Neurodegenerative diseases (NDDs) are a group of heterogeneous neurological disorders that can cause progressive loss of neurons in the central nervous system or peripheral nervous system, resulting in a decline in motor function. Motion capture, as a high-precision and high-resolution technology for capturing human motion data, drives NDDs motor assessment to effectively extract kinematic features and thus assess the patient's motor ability or disease severity. This paper focuses on the recent research progress in motor assessment of NDDs driven by motion capture data. Based on a brief introduction of NDDs motor assessment datasets, we categorized the assessment methods into three types according to the way of feature extraction and processing: NDDs motor assessment methods based on statistical analysis, machine learning and deep learning. Then, we comparatively analyzed the technical points and characteristics of the three types of methods from the aspects of data composition, data preprocessing, assessment methods, assessment purposes and effects. Finally, we discussed and prospected the development trends of NDDs motor assessment.
Humans ; Neurodegenerative Diseases/diagnosis* ; Machine Learning ; Biomechanical Phenomena ; Deep Learning ; Motion ; Motion Capture

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.
Humans ; Alzheimer Disease/diagnosis* ; Neurodegenerative Diseases ; Early Diagnosis ; Cognitive Dysfunction ; Biomarkers

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.
Humans ; Alzheimer Disease/diagnosis* ; Neurodegenerative Diseases ; Early Diagnosis ; Cognitive Dysfunction ; Biomarkers

The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD
Alzheimer Disease/diagnosis* ; Brain/diagnostic imaging* ; Cognitive Dysfunction/diagnosis* ; Early Diagnosis ; Humans ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Neurodegenerative Diseases

BACKGROUND: Parkinson's disease (PD) is one of the most common neurodegenerative diseases. However, the history of PD and famous persons with PD have not been described in detail yet. METHODS: We summarized the history of PD before the first description of James Parkinson's. The four famous patients who were suspected or diagnosed with PD were reviewed through peer-reviewed journals as well as biographies, books, and media. RESULTS: Before the definition of PD was established, there were descriptions of various Parkinsonian symptoms in several literatures. The diagnoses of Adolf Hitler and Na Hyeseok are not certain and we only suspect that they had parkinsonism. The diagnoses of PD of the Pope John Paul II and Muhammad Ali are certain as they had medical records as well as video records that shows progressive deterioration. CONCLUSIONS: Even before James Parkinson, PD have been recognized and described focusing on the bradykinesia and tremor. We should keep in mind that detailed examination as well as transcriptions are important, and that long-term follow-up is needed to document or differentiate PD and its mimics.
Diagnosis ; Famous Persons ; Follow-Up Studies ; Humans ; Hypokinesia ; Medical Records ; Neurodegenerative Diseases ; Parkinson Disease ; Parkinsonian Disorders ; Tremor

Dementia with Lewy bodies(DLB) is the second most common neurodegenerative disease. However, DLB might not be adequately diagnosed due to its variety of clinical symptoms. The authors present 65-year-old Mrs. A. who showed Parkinson's movement, cognitive decline, psychological symptoms, and autonomic dysfunction. According to the clinical features and biological markers in the recently revised DLB criteria, Mrs. A. was diagnosed with probable DLB. Differential diagnoses of delirium, Parkinson's dementia, and Alzheimer's dementia were discussed. Psychopharmacological treatments of antidepressants or anxiolytics caused intolerable side effects and showed little efficacy to Mrs. A. She experienced two episodes of hyponatremia during her one-year treatment. Recovery from neurological symptoms due to the first hyponatremia was time-consuming, and in the second, it was associated with changes in the level of consciousness despite relatively mild hyponatremia. A fall that occurred in the latter part of treatment triggered remarkable deterioration of DLB symptoms and daily life function. Prevention of falls is important for maintaining the quality of life of patients with DLB.
Accidental Falls ; Aged ; alpha-Synuclein ; Anti-Anxiety Agents ; Antidepressive Agents ; Biomarkers ; Consciousness ; Delirium ; Dementia ; Diagnosis ; Diagnosis, Differential ; Humans ; Hyponatremia ; Lewy Bodies ; Neurodegenerative Diseases ; Quality of Life

