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https://www.readbyqxmd.com/read/28641250/deep-convolutional-neural-network-for-inverse-problems-in-imaging
#1
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, Michael Unser
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution...
June 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28641247/modeling-task-fmri-data-via-deep-convolutional-autoencoder
#2
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28641239/automatic-recognition-of-fmri-derived-functional-networks-using-3d-convolutional-neural-networks
#3
Yu Zhao, Qinglin Dong, Shu Zhang, Wei Zhang, Hanbo Chen, Xi Jiang, Lei Guo, Xintao Hu, Junwei Han, Tianming Liu
Current fMRI data modeling techniques such as Independent Component Analysis (ICA) and Sparse Coding methods can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications...
June 15, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/28641124/neural-repair-by-nt3-chitosan-via-enhancement-of-endogenous-neurogenesis-after-adult-focal-aspiration-brain-injury
#4
Peng Hao, Hongmei Duan, Fei Hao, Lan Chen, Min Sun, Kevin S Fan, Yi Eve Sun, David Williams, Zhaoyang Yang, Xiaoguang Li
The latent regenerative potential of endogenous neural stem/progenitor cells (NSCs) in the adult mammalian brain has been postulated as a likely source for neural repair. However, the inflammatory and inhibitory microenvironment after traumatic brain injury (TBI) prohibits NSCs from generating new functional neurons to restore brain function. Here we report a biodegradable material, chitosan, which, when loaded with neurotrophin-3 (NT3) and injected into the lesion site after TBI, effectively engaged endogenous NSCs to proliferate and migrate to the injury area...
April 26, 2017: Biomaterials
https://www.readbyqxmd.com/read/28640883/an-artificial-emg-generation-model-based-on-signal-dependent-noise-and-related-application-to-motion-classification
#5
Akira Furui, Hideaki Hayashi, Go Nakamura, Takaaki Chin, Toshio Tsuji
This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance...
2017: PloS One
https://www.readbyqxmd.com/read/28640840/applying-artificial-intelligence-to-disease-staging-deep-learning-for-improved-staging-of-diabetic-retinopathy
#6
Hidenori Takahashi, Hironobu Tampo, Yusuke Arai, Yuji Inoue, Hidetoshi Kawashima
PURPOSE: Disease staging involves the assessment of disease severity or progression and is used for treatment selection. In diabetic retinopathy, disease staging using a wide area is more desirable than that using a limited area. We investigated if deep learning artificial intelligence (AI) could be used to grade diabetic retinopathy and determine treatment and prognosis. METHODS: The retrospective study analyzed 9,939 posterior pole photographs of 2,740 patients with diabetes...
2017: PloS One
https://www.readbyqxmd.com/read/28640825/olfactory-learning-without-the-mushroom-bodies-spiking-neural-network-models-of-the-honeybee-lateral-antennal-lobe-tract-reveal-its-capacities-in-odour-memory-tasks-of-varied-complexities
#7
HaDi MaBouDi, Hideaki Shimazaki, Martin Giurfa, Lars Chittka
The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn...
June 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28636933/drosophila-neuropeptide-f-signaling-independently-regulates-feeding-and-sleep-wake-behavior
#8
Brian Y Chung, Jennifer Ro, Sabine A Hutter, Kylie M Miller, Lakshmi S Guduguntla, Shu Kondo, Scott D Pletcher
Proper regulation of sleep-wake behavior and feeding is essential for organismal health and survival. While previous studies have isolated discrete neural loci and substrates important for either sleep or feeding, how the brain is organized to coordinate both processes with respect to one another remains poorly understood. Here, we provide evidence that the Drosophila Neuropeptide F (NPF) network forms a critical component of both adult sleep and feeding regulation. Activation of NPF signaling in the brain promotes wakefulness and adult feeding, likely through its cognate receptor NPFR...
