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Iorio EG, Khanteymoori A, Fond KA, Keller AV, Davis LM, Schwab JM, Ferguson AR, Torres-Espin A, Watzlawick R. Effect-Size Discrepancies in Literature Versus Raw Datasets from Experimental Spinal Cord Injury Studies: A CLIMBER Meta-Analysis. Neurotrauma Rep 2024; 5:686-698. [PMID: 39071986 PMCID: PMC11271150 DOI: 10.1089/neur.2024.0038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024] Open
Abstract
Translation of spinal cord injury (SCI) therapeutics from pre-clinical animal studies into human studies is challenged by effect size variability, irreproducibility, and misalignment of evidence used by pre-clinical versus clinical literature. Clinical literature values reproducibility, with the highest grade evidence (class 1) consisting of meta-analysis demonstrating large therapeutic efficacy replicating across multiple studies. Conversely, pre-clinical literature values novelty over replication and lacks rigorous meta-analyses to assess reproducibility of effect sizes across multiple articles. Here, we applied modified clinical meta-analysis methods to pre-clinical studies, comparing effect sizes extracted from published literature to raw data on individual animals from these same studies. Literature-extracted data (LED) from numerical and graphical outcomes reported in publications were compared with individual animal data (IAD) deposited in a federally supported repository of SCI data. The animal groups from the IAD were matched with the same cohorts in the LED for a direct comparison. We applied random-effects meta-analysis to evaluate predictors of neuroconversion in LED versus IAD. We included publications with common injury models (contusive injuries) and standardized end-points (open field assessments). The extraction of data from 25 published articles yielded n = 1841 subjects, whereas IAD from these same articles included n = 2441 subjects. We observed differences in the number of experimental groups and animals per group, insufficient reporting of dropout animals, and missing information on experimental details. Meta-analysis revealed differences in effect sizes across LED versus IAD stratifications, for instance, severe injuries had the largest effect size in LED (standardized mean difference [SMD = 4.92]), but mild injuries had the largest effect size in IAD (SMD = 6.06). Publications with smaller sample sizes yielded larger effect sizes, while studies with larger sample sizes had smaller effects. The results demonstrate the feasibility of combining IAD analysis with traditional LED meta-analysis to assess effect size reproducibility in SCI.
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Affiliation(s)
- Emma G. Iorio
- Department of Neurological Surgery, University of California, San Francisco, California, USA
| | - Alireza Khanteymoori
- Department of Neurosurgery, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kenneth A. Fond
- Department of Neurological Surgery, University of California, San Francisco, California, USA
| | - Anastasia V. Keller
- Department of Neurological Surgery, University of California, San Francisco, California, USA
| | - Lex Maliga Davis
- Department of Neurological Surgery, University of California, San Francisco, California, USA
| | - Jan M. Schwab
- Departments of Neurology and Neurosciences, The Ohio State University, Columbus, Ohio, USA
- Belford Center for Spinal Cord Injury, The Ohio State University, Columbus, Ohio, USA
| | - Adam R. Ferguson
- Department of Neurological Surgery, University of California, San Francisco, California, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
| | - Abel Torres-Espin
- Department of Neurological Surgery, University of California, San Francisco, California, USA
- Department of Physical Therapy, University of Alberta, Edmonton, Canada
- School of Public Health Sciences, University of Waterloo, Waterloo, Canada
| | - Ralf Watzlawick
- Department of Neurosurgery, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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2
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Radabaugh HL, Ferguson AR, Bramlett HM, Dietrich WD. Increasing Rigor of Preclinical Research to Maximize Opportunities for Translation. Neurotherapeutics 2023; 20:1433-1445. [PMID: 37525025 PMCID: PMC10684440 DOI: 10.1007/s13311-023-01400-5] [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] [Accepted: 06/09/2023] [Indexed: 08/02/2023] Open
Abstract
The use of animal models in pre-clinical research has significantly broadened our understanding of the pathologies that underlie traumatic brain injury (TBI)-induced damage and deficits. However, despite numerous pre-clinical studies reporting the identification of promising neurotherapeutics, translation of these therapies to clinical application has so far eluded the TBI research field. A concerted effort to address this lack of translatability is long overdue. Given the inherent heterogeneity of TBI and the replication crisis that continues to plague biomedical research, this is a complex task that will require a multifaceted approach centered around rigor and reproducibility. Here, we discuss the role of three primary focus areas for better aligning pre-clinical research with clinical TBI management. These focus areas are (1) reporting and standardization of protocols, (2) replication of prior knowledge including the confirmation of expected pharmacodynamics, and (3) the broad application of open science through inter-center collaboration and data sharing. We further discuss current efforts that are establishing the core framework needed for successfully addressing the translatability crisis of TBI.
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Affiliation(s)
- Hannah L Radabaugh
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Helen M Bramlett
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA
| | - W Dalton Dietrich
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.
