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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
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
Petr Nejedly, Jan Cimbalnik, Petr Klimes, Filip Plesinger, Josef Halamek, Vaclav Kremen, Ivo Viscor, Benjamin H Brinkmann, Martin Pail, Milan Brazdil, Gregory Worrell, Pavel Jurak
Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordings is time consuming and inaccurate. To date, unsupervised methods to accurately detect iEEG artifacts are not available. This study introduces a novel machine-learning approach for detection of artifacts in iEEG signals in clinically controlled conditions using convolutional neural networks (CNN) and benchmarks the method's performance against expert annotations...
August 13, 2018: Neuroinformatics
Michael de Ridder, Karsten Klein, Jean Yang, Pengyi Yang, Jim Lagopoulos, Ian Hickie, Max Bennett, Jinman Kim
Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction...
August 11, 2018: Neuroinformatics
Muhammad Yousefnezhad, Daoqiang Zhang
In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models...
August 9, 2018: Neuroinformatics
Jian Yang, Ming Hao, Xiaoyang Liu, Zhijiang Wan, Ning Zhong, Hanchuan Peng
Neuron reconstruction is an important technique in computational neuroscience. Although there are many reconstruction algorithms, few can generate robust results. In this paper, we propose a reconstruction algorithm called fast marching spanning tree (FMST). FMST is based on a minimum spanning tree method (MST) and improve its performance in two aspects: faster implementation and no loss of small branches. The contributions of the proposed method are as follows. Firstly, the Euclidean distance weight of edges in MST is improved to be a more reasonable value, which is related to the probability of the existence of an edge...
July 23, 2018: Neuroinformatics
Sebastian Schwanke, Jörg Jenssen, Peter Eipert, Oliver Schmitt
The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system)...
July 16, 2018: Neuroinformatics
Evelyn Perez Cervantes, Cesar Henrique Comin, Roberto Marcondes Cesar Junior, Luciano da Fontoura Costa
The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells...
July 14, 2018: Neuroinformatics
Kurt G Schilling, Yurui Gao, Matthew Christian, Vaibhav Janve, Iwona Stepniewska, Bennett A Landman, Adam W Anderson
The squirrel monkey (Saimiri sciureus) is a commonly-used surrogate for humans in biomedical research. In the neuroimaging community, MRI and histological atlases serve as valuable resources for anatomical, physiological, and functional studies of the brain; however, no digital MRI/histology atlas is currently available for the squirrel monkey. This paper describes the construction of a web-based multi-modal atlas of the squirrel monkey brain. The MRI-derived information includes anatomical MRI contrast (i...
July 13, 2018: Neuroinformatics
Ricardo Pizarro, Haz-Edine Assemlal, Dante De Nigris, Colm Elliott, Samson Antel, Douglas Arnold, Amir Shmuel
Neuroimaging science has seen a recent explosion in dataset size driving the need to develop database management with efficient processing pipelines. Multi-center neuroimaging databases consistently receive magnetic resonance imaging (MRI) data with unlabeled or incorrectly labeled contrast. There is a need to automatically identify the contrast of MRI scans to save database-managing facilities valuable resources spent by trained technicians required for visual inspection. We developed a deep learning (DL) algorithm with convolution neural network architecture to automatically infer the contrast of MRI scans based on the image intensity of multiple slices...
June 29, 2018: Neuroinformatics
Francisco J López-González, José Paredes-Pacheco, Karl Thurnhofer-Hemsi, Carlos Rossi, Manuel Enciso, Daniel Toro-Flores, Belén Murcia-Casas, Antonio L Gutiérrez-Cardo, Núria Roé-Vellvé
Kinetic modeling is at the basis of most quantification methods for dynamic PET data. Specific software is required for it, and a free and easy-to-use kinetic analysis toolbox can facilitate routine work for clinical research. The relevance of kinetic modeling for neuroimaging encourages its incorporation into image processing pipelines like those of SPM, also providing preprocessing flexibility to match the needs of users. The aim of this work was to develop such a toolbox: QModeling. It implements four widely-used reference-region models: Simplified Reference Tissue Model (SRTM), Simplified Reference Tissue Model 2 (SRTM2), Patlak Reference and Logan Reference...
June 28, 2018: Neuroinformatics
Paul A Yushkevich, Artem Pashchinskiy, Ipek Oguz, Suyash Mohan, J Eric Schmitt, Joel M Stein, Dženan Zukić, Jared Vicory, Matthew McCormick, Natalie Yushkevich, Nadav Schwartz, Yang Gao, Guido Gerig
ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest...
June 27, 2018: Neuroinformatics
Luping Zhou, Islem Rekik, Chenggang Yan, Guorong Wu
No abstract text is available yet for this article.
October 2018: Neuroinformatics
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
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
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
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
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
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
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
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