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https://www.readbyqxmd.com/read/29161639/specification-plasticity-and-evolutionary-origin-of-peripheral-glial-cells
#1
REVIEW
Maria Eleni Kastriti, Igor Adameyko
Peripheral glia includes predominantly myelinating and non-myelinating Schwann cells in addition to satellite, terminal and enteric glia as well as other unresolved subtypes with localized functions. Of these subtypes, all of them originate from neural crest-derived embryonic Schwann cell precursors (SCPs). Specific gene regulatory networks control neural crest specification and downstream events, including SCP differentiation and myelination. Embryonic SCPs are multipotent and generate neuroendocrine cells, parasympathetic and enteric neurons, melanocytes and other cell types...
November 18, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/29161383/basal-forebrain-gating-by-somatostatin-neurons-drives-prefrontal-cortical-activity
#2
Nelson Espinosa, Alejandra Alonso, Cristian Morales, Pedro Espinosa, Andrés E Chávez, Pablo Fuentealba
The basal forebrain provides modulatory input to the cortex regulating brain states and cognitive processing. Somatostatin-expressing neurons constitute a heterogeneous GABAergic population known to functionally inhibit basal forebrain cortically projecting cells thus favoring sleep and cortical synchronization. However, it remains unclear if somatostatin cells can regulate population activity patterns in the basal forebrain and modulate cortical dynamics. Here, we demonstrate that somatostatin neurons regulate the corticopetal synaptic output of the basal forebrain impinging on cortical activity and behavior...
November 17, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/29161358/neural-mechanisms-of-episodic-retrieval-support-divergent-creative-thinking
#3
Kevin P Madore, Preston P Thakral, Roger E Beaty, Donna Rose Addis, Daniel L Schacter
Prior research has indicated that brain regions and networks that support semantic memory, top-down and bottom-up attention, and cognitive control are all involved in divergent creative thinking. Kernels of evidence suggest that neural processes supporting episodic memory-the retrieval of particular elements of prior experiences-may also be involved in divergent thinking, but such processes have typically been characterized as not very relevant for, or even a hindrance to, creative output. In the present study, we combine functional magnetic resonance imaging with an experimental manipulation to test formally, for the first time, episodic memory's involvement in divergent thinking...
November 17, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/29161352/delta-rhythm-orchestrates-the-neural-activity-underlying-the-resting-state-bold-signal-via-phase-amplitude-coupling
#4
Saul Jaime, Hong Gu, Brian F Sadacca, Elliot A Stein, Jose E Cavazos, Yihong Yang, Hanbing Lu
Spontaneous ongoing neuronal activity is a prominent feature of the mammalian brain. Temporal and spatial patterns of such ongoing activity have been exploited to examine large-scale brain network organization and function. However, the neurophysiological basis of this spontaneous brain activity as detected by resting-state functional Magnetic Resonance Imaging (fMRI) remains poorly understood. To this end, multi-site local field potentials (LFP) and blood oxygenation level-dependent (BOLD) fMRI were simultaneously recorded in the rat striatum along with local pharmacological manipulation of striatal activity...
November 17, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/29160744/modulation-of-neural-oscillatory-activity-during-dynamic-face-processing
#5
Elaine Foley, Gina Rippon, Carl Senior
Various neuroimaging and neurophysiological methods have been used to examine neural activation patterns in response to faces. However, much of previous research has relied on static images of faces, which do not allow a complete description of the temporal structure of face-specific neural activities to be made. More recently, insights are emerging from fMRI studies about the neural substrates that underpin our perception of naturalistic dynamic face stimuli, but the temporal and spectral oscillatory activity associated with processing dynamic faces has yet to be fully characterized...
November 21, 2017: Journal of Cognitive Neuroscience
https://www.readbyqxmd.com/read/29160739/neural-correlates-of-effort-dependent-and-effort-independent-cognitive-fatigue-components-in-patients-with-multiple-sclerosis
#6
Stefan Spiteri, Thomas Hassa, Dolores Claros-Salinas, Christian Dettmers, Mircea Ariel Schoenfeld
BACKGROUND: Among patients with multiple sclerosis (MS), fatigue is the most commonly reported symptom. It can be subdivided into an effort-dependent (fatigability) and an effort-independent component (trait-fatigue). OBJECTIVE: The objective was to disentangle activity changes associated with effort-independent "trait-fatigue" from those associated with effort-dependent fatigability in MS patients. METHODS: This study employed behavioral measures and functional magnetic imaging to investigate neural changes in MS patients associated with fatigue...