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3
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Aaltonen J, Heikkinen V, Kaltiainen H, Salmelin R, Renvall H. Sensor-level MEG combined with machine learning yields robust classification of mild traumatic brain injury patients. Clin Neurophysiol 2023; 153:79-87. [PMID: 37459668 DOI: 10.1016/j.clinph.2023.06.010] [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: 03/02/2023] [Revised: 05/06/2023] [Accepted: 06/09/2023] [Indexed: 08/21/2023]
Abstract
OBJECTIVE Diagnosis of mild traumatic brain injury (mTBI) is challenging despite its high incidence, due to the unspecificity and variety of symptoms and the frequent lack of structural imaging findings. There is a need for reliable and simple-to-use diagnostic tools that would be feasible across sites and patient populations. METHODS We evaluated linear machine learning (ML) methods' ability to separate mTBI patients from healthy controls, based on their sensor-level magnetoencephalographic (MEG) power spectra in the subacute phase (<2 months) after a head trauma. We recorded resting-state MEG data from 25 patients and 25 age-sex matched controls and utilized a previously collected data set of 20 patients and 20 controls from a different site. The data sets were analyzed separately with three ML methods. RESULTS The median classification accuracies varied between 80 and 95%, without significant differences between the applied ML methods or data sets. The classification accuracies were significantly higher with ML than with traditional sensor-level MEG analysis based on detecting pathological low-frequency activity. CONCLUSIONS Easily applicable linear ML methods provide reliable and replicable classification of mTBI patients using sensor-level MEG data. SIGNIFICANCE Power spectral estimates combined with ML can classify mTBI patients with high accuracy and have high promise for clinical use.
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Affiliation(s)
- Juho Aaltonen
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland.
| | - Verna Heikkinen
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland
| | - Hanna Kaltiainen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland; Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, P.O. Box 340, 00029 HUS, Helsinki, Finland
| | - Riitta Salmelin
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland
| | - Hanna Renvall
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, P.O. Box 340, 00029 HUS Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto University, P.O. Box 12200, 00760 AALTO, Finland; Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, P.O. Box 340, 00029 HUS, Helsinki, Finland
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4
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Feld SI, Hippe DS, Miljacic L, Polissar NL, Newman SF, Nair BG, Vavilala MS. A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury. J Neurosurg Anesthesiol 2023; 35:215-223. [PMID: 34759236 PMCID: PMC9091057 DOI: 10.1097/ana.0000000000000819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/08/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Traumatic brain injury (TBI) is a major cause of death and disability. Episodes of hypotension are associated with worse TBI outcomes. Our aim was to model the real-time risk of intraoperative hypotension in TBI patients, compare machine learning and traditional modeling techniques, and identify key contributory features from the patient monitor and medical record for the prediction of intraoperative hypotension. METHODS The data included neurosurgical procedures in 1005 TBI patients at an academic level 1 trauma center. The clinical event was intraoperative hypotension, defined as mean arterial pressure <65 mm Hg for 5 or more consecutive minutes. Two types of models were developed: one based on preoperative patient-level predictors and one based on intraoperative predictors measured per minute. For each of these models, we took 2 approaches to predict the occurrence of a hypotensive event: a logistic regression model and a gradient boosting tree model. RESULTS The area under the receiver operating characteristic curve for the intraoperative logistic regression model was 0.80 (95% confidence interval [CI]: 0.78-0.83), and for the gradient boosting model was 0.83 (95% CI: 0.81-0.85). The area under the precision-recall curve for the intraoperative logistic regression model was 0.16 (95% CI: 0.12-0.20), and for the gradient boosting model was 0.19 (95% CI: 0.14-0.24). Model performance based on preoperative predictors was poor. Features derived from the recent trend of mean arterial pressure emerged as dominantly predictive in both intraoperative models. CONCLUSIONS This study developed a model for real-time prediction of intraoperative hypotension in TBI patients, which can use computationally efficient machine learning techniques and a streamlined feature-set derived from patient monitor data.
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Affiliation(s)
- Shara I Feld
- Anesthesiology and Pain Medicine, University of Washington
| | - Daniel S Hippe
- The Mountain-Whisper-Light: Statistics & Data Science, Seattle, WA
| | | | - Nayak L Polissar
- The Mountain-Whisper-Light: Statistics & Data Science, Seattle, WA
| | | | - Bala G Nair
- Anesthesiology and Pain Medicine, University of Washington
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5
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Martín-Noguerol T, Oñate Miranda M, Amrhein TJ, Paulano-Godino F, Xiberta P, Vilanova JC, Luna A. The role of Artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol 2023; 161:110726. [PMID: 36758280 DOI: 10.1016/j.ejrad.2023.110726] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/13/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.
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Affiliation(s)
| | - Marta Oñate Miranda
- Department of Radiology, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada.
| | - Timothy J Amrhein
- Department of Radiology, Duke University Medical Center, Durham, USA.
| | | | - Pau Xiberta
- Graphics and Imaging Laboratory (GILAB), University of Girona, 17003 Girona, Spain.
| | - Joan C Vilanova
- Department of Radiology. Clinica Girona, Diagnostic Imaging Institute (IDI), University of Girona, 17002 Girona, Spain.
| | - Antonio Luna
- MRI unit, Radiology department. HT medica, Carmelo Torres n°2, 23007 Jaén, Spain.
