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Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury

Stavros I. Dimitriadis, George Zouridakis, Roozbeh Rezaie, Abbas Babajani-Feremi, Andrew C. Papanicolaou
2015 NeuroImage: Clinical  
Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions.  ...  These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness  ...  Acknowledgment This project is part of a larger study, the Integrated Clinical Protocol, conducted by the Investigators and staff of The Mission Connect Mild Traumatic Brain Injury Translational Research  ... 
doi:10.1016/j.nicl.2015.09.011 pmid:26640764 pmcid:PMC4632071 fatcat:dyegulnigzddzdy5ys247ksjta

Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review

Nor Safira Elaina Mohd Noor, Haidi Ibrahim
2020 IEEE Access  
Recent developments in the field of machine learning (ML) have led to a renewed interest in the use of electroencephalography (EEG) to predict the outcome after traumatic brain injury (TBI).  ...  A systematic search in the PubMed and Google Scholar databases was performed to identify all predictive models for the extended Glasgow outcome scale (GOSE) and Glasgow outcome scale (GOS) based on EEG  ...  '', ''traumatic brain injury'' and ''electroencephalography''.  ... 
doi:10.1109/access.2020.2998934 fatcat:ltce5rfdszgnvcydw2tilwyrcm

Identification of relevant diffusion MRI metrics impacting cognitive functions using a novel feature selection method [article]

Tongda Xu, Xiyan Cai, Yao Wang, Xiuyuan Wang, Sohae Chung, Els Fieremans, Joseph Rath, Steven Flanagan, Yvonne W Lui
2019 arXiv   pre-print
Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints.  ...  In this study, we use diffusion MRI to formulate a predictive model for performance on working memory based on the most relevant MRI features.  ...  NS090349, R01 NS039135-11, R01 NS088040 and NIBIB Biomedical Technology Resource Center Grant NIH P41 EB01718.  ... 
arXiv:1908.04752v2 fatcat:mtvhyg43pba5pn4vsrfy774cju

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury

Marios Antonakakis, Stavros I. Dimitriadis, Michalis Zervakis, Andrew C. Papanicolaou, George Zouridakis
2020 Frontiers in Computational Neuroscience  
In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings.  ...  Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity.  ...  INTRODUCTION Mild traumatic brain injury (mTBI) accounts for ∼90% of all brain injuries (Len and Neary, 2011) , establishing it as a major cause of brain insult (Huang et al., 2014) .  ... 
doi:10.3389/fncom.2019.00090 pmid:32009921 pmcid:PMC6974679 fatcat:utpsppqfrjgitn72ir765z4a4e

Differential Effects of FK506 on Structural and Functional Axonal Deficits After Diffuse Brain Injury in the Immature Rat

Ann Mae DiLeonardi, Jimmy W. Huh, Ramesh Raghupathi
2012 Journal of Neuropathology and Experimental Neurology  
Diffuse axonal injury is a major component of traumatic brain injury in children and correlates with long-term cognitive impairment.  ...  Traumatic brain injury in adult rodents has been linked to a decrease in compound action potential (CAP) in the corpus callosum, but information on trauma-associated diffuse axonal injury in immature rodents  ...  David Kowalski for writing the Matlab codes that were used to analyze the electrophysiological data and Drs. Ime Udoekwere and Corey Hart for their help with the Matlab image processing code.  ... 
doi:10.1097/nen.0b013e31826f5876 pmid:23095847 pmcid:PMC3495060 fatcat:ewewct7y3bgbncwxznxr6jewjm

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya V., Anjan Gudigar, U. Raghavendra, Ajay Hegde, Girish R. Menon, Filippo Molinari, Edward J. Ciaccio, U. Rajendra Acharya
2021 International Journal of Environmental Research and Public Health  
Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces.  ...  The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and  ...  Introduction Traumatic brain injury (TBI) arises when sudden and direct/indirect external forces, such as a bump, blow to the head, or other kind of injury, result in neuropathological damage and brain  ... 
doi:10.3390/ijerph18126499 pmid:34208596 fatcat:6xikhm22rvh7vb7sberifgy6ym

Consciousness and the vegetative state: Today Articles from the workshop held in July 6, 2010 in Salerno, Italy

G Dolce, L Sannita
2012 Journal of Rehabilitation Medicine  
ACKNOWLEDGMENTS This research has been funded by a contractual agreement between the Center for Brain Injury Rehabilitation (C.RE.CER) and the Human Neuropsychology Laboratory at the University of Seville  ...  ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS The study has been carried on at the S. Anna -RAN Institute with support from the institute; authors are all employees of the institute.  ...  HEART RATE VARIABILITY AND PREDICTION OF OUTCOME HRV has been proposed as a useful predictor of outcome in brain-injured patients (27, 58, 59) .  ... 
doi:10.2340/16501977-0992 fatcat:vraw64cmbjeg7an3ps3xaaq4le