November 1, 2017: Multiple Sclerosis: Clinical and Laboratory Research
https://www.readbyqxmd.com/read/29159811/a-deep-learning-method-for-classifying-mammographic-breast-density-categories
#7
Aly A Mohamed, Wendie A Berg, Hong Peng, Yahong Luo, Rachel C Jankowitz, Shandong Wu
PURPOSE: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow...
November 21, 2017: Medical Physics
https://www.readbyqxmd.com/read/29159706/alcoholism-detection-by-data-augmentation-and-convolutional-neural-network-with-stochastic-pooling
#8
Shui-Hua Wang, Yi-Ding Lv, Yuxiu Sui, Shuai Liu, Su-Jing Wang, Yu-Dong Zhang
Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer...
November 17, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29159541/automated-detection-of-exudative-age-related-macular-degeneration-in-spectral-domain-optical-coherence-tomography-using-deep-learning
#9
Maximilian Treder, Jost Lennart Lauermann, Nicole Eter
PURPOSE: Our purpose was to use deep learning for the automated detection of age-related macular degeneration (AMD) in spectral domain optical coherence tomography (SD-OCT). METHODS: A total of 1112 cross-section SD-OCT images of patients with exudative AMD and a healthy control group were used for this study. In the first step, an open-source multi-layer deep convolutional neural network (DCNN), which was pretrained with 1.2 million images from ImageNet, was trained and validated with 1012 cross-section SD-OCT scans (AMD: 701; healthy: 311)...
November 20, 2017: Graefe's Archive for Clinical and Experimental Ophthalmology
https://www.readbyqxmd.com/read/29159437/exploring-the-potential-relationship-between-indoor-air-quality-and-the-concentration-of-airborne-culturable-fungi-a-combined-experimental-and-neural-network-modeling-study
#10
Zhijian Liu, Kewei Cheng, Hao Li, Guoqing Cao, Di Wu, Yunjie Shi
Indoor airborne culturable fungi exposure has been closely linked to occupants' health. However, conventional measurement of indoor airborne fungal concentration is complicated and usually requires around one week for fungi incubation in laboratory. To provide an ultra-fast solution, here, for the first time, a knowledge-based machine learning model is developed with the inputs of indoor air quality data for estimating the concentration of indoor airborne culturable fungi. To construct a database for statistical analysis and model training, 249 data groups of air quality indicators (concentration of indoor airborne culturable fungi, indoor/outdoor PM2...
November 20, 2017: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/29159042/evaluation-of-a-deep-learning-approach-for-the-segmentation-of-brain-tissues-and-white-matter-hyperintensities-of-presumed-vascular-origin-in%C3%A2-mri
#11
Pim Moeskops, Jeroen de Bresser, Hugo J Kuijf, Adriënne M Mendrik, Geert Jan Biessels, Josien P W Pluim, Ivana Išgum
Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts...
2018: NeuroImage: Clinical
https://www.readbyqxmd.com/read/29158992/hrv-derived-data-similarity-and-distribution-index-based-on-ensemble-neural-network-for-measuring-depth-of-anaesthesia
#12
Quan Liu, Li Ma, Ren-Chun Chiu, Shou-Zen Fan, Maysam F Abbod, Jiann-Shing Shieh
Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying level of consciousness, studies have been conducted to develop improved methods of measuring DoA...
2017: PeerJ
https://www.readbyqxmd.com/read/29158879/changes-in-effective-connectivity-network-patterns-in-drug-abusers-treated-with-different-methods
#13
Arash Zare Sadeghi, Amir Homayoun Jafari, Mohammad Ali Oghabian, Hamid Reza Salighehrad, Seyed Amir Hossein Batouli, Samira Raminfard, Hamed Ekhtiari
Introduction: Various treatment methods for drug abusers will result in different success rates. This is partly due to different neural assumptions and partly due to various rate of relapse in abusers because of different circumstances. Investigating the brain activation networks of treated subjects can reveal the hidden mechanisms of the therapeutic methods. Methods: We studied three groups of subjects: heroin abusers treated with abstinent based therapy (ABT) method, heroin abusers treated with Methadone Maintenance Therapy (MMT) method, and a control group...