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6
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Huie JR, Vashisht R, Galivanche A, Hadjadj C, Morshed S, Butte AJ, Ferguson AR, O'Neill C. Toward a causal model of chronic back pain: Challenges and opportunities. Front Comput Neurosci 2023; 16:1017412. [PMID: 36714527 PMCID: PMC9874096 DOI: 10.3389/fncom.2022.1017412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 11/21/2022] [Indexed: 01/13/2023] Open
Abstract
Chronic low back pain (cLBP) afflicts 8. 2% of adults in the United States, and is the leading global cause of disability. Neuropsychiatric co-morbidities including anxiety, depression, and substance abuse- are common in cLBP patients. In particular, cLBP is a risk factor for opioid addiction, as more than 50% of opioid prescriptions in the United States are for cLBP. Misuse of these prescriptions is a common precursor to addiction. While associations between cLBP and neuropsychiatric disorders are well established, causal relationships for the most part are unknown. Developing effective treatments for cLBP, and associated co-morbidities, requires identifying and understanding causal relationships. Rigorous methods for causal inference, a process for quantifying causal effects from observational data, have been developed over the past 30 years. In this review we first discuss the conceptual model of cLBP that current treatments are based on, and how gaps in causal knowledge contribute to poor clinical outcomes. We then present cLBP as a "Big Data" problem and identify how advanced analytic techniques may close knowledge gaps and improve clinical outcomes. We will focus on causal discovery, which is a data-driven method that uses artificial intelligence (AI) and high dimensional datasets to identify causal structures, discussing both constraint-based (PC and Fast Causal Inference) and score-based (Fast Greedy Equivalent Search) algorithms.
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Affiliation(s)
- J. Russell Huie
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - Rohit Vashisht
- Bakar Computational Health Sciences Center, University of California, San Francisco, San Francisco, CA, United States
| | - Anoop Galivanche
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Constance Hadjadj
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United States
| | - Saam Morshed
- Departments of Orthopaedic Surgery and of Epidemiology, University of California, San Francisco, San Francisco, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Center, University of California, San Francisco, San Francisco, CA, United States
| | - Adam R. Ferguson
- Department of Neurosurgery, Brain and Spinal Injury Center, Weill Institutes for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA, United States
| | - Conor O'Neill
- Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA, United States
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7
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Huie JR, Nielson JL, Wolfsbane J, Andersen CR, Spratt HM, DeWitt DS, Ferguson AR, Hawkins BE. Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research. Front Bioeng Biotechnol 2023; 10:887898. [PMID: 36704298 PMCID: PMC9871446 DOI: 10.3389/fbioe.2022.887898] [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: 03/02/2022] [Accepted: 11/07/2022] [Indexed: 01/12/2023] Open
Abstract
Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of chronic TBI (cTBI). Data collected by the Moody Project for Translational TBI Research included rats with no injury (naïve animals with similar amounts of anesthetic exposure to TBI and sham-injured animals), sham injury, or lateral fluid percussion TBI, followed by recovery periods up to 12 months. Behavioral measures included locomotor coordination (beam balance neuroscore) and memory and cognition assessments (Morris water maze: MWM) at multiple timepoints. Gene arrays were performed using hippocampal and cortical samples to probe 45,610 genes. To reduce the high dimensionality of molecular and behavioral domains and uncover gene-behavior associations, we performed non-linear principal components analyses (NL-PCA), which de-noised the data. Genomic NL-PCA unveiled three interpretable eigengene components (PC2, PC3, and PC4). Ingenuity pathway analysis (IPA) identified the PCs as an integrated stress response (PC2; EIF2-mTOR, corticotropin signaling, etc.), inflammatory factor translation (PC3; PI3K-p70S6K signaling), and neurite growth inhibition (PC4; Rho pathways). Behavioral PCA revealed three principal components reflecting the contribution of MWM overall speed and distance, neuroscore/beam walk, and MWM platform measures. Integrating the genomic and behavioral domains, we then performed a 'meta-PCA' on individual PC scores for each rat from genomic and behavioral PCAs. This meta-PCA uncovered three unique multimodal PCs, characterized by robust associations between inflammatory/stress response and neuroscore/beam walk performance (meta-PC1), stress response and MWM performance (meta-PC2), and stress response and neuroscore/beam walk performance (meta-PC3). Multivariate analysis of variance (MANOVA) on genomic-behavioral meta-PC scores tested separately on cortex and hippocampal samples revealed the main effects of TBI and recovery time. These findings are a proof of concept for the integration of disparate data domains for translational knowledge discovery, harnessing the full syndromic space of TBI.
