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Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

Gengyan Zhao, Fang Liu, Jonathan A. Oler, Mary E. Meyerand, Ned H. Kalin, Rasmus M. Birn
2018 NeuroImage  
To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected  ...  The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects...  ...  Imaging and Behavior, and the Wisconsin National Primate Research Center.  ... 
doi:10.1016/j.neuroimage.2018.03.065 pmid:29604454 pmcid:PMC6095475 fatcat:jmf4m5sadbaelk4dyvwrnclgmm

Front Matter: Volume 11317

Barjor S. Gimi, Andrzej Krol
2020 Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging  
The diverse sessions included Keynote and Invited Talk, Bone and Skeletal Imaging, Segmentation, Registration and Decision-making, Cardiac Imaging and Nanoparticle Imaging, Deep Convolutional Neural Networks  ...  Bayesian analysis and the PC algorithm [11317-32] DEEP CONVOLUTIONAL NEURAL NETWORKS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING II 0Y Efficacy of radiomics and genomics in predicting TP53 mutations  ...  MR images after stroke based on fully convolutional networks 11317 2H Left ventricular myocardium segmentation in coronary computed tomography angiography using 3D deep attention u-net Connectivity  ... 
doi:10.1117/12.2570187 fatcat:5hec55iwfneuhbhtjbqjcf2jqu

U-Net Model for Brain Extraction on Non-human Primates [article]

Xindi Wang, Xin-Hui Li, Jae Wook Cho, Brian Russ, Nanditha Rajamani, Alisa Omelchenko, Lei Ai, Annachiara Korchmaros, Pamela Garcia-Saldivar, Zheng Wang, Ned H. Kalin, Charles E. Schroeder (+6 others)
2020 bioRxiv   pre-print
To overcome this challenge, we propose to use transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e.  ...  Most brain extraction tools have been mainly orientated for human data and are often challenging for non-human primates (NHP).  ...  to NHK, P50MH084051 to NHK, National Nature Science Foundation of China (81571300, 81527901, 31771174 to ZW), and NIH BRAIN Initiative Grant RF1MH117040 to BER.  ... 
doi:10.1101/2020.11.17.385898 fatcat:zhsaleomt5dd3ayp7fz3ei4kee

U-Net Model for Brain Extraction: Trained on Humans for Transfer to Non-human Primates

Xindi Wang, Xin-Hui Li, Jae Wook Cho, Brian E. Russ, Nanditha Rajamani, Alisa Omelchenko, Lei Ai, Annachiara Korchmaros, Stephen Sawiak, R. Austin Benn, Pamela Garcia-Saldivar, Zheng Wang (+8 others)
2021 NeuroImage  
To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e.  ...  Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data.  ...  Healey, Phyllis Green, and Randolph Cowen to the Child Mind Institute, fundings from National Institutes of Health (NIH BRAIN Initiative Grant R01-MH111439 to CES and MPM, P50-MH109429 to CES, R24MH114806  ... 
doi:10.1016/j.neuroimage.2021.118001 pmid:33789137 fatcat:6qmoge2jwjhvzmeeypdaql2yci

MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

Adoui, Mahmoudi, Larhmam, Benjelloun
2019 Computers  
networks (CNN) based on SegNet and U-Net.  ...  In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural  ...  CNN Nonhuman primate brain extraction MRI-T1 98.00 DCS Table 2 . 2 The tried and used learning parameters with their optimal values for SegNet and U-Net.  ... 
doi:10.3390/computers8030052 fatcat:rpnk424tkbet5o3iitr5zk6xsq

What is consciousness, and could machines have it?

Stanislas Dehaene, Hakwan Lau, Sid Kouider
2017 Science  
The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the  ...  human brain.  ...  Many of these C0 computations have now been captured by AI, particularly by using feedforward convolutional neural networks (CNNs).  ... 
doi:10.1126/science.aan8871 pmid:29074769 fatcat:vpnnewgdqbdm3axnluoqllderq

What Is Consciousness, and Could Machines Have It? [chapter]

Stanislas Dehaene, Hakwan Lau, Sid Kouider
2021 Robotics, AI, and Humanity  
AbstractThe controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses  ...  it: the human brain.  ...  Many of these C0 computations have now been captured by AI, particularly using feedforward convolutional neural networks (CNNs).  ... 
doi:10.1007/978-3-030-54173-6_4 fatcat:fu7qvt433zd3nbnpcfnz5cclni

Deep Convolutional Neural Networks With Multiplane Consensus Labeling for Lung Function Quantification Using UTE Proton MRI

Wei Zha, Sean B. Fain, Mark L. Schiebler, Michael D. Evans, Scott K. Nagle, Fang Liu
2019 Journal of Magnetic Resonance Imaging  
To evaluate a deep learning (DL) approach for automated lung segmentation to extract image-based biomarkers from functional lung imaging using 3D radial UTE oxygen-enhanced (OE) MRI.  ...  Automated segmentation of the lungs using 2D convolutional encoder-decoder based DL method, and the subsequent functional quantification via adaptive K-means were compared with the results obtained from  ...  brain segmentation on nonhuman primates (26) .  ... 
doi:10.1002/jmri.26734 pmid:30945385 pmcid:PMC7039686 fatcat:ylxfymbgmjemxhjmodiapu27dq

Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach [article]

