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Neuroinformatics

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https://www.readbyqxmd.com/read/30406865/an-end-to-end-system-for-automatic-characterization-of-iba1-immunopositive-microglia-in-whole-slide-imaging
#1
Alexander D Kyriazis, Shahriar Noroozizadeh, Amir Refaee, Woongcheol Choi, Lap-Tak Chu, Asma Bashir, Wai Hang Cheng, Rachel Zhao, Dhananjay R Namjoshi, Septimiu E Salcudean, Cheryl L Wellington, Guy Nir
Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. Detailed studies of the microglial response after TBI require high throughput quantification of changes in microglial count and morphology in histological sections throughout the brain. In this paper, we present a fully automated end-to-end system that is capable of assessing microglial activation in white matter regions on whole slide images of Iba1 stained sections. Our approach involves the division of the full brain slides into smaller image patches that are subsequently automatically classified into white and grey matter sections...
November 8, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30382537/automated-metadata-suggestion-during-repository-submission
#2
Robert A McDougal, Isha Dalal, Thomas M Morse, Gordon M Shepherd
Knowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these categories risks reducing completeness in the other because manually curating metadata is time consuming and is restricted by familiarity with both the data and the metadata annotation scheme. The diverse interests of a research community may drive a resource to have hundreds of metadata tags with few examples for each making it challenging for humans or machine learning algorithms to learn how to assign metadata tags properly...
October 31, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30368637/eeg-eog-based-virtual-keyboard-toward-hybrid-brain-computer-interface
#3
REVIEW
Sarah M Hosni, Howida A Shedeed, Mai S Mabrouk, Mohamed F Tolba
The past twenty years have ignited a new spark in the research of Electroencephalogram (EEG), which was pursued to develop innovative Brain Computer Interfaces (BCIs) in order to help severely disabled people live a better life with a high degree of independence. Current BCIs are more theoretical than practical and are suffering from numerous challenges. New trends of research propose combining EEG to other simple and efficient bioelectric inputs such as Electro-oculography (EOG) resulting from eye movements, to produce more practical and robust Hybrid Brain Computer Interface systems (hBCI) or Brain/Neuronal Computer Interface (BNCI)...
October 27, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30357708/cocaine-induced-preference-conditioning-a-machine-vision-perspective
#4
V Javier Traver, Filiberto Pla, Marta Miquel, Maria Carbo-Gas, Isis Gil-Miravet, Julian Guarque-Chabrera
Existing work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly relied on limited manually-driven procedures, and lack some more rigorous statistical approaches and more automated techniques. In this work, established methods from computer vision and machine learning are considered to build stronger evidence of those previous findings. To that end, an image descriptor is designed to characterize PNNs images; unsupervised learning (clustering) is used to automatically find distinctive patterns of PNNs; and supervised learning (classification) is adopted for predicting the experiment group of the mice from their PNN images...
October 24, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30328551/the-residual-center-of-mass-an-image-descriptor-for-the-diagnosis-of-alzheimer-disease
#5
Alexandre Yukio Yamashita, Alexandre Xavier Falcão, Neucimar Jerônimo Leite
A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that such biomarkers can be obtained by image analysis techniques. However, these techniques cannot be directly compared since they use different databases and validation protocols. In this paper, we present an extensive study of image descriptors for the diagnosis of Alzheimer Disease (AD) and introduce a new one, named Residual Center of Mass (RCM). The RCM descriptor explores image moments and other techniques to enhance brain regions and select discriminative features for the diagnosis of AD...
October 17, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30291569/rhesus-macaque-brain-atlas-regions-aligned-to-an-mri-template
#6
Jeffrey M Moirano, Gleb Y Bezgin, Elizabeth O Ahlers, Rolf Kötter, Alexander K Converse
To aid in the analysis of rhesus macaque brain images, we aligned digitized anatomical regions from the widely used atlas of Paxinos et al. to a published magnetic resonance imaging (MRI) template based on a large number of subjects. Digitally labelled atlas images were aligned to the template in 2D and then in 3D. The resulting grey matter regions appear qualitatively to be well registered to the template. To quantitatively validate the procedure, MR brain images of 20 rhesus macaques were aligned to the template along with regions drawn by hand in striatal and cortical areas in each subject's MRI...
