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Pyrzowski J, Kałas M, Mazurkiewicz-Bełdzińska M, Siemiński M. EEG biomarkers for the prediction of post-traumatic epilepsy - a systematic review of an emerging field. Seizure 2024; 119:71-77. [PMID: 38796954 DOI: 10.1016/j.seizure.2024.05.006] [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: 01/19/2024] [Revised: 04/24/2024] [Accepted: 05/12/2024] [Indexed: 05/29/2024] Open
Abstract
Traumatic brain injury (TBI) is often followed by post-traumatic epilepsy (PTE), a condition often difficult to treat and leading to a substantial decline in quality of life as well as increased long-term mortality. The latent period between TBI and the emergence of spontaneous recurrent seizures provides an opportunity for pharmacological intervention to prevent epileptogenesis. Biomarkers capable of predicting PTE development are urgently needed to facilitate clinical trials of putative anti-epileptogenic drugs. EEG is a widely available and flexible diagnostic modality that plays a fundamental role in epileptology. We systematically review the advances in the field of the discovery of EEG biomarkers for the prediction of PTE in humans. Despite recent progress, the field faces several challenges including short observation periods, a focus on early post-injury monitoring, difficulties in translating findings from animal models to scalp EEG, and emerging evidence indicating the importance of assessing altered background scalp EEG activity alongside epileptiform activity using quantitative EEG methods while also considering sleep abnormalities in future studies.
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Affiliation(s)
- Jan Pyrzowski
- Department of Emergency Medicine, Medical University of Gdańsk, Gdańsk, Poland.
| | - Maria Kałas
- Department of Emergency Medicine, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Mariusz Siemiński
- Department of Emergency Medicine, Medical University of Gdańsk, Gdańsk, Poland
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2
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Anderson JT, Roth JD, Rosenau KA, Dwyer PS, Kuo AA, Martinez-Agosto JA. Enhancing multi-site autism research through the development of a collaborative data platform. Autism Res 2024. [PMID: 38794841 DOI: 10.1002/aur.3167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
Data repositories, particularly those storing data on vulnerable populations, increasingly need to carefully consider not only what data is being collected, but how it will be used. As such, the Autism Intervention Research Network on Physical Health (AIR-P) has created the Infrastructure for Collaborative Research (ICR) to establish standards on data collection practices in Autism repositories. The ICR will strive to encourage inter-site collaboration, amplify autistic voices, and widen accessibility to data. The ICR is staged as a three-tiered framework consisting of (1) a request for proposals system, (2) a REDCap-based data repository, and (3) public data dashboards to display aggregate de-identified data. Coupled with a review process including autistic and non-autistic researchers, this framework aims to propel the implementation of equitable autism research, enhance standardization within and between studies, and boost transparency and dissemination of findings. In addition, the inclusion of a contact registry that study participants can opt into creates the base for a robust participant pool. As such, researchers can leverage the platform to identify, reach, and distribute electronic materials to a greater proportion of potential participants who likely fall within their eligibility criteria. By incorporating practices that promote effective communication between researchers and participants, the ICR can facilitate research that is both considerate of and a benefit to autistic people.
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Affiliation(s)
- Jeffrey T Anderson
- Department of Medicine, University of California, Los Angeles, California, USA
| | - Jeffrey D Roth
- Department of Medicine, University of California, Los Angeles, California, USA
| | - Kashia A Rosenau
- Department of Medicine, University of California, Los Angeles, California, USA
| | - Patrick S Dwyer
- Department of Psychology, University of California, Los Angeles, California, USA
- Olga Tennison Autism Research Centre, School of Psychology and Public Health, La Trobe University, Bundoora, Victoria, Australia
| | - Alice A Kuo
- Department of Medicine, University of California, Los Angeles, California, USA
- Department of Pediatrics, University of California, Los Angeles, California, USA
| | - Julian A Martinez-Agosto
- Department of Pediatrics, University of California, Los Angeles, California, USA
- Department of Human Genetics, University of California, Los Angeles, California, USA
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3
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Pease M, Gupta K, Moshé SL, Correa DJ, Galanopoulou AS, Okonkwo DO, Gonzalez-Martinez J, Shutter L, Diaz-Arrastia R, Castellano JF. Insights into epileptogenesis from post-traumatic epilepsy. Nat Rev Neurol 2024; 20:298-312. [PMID: 38570704 DOI: 10.1038/s41582-024-00954-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies. The incidence of PTE after traumatic brain injury (TBI) depends on the severity of injury, approaching one in three in groups with the most severe injuries. The repeated seizures that characterize PTE impair neurological recovery and increase the risk of poor outcomes after TBI. Given this high risk of recurrent seizures and the relatively short latency period for their development after injury, PTE serves as a model disease to understand human epileptogenesis and trial novel anti-epileptogenic therapies. Epileptogenesis is the process whereby previously normal brain tissue becomes prone to recurrent abnormal electrical activity, ultimately resulting in seizures. In this Review, we describe the clinical course of PTE and highlight promising research into epileptogenesis and treatment using animal models of PTE. Clinical, imaging, EEG and fluid biomarkers are being developed to aid the identification of patients at high risk of PTE who might benefit from anti-epileptogenic therapies. Studies in preclinical models of PTE have identified tractable pathways and novel therapeutic strategies that can potentially prevent epilepsy, which remain to be validated in humans. In addition to improving outcomes after TBI, advances in PTE research are likely to provide therapeutic insights that are relevant to all epilepsies.