Md Navid Akbar, Mathew Yarossi, Marc Martinez-Gost, Marc A. Sommer, Moritz Dannhauer, Sumientra Rampersad, Dana Brooks, Eugene Tunik, Deniz Erdoğmuş
2020 arXiv   pre-print
A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for numerous basic science  ...  of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings  ...  segmentation of the subject specific MRI [4, 18] . (2) A nonlinear mapping of local E-fields to the expected neural ensemble firing rates, based on nonhuman primate experimental data.  ... 
arXiv:2002.06250v1 fatcat:4ksulhe7ybfpvnop6mdy3i5zve

The NonHuman Primate Neuroimaging Neuroanatomy Project [article]

Takuya Hayashi, Yujie Hou, Matthew F Glasser, Joonas A Autio, Kenneth Knoblauch, Miho Inoue-Murayama, Tim Coalson, Essa Yacoub, Stephen Smith, Henry Kennedy, David C Van Essen
2021 arXiv   pre-print
Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of  ...  We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth  ...  Introduction For over a century, neuroscientists investigated nonhuman primates (NHP) as a mesoscale model for understanding the anatomy, physiology and pathology of the human brain.  ... 
arXiv:2010.00308v3 fatcat:xqccnaz2hzcs7pqedcutkvktea

The NonHuman Primate Neuroimaging & Neuroanatomy Project

Takuya Hayashi, Yujie Hou, Matthew F Glasser, Joonas A Autio, Kenneth Knoblauch, Miho Inoue-Murayama, Tim Coalson, Essa Yacoub, Stephen Smith, Henry Kennedy, David C Van Essen
2021 NeuroImage  
Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of  ...  We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth  ...  Acknowledgements This study is supported by a grant Brain/MINDS -beyond from References under revision of this special issue of NeuroImage Autio, J.A., Zhu, Q., Li, X., Glasser, M.F., Schwiedrzik,  ... 
doi:10.1016/j.neuroimage.2021.117726 pmid:33484849 pmcid:PMC8079967 fatcat:tfv3sm6xpjfnfj2uarpqwnyc5u

Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data

M. Fiona Molloy, Giwon Bahg, Xiangrui Li, Mark Steyvers, Zhong-Lin Lu, Brandon M. Turner
2018 Computational Brain & Behavior  
One such restriction is convolution, a technique often used in neuroimaging analyses to relate experimental variables to models describing neural activation.  ...  We use the convolution technique as a basis for describing neural time series data and develop five models to describe how subject-, condition-, and brain-area-specific effects interact.  ...  While many of these models focus on purely behavioral data, some have incorporated neuroscience by using single-unit neurophysiology in experiments involving nonhuman primates to constrain behavioral models  ... 
doi:10.1007/s42113-018-0013-5 fatcat:h4o6m7agzne35g3dxhvw36rzai

Spontaneous Neural Dynamics and Multi-scale Network Organization

Brett L. Foster, Biyu J. He, Christopher J. Honey, Karim Jerbi, Alexander Maier, Yuri B. Saalmann
2016 Frontiers in Systems Neuroscience  
patterns and the structural network architecture of functional brain circuits.  ...  We highlight that, across many spatiotemporal scales, the covariance structure of spontaneous neural activity reflects structural properties of neural networks and dynamically tracks their functional repertoire  ...  Importantly, the recent interest in rsfMRI investigation of largescale brain networks is supported by a clear correspondence to invasively recorded neural activity in humans and nonhuman primates.  ... 
doi:10.3389/fnsys.2016.00007 pmid:26903823 pmcid:PMC4746329 fatcat:tlaydadeczem5nan5jgwp3a574

Convergence of cortical types and functional motifs in the mesiotemporal lobe [article]

Casey Paquola, Oualid Benkarim, Jordan DeKraker, Sara Lariviere, Stefan Frassle, Jessica Royer, Shahin Tavakol, Sofie Louise Valk, Andrea Bernasconi, Neda Bernasconi, Ali Khan, Alan C Evans (+3 others)
2020 bioRxiv   pre-print
Our findings establish a novel model of the MTL, in which its broad influence on neural function emerges through the combination micro- and macro-scale structural features.  ...  Our study leveraged an ultra-high-resolution histological reconstruction of a human brain to (i) develop a continuous surface model of the MTL iso-to-allocortex transition and (ii) quantitatively characterise  ...  Together, this work shows a gradual differentiation in cyto-and myelo-architecture from sensory to limbic areas defines a core organizational axis in humans, extending from prior work in nonhuman primates  ... 
doi:10.1101/2020.06.12.148643 fatcat:yldqtso4pvcy3o22vbackn57f4

Impaired social brain network for processing dynamic facial expressions in autism spectrum disorders

Wataru Sato, Motomi Toichi, Shota Uono, Takanori Kochiyama
2012 BMC Neuroscience  
However, the neural correlates of this dysfunction remain unidentified.  ...  Conclusions: These results suggest that weak activity and connectivity of the social brain network underlie the impairment in social interaction involving dynamic facial expressions in individuals with  ...  Yoshikawa for her helpful advice and ATR Brain Activity Imaging Center for their supports of acquiring fMRI data.  ... 
doi:10.1186/1471-2202-13-99 pmid:22889284 pmcid:PMC3459703 fatcat:trjcbalhfzhjnaqvi4m5xoqgbi
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