October 5, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30284672/fused-group-lasso-regularized-multi-task-feature-learning-and-its-application-to-the-cognitive-performance-prediction-of-alzheimer-s-disease
#7
Xiaoli Liu, Peng Cao, Jianzhong Wang, Jun Kong, Dazhe Zhao
Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. Recently, multi-task based feature learning (MTFL) methods with sparsity-inducing [Formula: see text]-norm have been widely studied to select a discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures...
October 4, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30215167/automated-neuron-detection-in-high-content-fluorescence-microscopy-images-using-machine-learning
#8
Gadea Mata, Miroslav Radojević, Carlos Fernandez-Lozano, Ihor Smal, Niels Werij, Miguel Morales, Erik Meijering, Julio Rubio
The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose...
September 13, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30128674/characterizing-regularization-techniques-for-spatial-filter-optimization-in-oscillatory-eeg-regression-problems-guidelines-derived-from-simulation-and-real-world-data
#9
Andreas Meinel, Sebastián Castaño-Candamil, Benjamin Blankertz, Fabien Lotte, Michael Tangermann
We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio...
August 20, 2018: Neuroinformatics
https://www.readbyqxmd.com/read/30022314/special-issue-on-high-performance-computing-in-bio-medical-informatics
#10
EDITORIAL
Luping Zhou, Islem Rekik, Chenggang Yan, Guorong Wu
No abstract text is available yet for this article.
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29948843/predict-mirna-disease-association-with-collaborative-filtering
#11
Yatong Jiang, Bingtao Liu, Linghui Yu, Chenggang Yan, Hujun Bian
The era of human brain science research is dawning. Researchers utilize the various multi-disciplinary knowledge to explore the human brain,such as physiology and bioinformatics. The emerging disease association prediction technology can speed up the study of diseases, so as to better understanding the structure and function of human body. There are increasing evidences that miRNA plays a significant role in nervous system development, adult function, plasticity, and vulnerability to neurological disease states...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29907892/a-robust-reduced-rank-graph-regression-method-for-neuroimaging-genetic-analysis
#12
Xiaofeng Zhu, Weihong Zhang, Yong Fan
To characterize associations between genetic and neuroimaging data, a variety of analytic methods have been proposed in neuroimaging genetic studies. These methods have achieved promising performance by taking into account inherent correlation in either the neuroimaging data or the genetic data alone. In this study, we propose a novel robust reduced rank graph regression based method in a linear regression framework by considering correlations inherent in neuroimaging data and genetic data jointly. Particularly, we model the association analysis problem in a reduced rank regression framework with the genetic data as a feature matrix and the neuroimaging data as a response matrix by jointly considering correlations among the neuroimaging data as well as correlations between the genetic data and the neuroimaging data...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29802511/cognitive-assessment-prediction-in-alzheimer-s-disease-by-multi-layer-multi-target-regression
#13
Xiaoqian Wang, Xiantong Zhen, Quanzheng Li, Dinggang Shen, Heng Huang
Accurate and automatic prediction of cognitive assessment from multiple neuroimaging biomarkers is crucial for early detection of Alzheimer's disease. The major challenges arise from the nonlinear relationship between biomarkers and assessment scores and the inter-correlation among them, which have not yet been well addressed. In this paper, we propose multi-layer multi-target regression (MMR) which enables simultaneously modeling intrinsic inter-target correlations and nonlinear input-output relationships in a general compositional framework...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29725916/segan-adversarial-network-with-multi-scale-l-1-loss-for-medical-image-segmentation
#14
Yuan Xue, Tao Xu, Han Zhang, L Rodney Long, Xiaolei Huang
