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Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Lauren E. Salminen, Rajendra A. Morey, Brandalyn C. Riedel, Neda Jahanshad, Emily L. Dennis, Paul M. Thompson
2019 Journal of Neuroimaging  
Here, we used a novel machine learning method - evolving partitions to improve classification (EPIC) - to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed  ...  Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities.  ...  Our group has previously shown that EPIC improves the accuracy of disease classification in Alzheimer's disease 11 and traumatic brain injury. 12 Here, we used EPIC to determine if unique combinations  ... 
doi:10.1111/jon.12600 pmid:30714246 pmcid:PMC6571150 fatcat:cp7jew3d6rh7ta3xc3tjpkcdxm

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder [article]

Lauren E Salminen, Rajendra A Morey, Brandalyn C Riedel, Neda Jahanshad, Emily L Dennis, Paul M Thompson
2018 bioRxiv   pre-print
Methods: We used EPIC with repeated cross-validation to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n=40) versus  ...  Here we used a novel machine learning method - evolving partitions to improve classification (EPIC) - to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed  ...  In functional connectivity analyses, however, the seed regions that act as nodes of the network could be adaptively refined to improve classification of the target groups, and some regions could be merged  ... 
doi:10.1101/482448 fatcat:jv7nc355cbgpxdi747hdns567i

Cerebral Palsy Research Network Clinical Registry: Methodology and Baseline Report

Paul Gross, Mary Gannotti, Amy Bailes, Susan D. Horn, Jacob Kean, Unni G. Narayanan, Jerry Oakes, Garey Noritz
2020 Archives of Rehabilitation Research and Clinical Translation  
Collecting data during routine clinical care using the electronic medical record was determined to be a sustainable plan for data acquisition and management.  ...  The Cerebral Palsy Research Network registry elements are implemented in a versatile electronic platform and minimize burden to clinicians.  ...  Using Epic decreased time burden for documentation by clinicians and eliminated duplicate data entry into the registry.  ... 
doi:10.1016/j.arrct.2020.100054 pmid:33543081 pmcid:PMC7853390 fatcat:nr3xtmyhpveqfnfv5k2t33je2i

Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort

R.A. Welikala, P.J. Foster, P.H. Whincup, A.R. Rudnicka, C.G. Owen, D.P. Strachan, S.A. Barman
2017 Computers in Biology and Medicine  
Associations between retinal vessel morphology and disease precursors/outcomes may be similar or opposing for arterioles and venules.  ...  This M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2 comprises of a convolutional neural network whose architecture contains six learned layers: three convolutional and three fully-connected.  ...  Acknowledgements: This research has been conducted using the UK Biobank resource under application number  ... 
doi:10.1016/j.compbiomed.2017.09.005 pmid:28917120 fatcat:e2jv5n25t5faxjykswlfk7o2ya

A 5G Cognitive System for Healthcare

Min Chen, Jun Yang, Yixue Hao, Shiwen Mao, Kai Hwang
2017 Big Data and Cognitive Computing  
Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications.  ...  diseases.  ...  In the scenario, remote survey is used for curing a patient's physiological disease.  ... 
doi:10.3390/bdcc1010002 fatcat:jxzuo5ohtbauxgqvs3ovzsa4ym

Governance and Sporting Success of Top 20 Football Clubs After Economic Crisis [chapter]

Domenico Marino
2010 Optimal Strategies in Sports Economics and Management  
The issue of sporting success is not simply an aspect that is linked to the epic and immeasurable size of a sporting talent, which nevertheless still remains an inescapable element to success, but it must  ...  The classification properties of a neural network constitute one of the richest fields for possible applications.  ...  This analysis will also allow us, using the neural network, to identify those factors that are crucial for sporting success.  ... 
doi:10.1007/978-3-642-13205-6_5 fatcat:xewsnpfrv5hmtczbqgy5syselu

Basic Electroencephalogram and It Is Common Clinical Applications in Children [chapter]

Raafat Hammad Seroor Jadah
2020 Electroencephalography [Working Title]  
EEG can also be used to diagnose and evaluate other conditions such as sleep disorders, neurometabolic diseases with encephalopathy and neuropsychiatric disorders.  ...  EEG is an essential investigational tool to analyze and record electrical impulses of the brain and considered to be the gold standard electrophysiological test which can be used to help diagnose epilepsy  ...  The connectivity of this complex brain network can be classified into three types: structural connectivity, functional connectivity and effective connectivity [13] .  ... 
doi:10.5772/intechopen.94247 fatcat:xa7adz5hurh43nkmj2y2wwmyzy

Cortical excitation:inhibition imbalance causes network specific functional hypoconnectivity: a DREADD-fMRI study [article]

Marija Markicevic, Ben D Fulcher, Christopher Lewis, Fritjof Helmchen, Markus Rudin, Valerio Zerbi, Nicole Wenderoth
2018 biorxiv/medrxiv   pre-print
Resting-state fMRI (rsfMRI) is a widespread method utilized for estimating brain-wide functional connectivity (FC) in health and disease.  ...  using hM3Dq-DREADDs, or (ii) locally suppress activity of inhibitory Parvalbumin interneurons with hM4Di-DREADDs in the somatosensory network.  ...  Introduction Complex behavior results from the interplay of distributed yet anatomically connected neuronal populations which form brain-wide networks.  ... 
doi:10.1101/492108 fatcat:c5hndpupjvdkhivhcyl32qwxte

Proceedings of the Sixth Deep Brain Stimulation Think Tank Modulation of Brain Networks and Application of Advanced Neuroimaging, Neurophysiology, and Optogenetics

