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https://www.readbyqxmd.com/read/28213446/acetylcholine-release-in-prefrontal-cortex-promotes-gamma-oscillations-and-theta-gamma-coupling-during-cue-detection
#1
W M Howe, H J Gritton, N Lusk, Erik A Roberts, Vaughn L Hetrick, Joshua D Berke, Martin Sarter
The capacity for using external cues to guide behavior ("cue detection") constitutes an essential aspect of attention and goal-directed behavior. The cortical cholinergic input system, via phasic increases in prefrontal acetylcholine release, plays an essential role in attention by mediating such cue detection. However, the relationship between cholinergic signaling during cue detection and neural activity dynamics in prefrontal networks remains unclear. Here we combined sub-second measures of cholinergic signaling, neurophysiological recordings, and cholinergic receptor blockade to delineate the cholinergic contributions to prefrontal oscillations during cue detection in rats...
February 17, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28213114/hierarchical-control-of-procedural-and-declarative-category-learning-systems
#2
Benjamin O Turner, Matthew J Crossley, F Gregory Ashby
Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems...
February 14, 2017: NeuroImage
https://www.readbyqxmd.com/read/28213065/requiring-collaboration-hippocampal-prefrontal-networks-needed-in-spatial-working-memory-and-ageing-a-multivariate-analysis-approach
#3
C Zancada-Menendez, P Alvarez-Suarez, P Sampedro-Piquero, M Cuesta, A Begega
Ageing is characterized by a decline in the processes of retention and storage of spatial information. We have examined the behavioural performance of adult rats (3 months old) and aged rats (18 months old) in a spatial complex task (delayed match to sample). The spatial task was performed in the Morris water maze and consisted of three sessions per day over a period of three consecutive days. Each session consisted of two trials (one sample and retention) and inter-session intervals of five minutes. Behavioural results showed that the spatial task was difficult for middle aged group...
February 14, 2017: Neurobiology of Learning and Memory
https://www.readbyqxmd.com/read/28212969/lessons-learned-when-introducing-pharmacogenomic-panel-testing-into-clinical-practice
#4
Marc B Rosenman, Brian Decker, Kenneth D Levy, Ann M Holmes, Victoria M Pratt, Michael T Eadon
OBJECTIVES: Implementing new programs to support precision medicine in clinical settings is a complex endeavor. We describe challenges and potential solutions based on the Indiana GENomics Implementation: an Opportunity for the Underserved (INGenious) program at Eskenazi Health-one of six sites supported by the Implementing GeNomics In pracTicE network grant of the National Institutes of Health/National Human Genome Research Institute. INGenious is an implementation of a panel of genomic tests...
January 2017: Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research
https://www.readbyqxmd.com/read/28212422/probability-matching-in-perceptrons-effects-of-conditional-dependence-and-linear-nonseparability
#5
Michael R W Dawson, Maya Gupta
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation...
2017: PloS One
https://www.readbyqxmd.com/read/28212138/deep-learning-in-mammography-diagnostic-accuracy-of-a-multipurpose-image-analysis-software-in-the-detection-of-breast-cancer
#6
Anton S Becker, Magda Marcon, Soleen Ghafoor, Moritz C Wurnig, Thomas Frauenfelder, Andreas Boss
OBJECTIVES: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set. MATERIALS AND METHODS: In this retrospective, Health Insurance Portability and Accountability Act-compliant study, all patients undergoing mammography in 2012 at our institution were reviewed (n = 3228). All of their prior and follow-up mammographies from a time span of 7 years (2008-2015) were considered as a reference for clinical diagnosis...
February 16, 2017: Investigative Radiology
https://www.readbyqxmd.com/read/28212116/developing-a-community-of-practice-for-hiv-care-supporting-knowledge-translation-in-a-regional-training-initiative
#7
Donna M Gallagher, Lisa R Hirschhorn, Laura S Lorenz, Priyatam Piya
INTRODUCTION: Ensuring knowledgeable, skilled HIV providers is challenged by rapid advances in the field, diversity of patients and providers, and the need to retain experienced providers while training new providers. These challenges highlight the need for education strategies, including training and clinical consultation to support translation of new knowledge to practice. New England AIDS Education and Training Center (NEAETC) provides a range of educational modalities including academic peer detailing and distance support to HIV providers in six states...
February 15, 2017: Journal of Continuing Education in the Health Professions
https://www.readbyqxmd.com/read/28210983/multi-view-ensemble-classification-of-brain-connectivity-images-for-neurodegeneration-type-discrimination
#8
Michele Fratello, Giuseppina Caiazzo, Francesca Trojsi, Antonio Russo, Gioacchino Tedeschi, Roberto Tagliaferri, Fabrizio Esposito
Brain connectivity analyses using voxels as features are not robust enough for single-patient classification because of the inter-subject anatomical and functional variability. To construct more robust features, voxels can be aggregated into clusters that are maximally coherent across subjects. Moreover, combining multi-modal neuroimaging and multi-view data integration techniques allows generating multiple independent connectivity features for the same patient. Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration by an ensemble learning method (random forest)...
February 16, 2017: Neuroinformatics
https://www.readbyqxmd.com/read/28208587/vehicle-detection-in-aerial-images-based-on-region-convolutional-neural-networks-and-hard-negative-example-mining
#9
Tianyu Tang, Shilin Zhou, Zhipeng Deng, Huanxin Zou, Lin Lei
Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well...