Inspired by classic Generative Adversarial Networks (GANs), we propose a novel end-to-end adversarial neural network, called SegAN, for the task of medical image segmentation. Since image segmentation requires dense, pixel-level labeling, the single scalar real/fake output of a classic GAN's discriminator may be ineffective in producing stable and sufficient gradient feedback to the networks. Instead, we use a fully convolutional neural network as the segmentor to generate segmentation label maps, and propose a novel adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local features that capture long- and short-range spatial relationships between pixels...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29691798/gpu-accelerated-browser-for-neuroimaging-genomics
#15
Bob Zigon, Huang Li, Xiaohui Yao, Shiaofen Fang, Mohammad Al Hasan, Jingwen Yan, Jason H Moore, Andrew J Saykin, Li Shen
Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29644547/sacmda-mirna-disease-association-prediction-with-short-acyclic-connections-in-heterogeneous-graph
#16
Biyao Shao, Bingtao Liu, Chenggang Yan
MiRNA-disease association is important to disease diagnosis and treatment. Prediction of miRNA-disease associations is receiving increasing attention. Using the huge number of known databases to predict potential associations between miRNAs and diseases is an important topic in the field of biology and medicine. In this paper, we propose a novel computational method of with Short Acyclic Connections in Heterogeneous Graph (SACMDA). SACMDA obtains AUCs of 0.8770 and 0.8368 during global and local leave-one-out cross validation, respectively...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29572601/multi-modality-cascaded-convolutional-neural-networks-for-alzheimer-s-disease-diagnosis
#17
Manhua Liu, Danni Cheng, Kundong Wang, Yaping Wang
Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29572600/decreased-cerebral-blood-flow-in-mesial-thalamus-and-precuneus-pcc-during-midazolam-induced-sedation-assessed-with-asl
#18
Peipeng Liang, Yachao Xu, Fei Lan, Daqing Ma, Kuncheng Li
While some previous work suggests that midazolam-induced light sedation results from the functional disconnection within resting state network, little is known about the underlying alterations of cerebral blood flow (CBF) associated with its effects. A randomized, double-blind, within-subject, cross-over design was adopted, while 12 healthy young volunteers were scanned with arterial spin-labeling (ASL) perfusion MRI both before and after an injection of either saline or midazolam. The contrast of MRI signal before and after midazolam administration revealed the CBF decrease in the bilateral mesial thalamus and precuneus/posterior cingulate cortex (PCC)...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29512026/patch-based-label-fusion-with-structured-discriminant-embedding-for-hippocampus-segmentation
#19
Yan Wang, Guangkai Ma, Xi Wu, Jiliu Zhou
Automatic and accurate segmentation of hippocampal structures in medical images is of great importance in neuroscience studies. In multi-atlas based segmentation methods, to alleviate the misalignment when registering atlases to the target image, patch-based methods have been widely studied to improve the performance of label fusion. However, weights assigned to the fused labels are usually computed based on predefined features (e.g. image intensities), thus being not necessarily optimal. Due to the lack of discriminating features, the original feature space defined by image intensities may limit the description accuracy...
October 2018: Neuroinformatics
https://www.readbyqxmd.com/read/29488069/decoding-auditory-saliency-from-brain-activity-patterns-during-free-listening-to-naturalistic-audio-excerpts
#20
Shijie Zhao, Junwei Han, Xi Jiang, Heng Huang, Huan Liu, Jinglei Lv, Lei Guo, Tianming Liu
In recent years, natural stimuli such as audio excerpts or video streams have received increasing attention in neuroimaging studies. Compared with conventional simple, idealized and repeated artificial stimuli, natural stimuli contain more unrepeated, dynamic and complex information that are more close to real-life. However, there is no direct correspondence between the stimuli and any sensory or cognitive functions of the brain, which makes it difficult to apply traditional hypothesis-driven analysis methods (e...
October 2018: Neuroinformatics
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