at earlier stages and that the response to therapies is monitored. Recent studies suggest this can be accomplished by magnetic resonance imaging (MRI) detection of neuromelanin (NM), the characteristic pigment of SN dopaminergic, and locus coeruleus (LC) noradrenergic neurons. NM is an autophagic product synthesized via oxidation of catecholamines and subsequent reactions, and in the SN and LC it increases linearly during normal aging. In PD, however, the pigment is lost when SN and LC neurons die. As shown nearly 25 years ago by Zecca and colleagues, NM’s avid binding of iron provides a paramagnetic source to enable electron and nuclear magnetic resonance detection, and thus a means for safe and noninvasive measure in living human brain. Recent technical improvements now provide a means for MRI to differentiate between PD patients and age-matched healthy controls, and should be able to identify changes in SN NM with age in individuals. We discuss how MRI detects NM and how this approach might be improved. We suggest that MRI of NM can be used to confirm PD diagnosis and monitor disease progression. We recommend that for subjects at risk for PD, and perhaps generally for older people, that MRI sequences performed at regular intervals can provide a pre-clinical means to detect presymptomatic PD.

Nigrosome Imaging and Neuromelanin Sensitive MRI in Diagnostic Evaluation of Parkinsonism

Background

Recent developments in magnetic resonance imaging (MRI) techniques have offered new research opportunities to visualize in vivo substantia nigra pathology in Parkinson's disease (PD). This paper summarizes the main findings of nigrosome imaging and neuromelanin sensitive MRI studies in patients with PD and other parkinsonisms.

Methods

The PubMed database was searched from 2005 to 2017 using the following keywords: Parkinson's disease and parkinsonism, in combination with MRI, nigrosome, neuromelanin, and iron. Only publications in English were included.

Results

Nigrosome or dorsal nigral hyperintensity abnormalities are studied using T2* and susceptibility weighted imaging MRI sequences in most studies, whereas Neuromelanin imaging is usually performed using T1-weighted fast spin echo sequence. Nigrosome abnormalities have been consistently demonstrated in PD patients, and nigrosome imaging has high sensitivity and specificity in distinguishing PD from healthy controls, though it is unable to reliably separate PD from atypical parkinsonisms. Reduced neuromelanin-related signals and/or volume loss in neuromelanin containing structures have been found in PD patients, and neuromelanin sensitive MRI imaging can also discriminate PD patients from healthy controls with high accuracy, though there is a degree of heterogeneity in the imaging findings. Preliminary findings suggested that longitudinal change of neuromelanin signal could be detected in PD, raising the possibility of using it as a marker of disease progression.

Conclusion

Nigrosome imaging and neuromelanin sensitive MRI are promising tools to study nigral pathology and to improve the diagnosis of PD. However, further studies are required to standardize analysis approaches, confirm longitudinal changes, and assess their generalizability.