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Affiliation(s)
- J. Russell Huie
- Weill Institutes for Neurosciences, Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States,San Francisco Veterans Administration Medical Center, San Francisco, CA, United States,*Correspondence: J. Russell Huie,
| | - Jessica L. Nielson
- Department of Psychiatry and Behavioral Sciences, Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
| | - Jorden Wolfsbane
- The Moody Project for Translational Traumatic Brain Injury Research, Department of Anesthesiology, University of Texas Medical Branch, Galveston, TX, United States
| | - Clark R. Andersen
- Office of Biostatistics, Department of Preventive Medicine Population Health, University of Texas Medical Branch, Galveston, TX, United States,Biostatistics Department, UT MD Anderson, Houston, TX, United States
| | - Heidi M. Spratt
- Office of Biostatistics, Department of Preventive Medicine Population Health, University of Texas Medical Branch, Galveston, TX, United States
| | - Douglas S. DeWitt
- The Moody Project for Translational Traumatic Brain Injury Research, Department of Anesthesiology, University of Texas Medical Branch, Galveston, TX, United States
| | - Adam R. Ferguson
- Weill Institutes for Neurosciences, Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States,San Francisco Veterans Administration Medical Center, San Francisco, CA, United States
| | - Bridget E. Hawkins
- The Moody Project for Translational Traumatic Brain Injury Research, Department of Anesthesiology, University of Texas Medical Branch, Galveston, TX, United States,Research Innovation and Scientific Excellence (RISE) Center, School of Nursing, University of Texas Medical Branch, Galveston, TX, United States
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8
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Torres-Espín A, Ferguson AR. Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine. HARVARD DATA SCIENCE REVIEW 2022; 4:10.1162/99608f92.a9717b34. [PMID: 36420049 PMCID: PMC9681014 DOI: 10.1162/99608f92.a9717b34] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication ('dark data'). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research.
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Affiliation(s)
- Abel Torres-Espín
- Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, United States of America
| | - Adam R Ferguson
- Brain and Spinal Injury Center (BASIC), Department of Neurological Surgery, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, United States of America
- San Francisco Veterans Affairs Health Care System, San Francisco, California, United States of America
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9
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Chou A, Torres-Espín A, Huie JR, Krukowski K, Lee S, Nolan A, Guglielmetti C, Hawkins BE, Chaumeil MM, Manley GT, Beattie MS, Bresnahan JC, Martone ME, Grethe JS, Rosi S, Ferguson AR. Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research. Neurotrauma Rep 2022; 3:139-157. [PMID: 35403104 PMCID: PMC8985540 DOI: 10.1089/neur.2021.0061] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.
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Affiliation(s)
- Austin Chou
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Abel Torres-Espín
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - J Russell Huie
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
| | - Karen Krukowski
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
| | - Sangmi Lee
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Amber Nolan
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
| | - Caroline Guglielmetti
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Bridget E Hawkins
- Department of Anesthesiology, University of Texas Medical Branch at Galveston, Galveston, Texas, USA
- Moody Project for Traumatic Brain Injury Research, University of Texas Medical Branch at Galveston, Galveston, Texas, USA
| | - Myriam M Chaumeil
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Geoffrey T Manley
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Michael S Beattie
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
| | - Jacqueline C Bresnahan
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
| | - Maryann E Martone
- Department of Neuroscience, University of California San Diego, San Diego, California, USA
| | - Jeffrey S Grethe
- Department of Neuroscience, University of California San Diego, San Diego, California, USA
| | - Susanna Rosi
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
- Kavli Institute of Fundamental Neuroscience, University of California San Francisco, San Francisco, California, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, University of California San Francisco, San Francisco, California, USA
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
- San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California, USA
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10
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Doerr SA, Weber-Levine C, Hersh AM, Awosika T, Judy B, Jin Y, Raj D, Liu A, Lubelski D, Jones CK, Sair HI, Theodore N. Automated prediction of the Thoracolumbar Injury Classification and Severity Score from CT using a novel deep learning algorithm. Neurosurg Focus 2022; 52:E5. [PMID: 35364582 DOI: 10.3171/2022.1.focus21745] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/18/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Damage to the thoracolumbar spine can confer significant morbidity and mortality. The Thoracolumbar Injury Classification and Severity Score (TLICS) is used to categorize injuries and determine patients at risk of spinal instability for whom surgical intervention is warranted. However, calculating this score can constitute a bottleneck in triaging and treating patients, as it relies on multiple imaging studies and a neurological examination. Therefore, the authors sought to develop and validate a deep learning model that can automatically categorize vertebral morphology and determine posterior ligamentous complex (PLC) integrity, two critical features of TLICS, using only CT scans. METHODS All patients who underwent neurosurgical consultation for traumatic spine injury or degenerative pathology resulting in spine injury at a single tertiary center from January 2018 to December 2019 were retrospectively evaluated for inclusion. The morphology of injury and integrity of the PLC were categorized on CT scans. A state-of-the-art object detection region-based convolutional neural network (R-CNN), Faster R-CNN, was leveraged to predict both vertebral locations and the corresponding TLICS. The network was trained with patient CT scans, manually labeled vertebral bounding boxes, TLICS morphology, and PLC annotations, thus allowing the model to output the location of vertebrae, categorize their morphology, and determine the status of PLC integrity. RESULTS A total of 111 patients were included (mean ± SD age 62 ± 20 years) with a total of 129 separate injury classifications. Vertebral localization and PLC integrity classification achieved Dice scores of 0.92 and 0.88, respectively. Binary classification between noninjured and injured morphological scores demonstrated 95.1% accuracy. TLICS morphology accuracy, the true positive rate, and positive injury mismatch classification rate were 86.3%, 76.2%, and 22.7%, respectively. Classification accuracy between no injury and suspected PLC injury was 86.8%, while true positive, false negative, and false positive rates were 90.0%, 10.0%, and 21.8%, respectively. CONCLUSIONS In this study, the authors demonstrate a novel deep learning method to automatically predict injury morphology and PLC disruption with high accuracy. This model may streamline and improve diagnostic decision support for patients with thoracolumbar spinal trauma.