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Affiliation(s)
- Matthew Pease
- Department of Neurosurgery, Indiana University, Bloomington, IN, USA.
| | - Kunal Gupta
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Solomon L Moshé
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York, NY, USA
- Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
- Department of Paediatrics, Albert Einstein College of Medicine, New York, NY, USA
| | - Daniel J Correa
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York, NY, USA
| | - Aristea S Galanopoulou
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, New York, NY, USA
- Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
| | - David O Okonkwo
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Lori Shutter
- Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
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4
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Shannon T, Cotter C, Fitzgerald J, Houle S, Levine N, Shen Y, Rajjoub N, Dobres S, Iyer S, Xenakis J, Lynch R, de Villena FPM, Kokiko-Cochran O, Gu B. Genetic diversity drives extreme responses to traumatic brain injury and post-traumatic epilepsy. Exp Neurol 2024; 374:114677. [PMID: 38185315 DOI: 10.1016/j.expneurol.2024.114677] [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: 09/15/2023] [Revised: 11/21/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Traumatic brain injury (TBI) is a complex and heterogeneous condition that can cause wide-spectral neurological sequelae such as behavioral deficits, sleep abnormalities, and post-traumatic epilepsy (PTE). However, understanding the interaction of TBI phenome is challenging because few animal models can recapitulate the heterogeneity of TBI outcomes. We leveraged the genetically diverse recombinant inbred Collaborative Cross (CC) mice panel and systematically characterized TBI-related outcomes in males from 12 strains of CC and the reference C57BL/6J mice. We identified unprecedented extreme responses in multiple clinically relevant traits across CC strains, including weight change, mortality, locomotor activity, cognition, and sleep. Notably, we identified CC031 mouse strain as the first rodent model of PTE that exhibit frequent and progressive post-traumatic seizures after moderate TBI induced by lateral fluid percussion. Multivariate analysis pinpointed novel biological interactions and three principal components across TBI-related modalities. Estimate of the proportion of TBI phenotypic variability attributable to strain revealed large range of heritability, including >70% heritability of open arm entry time of elevated plus maze. Our work provides novel resources and models that can facilitate genetic mapping and the understanding of the pathobiology of TBI and PTE.