June 20, 2017: Cell Reports
https://www.readbyqxmd.com/read/28636585/microbiome-a-potential-component-in-the-origin-of-mental-disorders
#9
George B Stefano, Radek Ptacek, Jiri Raboch, Richard M Kream
It is not surprising to find microbiome abnormalities present in psychiatric disorders such as depressive disorders, bipolar disorders, etc. Evolutionary pressure may provide an existential advantage to the host eukaryotic cells in that it survives in an extracellular environment containing non-self cells (e.g., bacteria). This phenomenon is both positive and negative, as with other intercellular processes. In this specific case, the phenomenal amount of information gained from combined bacterial genome could enhance communication between self and non-self cells...
June 21, 2017: Medical Science Monitor: International Medical Journal of Experimental and Clinical Research
https://www.readbyqxmd.com/read/28635678/evaluation-of-classifier-performance-for-multiclass-phenotype-discrimination-in-untargeted-metabolomics
#10
Patrick J Trainor, Andrew P DeFilippis, Shesh N Rai
Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination...
June 21, 2017: Metabolites
https://www.readbyqxmd.com/read/28635625/soil-moisture-content-estimation-based-on-sentinel-1-and-auxiliary-earth-observation-products-a-hydrological-approach
#11
Dimitrios D Alexakis, Filippos-Dimitrios K Mexis, Anthi-Eirini K Vozinaki, Ioannis N Daliakopoulos, Ioannis K Tsanis
A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested...
June 21, 2017: Sensors
https://www.readbyqxmd.com/read/28634789/detection-and-grading-of-prostate-cancer-using-temporal-enhanced-ultrasound-combining-deep-neural-networks-and-tissue-mimicking-simulations
#12
Shekoofeh Azizi, Sharareh Bayat, Pingkun Yan, Amir Tahmasebi, Guy Nir, Jin Tae Kwak, Sheng Xu, Storey Wilson, Kenneth A Iczkowski, M Scott Lucia, Larry Goldenberg, Septimiu E Salcudean, Peter A Pinto, Bradford Wood, Purang Abolmaesumi, Parvin Mousavi
PURPOSE  : Temporal Enhanced Ultrasound (TeUS) has been proposed as a new paradigm for tissue characterization based on a sequence of ultrasound radio frequency (RF) data. We previously used TeUS to successfully address the problem of prostate cancer detection in the fusion biopsies. METHODS  : In this paper, we use TeUS to address the problem of grading prostate cancer in a clinical study of 197 biopsy cores from 132 patients. Our method involves capturing high-level latent features of TeUS with a deep learning approach followed by distribution learning to cluster aggressive cancer in a biopsy core...
June 20, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28634230/similarity-in-gene-regulatory-networks-suggests-that-cancer-cells-share-characteristics-of-embryonic-neural-cells
#13
Zan Zhang, Anhua Lei, Liyang Xu, Lu Chen, Yonglong Chen, Xuena Zhang, Yan Gao, Xiaoli Yang, Min Zhang, Ying Cao
Cancer cells are immature cells resulting from cellular reprogramming by gene misregulation, and re-differentiation is expected to reduce malignancy. It is unclear, however, whether cancer cells can undergo terminal differentiation. Here, we show that, inhibition of the epigenetic modification enzymes enhancer of zeste homolog 2 (EZH2), histone deacetylases (HDACs) 1 and 3, lysine demethylase 1A (LSD1), or DNA methyltransferase 1 (DNMT1), which all promote cancer development and progression, leads to postmitotic neuron-like differentiation with loss of malignant features in distinct solid cancer cell lines...