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Affiliation(s)
- Sophia A Doerr
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Carly Weber-Levine
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Andrew M Hersh
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Tolulope Awosika
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Brendan Judy
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Yike Jin
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Divyaansh Raj
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Ann Liu
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Daniel Lubelski
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
| | - Craig K Jones
- 2Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore; and
| | - Haris I Sair
- 3Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Nicholas Theodore
- 1Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore
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11
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Estrada V, Oldenburg E, Popa O, Muller HW. Mapping the long rocky road to effective spinal cord injury therapy - A meta-review of pre-clinical and clinical research. J Neurotrauma 2022; 39:591-612. [PMID: 35196894 DOI: 10.1089/neu.2021.0298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Spinal cord injury (SCI) is a rare condition, which even after decades of research, to date still presents an incurable condition with a complex symptomatology. SCI can result in paralysis, pain, loss of sensation, bladder and sexual dysfunction, and muscle degeneration to name but a few. The large number of publications makes it difficult to keep track of current progress in the field and of the many treatment options, which have been suggested and are being proposed with increasing frequency. Scientific databases with user-oriented search options will offer possible solutions, but they are still mostly in the development phase. In this meta-analysis, we summarize and narrow down SCI therapeutic approaches applied in pre-clinical and clinical research. Statistical analyses of treatment clusters - assorted after counting annual publication numbers in PubMed and ClinicalTrials.gov databases - were performed to allow the comparison of research foci and of their translation efficacy into clinical therapy. Using the example of SCI research, our findings demonstrate the challenges that come with the accelerating research progress - an issue, which many research fields are faced with today. The analyses point out similarities and differences in the prioritization of SCI research in pre-clinical versus clinical therapy strategies. Moreover, the results demonstrate the rapidly growing importance of modern (bio-)engineering technologies.
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Affiliation(s)
- Veronica Estrada
- Heinrich Heine University Düsseldorf, 9170, Neurology, Molecular Neurobiology Laboratory, Düsseldorf, Germany;
| | - Ellen Oldenburg
- Heinrich Heine University Düsseldorf, 9170, Institute of Quantitative and Theoretical Biology, Düsseldorf, Germany;
| | - Ovidiu Popa
- Heinrich Heine University Düsseldorf, 9170, Institute of Quantitative and Theoretical Biology, Düsseldorf, Germany;
| | - Hans W Muller
- Heinrich Heine University Düsseldorf, 9170, Neurology, Düsseldorf, Germany;
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12
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Almeida CA, Torres-Espin A, Huie JR, Sun D, Noble-Haeusslein LJ, Young W, Beattie MS, Bresnahan JC, Nielson JL, Ferguson AR. Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery. Neuroinformatics 2022; 20:39-52. [PMID: 33651310 PMCID: PMC9015816 DOI: 10.1007/s12021-021-09512-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2021] [Indexed: 01/07/2023]
Abstract
Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming 'dark data' unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omission of data that do not. Publication bias contributes to scientific irreproducibility and failures in bench-to-bedside translation. Sharing dark data by making it Findable, Accessible, Interoperable, and Reusable (FAIR) may reduce the burden of irreproducible science by increasing transparency and support data-driven discoveries beyond the lifecycle of the original study. We illustrate feasibility of dark data sharing by recovering original raw data from the Multicenter Animal Spinal Cord Injury Study (MASCIS), an NIH-funded multi-site preclinical drug trial conducted in the 1990s that tested efficacy of several therapies after a spinal cord injury (SCI). The original drug treatments did not produce clear positive results and MASCIS data were stored in boxes for more than two decades. The goal of the present study was to independently confirm published machine learning findings that perioperative blood pressure is a major predictor of SCI neuromotor outcome (Nielson et al., 2015). We recovered, digitized, and curated the data from 1125 rats from MASCIS. Analyses indicated that high perioperative blood pressure at the time of SCI is associated with poorer health and worse neuromotor outcomes in more severe SCI, whereas low perioperative blood pressure is associated with poorer health and worse neuromotor outcome in moderate SCI. These findings confirm and expand prior results that a narrow window of blood-pressure control optimizes outcome, and demonstrate the value of recovering dark data for assessing reproducibility of findings with implications for precision therapeutic approaches.
