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Bayesian convolutional neural network based MRI brain extraction on nonhuman primates
2018
NeuroImage
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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
2020
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
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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]
2020
bioRxiv
pre-print
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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
2021
NeuroImage
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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
2019
Computers
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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?
2017
Science
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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]
2021
Robotics, AI, and Humanity
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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
2019
Journal of Magnetic Resonance Imaging
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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]
2020
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2021
NeuroImage
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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
2018
Computational Brain & Behavior
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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
2016
Frontiers in Systems Neuroscience
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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]
2020
bioRxiv
pre-print
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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
2012
BMC Neuroscience
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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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