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A multivariate MRI model of Alzheimer’s disease risk is associated with clinical diagnosis, PET imaging, and plasma biomarkers in a mixed dementia sample

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Jeffrey S Phillips Sindhuja Tirumalai Govindarajan Gyujoon Hwang Guray Erus Katheryn A Q Cousins Sandhitsu R. Das David A. Wolk David J. Irwin Murray Grossman Ilya M. Nasrallah Christos Davatzikos Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Frontotemporal Degeneration Center Department of Neurology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA

Background We sought to validate two structural MRI-based brain health models: Spatial Pattern of Atrophy for REcognition of Alzheimer’s disease (SPARE-AD) and SPARE-Brain Age Gap (SPARE-BAG), which estimates the discrepancy between biological brain age and chronological age. These models' generalization to non-amnestic AD (naAD) syndromes and associations with positron emission tomography (PET) and plasma biomarkers remain underinvestigated. We hypothesized that SPARE-AD scores in naAD would be lower (less abnormal) than in amnestic AD (aAD) but elevated (more abnormal) relative to controls; and that SPARE-AD but not SPARE-BAG scores would be associated with PET and plasma biomarkers of AD. Method SPARE-AD and SPARE-BAG scores were estimated for each of 1607 MRI scans from 277 cognitively normal adults (62.8% female; mean age = 69.5 years) and 902 clinical participants (54.2% female; mean age = 73.1 years), including 585 with amnestic mild cognitive impairment (aMCI) or aAD; 56 with naAD syndromes; 28 with vascular disease; 28 with Lewy body disorders (LBD); 18 with suspected frontotemporal lobar degeneration (FTLD); 56 with non-amnestic MCI of uncertain etiology; and 131 with other clinical diagnoses. We used mixed effects models adjusting for age, sex, and global cognition to test group differences in SPARE-AD and SPARE-BAG. Additionally, we assessed associations with tau (n = 122) and amyloid-beta (n = 199) PET imaging and plasma-based phosphorylated tau-181 (p-tau181) and glial fibrillary acidic protein (GFAP) (n = 358). Result SPARE-AD scores were elevated vs. controls in aMCI/aAD, naAD, vascular disease, FTLD, and naMCI (all p<0.003) but not in LBD or other diagnoses (p>0.1). NaAD patients had lower SPARE-AD (p<0.02) but higher SPARE-BAG scores (p<0.02) than aMCI/aAD patients. SPARE-AD scores were positively associated with mean cortical tau PET uptake, amyloid PET positivity, and plasma p-tau181 and GFAP (all p<0.001). In contrast, SPARE-BAG scores were not

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COVID-19 mRNA Vaccine Degradation Prediction Using LR and LGBM Algorithms

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Journal of Physics: Conference Series 2021年第1期1997卷

作者： Soon Hwai Ing Azian Azamimi Abdullah Nor Hazlyna Harun Shigehiko Kanaya Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Medical Devices and Life Sciences Cluster Sport Engineering Research Centre Centre of Excellence (SERC) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Data Science Research Lab School of Computing School of Computing Universiti Utara Malaysia Sintok Kedah. Computational Systems Biology Lab Graduate School of Information Science Nara Institute of Science and Technology Ikoma Nara Japan.

The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvellous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19. Two machine learning algorithms, which are, Linear Regression (LR) and Light Gradient Boosting Machine (LGBM) are proposed for models development using Python language. dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that LGBM (0.2447) performs better than LR (0.3957) for this study when evaluated with the RMSE metric.

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An AI-based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Samad Amini Boran Hao Lifu Zhang Mengting Song Aman Gupta Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo

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Observability quantification for linear stochastic system and its application in state estimation

Observability quantification for linear stochastic system an...

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Chinese Automation Congress (CAC)

作者： Tinghui Xin Yuan Liang Xiwang Dong Sibo Hu Qingdong Li Zhang Ren China Innovation Fund Management Co. Ltd. Beijing P. R. China School of Automation Science and Electrical Engineering Beihang University Beijing P. R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper presents a method for quantitatively evaluating the observability of linear stochastic systems and pre-evaluating the accuracy of state estimations. In this method, according to the relation among the current state vector, measurement sequence and noise sequence, the observability degree for the state variables of linear stochastic systems is defined. And it is further proved that, the proposed observability degree can be used to examine the existence of unbiased estimated states and measure the estimation error lower bound. In addition, the necessary and sufficient condition for the state variable to be observable is also given according to the proposed observability degree. Finally, a numerical simulation example on target tracking problem is presented to validate the efficiency of the proposed observability degree.

关键词： Observability Estimation Estimation error Stochastic systems Noise measurement Time-varying systems Target tracking

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Finite Time Decentralized Cubature Information Filtering with Directed Graph

Finite Time Decentralized Cubature Information Filtering wit...

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Chinese Automation Congress (CAC)

作者： Yuan Liang Tinghui Xin Sibo Hu Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Beihang University Beijing P. R. China China Innovation Fund Management Co. Ltd. Beijing P. R. China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper focuses on the distributed state estimation problem with finite communication time and directed graph for the nonlinear discrete system. A finite time decentralized cubature information filtering algorithm is proposed, of which the goal is to estimate the interested states in a fully distributed manner. The proposed filtering algorithm is applicable for the directed graph and only demands finite communication time to obtain satisfactory performance. Besides, the proposed filtering algorithm possesses guaranteed stability in the mean square sense. Finally, the effectiveness of the proposed algorithm is validated through the numerical simulation.