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Affiliation(s)
- Carlos A Almeida
- Department of Neurological Surgery, Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California San Francisco, San Francisco, CA, USA
| | - Abel Torres-Espin
- Department of Neurological Surgery, Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California San Francisco, San Francisco, CA, USA
| | - J Russell Huie
- Department of Neurological Surgery, Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California San Francisco, San Francisco, CA, USA
| | - Dongming Sun
- W.M. Keck Center for Collaborative Neuroscience, Rutgers University, New Brunswick, NJ, USA
| | - Linda J Noble-Haeusslein
- Department of Neurology, University of Texas, Austin, TX, USA
- Department of Psychology, University of Texas, Austin, TX, USA
| | - Wise Young
- W.M. Keck Center for Collaborative Neuroscience, Rutgers University, New Brunswick, NJ, USA
| | - Michael S Beattie
- Department of Neurological Surgery, Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California San Francisco, San Francisco, CA, USA
| | - Jacqueline C Bresnahan
- Department of Neurological Surgery, Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California San Francisco, San Francisco, CA, USA
| | - Jessica L Nielson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
| | - Adam R Ferguson
- Department of Neurological Surgery, Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California San Francisco, San Francisco, CA, USA.
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA.
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13
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Agoston DV. COVID-19 and Traumatic Brain Injury (TBI); What We Can Learn From the Viral Pandemic to Better Understand the Biology of TBI, Improve Diagnostics and Develop Evidence-Based Treatments. Front Neurol 2021; 12:752937. [PMID: 34987462 PMCID: PMC8720751 DOI: 10.3389/fneur.2021.752937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 12/01/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Denes V. Agoston
- Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, United States
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14
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Huie JR, Chou A, Torres-Espin A, Nielson JL, Yuh EL, Gardner RC, Diaz-Arrastia R, Manley GT, Ferguson AR. FAIR Data Reuse in Traumatic Brain Injury: Exploring Inflammation and Age as Moderators of Recovery in the TRACK-TBI Pilot. Front Neurol 2021; 12:768735. [PMID: 34803899 PMCID: PMC8595404 DOI: 10.3389/fneur.2021.768735] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/01/2021] [Indexed: 12/12/2022] Open
Abstract
The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients.
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Affiliation(s)
- J. Russell Huie
- Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States
| | - Austin Chou
- Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
| | - Abel Torres-Espin
- Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
| | - Jessica L. Nielson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
| | - Esther L. Yuh
- Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
- Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
| | - Raquel C. Gardner
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Ramon Diaz-Arrastia
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Geoff T. Manley
- Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
| | - Adam R. Ferguson
- Brain and Spinal Injury Center, Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, United States
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States
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15
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Carmichael J, Hicks AJ, Spitz G, Gould KR, Ponsford J. Moderators of gene-outcome associations following traumatic brain injury. Neurosci Biobehav Rev 2021; 130:107-124. [PMID: 34411558 DOI: 10.1016/j.neubiorev.2021.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 07/04/2021] [Accepted: 08/13/2021] [Indexed: 12/14/2022]
Abstract
The field of genomics is the principal avenue in the ongoing development of precision/personalised medicine for a variety of health conditions. However, relating genes to outcomes is notoriously complex, especially when considering that other variables can change, or moderate, gene-outcome associations. Here, we comprehensively discuss moderation of gene-outcome associations in the context of traumatic brain injury (TBI), a common, chronically debilitating, and costly neurological condition that is under complex polygenic influence. We focus our narrative review on single nucleotide polymorphisms (SNPs) of three of the most studied genes (apolipoprotein E, brain-derived neurotrophic factor, and catechol-O-methyltransferase) and on three demographic variables believed to moderate associations between these SNPs and TBI outcomes (age, biological sex, and ethnicity). We speculate on the mechanisms which may underlie these moderating effects, drawing widely from biomolecular and behavioural research (n = 175 scientific reports) within the TBI population (n = 72) and other neurological, healthy, ageing, and psychiatric populations (n = 103). We conclude with methodological recommendations for improved exploration of moderators in future genetics research in TBI and other populations.
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Affiliation(s)
- Jai Carmichael
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia.