June 20, 2017: Journal of Biological Chemistry
https://www.readbyqxmd.com/read/28633970/unified-neural-field-theory-of-brain-dynamics-underlying-oscillations-in-parkinson-s-disease-and-generalized-epilepsies
#14
E J Müller, S J van Albada, J W Kim, P A Robinson
The mechanisms underlying pathologically synchronized neural oscillations in Parkinson's disease (PD) and generalized epilepsies are explored in parallel via a physiologically-based neural field model of the corticothalamic-basal ganglia (CTBG) system. The basal ganglia (BG) are approximated as a single effective population and their roles in the modulation of oscillatory dynamics of the corticothalamic (CT) system and vice versa are analyzed. In addition to normal EEG rhythms, enhanced activity around 4 Hz and 20 Hz exists in the model, consistent with the characteristic frequencies observed in PD...
June 17, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28633886/remembering-and-imagining-alternative-versions-of-the-personal-past
#15
Peggy L St Jacques, Alexis C Carpenter, Karl K Szpunar, Daniel L Schacter
Although autobiographical memory and episodic simulations recruit similar core brain regions, episodic simulations engage additional neural recruitment in the frontoparietal control network due to greater demands on constructive processes. However, previous functional neuroimaging studies showing differences in remembering and episodic simulation have focused on veridical retrieval of past experiences, and thus have not fully considered how retrieving the past in different ways from how it was originally experienced may also place similar demands on constructive processes...
June 17, 2017: Neuropsychologia
https://www.readbyqxmd.com/read/28633707/qualitative-analysis-of-biological-tuberculosis-samples-by-an-electronic-nose-based-artificial-neural-network
#16
E I Mohamed, M A Mohamed, M H Moustafa, S M Abdel-Mageed, A M Moro, A I Baess, S M El-Kholy
OBJECTIVE: To apply an e-nose system for monitoring headspace volatiles in biological samples from Egyptian patients with active pulmonary tuberculosis (TB) and healthy controls (HCs) and compare them with standard sputum analysis. DESIGN: The study population comprised 260 (140 males, 120 females) newly diagnosed TB patients and 240 (120 males, 120 females) HCs matched by age and socio-economic level admitted to hospitals specialising in chest diseases in Alexandria, Behera, Giza and Damietta Governorates, Egypt...
July 1, 2017: International Journal of Tuberculosis and Lung Disease
https://www.readbyqxmd.com/read/28633299/structural-covariance-networks-in-children-with-autism-or-adhd
#17
R A I Bethlehem, R Romero-Garcia, E Mak, E T Bullmore, S Baron-Cohen
Background: While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. Method: Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69)...
June 13, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/28633164/ann-prediction-of-ligament-stiffnesses-for-the-enhanced-predictive-ability-of-a-patient-specific-computational-foot-ankle-model
#18
Ruchi Chande, Jennifer S Wayne
Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data is not always available for all soft tissues nor is it known for patient specific work...
June 20, 2017: Journal of Biomechanical Engineering
https://www.readbyqxmd.com/read/28633048/affective-traits-and-history-of-depression-are-related-to-ventral-striatum-connectivity
#19
Sophie R DelDonno, Lisanne M Jenkins, Natania A Crane, Robin Nusslock, Kelly A Ryan, Stewart A Shankman, K Luan Phan, Scott A Langenecker
INTRODUCTION: Studying remitted Major Depressive Disorder (rMDD) facilitates a better understanding of neural mechanisms for risk, given that confounding effects of active symptoms are removed. Disrupted functional connectivity has been reported in multiple networks in MDD. However, no study to date of rMDD has specifically examined connectivity of the ventral striatum (VS), a region highly implicated in reward and motivation. We investigated functional connectivity of the VS in individuals with and without a history of MDD, and in relation to affective personality traits...
June 15, 2017: Journal of Affective Disorders
https://www.readbyqxmd.com/read/28633028/visual-perception-neural-networks-for-stereopsis
#20
Jenny C A Read, Bruce G Cumming
How does our brain use differences between the images in our two eyes, binocular disparities, to generate depth perception? New work shows that a type of neural network trained on natural binocular images can learn parameters that match key properties of visual cortex. Most information is conveyed by cells which sense differences between the two eyes' images.
June 19, 2017: Current Biology: CB
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