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Affiliation(s)
- Tyler Shannon
- Department of Neuroscience, Ohio State University, Columbus, USA
| | - Christopher Cotter
- Department of Neuroscience, Ohio State University, Columbus, USA; Institute for Behavioral Medicine Research, Neurological Institute, Ohio State University, Columbus, USA
| | - Julie Fitzgerald
- Department of Neuroscience, Ohio State University, Columbus, USA; Institute for Behavioral Medicine Research, Neurological Institute, Ohio State University, Columbus, USA
| | - Samuel Houle
- Department of Neuroscience, Ohio State University, Columbus, USA; Institute for Behavioral Medicine Research, Neurological Institute, Ohio State University, Columbus, USA
| | - Noah Levine
- Electrical and Computer Engineering Program, Ohio State University, Columbus, USA
| | - Yuyan Shen
- Department of Neuroscience, Ohio State University, Columbus, USA; College of Veterinary Medicine, Ohio State University, Columbus, USA
| | - Noora Rajjoub
- Department of Neuroscience, Ohio State University, Columbus, USA
| | - Shannon Dobres
- Department of Neuroscience, Ohio State University, Columbus, USA
| | - Sidharth Iyer
- Department of Neuroscience, Ohio State University, Columbus, USA
| | - James Xenakis
- Department of Genetics, University of North Carolina, Chapel Hill, USA
| | - Rachel Lynch
- Department of Genetics, University of North Carolina, Chapel Hill, USA
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, Chapel Hill, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, USA
| | - Olga Kokiko-Cochran
- Department of Neuroscience, Ohio State University, Columbus, USA; Institute for Behavioral Medicine Research, Neurological Institute, Ohio State University, Columbus, USA; Chronic Brain Injury Program, Ohio State University, Columbus, USA
| | - Bin Gu
- Department of Neuroscience, Ohio State University, Columbus, USA; Chronic Brain Injury Program, Ohio State University, Columbus, USA.
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Bhagavatula S, Cabeen R, Harris NG, Gröhn O, Wright DK, Garner R, Bennett A, Alba C, Martinez A, Ndode-Ekane XE, Andrade P, Paananen T, Ciszek R, Immonen R, Manninen E, Puhakka N, Tohka J, Heiskanen M, Ali I, Shultz SR, Casillas-Espinosa PM, Yamakawa GR, Jones NC, Hudson MR, Silva JC, Braine EL, Brady RD, Santana-Gomez CE, Smith GD, Staba R, O'Brien TJ, Pitkänen A, Duncan D. Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies. Epilepsy Res 2023; 195:107201. [PMID: 37562146 PMCID: PMC10528111 DOI: 10.1016/j.eplepsyres.2023.107201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/04/2023] [Accepted: 07/31/2023] [Indexed: 08/12/2023]
Abstract
Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.
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Affiliation(s)
- Sweta Bhagavatula
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Ryan Cabeen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Neil G Harris
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Olli Gröhn
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - David K Wright
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Rachael Garner
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Alexis Bennett
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Celina Alba
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Aubrey Martinez
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Pedro Andrade
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tomi Paananen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Robert Ciszek
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Riikka Immonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Eppu Manninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Noora Puhakka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mette Heiskanen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Idrish Ali
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Sandy R Shultz
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Pablo M Casillas-Espinosa
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Glenn R Yamakawa
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Nigel C Jones
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Matthew R Hudson
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Juliana C Silva
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Emma L Braine
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Rhys D Brady
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Cesar E Santana-Gomez
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Gregory D Smith
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Terence J O'Brien
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dominique Duncan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
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Maharathi B, Mir F, Hosur K, Loeb JA. INTUITION: a data platform to integrate human epilepsy clinical care and support for discovery. Front Digit Health 2023; 5:1091508. [PMID: 37363274 PMCID: PMC10285513 DOI: 10.3389/fdgth.2023.1091508] [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: 11/07/2022] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
To make appropriate clinical decisions, clinicians consider many types of data from multiple sources to arrive at a diagnosis and plan. However, the current health systems have siloed data, making it challenging to develop information platforms that integrate this process into a single place for comprehensive clinical evaluation and research. INTUITION is a human brain integrative data system that facilitates multimodal data integration, unified storage, cohort selection, and analysis of multidisciplinary datasets. In this article, we describe the use of INTUITION to include electronic health records together with co-registered neuroimaging and EEG from patients who undergo invasive brain surgery for epilepsy. In addition to providing clinically useful visualizations and analytics to help guide surgical planning, INTUITION also links a bank of human brain epileptic tissues from specific brain locations to quantitative EEG, imaging, histology, and omics studies in a unique, completely integrated informatics platform. Having a clinically useful platform for integrating multimodal datasets can not only aid in clinical management decisions but also in creating a unique resource for research and discovery when linked to spatially mapped tissue samples.