| | - Amelia J Hicks
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Gershon Spitz
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Kate Rachel Gould
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Jennie Ponsford
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
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16
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LaPlaca MC, Huie JR, Alam HB, Bachstetter AD, Bayir H, Bellgowan PF, Cummings D, Dixon CE, Ferguson AR, Ferland-Beckham C, Floyd CL, Friess SH, Galanopoulou AS, Hall ED, Harris NG, Hawkins BE, Hicks RR, Hulbert LE, Johnson VE, Kabitzke PA, Lafrenaye AD, Lemmon VP, Lifshitz CW, Lifshitz J, Loane DJ, Misquitta L, Nikolian VC, Noble-Haeusslein LJ, Smith DH, Taylor-Burds C, Umoh N, Vovk O, Williams AM, Young M, Zai LJ. Pre-Clinical Common Data Elements for Traumatic Brain Injury Research: Progress and Use Cases. J Neurotrauma 2021; 38:1399-1410. [PMID: 33297844 PMCID: PMC8082734 DOI: 10.1089/neu.2020.7328] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Traumatic brain injury (TBI) is an extremely complex condition due to heterogeneity in injury mechanism, underlying conditions, and secondary injury. Pre-clinical and clinical researchers face challenges with reproducibility that negatively impact translation and therapeutic development for improved TBI patient outcomes. To address this challenge, TBI Pre-clinical Working Groups expanded upon previous efforts and developed common data elements (CDEs) to describe the most frequently used experimental parameters. The working groups created 913 CDEs to describe study metadata, animal characteristics, animal history, injury models, and behavioral tests. Use cases applied a set of commonly used CDEs to address and evaluate the degree of missing data resulting from combining legacy data from different laboratories for two different outcome measures (Morris water maze [MWM]; RotorRod/Rotarod). Data were cleaned and harmonized to Form Structures containing the relevant CDEs and subjected to missing value analysis. For the MWM dataset (358 animals from five studies, 44 CDEs), 50% of the CDEs contained at least one missing value, while for the Rotarod dataset (97 animals from three studies, 48 CDEs), over 60% of CDEs contained at least one missing value. Overall, 35% of values were missing across the MWM dataset, and 33% of values were missing for the Rotarod dataset, demonstrating both the feasibility and the challenge of combining legacy datasets using CDEs. The CDEs and the associated forms created here are available to the broader pre-clinical research community to promote consistent and comprehensive data acquisition, as well as to facilitate data sharing and formation of data repositories. In addition to addressing the challenge of standardization in TBI pre-clinical studies, this effort is intended to bring attention to the discrepancies in assessment and outcome metrics among pre-clinical laboratories and ultimately accelerate translation to clinical research.
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Affiliation(s)
- Michelle C. LaPlaca
- Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, Georgia, USA
- San Francisco Veterans Affairs Health Care System, San Francisco, California, USA
| | - J. Russell Huie
- Brain and Spinal Injury Center, Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Hasan B. Alam
- Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Adam D. Bachstetter
- Department of Neuroscience, University of Kentucky, Lexington, Kentucky, USA
| | - Hűlya Bayir
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | | | - C. Edward Dixon
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Adam R. Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | | | - Candace L. Floyd
- Department of Physical Medicine and Rehabilitation, University of Utah, Salt Lake City, Utah, USA
| | - Stuart H. Friess
- Division of Critical Care Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Edward D. Hall
- Department of Neuroscience, University of Kentucky, Lexington, Kentucky, USA
| | - Neil G. Harris
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, California, USA
| | - Bridget E. Hawkins
- Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, USA
| | | | - Lindsey E. Hulbert
- Department of Animal Sciences and Industry, Kansas State University, Manhattan, Kansas, USA
| | - Victoria E. Johnson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Audrey D. Lafrenaye
- Department of Anatomy and Neurobiology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Vance P. Lemmon
- Department of Neurological Surgery, University of Miami, Miami, Florida, USA
| | - Carrie W. Lifshitz
- Department of Child Health, University of Arizona College of Medicine Phoenix, Phoenix, Arizona, USA
| | - Jonathan Lifshitz
- Department of Child Health, University of Arizona College of Medicine Phoenix, Phoenix, Arizona, USA
| | - David J. Loane
- School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
| | | | | | | | - Douglas H. Smith
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Nsini Umoh
- Department of Defense, U.S. Army Medical Research and Materiel Command, Fort Detrick, Frederick, Maryland, USA
| | - Olga Vovk
- National Institutes of Health, Bethesda, Maryland, USA
| | - Aaron M. Williams
- Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Margaret Young
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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17
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Huie JR, Ferguson AR, Kyritsis N, Pan JZ, Irvine KA, Nielson JL, Schupp PG, Oldham MC, Gensel JC, Lin A, Segal MR, Ratan RR, Bresnahan JC, Beattie MS. Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury. Sci Rep 2021; 11:3442. [PMID: 33564058 PMCID: PMC7873211 DOI: 10.1038/s41598-021-82951-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 01/21/2021] [Indexed: 12/12/2022] Open
Abstract
Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five different blinded, fully counter-balanced treatment trials for different acute anti-inflammatory treatments for cervical spinal cord injury in rats. Multi-dimensional discovery, using topological data analysis (TDA) and principal components analysis (PCA) revealed that only one showed consistent multidimensional syndromic benefit: intrathecal application of recombinant soluble TNFα receptor 1 (sTNFR1), which showed an inverse-U dose response efficacy. Using the optimal acute dose, we showed that clinically-relevant 90 min delayed treatment profoundly affected multiple biological indices of NI in the first 48 h after injury, including reduction in pro-inflammatory cytokines and gene expression of a coherent complex of acute inflammatory mediators and receptors. Further, a 90 min delayed bolus dose of sTNFR1 reduced the expression of NI markers in the chronic perilesional spinal cord, and consistently improved neurological function over 6 weeks post SCI. These results provide validation of a novel strategy for precision preclinical drug discovery that is likely to improve translation in the difficult landscape of CNS trauma, and confirm the importance of TNFα signaling as a therapeutic target.
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Affiliation(s)
- J R Huie
- Department of Neurological Surgery, Brain and Spinal Injury Center (BASIC), University of California, San Francisco, CA, USA
| | - A R Ferguson
- Department of Neurological Surgery, Brain and Spinal Injury Center (BASIC), University of California, San Francisco, CA, USA.