Patient Similarity Analysis with Longitudinal Health Data [article]

Ahmed Allam, Matthias Dittberner, Anna Sintsova, Dominique Brodbeck, Michael Krauthammer
2020 arXiv   pre-print
The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection.  ...  In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision  ...  Using data from patients who sustained mild traumatic brain injury (mTBI) and developed post-traumatic stress disorder (PTSD), the authors were able to identify the most common path patients took from  ... 
arXiv:2005.06630v1 fatcat:7u5pj6uqyvgelpj4iwkqwf3e5y

Benchmarking functional connectome-based predictive models for resting-state fMRI

Kamalaker Dadi, Mehdi Rahim, Alexandre Abraham, Darya Chyzhyk, Michael Milham, Bertrand Thirion, Gaël Varoquaux
2019 NeuroImage  
The typical prediction procedure from rest-fMRI consists of three main steps: defining brain regions, representing the interactions, and supervised learning.  ...  We systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro-degenerative (Alzheimer's, Post-traumatic stress disorder), neuro-psychiatric  ...  For non-linear methods, we consider Nearest Neighbors (K-NN) (Cover and Hart, 1967) with K=1 and Euclidean distance metric, Gaussian Naïve Bayes (GNB) and Random Forests Classifier (RF) (Breiman, 2001  ... 
doi:10.1016/j.neuroimage.2019.02.062 pmid:30836146 fatcat:gyc6jxopp5gihn3alwgrf7zcge

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
2018 IEEE Transactions on Neural Networks and Learning Systems  
, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques.  ...  This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data.  ...  Pawel Raif and Dr. Kamal Abu-Hassan for useful discussions during the early stage of the work. This work was supported by the ACSLab (www.acslab.info).  ... 
doi:10.1109/tnnls.2018.2790388 pmid:29771663 fatcat:6r63zihrfvea7cto4ei3mlvqtu

Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks

David Gabrieli, Samantha N Schumm, Nicholas F Vigilante, Brandon Parvesse, David F Meaney
2020 PLoS ONE  
Traumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes.  ...  Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear.  ...  ., in developing parts of this manuscript focusing on excitatory/inhibitory balance and activity.  ... 
doi:10.1371/journal.pone.0234749 pmid:32966291 pmcid:PMC7510994 fatcat:qwndyexzxjhbzglxx7hxjs2b5a

Predictive modelling of football injuries [article]

Stylianos Kampakis
2016 arXiv   pre-print
Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied  ...  Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue.  ...  K-NN fails completely in this task, since both the kappa and the precision are zero.  ... 
arXiv:1609.07480v1 fatcat:rlw2bbzdhrfxvlkh6luxdbrkha

Denouements of machine learning and multimodal diagnostic classification of Alzheimer's disease

Binny Naik, Ashir Mehta, Manan Shah
2020 Visual Computing for Industry, Biomedicine, and Art  
However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention.  ...  /MCI and healthy controls.  ...  patterns SVM, NN MRI OASIS SVM (84.21%) NN (65.78%) [75] K-OPLS, OPLS Multivariate data analysis MRI ADNI K-OPLS (88.70%) OPLS (88.40%) [76] Hippocampal shape feature SVM MRI ADNI CASE  ... 
doi:10.1186/s42492-020-00062-w pmid:33151420 fatcat:cwmdiv6kmrfypnrqy2p3lkpe7m

Development three years after perinatal anoxia and other potentially damaging newborn experiences

Frances K. Graham, Claire B. Ernhart, Don Thurston, Marguerite Craft
1962 The Psychological Monographs  
and of brain injury.  ...  and Brain Injury, Parent Binet and Brain Injury, Examiner Binet and Copy-Forms Binet and Composite Personality Binet and Brain Injury, Parent Binet and Brain Injury, Examiner Binet and Maladjustment,  ... 
doi:10.1037/h0093831 fatcat:zea3nctvrbdz3hukmms4bk77bq

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Siuly Siuly, Yanchun Zhang
2016 Data Science and Engineering  
Acknowledgments This work is supported by the National Natural Science Foundation of China (NSFC 61332013) and the Australian Research Council (ARC) Linkage Project (LP100200682) and Discovery Project  ...  reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ...  [71] introduced a method based on Fourier methods to extract EEG features and k nearest neighbours (kNN) in classifying between normal and autistic children. Bosl et al.  ... 
doi:10.1007/s41019-016-0011-3 fatcat:gdebiikzjvghjaegsggrpmz24q
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