OBJECTIVE: The provisional diagnosis of progressive supranuclear palsy (PSP) depends on a combination of typical clinical features and specific MRI findings, such as atrophy of the tegmentum in the midbrain. Atrophy of the superior cerebellar peduncle (SCP) distinguishes PSP from other types of parkinsonism. Histological factors affect the conventional fluid-attenuated inversion recovery (FLAIR) signals, such as the extent of neuronal loss and gliosis. METHODS: We investigated patients with PSP to verify the percentage of patients with various PSP phenotypes presenting a high signal intensity in the SCP. Three interviewers, who were not informed about the clinical data, visually inspected the presence or absence of a high signal intensity in the SCP on the FLAIR images. We measured the pixel value in the SCP of each patient. Clinical characteristics were evaluated using the Mann-Whitney test, followed by the χ² test. RESULTS: Ten of the 51 patients with PSP showed a high signal intensity in the SCP on FLAIR MRI. Higher pixel values were observed within the SCP of patients with a high signal intensity in the SCP than in patients without a high signal intensity (p < 0.001). The sensitivity and specificity of the high signal intensity in the SCP of patients with PSP was 19.6% and 100%, respectively. This finding was more frequently observed in patients with PSP with Richardson's syndrome (PSP-RS) (25.7%) than other phenotypes (6.2%). CONCLUSION: The high signal intensity in the SCP on FLAIR MRI might be an effective diagnostic tool for PSP-RS.
Atrophy ; Diagnosis ; Gliosis ; Humans ; Magnetic Resonance Imaging ; Mesencephalon ; Neurodegenerative Diseases ; Neurons ; Parkinsonian Disorders ; Phenotype ; Sensitivity and Specificity ; Supranuclear Palsy, Progressive

The pathological features of Alzheimer's disease are senile plaques which are aggregates of β-amyloid peptides and neurofibrillary tangles in the brain. Neurofibrillary tangles are aggregates of hyperphosphorylated tau proteins, and these induce various other neurodegenerative diseases, such as progressive supranuclear palsy, corticobasal degeneration, frontotemporal lobar degeneration, frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17), and chronic traumatic encephalopathy. In the case of Alzheimer's disease, the measurement of neurofibrillary tangles associated with cognitive decline is suitable for differential diagnosis, disease progression assessment, and to monitor the effects of therapeutic treatment. This review discusses considerations for the development of tau ligands for imaging and summarizes the results of the first-in-human and preclinical studies of the tau tracers that have been developed thus far. The development of tau ligands for imaging studies will be helpful for differential diagnosis and for the development of therapeutic treatments for tauopathies including Alzheimer's disease.
Alzheimer Disease ; Brain ; Brain Injury, Chronic ; Chromosomes, Human, Pair 17 ; Diagnosis, Differential ; Disease Progression ; Frontotemporal Dementia ; Frontotemporal Lobar Degeneration ; Ligands ; Neurodegenerative Diseases ; Neurofibrillary Tangles ; Parkinsonian Disorders ; Peptides ; Plaque, Amyloid ; Supranuclear Palsy, Progressive ; tau Proteins ; Tauopathies

Primary progressive aphasia (PPA) is a heterogenous neurodegenerative disorder characterized by declining language and speech ability. Various underlying neuropathologies can induce PPA, and the disorder is divided into three subtypes—progressive non-fluent aphasia, semantic variant aphasia, and logopenic aphasia—according to clinical features. Accurate disease classification and prediction of underlying diseases are necessary for appropriate treatment, but proper use of imaging tests is important because clinical information alone often makes it difficult to make accurate decisions. Because there is a characteristic metabolic pattern according to the subtypes, F-18 fluorodeoxyglucose positron emission tomography (PET) can indicate subtype classification. In addition, PETstudies for imaging amyloid or dopamine transporters play an important role in demonstrating underlying disease. The present case showed that PET imaging studies are useful in diagnosis and could be used as a biomarker in PPA.
Amyloid ; Aphasia ; Aphasia, Primary Progressive ; Biomarkers ; Classification ; Diagnosis ; Dopamine ; Dopamine Plasma Membrane Transport Proteins ; Electrons ; Neurodegenerative Diseases ; Neuropathology ; Positron-Emission Tomography