- San Francisco Veterans Affairs Medical Center, San Francisco, USA.
| | - N Kyritsis
- Department of Neurological Surgery, Brain and Spinal Injury Center (BASIC), University of California, San Francisco, CA, USA
| | - J Z Pan
- Department of Anesthesiology, University of California San Francisco, San Francisco, USA
| | - K-A Irvine
- Department of Anesthesiology, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Anesthesia, Perioperative Medicine and Pain, Stanford University, Stanford, CA, USA
| | - J L Nielson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, USA
- Institute for Health Informatics, University of Minnesota, Minneapolis, USA
| | - P G Schupp
- Brain Tumor Research Center, University of California, San Francisco, USA
| | - M C Oldham
- Brain Tumor Research Center, University of California, San Francisco, USA
| | - J C Gensel
- SCoBIRC, University of Kentucky, Lexington, USA
| | - A Lin
- Department of Neurological Surgery, Brain and Spinal Injury Center (BASIC), University of California, San Francisco, CA, USA
| | - M R Segal
- Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California San Francisco, San Francisco, USA
| | - R R Ratan
- Department of Neurology and Neuroscience, Burke-Cornell Medical Research Institute, Weill Medical College of Cornell University, New York, USA
| | - J C Bresnahan
- Department of Neurological Surgery, Brain and Spinal Injury Center (BASIC), University of California, San Francisco, CA, USA
| | - M S Beattie
- Department of Neurological Surgery, Brain and Spinal Injury Center (BASIC), University of California, San Francisco, CA, USA.
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18
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Torres-Espín A, Chou A, Huie JR, Kyritsis N, Upadhyayula PS, Ferguson AR. Reproducible analysis of disease space via principal components using the novel R package syndRomics. eLife 2021; 10:61812. [PMID: 33443012 PMCID: PMC7857733 DOI: 10.7554/elife.61812] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/13/2021] [Indexed: 01/12/2023] Open
Abstract
Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. ‘Syndromics’ refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.
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Affiliation(s)
- Abel Torres-Espín
- Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.,Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.,Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
| | - Austin Chou
- Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.,Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.,Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
| | - J Russell Huie
- Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.,Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.,Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
| | - Nikos Kyritsis
- Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.,Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.,Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
| | - Pavan S Upadhyayula
- School of Medicine, University of California San Diego (UCSD), San Diego, United States
| | - Adam R Ferguson
- Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States.,Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States.,Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States.,San Francisco VA Health Care System, San Francisco, United States
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19
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Singh T, Robles D, Vazquez M. Neuronal substrates alter the migratory responses of nonmyelinating Schwann cells to controlled brain‐derived neurotrophic factor gradients. J Tissue Eng Regen Med 2020; 14:609-621. [DOI: 10.1002/term.3025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/16/2020] [Accepted: 02/02/2020] [Indexed: 12/19/2022]
Affiliation(s)
- Tanya Singh
- Department of Biomedical EngineeringCity College of New York New York NY USA
| | - Denise Robles
- Department of Biomedical EngineeringRutgers University, The State University of New Jersey New Brunswick NJ USA
| | - Maribel Vazquez
- Department of Biomedical EngineeringRutgers University, The State University of New Jersey New Brunswick NJ USA
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Hawkins BE, Huie JR, Almeida C, Chen J, Ferguson AR. Data Dissemination: Shortening the Long Tail of Traumatic Brain Injury Dark Data. J Neurotrauma 2019; 37:2414-2423. [PMID: 30794049 DOI: 10.1089/neu.2018.6192] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multi-species data across diverse data types including imaging and molecular biomarkers, histopathology, behavior, and functional outcomes. In this review we argue that TBI translation should be acknowledged for what it is: a problem of big data that can be addressed using modern data science approaches. We review the history of the term big data, tracing its origins in Internet technology as data that are "big" according to the "4Vs" of volume, velocity, variety, veracity and discuss how the term has transitioned into the mainstream of biomedical research. We argue that the problem of TBI translation fundamentally centers around data variety and that solutions to this problem can be found in modern machine learning and other cutting-edge analytical approaches. Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature). We review a few early examples of published articles in both the pre-clinical and clinical TBI research literature to demonstrate how data reuse can drive new discoveries leading into translational therapies. Making TBI data resources more Findable, Accessible, Interoperable, and Reusable (FAIR) through better data stewardship has great potential to accelerate discovery and translation for the silent epidemic of TBI.
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Affiliation(s)
- Bridget E Hawkins
- The Moody Project for Translational Traumatic Brain Injury Research, Department of Anesthesiology, University of Texas Medical Branch, Galveston, Texas, USA
| | - J Russell Huie
- Weill Institutes for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Carlos Almeida
- Weill Institutes for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Jiapei Chen
- Weill Institutes for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA
| | - Adam R Ferguson
- Weill Institutes for Neurosciences, Brain and Spinal Injury Center, Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.,San Francisco Veterans Affairs Health Care System (SFVAHCS), San Francisco, California, USA
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