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Devoto C, Lai C, Qu BX, Guedes VA, Leete J, Wilde E, Walker WC, Diaz-Arrastia R, Kenney K, Gill J. Exosomal MicroRNAs in Military Personnel with Mild Traumatic Brain Injury: Preliminary Results from the Chronic Effects of Neurotrauma Consortium Biomarker Discovery Project. J Neurotrauma 2020; 37:2482-2492. [PMID: 32458732 PMCID: PMC7698974 DOI: 10.1089/neu.2019.6933] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Chronic symptoms after mild traumatic brain injury (mTBI) are common among veterans and service members, and represent a significant source of morbidity, with those who sustain multiple mTBIs at greatest risk. Exosomal micro-RNAs (miRNAs), mediators of intercellular communication, may be involved in chronic TBI symptom persistence. Exosomal miRNA (exomiR) was extracted from 153 participants enrolled in the Chronic Effect of Neurotrauma Consortium (CENC) longitudinal study (no TBI, n = 35; ≥ 3 mTBIs (rTBI), n = 45; 1-2 mTBIs, n = 73). Analyses were performed with nCounter® Human miRNA Expression Panels and Ingenuity Pathway Analysis (IPA) for identification of gene networks associated with TBI. Generalized linear models were used to analyze the predictive value of exomiR dysregulation and remote neurobehavioral symptoms. Compared with controls, there were 17 dysregulated exomiRs in the entire mTBI group and 32 dysregulated exomiRs in the rTBI group. Two miRNAs, hsa-miR-139-5p and hsa-miR-18a-5p, were significantly differentially expressed in the rTBI and 1-2 mTBI groups. IPA analyses showed that these dysregulated exomiRs correlated with pathways of inflammatory regulation, neurological disease, and cell development. Within the rTBI group, exomiRs correlated with gene activity for hub-genes of tumor protein TP53, insulin-like growth factor 1 receptor, and transforming growth factor beta. TBI history and neurobehavioral symptom survey scores negatively and significantly correlated with hsa-miR-103a-3p expression. Participants with remote mTBI have distinct exomiR profiles, which are significantly linked to inflammatory and neuronal repair pathways. These profiles suggest that analysis of exosomal miRNA expression may provide novel insights into the underlying pathobiology of chronic TBI symptom persistence.
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Affiliation(s)
- Christina Devoto
- Tissue Injury Branch, National Institutes of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA
- Center for Neuroscience and Rehabilitation Medicine, Uniformed Services University of the Health Sciences and National Institutes of Health, Bethesda, Maryland, USA
| | - Chen Lai
- Tissue Injury Branch, National Institutes of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Bao-Xi Qu
- Department of Neurology, Uniformed Services University of the Health Sciences and National Institutes of Health, Bethesda, Maryland, USA
- CENC Biorepository, Uniformed Services University of the Health Sciences, Twinbrook, Rockville, Maryland, USA
| | - Vivian A. Guedes
- Tissue Injury Branch, National Institutes of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Jacqueline Leete
- Tissue Injury Branch, National Institutes of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Elisabeth Wilde
- CENC Imaging Core, University of Utah, Salt Lake City, Utah, USA
| | - William C. Walker
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Kimbra Kenney
- Department of Neurology, Uniformed Services University of the Health Sciences and National Institutes of Health, Bethesda, Maryland, USA
- CENC Biorepository, Uniformed Services University of the Health Sciences, Twinbrook, Rockville, Maryland, USA
| | - Jessica Gill
- Tissue Injury Branch, National Institutes of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA
- Center for Neuroscience and Rehabilitation Medicine, Uniformed Services University of the Health Sciences and National Institutes of Health, Bethesda, Maryland, USA
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Bucholc M, Ding X, Wang H, Glass DH, Wang H, Prasad G, Maguire LP, Bjourson AJ, McClean PL, Todd S, Finn DP, Wong-Lin K. A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual. EXPERT SYSTEMS WITH APPLICATIONS 2019; 130:157-171. [PMID: 31402810 PMCID: PMC6688646 DOI: 10.1016/j.eswa.2019.04.022] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.
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Affiliation(s)
- Magda Bucholc
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Xuemei Ding
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
- Fujian Provincial Engineering Technology Research Centre for Public Service Big Data Mining and Application, College of Mathematics and Informatics, Fujian Normal University, Fuzhou, Fujian, 350108, China
| | - Haiying Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - David H. Glass
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Hui Wang
- School of Computing and Mathematics, Ulster University, Jordanstown campus, Northern Ireland, United Kingdom
| | - Girijesh Prasad
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Liam P. Maguire
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
| | - Anthony J. Bjourson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Paula L. McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, United Kingdom
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Northern Ireland, United Kingdom
| | - David P. Finn
- Pharmacology and Therapeutics, School of Medicine, and NCBES Galway Neuroscience Centre, National University of Ireland, Galway, Republic of Ireland
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Magee campus, Northern Ireland, United Kingdom
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4
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Mirzaei G, Adeli A, Adeli H. Imaging and machine learning techniques for diagnosis of Alzheimer's disease. Rev Neurosci 2018; 27:857-870. [PMID: 27518905 DOI: 10.1515/revneuro-2016-0029] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 06/19/2016] [Indexed: 11/15/2022]
Abstract
Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.
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Manzine PR, Pelucchi S, Horst MA, Vale FA, Pavarini SC, Audano M, Mitro N, Di Luca M, Marcello E, Cominetti MR. microRNA 221 Targets ADAM10 mRNA and is Downregulated in Alzheimer’s Disease. J Alzheimers Dis 2017; 61:113-123. [DOI: 10.3233/jad-170592] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Patricia R. Manzine
- Department of Gerontology, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Silvia Pelucchi
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
- Department of Neurosciences, Psychology, Drug Research and Child Health, Section of Pharmacology and Toxicology, Università di Firenze, Florence, Italy
| | - Maria A. Horst
- Faculty of Nutrition, Federal University of Goiás, Goiânia, MG, Brazil
| | - Francisco A.C. Vale
- Department of Medicine, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Sofia C.I. Pavarini
- Department of Gerontology, Federal University of São Carlos, São Carlos, SP, Brazil
| | - Matteo Audano
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Nico Mitro
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Monica Di Luca
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Elena Marcello
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Márcia R. Cominetti
- Department of Gerontology, Federal University of São Carlos, São Carlos, SP, Brazil
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