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Affiliation(s)
- Biswajit Maharathi
- Department of Neurology and Rehabilitation, University of Illinois, Chicago, IL, United States
- Center for Clinical and Translational Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Fozia Mir
- Department of Neurology and Rehabilitation, University of Illinois, Chicago, IL, United States
| | - Karthik Hosur
- Department of Neurology and Rehabilitation, University of Illinois, Chicago, IL, United States
| | - Jeffrey A. Loeb
- Department of Neurology and Rehabilitation, University of Illinois, Chicago, IL, United States
- Center for Clinical and Translational Science, University of Illinois at Chicago, Chicago, IL, United States
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Maharathi B, Wong J, Geraghty JR, Serafini A, Davis JM, Butler M, Kolar SK, Pandey DK, Loeb JA. Multi-modal data integration platform combining clinical and preclinical models of post subarachnoid hemorrhage epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3459-3463. [PMID: 36086190 DOI: 10.1109/embc48229.2022.9871864] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Subarachnoid hemorrhage (SAH) is a devastating neurological injury that can lead to many downstream complications including epilepsy. Predicting who will get epilepsy in order to find ways to prevent it as well as stratify patients for future interventions is a major challenge given the large number of variables not only related to the injury itself, but also to what happens after the injury. Extensive multimodal data are generated during the process of SAH patient care. In parallel, preclinical models are under development that attempt to imitate the variables observed in patients. Computational tools that consider all variables from both human data and animal models are lacking and demand an integrated, time-dependent platform where researchers can aggregate, store, visualize, analyze, and share the extensive integrated multimodal information. We developed a multi-tier web-based application that is secure, extensible, and adaptable to all available data modalities using flask micro-web framework, python, and PostgreSQL database. The system supports data visualization, data sharing and downloading for offline processing. The system is currently hosted inside the institutional private network and holds [Formula: see text] of data from 164 patients and 71 rodents. Clinical Relevance-Our platform supports clinical and preclinical data management. It allows users to comprehensively visualize patient data and perform visual analytics. These utilities can improve research and clinical practice for subarachnoid hemorrhage and other brain injuries.
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Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol 2022; 32:3173-3186. [PMID: 35001159 DOI: 10.1007/s00330-021-08431-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/01/2021] [Accepted: 10/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVE The systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks. METHODS Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database ( https://cordis.europa.eu/projects ) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes. RESULTS Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request. CONCLUSIONS Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks. KEY POINTS • Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request. • While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.
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Abstract
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.
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Affiliation(s)
- John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
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Galanopoulou AS, Löscher W, Lubbers L, O’Brien TJ, Staley K, Vezzani A, D’Ambrosio R, White HS, Sontheimer H, Wolf JA, Twyman R, Whittemore V, Wilcox KS, Klein B. Antiepileptogenesis and disease modification: Progress, challenges, and the path forward-Report of the Preclinical Working Group of the 2018 NINDS-sponsored antiepileptogenesis and disease modification workshop. Epilepsia Open 2021; 6:276-296. [PMID: 34033232 PMCID: PMC8166793 DOI: 10.1002/epi4.12490] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/04/2021] [Accepted: 04/12/2021] [Indexed: 12/12/2022] Open
Abstract
Epilepsy is one of the most common chronic brain diseases and is often associated with cognitive, behavioral, or other medical conditions. The need for therapies that would prevent, ameliorate, or cure epilepsy and the attendant comorbidities is a priority for both epilepsy research and public health. In 2018, the National Institute of Neurological Disease and Stroke (NINDS) convened a workshop titled "Accelerating the Development of Therapies for Antiepileptogenesis and Disease Modification" that brought together preclinical and clinical investigators and industry and regulatory bodies' representatives to discuss and propose a roadmap to accelerate the development of antiepileptogenic (AEG) and disease-modifying (DM) new therapies. This report provides a summary of the discussions and proposals of the Preclinical Science working group. Highlights of the progress of collaborative preclinical research projects on AEG/DM of ongoing research initiatives aiming to improve infrastructure and translation to clinical trials are presented. Opportunities and challenges of preclinical epilepsy research, vis-à-vis clinical research, were extensively discussed, as they pertain to modeling of specific epilepsy types across etiologies and ages, the utilization of preclinical models in AG/DM studies, and the strategies and study designs, as well as on matters pertaining to transparency, data sharing, and reporting research findings. A set of suggestions on research initiatives, infrastructure, workshops, advocacy, and opportunities for expanding the borders of epilepsy research were discussed and proposed as useful initiatives that could help create a roadmap to accelerate and optimize preclinical translational AEG/DM epilepsy research.
