Giancarlo Crocetti, Michael Coakley, Phil Dressner, Wanda Kellum, Tamba Lamin
备注：
5 pages, 4 figures, 3 tables, published at the Research Day at Pace University, New York. Proceedings of 12th Annual Research Day, 2014 - Pace University
In this study, we executed a genomic analysis with the objective of selecting a set of genes (possibly small) that would help in the detection and classification of samples from patients affected by Parkinson Disease. We performed a complete data analysis and during the exploratory phase, we selected a list of differentially expressed genes. Despite their association with the diseased state, we could not use them as a biomarker tool. Therefore, our research was extended to include a multivariate analysis approach resulting in the identification and selection of a group of 20 genes that showed a clear potential in detecting and correctly classify Parkinson Disease samples even in the presence of other neurodegenerative disorders.
From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease
McGill University（麦吉尔大学）；Centre for Research on Brain, Language, and Music（大脑、语言和音乐研究中心）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Douglas Research Centre（道格拉斯研究中心）；Concordia University（Concordia大学）
Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms
New Jersey City University（新泽西城市大学）；University of South Dakota（南达科他大学）；Monroe College（门罗学院）；University of Texas at Dallas（德州大学达拉斯分校）；International American University（国际美国大学）；Gannon University（甘农大学）；Saint Francis College（圣弗朗西斯学院）
Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection
USD AI Research, Department of Computer Science, University of South Dakota（USD人工智能研究机构、计算机科学系、南达科他大学）；Biomedical and Translational Sciences, Sanford School of Medicine, University of South Dakota（生物医学与转化科学、桑福德医学院、南达科他大学）；Department of Sport, Exercise and Rehabilitation, Northumbria University（体育、运动与康复系、北umbria大学）；Department of Neurology, Oregon Health & Science University（神经病学系、俄勒冈健康与科学大学）
Modeling Parkinson's Disease Progression Using Longitudinal Voice Biomarkers: A Comparative Study of Statistical and Neural Mixed-Effects Models
Department of Mathematical Sciences（数学科学系）；Naveen Jindal School of Management（奈文·金达管理学院）；Department of Biology（生物系）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Duke University（杜克大学）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院）；Department of Neurosurgery（神经外科系）
University of Münster（明斯特大学）；Klinikum Osnabrück（奥斯纳布吕克医院）
Sensors from smart consumer devices have demonstrated high potential to serve as digital biomarkers in the identification of movement disorders in recent years. With the usage of broadly available smartwatches we have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD). In total, 504 participants, including PD patients, differential diagnoses (DD) and healthy controls (HC), were captured with a comprehensive system utilizing two smartwatches and two smartphones. To the best of our knowledge, this study provided the largest P…
University of Münster（明斯特大学）；Klinikum Osnabrück（奥斯纳布吕克医院）
In recent years, sensors from smart consumer devices have shown great diagnostic potential in movement disorders. In this context, data modalities such as electronic questionnaires, hand movement and voice captures have successfully captured biomarkers and allowed discrimination between Parkinson's disease (PD) and healthy controls (HC) or differential diagnosis (DD). However, to the best of our knowledge, a comprehensive evaluation of assessments with a multi-modal smart device system has still been lacking. In a prospective study exploring PD, we used smartwatches and smartphones to collect …
Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number and speed of room to room transitions, provides a proxy outcome which represents mobility and could be used as a digital biomarker to quantify how mobility changes as this disease progresses. We use data collected from 10 people with Parkinson's, and 10 controls, each of whom lived for five days in a smart home with various sensors. In order to more effectively localise them indoors, we propose a transformer-based ap…
Explainable Graph Neural Networks: Understanding Brain Connectivity and Biomarkers in Dementia
School of Computing Technologies RMIT University Melbourne VIC Australia；Department of Biological Sciences；Department of Computer Science \& Information Systems Birla Institute of Technology；Institute of Innovation, Science；Sustainability Federation University Australia Ballarat VIC Australia；RMIT University；Birla Institute of Technology；Federation University Australia
Indian Institute of Technology Bhubaneswar（布巴内斯瓦尔印度理工学院）；G.G Vishwavidyalaya, Central University（G.G大学（中央大学））
Parkinson's disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson's disease in the United States whereas the number of people suffering from Parkinson's disease worldwide is around 5 million. Thus, it is important to predict Parkinson's disease in early stages so that early plan for the necessary treatment can be made. People are mostly familiar with the motor symptoms of Parkinson's disease, however, an increasing amount of research is being done to predict the Parkinson's disease from non-motor sympt…
Institute of Robotics and Intelligent Systems（机器人与智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）
Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping…
Detecting and explaining clinical-omics inconsistencies to improve patient cohort stratification: an application to Parkinson's disease
University of Murcia（穆尔西亚大学）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经学研究所）；UK Dementia Research Institute（英国痴呆症研究所）；University of Cambridge（剑桥大学）；Washington University School of Medicine（华盛顿大学医学院）
Cross-dataset Multivariate Time-series Model for Parkinson's Diagnosis via Keyboard Dynamics
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy（人工智能与计算机系统单位，工程系，罗马生物医学大学）；Eustema S.p.A., Research and Development Centre, Naples, Italy（Eustema公司，研发中心，意大利那不勒斯）；Department of Radiation Sciences, Biomedical Engineering, Umeå University, Umeå, Sweden（辐射科学系，生物医学工程，乌梅大学）