July 2017: Basic and Clinical Neuroscience
https://www.readbyqxmd.com/read/29158813/vascularized-tissue-engineered-model-for-studying-drug-resistance-in-neuroblastoma
#14
A Villasante, K Sakaguchi, J Kim, N K Cheung, M Nakayama, H Parsa, T Okano, T Shimizu, G Vunjak-Novakovic
Neuroblastoma is a vascularized pediatric tumor derived from neural crest stem cells that displays vasculogenic mimicry and can express a number of stemness markers, such as SOX2 and NANOG. Tumor relapse is the major cause of succumbing to this disease, and properties attributed to cancer stem-like cells (CSLC), such as drug-resistance and cell plasticity, seem to be the key mechanisms. However, the lack of controllable models that recapitulate the features of human neuroblastoma limits our understanding of the process and impedes the development of new therapies...
2017: Theranostics
https://www.readbyqxmd.com/read/29158603/realization-of-ground-state-in-artificial-kagome-spin-ice-via-topological-defect-driven-magnetic-writing
#15
Jack C Gartside, Daan M Arroo, David M Burn, Victoria L Bemmer, Andy Moskalenko, Lesley F Cohen, Will R Branford
Arrays of non-interacting nanomagnets are widespread in data storage and processing. As current technologies approach fundamental limits on size and thermal stability, enhancing functionality through embracing the strong interactions present at high array densities becomes attractive. In this respect, artificial spin ices are geometrically frustrated magnetic metamaterials that offer vast untapped potential due to their unique microstate landscapes, with intriguing prospects in applications from reconfigurable logic to magnonic devices or hardware neural networks...
November 20, 2017: Nature Nanotechnology
https://www.readbyqxmd.com/read/29157458/robust-breast-cancer-prediction-system-based-on-rough-set-theory-at-national-cancer-institute-of-egypt
#16
Saeed Khodary M Hamouda, Mohammed E Wahed, Reda H Abo Alez, Khaled Riad
BACKGROUND: Breast cancer is one of the major death causing diseases of the women in the world. Every year more than million women are diagnosed with breast cancer more than half of them will die because of inaccuracies and delays in diagnosis of the disease. High accuracy in cancer prediction is important to improve the treatment quality and the survivability rate of patients. OBJECTIVES: In this paper, we are going to propose a new and robust breast cancer prediction and diagnosis system based on the Rough Set (RS)...
January 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29157445/an-ensemble-deep-learning-based-approach-for-red-lesion-detection-in-fundus-images
#17
José Ignacio Orlando, Elena Prokofyeva, Mariana Del Fresno, Matthew B Blaschko
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy...
January 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29157240/automatic-diagnosis-of-imbalanced-ophthalmic-images-using-a-cost-sensitive-deep-convolutional-neural-network
#18
Jiewei Jiang, Xiyang Liu, Kai Zhang, Erping Long, Liming Wang, Wangting Li, Lin Liu, Shuai Wang, Mingmin Zhu, Jiangtao Cui, Zhenzhen Liu, Zhuoling Lin, Xiaoyan Li, Jingjing Chen, Qianzhong Cao, Jing Li, Xiaohang Wu, Dongni Wang, Jinghui Wang, Haotian Lin
BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. METHODS: In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images...
November 21, 2017: Biomedical Engineering Online
https://www.readbyqxmd.com/read/29156419/segmentation-of-the-hippocampus-by-transferring-algorithmic-knowledge-for-large-cohort-processing
#19
Benjamin Thyreau, Kazunori Sato, Hiroshi Fukuda, Yasuyuki Taki
The hippocampus is a particularly interesting target for neuroscience research studies due to its essential role within the human brain. In large human cohort studies, bilateral hippocampal structures are frequently identified and measured to gain insight into human behaviour or genomic variability in neuropsychiatric disorders of interest. Automatic segmentation is performed using various algorithms, with FreeSurfer being a popular option. In this manuscript, we present a method to segment the bilateral hippocampus using a deep-learned appearance model...
November 10, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/29155996/hierarchical-attention-networks-for-information-extraction-from-cancer-pathology-reports
#20
Shang Gao, Michael T Young, John X Qiu, Hong-Jun Yoon, James B Christian, Paul A Fearn, Georgia D Tourassi, Arvind Ramanthan
Objective: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents. Materials and Methods: Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program...
November 16, 2017: Journal of the American Medical Informatics Association: JAMIA
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