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Affiliation(s)
- Aristea S. Galanopoulou
- Saul R. Korey Department of NeurologyDominick P. Purpura Department of NeuroscienceIsabelle Rapin Division of Child NeurologyAlbert Einstein College of MedicineBronxNYUSA
| | - Wolfgang Löscher
- Department of Pharmacology, Toxicology, and PharmacyUniversity of Veterinary Medicine HannoverHannoverGermany
| | | | - Terence J. O’Brien
- Department of NeuroscienceCentral Clinical SchoolAlfred HealthMonash UniversityMelbourneVic.Australia
| | - Kevin Staley
- Department of NeurologyMassachusetts General HospitalBostonMAUSA
| | - Annamaria Vezzani
- Department of NeuroscienceIRCCS‐Mario Negri Institute for Pharmacological ResearchMilanoItaly
| | | | - H. Steve White
- Department of PharmacySchool of PharmacyUniversity of WashingtonSeattleWAUSA
| | | | - John A. Wolf
- Center for Brain Injury and RepairDepartment of NeurosurgeryUniversity of PennsylvaniaPhiladelphiaPAUSA
- Corporal Michael J. Crescenz Veterans Affairs Medical CenterPhiladelphiaPAUSA
| | | | - Vicky Whittemore
- National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMDUSA
| | - Karen S. Wilcox
- Department of Pharmacology & ToxicologyUniversity of UtahSalt Lake CityUTUSA
| | - Brian Klein
- National Institute of Neurological Disorders and StrokeNational Institutes of HealthBethesdaMDUSA
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Gagalova KK, Leon Elizalde MA, Portales-Casamar E, Görges M. What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions. JMIR Form Res 2020; 4:e17687. [PMID: 32852280 PMCID: PMC7484778 DOI: 10.2196/17687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 06/09/2020] [Accepted: 07/17/2020] [Indexed: 12/23/2022] Open
Abstract
Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.
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Affiliation(s)
- Kristina K Gagalova
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.,Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada.,Research Institute, BC Children's Hospital, Vancouver, BC, Canada
| | - M Angelica Leon Elizalde
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Elodie Portales-Casamar
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Matthias Görges
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
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12
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Baldassano SN, Hill CE, Shankar A, Bernabei J, Khankhanian P, Litt B. Big data in status epilepticus. Epilepsy Behav 2019; 101:106457. [PMID: 31444029 PMCID: PMC6944751 DOI: 10.1016/j.yebeh.2019.106457] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Accepted: 07/26/2019] [Indexed: 12/23/2022]
Abstract
Status epilepticus care and treatment are already being touched by the revolution in data science. New approaches designed to leverage the tremendous potential of "big data" in the clinical sphere are enabling researchers and clinicians to extract information from sources such as administrative claims data, the electronic medical health record, and continuous physiologic monitoring data streams. Algorithmic methods of data extraction also offer potential to fuse multimodal data (including text-based documentation, imaging data, and time-series data) to improve patient assessment and stratification beyond the manual capabilities of individual physicians. Still, the potential of data science to impact the diagnosis, treatment, and minute-to-minute care of patients with status epilepticus is only starting to be appreciated. In this brief review, we discuss how data science is impacting the field and draw examples from the following three main areas: (1) analysis of insurance claims from large administrative datasets to evaluate the impact of continuous electroencephalogram (EEG) monitoring on clinical outcomes; (2) natural language processing of the electronic health record to find, classify, and stratify patients for prognostication and treatment; and (3) real-time systems for data analysis, data reduction, and multimodal data fusion to guide therapy in real time. While early, it is our hope that these examples will stimulate investigators to leverage data science, computer science, and engineering methods to improve the care and outcome of patients with status epilepticus and other neurological disorders. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".