One major challenge in the medication of Parkinson's disease is that the severity of the disease, reflected in the patients' motor state, cannot be measured using accessible biomarkers. Therefore, we develop and examine a variety of statistical models to detect the motor state of such patients based on sensor data from a wearable device. We find that deep learning models consistently outperform a classical machine learning model applied on hand-crafted features in this time series classification task. Furthermore, our results suggest that treating this problem as a regression instead of an ord…
Healthcare datasets often contain groups of highly correlated features, such as features from the same biological system. When feature selection is applied to these datasets to identify the most important features, the biases inherent in some multivariate feature selectors due to correlated features make it difficult for these methods to distinguish between the important and irrelevant features and the results of the feature selection process can be unstable. Feature selection ensembles, which aggregate the results of multiple individual base feature selectors, have been investigated as a mean…
Université du Québec à Trois-Rivières (UQTR)（魁北克大学三河分校）；École de technologie supérieure（高等技术学院）；Centre de Recherche de l’Hôpital du Sacré-Coeur de Montréal（蒙特利尔圣心医院研究中心）；Centre intégré universitaire de santé et de services sociaux du Nord-de-l’Île-de-Montréal (CIUSSS du Nord-de-l’Île-de-Montréal)（蒙特利尔岛北部综合大学健康与社会服务中心）
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stag…
Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or…
OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values
University Lausanne Hospital (CHUV)（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；Zhejiang University（浙江大学）；Zhejiang University School of Medicine（浙江大学医学院）
Interpretable machine learning predicts Parkinson's disease severity using motion-corrected QSM MRI and multiband multiecho fMRI features
Lyda Hill Department of Bioinformatics（Lyda Hill 生物信息学系）；Department of Biomedical Engineering（生物医学工程系）；University of Texas Southwestern Medical Center, Dallas, Texas, USA（德克萨斯西南医学中心，德克萨斯州达拉斯）
Parkinson's disease (PD) is a common and complex neurodegenerative disorder with 5 stages in the Hoehn and Yahr scaling. Given the heterogeneity of PD, it is challenging to classify early stages 1 and 2 and detect brain function alterations. Functional magnetic resonance imaging (fMRI) is a promising tool in revealing functional connectivity (FC) differences and developing biomarkers in PD. Some machine learning approaches like support vector machine and logistic regression have been successfully applied in the early diagnosis of PD using fMRI data, which outperform classifiers based on manual…
Sample-Efficient Reinforcement Learning Controller for Deep Brain Stimulation in Parkinson's Disease
Department of Electrical and Computer Engineering, Clemson University（电子与计算机工程系，克莱姆森大学）；Department of Biomedical Engineering, Michigan Technological University（生物医学工程系，密歇根技术大学）
Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure. Among the univariate methods, differential expression analysis (DEA) is often applied for its simplicity and interpretability. A characteristic of methods for DEA is that they treat genes individually, disregarding the correlation that exists between them. On the other hand, some multivariate feature selection methods are proposed for biomarker discovery. Provided with various biomarker discovery methods, how to choose the most suitable method for a specific dataset becom…
University of Basel（巴塞尔大学）；Hospital of the University of Basel（巴塞尔大学医院）
Recordings of electrical brain activity carry information about a person's cognitive health. For recording EEG signals, a very common setting is for a subject to be at rest with its eyes closed. Analysis of these recordings often involve a dimensionality reduction step in which electrodes are grouped into 10 or more regions (depending on the number of electrodes available). Then an average over each group is taken which serves as a feature in subsequent evaluation. Currently, the most prominent features used in clinical practice are based on spectral power densities. In our work we consider a …
Limitations of Sequence-Based Protein Representations for Parkinson's Disease Classification: A Leakage-Free Benchmark
Higher School of Mechanical and Electrical Engineering, Instituto Politécnico Nacional（机械与电气工程高等专科学校，墨西哥国立理工学院）；Research Center for Computing, Instituto Politécnico Nacional（计算研究中心，墨西哥国立理工学院）
BenSParX: A Robust Explainable Machine Learning Framework for Parkinson's Disease Detection from Bengali Conversational Speech
Chittagong University of Engineering and Technology（吉大港工程技术大学）；East Delta University（东三角洲大学）；Charles Sturt University（查尔斯特大学）；Bangabandhu Sheikh Mujib Medical University（班加班杜·谢赫·穆吉布医科大学）
Arizona State University（亚利桑那州立大学）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Children’s Hospital Los Angeles（洛杉矶儿童医院）；Mayo Clinic Arizona（梅奥诊所亚利桑那分院）；University of Southern California（南加州大学）
Background: Beta-amyloid (A$β$) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the ''ATN framework'' of AD. Current methods to detect A$β$/tau pathology include cerebrospinal fluid (CSF; invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (BBBM; promising but mainly still in development). Objective: To develop a non-invasiv…
Arizona State University（亚利桑那州立大学）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）；Southeast University（东南大学）；Children’s Hospital Los Angeles（洛杉矶儿童医院）；Mayo Clinic Arizona（梅奥诊所亚利桑那分院）；University of Southern California（南加州大学）
Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and ma…
Kyoto University（京都大学）；Kyowa Hakko Bio Co., Ltd.（协和发酵生物株式会社）；Yokohama City University（横滨市立大学）；Hirosaki University（弘前大学）
Early disease detection and prevention methods based on effective interventions are gaining attention. Machine learning technology has enabled precise disease prediction by capturing individual differences in multivariate data. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in the development of chronic diseases. However, it remains a challenge to identify individual physiological state changes in cross-disease onset processes because of the complex relationships among multipl…