关键词： Directed graphs Protocols State estimation Nonlinear systems Prediction algorithms Numerical models Information filtering

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Development of a Predictive Analytic Tool for Detection of Alzheimer's Disease

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Journal of Physics: Conference Series 2021年第1期1997卷

作者： Aw Hui Yee Azian Azamimi Abdullah Juhaida Abu Bakar Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Medical Devices and Life Sciences Cluster Sport Engineering Research Centre Centre of Excellence (SERC) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia. Data Science Research Lab School of Computing School of Computing Universiti Utara Malaysia Sintok Kedah.

Alzheimer's disease (AD) is the common neurodegenerative disease that causes the impairment of the brain tissue with a gradual decline in the memory and thinking skills among the elderly population. However, there is a lack of reliable biomarker and effective medical treatment to detect and cure the AD respectively. Since there is a commitment agrees that AD should be the focus on the early diagnosis stage due to the great improvement on the treatment efficacy, hence with the help of imaging modality such as Magnetic Resonance Imaging (MRI), it can provide a detailed brain's features information for analysis and interpretation purposes. Besides that, this project is focusing on the implementation of the supervised machine learning algorithms on the prediction of AD based on the Graphical User Interface (GUI). By referring to the results obtained from classifiers' performance evaluation, Random Forest achieved the best performance in classification of AD with the highest accuracy of 0.7825 and AUC score of 0.8314 along with Lasso Regularization, followed by Gradient Boosting Machine (GBM) with accuracy of 0.7352 and Deep Neural Network (DNN) with accuracy of 0.6201 only. The machine learning with feature selection, Lasso Regularization is performed much better than those without feature selection. Thus, the GUI is then developed to provide convenience for the user to accurately predict their current AD's status.

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Cooperative bi-path metric for few-shot learning

arXiv

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arXiv 2020年

作者： Wang, Zeyuan Zhao, Yifan Li, Jia Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing100191 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518066 China

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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PHD: A Deep Learning Based Human Detection Framework for Panoramic Videos

PHD: A Deep Learning Based Human Detection Framework for Pan...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Jinting Tang Zhenhui Chen Yongkai Huo Peichang Zhang National Engineering Laboratory for Big Data System Computing Technology Research Institute for Future Media Computing School of Computer Science and Software Engineering Shenzhen University Shenzhen China The College of Information Engineering Shenzhen University Shenzhen China

Panoramic video has attracted substantial research attention as the coming video format. It is capable of providing 360 degree immersive experience of omnidirectional visual information. State-of-the-art detection networks may fail to detect humans on spherical images, which are normally represented in deformed rectangular shapes. In this paper, we propose a socalled Panoramic Human Detection (PHD) scheme to address the task of human detection in panoramic videos. Moreover, the PHD method is designed to detect humans by extracting multiple overlapping sub-images from each integral spherical image, where three-dimensional rotation of spherical images is employed to ensure consistency of sub-images. Two detection box filters are designed for removing redundant boxes. Our PHD method is capable of accomplishing the task of human detection in various panoramic video types. Experiments prove that our PHD method outperforms the baseline by 35% and 48.6% in terms of precision and recall, respectively.

关键词： Videos Task analysis Feature extraction Matrices Visualization Strain Transmission line matrix methods

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Deep learning for image and point cloud fusion in autonomous driving: A review

arXiv

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arXiv 2020年

作者： Cui, Yaodong Chen, Ren Chu, Wenbo Chen, Long Tian, Daxin Li, Ying Cao, Dongpu The Waterloo CogDrive Lab Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave West WaterlooONN2L3G1 Canada Research Institute Co. Ltd. Beijing100176 China School of Data and Computer Science Sun Yat-sen University Zhuhai519082 China Waytous Inc. Qingdao266109 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this paper devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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datasets of Smartphone Modalities for Depression Assessment: A Scoping Review

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IEEE Transactions on Affective computing 2025年

作者： Tlachac, Ml Heinz, Michael V. Bryan, Anastasia C. LaPreay, Arielle Dimas, Geri Louise Zhao, Tingting Jacobson, Nicholas C. Ogden, Samuel S. Bryant University Department of Information Systems and Analytics Smithfield 02911 RI United States Dartmouth-Hitchcock Medical Center Department of Psychiatry Lebanon 03766 NH United States Dartmouth College Center for Technology and Behavioral Health Lebanon 03766 NH United States University of Rhode Island College of Business Kingston 02881 RI United States Dartmouth College Departments of Biomedical Data Science and Psychiatry Lebanon 03766 NH United States California State University Monterey Bay School of Computing and Design Seaside 93955 CA United States

As mobile sensing research for depression assessment is rapidly expanding, it is vital to uncover fundamental patterns and overarching limitations in the datasets available for such research, especially as many analyses are conducted on the same datasets. As such, we conduct a scoping review by identifying 80 datasets through the start of 2024 that contain smartphone modalities and depression labels. These datasets originated from 72 manuscripts and approximately 60 research groups. The most collected smartphone modalities included location and/or activity for 68.75%, communication logs for 47.5%, phone use for 41.25%, vocal utterances for 30%, and WiFi and/or Bluetooth connectivity for 28.75% of the datasets. The PHQ-8 and PHQ-9 (Patient Health Questionnaire) were the most popular screening instruments, administered for 53.75% of datasets. Of the 80 datasets, 31.25% recruited from student populations, 22.5% from patient populations, and 7.5% recruited crowdsourced workers. Excluding the nine datasets that did not report demographics, 73% reported a majority of women participants. Given the importance of datasets on analysis outcomes, this scoping review is an invaluable resource to inform the state of science and guide future mobile health research. © 2010-2012 IEEE.

关键词： data Collection Digital Biomarker Digital Health Digital Phenotype Mobile Health

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