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Affiliation(s)
- Steven N. Baldassano
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Chloé E. Hill
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States
| | - Arjun Shankar
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - John Bernabei
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States
| | - Pouya Khankhanian
- Department of Neurology, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, 210 South 33rd Street, Philadelphia, PA 19104, United States,Center for Neuroengineering and Therapeutics, University of Pennsylvania, 240 South 33rd Street, Philadelphia, PA 19104, United States,Department of Neurology, Penn Epilepsy Center, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
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13
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Garner R, La Rocca M, Vespa P, Jones N, Monti MM, Toga AW, Duncan D. Imaging biomarkers of posttraumatic epileptogenesis. Epilepsia 2019; 60:2151-2162. [PMID: 31595501 PMCID: PMC6842410 DOI: 10.1111/epi.16357] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 09/10/2019] [Accepted: 09/10/2019] [Indexed: 12/14/2022]
Abstract
Traumatic brain injury (TBI) affects 2.5 million people annually within the United States alone, with over 300 000 severe injuries resulting in emergency room visits and hospital admissions. Severe TBI can result in long-term disability. Posttraumatic epilepsy (PTE) is one of the most debilitating consequences of TBI, with an estimated incidence that ranges from 2% to 50% based on severity of injury. Conducting studies of PTE poses many challenges, because many subjects with TBI never develop epilepsy, and it can be more than 10 years after TBI before seizures begin. One of the unmet needs in the study of PTE is an accurate biomarker of epileptogenesis, or a panel of biomarkers, which could provide early insights into which TBI patients are most susceptible to PTE, providing an opportunity for prophylactic anticonvulsant therapy and enabling more efficient large-scale PTE studies. Several recent reviews have provided a comprehensive overview of this subject (Neurobiol Dis, 123, 2019, 3; Neurotherapeutics, 11, 2014, 231). In this review, we describe acute and chronic imaging methods that detect biomarkers for PTE and potential mechanisms of epileptogenesis. We also describe shortcomings in current acquisition methods, analysis, and interpretation that limit ongoing investigations that may be mitigated with advancements in imaging techniques and analysis.
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Affiliation(s)
- Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Marianna La Rocca
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Paul Vespa
- Division of Neurosurgery, Department of Neurology, University of California Los Angeles School of Medicine, Los Angeles, CA, United States
| | - Nigel Jones
- Van Cleef Centre for Nervous Diseases, Department of Neuroscience, Monash University, Clayton, VIC, Australia
| | - Martin M. Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, United States
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
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14
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Abstract
Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.
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15
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Carvill GL, Dulla CG, Lowenstein DH, Brooks-Kayal AR. The path from scientific discovery to cures for epilepsy. Neuropharmacology 2019; 167:107702. [PMID: 31301334 DOI: 10.1016/j.neuropharm.2019.107702] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 07/05/2019] [Accepted: 07/06/2019] [Indexed: 02/06/2023]
Abstract
The epilepsies are a complex group of disorders that can be caused by a myriad of genetic and acquired factors. As such, identifying interventions that will prevent development of epilepsy, as well as cure the disorder once established, will require a multifaceted approach. Here we discuss the progress in scientific discovery propelling us towards this goal, including identification of genetic risk factors and big data approaches that integrate clinical and molecular 'omics' datasets to identify common pathophysiological signatures and biomarkers. We discuss the many animal and cellular models of epilepsy, what they have taught us about pathophysiology, and the cutting edge cellular, optogenetic, chemogenetic and anti-seizure drug screening approaches that are being used to find new cures in these models. Finally, we reflect on the work that still needs to be done towards identify at-risk individuals early, targeting and stopping epileptogenesis, and optimizing promising treatment approaches. Ultimately, developing and implementing cures for epilepsy will require a coordinated and immense effort from clinicians and basic scientists, as well as industry, and should always be guided by the needs of individuals affected by epilepsy and their families. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.
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Affiliation(s)
- Gemma L Carvill
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
| | - Chris G Dulla
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA.
| | - Dan H Lowenstein
- Department of Neurology, University of California, San Francisco, CA, 94941, USA
| | - Amy R Brooks-Kayal
- Department of Pediatrics and Neurology, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO, 80045, USA
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16
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Pitkänen A, O'Brien TJ, Staba R. Preface - Practical and theoretical considerations for performing a multi-center preclinical biomarker discovery study of post-traumatic epileptogenesis: lessons learned from the EpiBioS4Rx consortium. Epilepsy Res 2019; 156:106080. [PMID: 30685321 DOI: 10.1016/j.eplepsyres.2019.01.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Accepted: 01/13/2019] [Indexed: 12/13/2022]
Abstract
The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a NINDS funded Center-Without-Walls international study aimed at preventing epileptogenesis after traumatic brain injury (TBI). One objective of EpiBioS4Rx relates to preclinical biomarker discovery for post-traumatic epilepsy. In order to perform a statistically appropriately powered biomarker discovery study, EpiBioS4Rx has made a rigorous attempt to harmonize the preclinical procedures performed at the three EpiBioS4Rx centers, located in Finland, Australia, and the USA. Moreover, we have also performed a rigorous interim analysis of the success of procedural harmonization, which is reported in this virtual special issue. The analysis included harmonization of the production of animal model, blood sampling, electroencephalogram analyses (seizures, high-frequency oscillations) and magnetic resonance imaging analysis. Based on lessons learned, we propose a 3-stage protocol to facilitate the success of preclinical multicenter studies: preparation ⇨ testing ⇨ multicenter study. The need of funding for preparation and testing phases, which precede the actual multicenter study and are necessary for its success, should be taken into account in the design of funding schemes.
