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Karabacak M, Jagtiani P, Jain A, Panov F, Margetis K. Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach. Epilepsia 2024; 65:861-872. [PMID: 38314969 DOI: 10.1111/epi.17890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/07/2024]
Abstract
Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.
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Affiliation(s)
- Mert Karabacak
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
| | - Pemla Jagtiani
- School of Medicine, SUNY Downstate Health Sciences University, New York, New York, USA
| | - Ankita Jain
- School of Medicine, New York Medical College, Valhalla, New York, USA
| | - Fedor Panov
- Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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Li X, Huang Y, Lhatoo SD, Tao S, Vilella Bertran L, Zhang GQ, Cui L. A hybrid unsupervised and supervised learning approach for postictal generalized EEG suppression detection. Front Neuroinform 2022; 16:1040084. [PMID: 36601382 PMCID: PMC9806125 DOI: 10.3389/fninf.2022.1040084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/07/2022] [Indexed: 12/23/2022] Open
Abstract
Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.
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Affiliation(s)
- Xiaojin Li
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Yan Huang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Samden D. Lhatoo
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shiqiang Tao
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Laura Vilella Bertran
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Guo-Qiang Zhang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States,*Correspondence: Guo-Qiang Zhang
| | - Licong Cui
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States,Licong Cui
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Li X, Tao S, Lhatoo SD, Cui L, Huang Y, Hampson JP, Zhang GQ. A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy. Front Big Data 2022; 5:965715. [PMID: 36059922 PMCID: PMC9428292 DOI: 10.3389/fdata.2022.965715] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/11/2022] [Indexed: 02/03/2023] Open
Abstract
Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.
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Affiliation(s)
- Xiaojin Li
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shiqiang Tao
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Samden D. Lhatoo
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Licong Cui
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Yan Huang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Johnson P. Hampson
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Guo-Qiang Zhang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States,*Correspondence: Guo-Qiang Zhang
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Chen ZS, Hsieh A, Sun G, Bergey GK, Berkovic SF, Perucca P, D'Souza W, Elder CJ, Farooque P, Johnson EL, Barnard S, Nightscales R, Kwan P, Moseley B, O'Brien TJ, Sivathamboo S, Laze J, Friedman D, Devinsky O. Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study. Front Neurol 2022; 13:858333. [PMID: 35370908 PMCID: PMC8973318 DOI: 10.3389/fneur.2022.858333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 02/08/2022] [Indexed: 12/04/2022] Open
Abstract
Objective Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged postictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve. Results The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved the mean AUC of 0.79 in leave-one-center-out prediction. Conclusions Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians to identify high-risk patients and initiate preventive strategies.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- *Correspondence: Zhe Sage Chen
| | - Aaron Hsieh
- Tandon School of Engineering, New York University, New York, NY, United States
| | - Guanghao Sun
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
| | - Gregory K. Bergey
- Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Samuel F. Berkovic
- Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, VIC, Australia
- Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | - Piero Perucca
- Department of Medicine (Austin Health), The University of Melbourne, Heidelberg, VIC, Australia
- Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Heidelberg, VIC, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Wendyl D'Souza
- Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Christopher J. Elder
- Division of Epilepsy and Sleep, Columbia University, New York, NY, United States
| | - Pue Farooque
- Yale University School of Medicine, New Haven, CT, United States
| | - Emily L. Johnson
- Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Sarah Barnard
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
| | - Russell Nightscales
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Brian Moseley
- Clinical Development Neurocrine Biosciences Inc., San Diego, CA, United States
| | - Terence J. O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Shobi Sivathamboo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Juliana Laze
- Comprehensive Epilepsy Center, New York University Langone Health, New York, NY, United States
| | - Daniel Friedman
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
- Comprehensive Epilepsy Center, New York University Langone Health, New York, NY, United States
| | - Orrin Devinsky
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
- Comprehensive Epilepsy Center, New York University Langone Health, New York, NY, United States
- Orrin Devinsky
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