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Carlos Platero, Lin Lin, M Carmen Tobar
Hippocampal atrophy measures from magnetic resonance imaging (MRI) are powerful tools for monitoring Alzheimer's disease (AD) progression. In this paper, we introduce a longitudinal image analysis framework based on robust registration and simultaneous hippocampal segmentation and longitudinal marker classification of brain MRI of an arbitrary number of time points. The framework comprises two innovative parts: a longitudinal segmentation and a longitudinal classification step. The results show that both steps of the longitudinal pipeline improved the reliability and the accuracy of the discrimination between clinical groups...
May 21, 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...
May 3, 2018: Neuroinformatics
Stefan Bode, Daniel Feuerriegel, Daniel Bennett, Phillip M Alday
In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig 2004; Lopez-Calderon and Luck 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables...
May 2, 2018: Neuroinformatics
Leonardo A Hernández-Pérez, Duniel Delgado-Castillo, Rainer Martín-Pérez, Rubén Orozco-Morales, Juan V Lorenzo-Ginori
This paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three one-dimensional (1D) time series are derived from the three-dimensional (3D) structure of neuron instead, and afterwards a spatial time series is finally constructed from which the features are calculated. Digitally reconstructed neurons were separated into control and pathological sets, which are related to three categories of alterations caused by epilepsy, Alzheimer's disease (long and local projections), and ischemia...
April 28, 2018: Neuroinformatics
David N Kennedy
No abstract text is available yet for this article.
April 27, 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...
April 25, 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...
April 11, 2018: Neuroinformatics
Yifeng Ling, Eric Jouvent, Louis Cousyn, Hugues Chabriat, François De Guio
White matter hyperintensities (WMH) are a hallmark of small vessel diseases (SVD). Yet, no automated segmentation method is readily and widely used, especially in patients with extensive WMH where lesions are close to the cerebral cortex. BIANCA (Brain Intensity AbNormality Classification Algorithm) is a new fully automated, supervised method for WMH segmentation. In this study, we optimized and compared BIANCA against a reference method with manual editing in a cohort of patients with extensive WMH. This was achieved in two datasets: a clinical protocol with 90 patients having 2-dimensional FLAIR and an advanced protocol with 66 patients having 3-dimensional FLAIR...
March 29, 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...
March 23, 2018: Neuroinformatics
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)...
March 23, 2018: Neuroinformatics
James Deraeve, William H Alexander
Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy...
March 21, 2018: Neuroinformatics
Meysam Hashemi, Axel Hutt, Laure Buhry, Jamie Sleigh
Mathematical modeling is a powerful tool that enables researchers to describe the experimentally observed dynamics of complex systems. Starting with a robust model including model parameters, it is necessary to choose an appropriate set of model parameters to reproduce experimental data. However, estimating an optimal solution of the inverse problem, i.e., finding a set of model parameters that yields the best possible fit to the experimental data, is a very challenging problem. In the present work, we use different optimization algorithms based on a frequentist approach, as well as Monte Carlo Markov Chain methods based on Bayesian inference techniques to solve the considered inverse problems...
March 7, 2018: Neuroinformatics
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...
March 7, 2018: Neuroinformatics
Bengt Ljungquist, Per Petersson, Anders J Johansson, Jens Schouenborg, Martin Garwicz
Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources...
March 5, 2018: Neuroinformatics
Ting-Yuan Wang, Nan-Yow Chen, Guan-Wei He, Guo-Tzau Wang, Chi-Tin Shih, Ann-Shyn Chiang
Effective 3D visualization is essential for connectomics analysis, where the number of neural images easily reaches over tens of thousands. A formidable challenge is to simultaneously visualize a large number of distinguishable single-neuron images, with reasonable processing time and memory for file management and 3D rendering. In the present study, we proposed an algorithm named "Kaleido" that can visualize up to at least ten thousand single neurons from the Drosophila brain using only a fraction of the memory traditionally required, without increasing computing time...
March 3, 2018: Neuroinformatics
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...
February 27, 2018: Neuroinformatics
Runa Bhaumik, Ashish Pradhan, Soptik Das, Dulal K Bhaumik
The advances in neuroimaging methods reveal that resting-state functional fMRI (rs-fMRI) connectivity measures can be potential diagnostic biomarkers for autism spectrum disorder (ASD). Recent data sharing projects help us replicating the robustness of these biomarkers in different acquisition conditions or preprocessing steps across larger numbers of individuals or sites. It is necessary to validate the previous results by using data from multiple sites by diminishing the site variations. We investigated partial least square regression (PLS), a domain adaptive method to adjust the effects of multicenter acquisition...
February 17, 2018: Neuroinformatics
Zhongyi Han, Benzheng Wei, Stephanie Leung, Ilanit Ben Nachum, David Laidley, Shuo Li
Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads...
February 15, 2018: Neuroinformatics
Zhongyu Li, Erik Butler, Kang Li, Aidong Lu, Shuiwang Ji, Shaoting Zhang
Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i...
February 12, 2018: Neuroinformatics
Zheng Xu, Sheng Wang, Yeqing Li, Feiyun Zhu, Junzhou Huang
The most recent history of parallel Magnetic Resonance Imaging (pMRI) has in large part been devoted to finding ways to reduce acquisition time. While joint total variation (JTV) regularized model has been demonstrated as a powerful tool in increasing sampling speed for pMRI, however, the major bottleneck is the inefficiency of the optimization method. While all present state-of-the-art optimizations for the JTV model could only reach a sublinear convergence rate, in this paper, we squeeze the performance by proposing a linear-convergent optimization method for the JTV model...
February 8, 2018: Neuroinformatics
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