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Affiliation(s)
- Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
| | - Terence J O'Brien
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Australia; Department of Neurology, The Alfred Hospital, Commercial Road, Melbourne, 3004 Victoria, Australia
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, United States
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17
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Duncan D, Barisano G, Cabeen R, Sepehrband F, Garner R, Braimah A, Vespa P, Pitkänen A, Law M, Toga AW. Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients. Front Neuroinform 2018; 12:86. [PMID: 30618695 PMCID: PMC6307529 DOI: 10.3389/fninf.2018.00086] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/02/2018] [Indexed: 12/16/2022] Open
Abstract
Epilepsy is among the most common serious disabling disorders of the brain, and the global burden of epilepsy exerts a tremendous cost to society. Most people with epilepsy have acquired forms of the disorder, and the development of antiepileptogenic interventions could potentially prevent or cure epilepsy in many of them. However, the discovery of potential antiepileptogenic treatments and clinical validation would require a means to identify populations of patients at very high risk for epilepsy after a potential epileptogenic insult, to know when to treat and to document prevention or cure. A fundamental challenge in discovering biomarkers of epileptogenesis is that this process is likely multifactorial and crosses multiple modalities. Investigators must have access to a large number of high quality, well-curated data points and study subjects for biomarker signals to be detectable above the noise inherent in complex phenomena, such as epileptogenesis, traumatic brain injury (TBI), and conditions of data collection. Additionally, data generating and collecting sites are spread worldwide among different laboratories, clinical sites, heterogeneous data types, formats, and across multi-center preclinical trials. Before the data can even be analyzed, these data must be standardized. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a multi-center project with the overarching goal that epileptogenesis after TBI can be prevented with specific treatments. The identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies. We have been analyzing human data collected from UCLA and rat data collected from the University of Eastern Finland, both centers collecting data for EpiBioS4Rx, to identify biomarkers of epileptogenesis. Big data techniques and rigorous analysis are brought to longitudinal data collected from humans and an animal model of TBI, epilepsy, and their interaction. The prolonged continuous data streams of intracranial, cortical surface, and scalp EEG from humans and an animal model of epilepsy span months. By applying our innovative mathematical tools via supervised and unsupervised learning methods, we are able to subject a robust dataset to recently pioneered data analysis tools and visualize multivariable interactions with novel graphical methods.
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Affiliation(s)
- Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Giuseppe Barisano
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Ryan Cabeen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Farshid Sepehrband
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Rachael Garner
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Adebayo Braimah
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Paul Vespa
- Division of Neurosurgery, Department of Neurology, University of California at Los Angeles School of Medicine Los Angeles, CA, United States
| | - Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences University of Eastern Finland, Kuopio, Finland
| | - Meng Law
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California Los Angeles, CA, United States
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18
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Galanopoulou AS, Engel J, Moshé SL. PREFACE: Antiepileptogenesis following traumatic brain injury. Neurobiol Dis 2018; 123:1-2. [PMID: 30312758 DOI: 10.1016/j.nbd.2018.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Aristea S Galanopoulou
- Saul R. Korey Department of Neurology, Isabelle Rapin Division of Child Neurology, Dominick P. Purpura Department of Neuroscience, Montefiore/Einstein Epilepsy Center, Laboratory of Developmental Epilepsy, Albert Einstein College of Medicine, Bronx, NY, USA.
| | - Jerome Engel
- David Geffen School of medicine, University of California, Los Angeles, CA, USA
| | - Solomon L Moshé
- Saul R. Korey Department of Neurology, Isabelle Rapin Division of Child Neurology, Dominick P. Purpura Department of Neuroscience, Montefiore/Einstein Epilepsy Center, Laboratory of Developmental Epilepsy, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
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