Adolfo Ramirez-Zamora, James Giordano, Edward S. Boyden, Viviana Gradinaru, Aysegul Gunduz, Philip A. Starr, Sameer A. Sheth, Cameron C. McIntyre, Michael D. Fox, Jerrold Vitek, Vinata Vedam-Mai, Umer Akbar (+22 others)
2019 Frontiers in Neuroscience  
The proceedings also offer insights into the new era of brain network neuromodulation and connectomic DBS to define and target dysfunctional brain networks.  ...  Each section of this overview provides insight about the understanding of neuromodulation for specific disease and discusses current challenges and future directions.  ...  ACKNOWLEDGMENTS MO acknowledges the support of Tyler's Hope for a Dystonia Cure and the Parkinson's Foundation Center of Excellence.  ... 
doi:10.3389/fnins.2019.00936 pmid:31572109 pmcid:PMC6751331 fatcat:hqoewqybk5gopp4a4vyovhpbkq

Recent Advancements in Brain Tumor Segmentation and Classification using Deep Learning: A Review

Muhmmad Irfan Sharif, Srinivas Institute of Technology
2019 International Journal of Engineering Research and  
The two most important tasks covered in literature for brain tumor analysis are tumor segmentation and tumor classification.  ...  The review aims to provide an introduction to recent works that use deep learning methodologies for brain tumor analysis.  ...  introduce fusion between handcrafted and CNN features to perform tumor classification [110] . P. Afshar et al. use capsule network in their work for brain tumor prediction [111] .  ... 
doi:10.17577/ijertv8is120190 fatcat:fma2gzxzmrb5daxiz2zumujgqq

A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants

Lili He, Hailong Li, Jinghua Wang, Ming Chen, Elveda Gozdas, Jonathan R. Dillman, Nehal A. Parikh
2020 Scientific Reports  
In this work, we developed a multi-task, multi-stage deep transfer learning framework using the fusion of brain connectome and clinical data for early joint prediction of multiple abnormal neurodevelopmental  ...  We first pre-trained a deep neural network prototype in a supervised fashion using 884 older children and adult subjects, and then re-trained this prototype using 291 neonatal subjects without supervision  ...  We sincerely thank our collaborators from the Marburger, NNP for serving as our Nationwide Children's study coordinators and Mark Smith, MS, for serving as the study MR technologist.  ... 
doi:10.1038/s41598-020-71914-x pmid:32934282 fatcat:mqllg23b5nbjbdneyl43gazi6e

Inhibiting mGluR5 activity by AFQ056/Mavoglurant rescues circuit-specific functional connectivity in Fmr1 knockout mice

Valerio Zerbi, Marija Markicevic, Fabrizio Gasparini, Aileen Schroeter, Markus Rudin, Nicole Wenderoth
2019 NeuroImage  
Previous work has demonstrated that neuroimaging biomarkers which capture functional connectivity of the brain can be used to define a specific and robust endophenotype in Fmr1-/y mice, a well-established  ...  In line with previous findings, we observed that Fmr1-/y mice exhibited impaired social interaction, reduced connectivity in three main functional networks and altered network topology.  ...  After standard adjustments, shim gradients were optimized using mapshim protocol, with an ellipsoid reference volume covering the whole brain.  ... 
doi:10.1016/j.neuroimage.2019.02.051 pmid:30807820 fatcat:26ranmg3kzezhml2sdafpyf5sa

Brain-Controlled Adaptive Lower Limb Exoskeleton for Rehabilitation of Post-Stroke Paralyzed

Vinoj P. G., Sunil Jacob, Varun G. Menon, Sreeja Rajesh, Mohammad Reza Khosravi
2019 IEEE Access  
The microcontroller controls the high torque motors connected to the exoskeleton's joints based on user intentions.  ...  The exoskeleton is modeled according to the human body anatomy, which makes it a perfect fit for the affected body part.  ...  Asha Jacob, SCMS Center for Robotics for providing assistance and guidance in testing and conducting the experiments. They would like to thank the EiC for the APC Waiver.  ... 
doi:10.1109/access.2019.2921375 fatcat:okf6xp66yjblhbim5p5wrzz754

A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications

Hemantha Krishna Bharadwaj, Aayush Agarwal, Vinay Chamola, Naga Rajiv Lakkaniga, Vikas Hassija, Mohsen Guizani, Biplab Sikdar
2021 IEEE Access  
Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements.  ...  In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks.  ...  Finally, for the classification stage, the weight values to be used in the model were optimized using the adaptive elephant herd optimization algorithm.  ... 
doi:10.1109/access.2021.3059858 fatcat:gwdku6me2fhzbfwf7agtt5vmse

Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy

Lili Zhang, Yongzheng Wu, Bin Zheng, Lizhong Su, Yuan Chen, Shuang Ma, Qinqin Hu, Xiang Zou, Lie Yao, Yinlong Yang, Liang Chen, Ying Mao (+2 others)
2019 Theranostics  
providing optimal surgical outcomes.  ...  to train and cross-validate our 34-layered residual convolutional neural network, which was used to classify 33 untrained fresh larynx surgical samples into normal and neoplasia.  ...  It is expected that larger image datasets provides opportunities for further development and optimization of DL based neural networks to accomplish more refined tasks, such as the classification of tumors  ... 
doi:10.7150/thno.32655 pmid:31131052 pmcid:PMC6526002 fatcat:zibdhjtqkna6zljvlgmlapvcgq
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