February 10, 2017: Sensors
https://www.readbyqxmd.com/read/28207733/beyond-negative-valence-2-week-administration-of-a-serotonergic-antidepressant-enhances-both-reward-and-effort-learning-signals
#10
Jacqueline Scholl, Nils Kolling, Natalie Nelissen, Michael Browning, Matthew F S Rushworth, Catherine J Harmer
To make good decisions, humans need to learn about and integrate different sources of appetitive and aversive information. While serotonin has been linked to value-based decision-making, its role in learning is less clear, with acute manipulations often producing inconsistent results. Here, we show that when the effects of a selective serotonin reuptake inhibitor (SSRI, citalopram) are studied over longer timescales, learning is robustly improved. We measured brain activity with functional magnetic resonance imaging (fMRI) in volunteers as they performed a concurrent appetitive (money) and aversive (effort) learning task...
February 2017: PLoS Biology
https://www.readbyqxmd.com/read/28207407/deep-pain-exploiting-long-short-term-memory-networks-for-facial-expression-classification
#11
Pau Rodriguez, Guillem Cucurull, Jordi Gonalez, Josep M Gonfaus, Kamal Nasrollahi, Thomas B Moeslund, F Xavier Roca
Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data...
February 9, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28207396/hd-mtl-hierarchical-deep-multi-task-learning-for-large-scale-visual-recognition
#12
Jianping Fan, Tianyi Zhao, Zhenzhong Kuang, Yu Zheng, Ji Zhang, Jun Yu, Jinye Peng
In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically...
February 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28207384/supervised-learning-of-semantics-preserving-hash-via-deep-convolutional-neural-networks
#13
Huei-Fang Yang, Kevin Lin, Chu-Song Chen
This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties...
February 9, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28207383/person-re-identification-by-camera-correlation-aware-feature-augmentation
#14
Ying-Cong Chen, Xiatian Zhu, Wei-Shi Zheng, Jian-Huang Lai
The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i...
February 9, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28205307/segmentation-of-organs-at-risks-in-head-and-neck-ct-images-using-convolutional-neural-networks
#15
Bulat Ibragimov, Lei Xing
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs in HaN CT images, and compared its performance against state-of-the-art automated segmentation algorithms, commercial software, and interobserver variability. METHODS: Convolutional neural networks (CNNs)-a concept from the field of deep learning-were used to study consistent intensity patterns of OARs from training CT images and to segment the OAR in a previously unseen test CT image...
February 2017: Medical Physics
https://www.readbyqxmd.com/read/28205298/automatic-segmentation-of-the-right-ventricle-from-cardiac-mri-using-a-learning-based-approach
#16
Michael R Avendi, Arash Kheradvar, Hamid Jafarkhani
PURPOSE: This study aims to accurately segment the right ventricle (RV) from cardiac MRI using a fully automatic learning-based method. METHODS: The proposed method uses deep learning algorithms, i.e., convolutional neural networks and stacked autoencoders, for automatic detection and initial segmentation of the RV chamber. The initial segmentation is then combined with the deformable models to improve the accuracy and robustness of the process. We trained our algorithm using 16 cardiac MRI datasets of the MICCAI 2012 RV Segmentation Challenge database and validated our technique using the rest of the dataset (32 subjects)...
February 16, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28203531/an-fmri-study-of-implicit-language-learning-in-developmental-language-impairment
#17
Elena Plante, Dianne Patterson, Michelle Sandoval, Christopher J Vance, Arve E Asbjørnsen
Individuals with developmental language impairment can show deficits into adulthood. This suggests that neural networks related to their language do not normalize with time. We examined the ability of 16 adults with and without impaired language to learn individual words in an unfamiliar language. Adults with impaired language were able to segment individual words from running speech, but needed more time to do so than their normal-language peers. ICA analysis of fMRI data indicated that adults with language impairment activate a neural network that is comparable to that of adults with normal language...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28202961/early-brain-development-in-infants-at-high-risk-for-autism-spectrum-disorder
#18
Heather Cody Hazlett, Hongbin Gu, Brent C Munsell, Sun Hyung Kim, Martin Styner, Jason J Wolff, Jed T Elison, Meghan R Swanson, Hongtu Zhu, Kelly N Botteron, D Louis Collins, John N Constantino, Stephen R Dager, Annette M Estes, Alan C Evans, Vladimir S Fonov, Guido Gerig, Penelope Kostopoulos, Robert C McKinstry, Juhi Pandey, Sarah Paterson, John R Pruett, Robert T Schultz, Dennis W Shaw, Lonnie Zwaigenbaum, Joseph Piven
Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life...
February 15, 2017: Nature
https://www.readbyqxmd.com/read/28201916/artificial-neural-network-for-the-configuration-problem-in-solids
#19
Hyunjun Ji, Yousung Jung
A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R(2) = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R(2) = 0...
February 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28199851/the-input-output-relationship-of-the-cholinergic-basal-forebrain
#20
Matthew R Gielow, Laszlo Zaborszky
Basal forebrain cholinergic neurons influence cortical state, plasticity, learning, and attention. They collectively innervate the entire cerebral cortex, differentially controlling acetylcholine efflux across different cortical areas and timescales. Such control might be achieved by differential inputs driving separable cholinergic outputs, although no input-output relationship on a brain-wide level has ever been demonstrated. Here, we identify input neurons to cholinergic cells projecting to specific cortical regions by infecting cholinergic axon terminals with a monosynaptically restricted viral tracer...
February 14, 2017: Cell Reports
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