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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
Wu Liu, Cheng Zhang, Huadong Ma, Shuangqun Li
The integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on. Among them, biometrics based automatic human identification is one of the most fundamental and significant research topic. Human gait, which is a biometric features with the unique capability, has gained significant attentions as the remarkable characteristics of remote accessed, robust and security in the biometrics based human identification...
February 6, 2018: Neuroinformatics
Lei Cai, Bian Wu, Shuiwang Ji
Visual cortex forms the basis of visual processing and plays important roles in visual encoding. By using the recently published Allen Brain Observatory dataset consisting of large-scale calcium imaging of mouse V1 activities under visual stimuli, we were able to obtain high-quality data capturing simultaneous neuronal activities at multiple sub-areas and cortical depths of V1. Using prediction models, we analyzed the activity profiles related to static and drifting grating stimuli. We conducted a comprehensive survey of the coding ability of multiple cortical locations toward different stimulus attributes...
February 5, 2018: Neuroinformatics
Yangming Ou, Lilla Zöllei, Xiao Da, Kallirroi Retzepi, Shawn N Murphy, Elizabeth R Gerstner, Bruce R Rosen, P Ellen Grant, Jayashree Kalpathy-Cramer, Randy L Gollub
Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs)...
January 20, 2018: Neuroinformatics
Erik De Schutter
No abstract text is available yet for this article.
January 20, 2018: Neuroinformatics
Marco Ganzetti, Quanying Liu, Dante Mantini
During aging the brain undergoes a series of structural changes, in size, shape as well as tissue composition. In particular, cortical atrophy and ventricular enlargement are often present in the brain of elderly individuals. This poses serious challenges in the spatial registration of structural MR images. In this study, we addressed this open issue by proposing an enhanced framework for MR registration and segmentation. Our solution was compared with other approaches based on the tools available in SPM12, a widely used software package...
January 19, 2018: Neuroinformatics
Bart Ferguson, Natalia Petridou, Alessio Fracasso, Martijn P van den Heuvel, Rachel M Brouwer, Hilleke E Hulshoff Pol, René S Kahn, René C W Mandl
Studies into cortical thickness in psychiatric diseases based on T1-weighted MRI frequently report on aberrations in the cerebral cortex. Due to limitations in image resolution for studies conducted at conventional MRI field strengths (e.g. 3 Tesla (T)) this information cannot be used to establish which of the cortical layers may be implicated. Here we propose a new analysis method that computes one high-resolution average cortical profile per brain region extracting myeloarchitectural information from T1-weighted MRI scans that are routinely acquired at a conventional field strength...
January 19, 2018: Neuroinformatics
Shancheng Fang, Hongtao Xie, Zhineng Chen, Yizhi Liu, Yan Li
How to read Uyghur text from biomedical graphic images is a challenge problem due to the complex layout and cursive writing of Uyghur. In this paper, we propose a system that extracts text from Uyghur biomedical images, and matches the text in a specific lexicon for semantic analysis. The proposed system possesses following distinctive properties: first, it is an integrated system which firstly detects and crops the Uyghur text lines using a single fully convolutional neural network, and then keywords in the lexicon are matched by a well-designed matching network...
January 19, 2018: Neuroinformatics
Donghao Zhang, Siqi Liu, Yang Song, Dagan Feng, Hanchuan Peng, Weidong Cai
The automatic neuron reconstruction is important since it accelerates the collection of 3D neuron models for the neuronal morphological studies. The majority of the previous neuron reconstruction methods only focused on tracing neuron fibres without considering the somatic surface. Thus, topological errors often present around the soma area in the results obtained by these tracing methods. Segmentation of the soma structures can be embedded in the existing neuron tracing methods to reduce such topological errors...
January 18, 2018: Neuroinformatics
Jessica Schrouff, J M Monteiro, L Portugal, M J Rosa, C Phillips, J Mourão-Miranda
Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not incorporate previous knowledge about the brain structure and function into the models. Previous knowledge can be embedded into pattern recognition models by imposing a grouping structure based on anatomically or functionally defined brain regions. In this work, we present a novel approach that uses group sparsity to model the whole brain multivariate pattern as a combination of regional patterns...
January 3, 2018: Neuroinformatics
Pawel J Markiewicz, Matthias J Ehrhardt, Kjell Erlandsson, Philip J Noonan, Anna Barnes, Jonathan M Schott, David Atkinson, Simon R Arridge, Brian F Hutton, Sebastien Ourselin
We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis...
December 26, 2017: Neuroinformatics
Christof Seiler, Tamar Green, David Hong, Lindsay Chromik, Lynne Huffman, Susan Holmes, Allan L Reiss
Girls and women with Turner syndrome (TS) have a completely or partially missing X chromosome. Extensive studies on the impact of TS on neuroanatomy and cognition have been conducted. The integration of neuroanatomical and cognitive information into one consistent analysis through multi-table methods is difficult and most standard tests are underpowered. We propose a new two-sample testing procedure that compares associations between two tables in two groups. The procedure combines multi-table methods with permutation tests...
December 21, 2017: Neuroinformatics
Sascha E A Muenzing, Martin Strauch, James W Truman, Katja Bühler, Andreas S Thum, Dorit Merhof
The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping...
November 10, 2017: Neuroinformatics
Žiga Lesjak, Alfiia Galimzianova, Aleš Koren, Matej Lukin, Franjo Pernuš, Boštjan Likar, Žiga Špiclin
Quantified volume and count of white-matter lesions based on magnetic resonance (MR) images are important biomarkers in several neurodegenerative diseases. For a routine extraction of these biomarkers an accurate and reliable automated lesion segmentation is required. To objectively and reliably determine a standard automated method, however, creation of standard validation datasets is of extremely high importance. Ideally, these datasets should be publicly available in conjunction with standardized evaluation methodology to enable objective validation of novel and existing methods...
November 4, 2017: Neuroinformatics
Heath R Pardoe, Ruben Kuzniecky
The availability of cloud computing services has enabled the widespread adoption of the "software as a service" (SaaS) approach for software distribution, which utilizes network-based access to applications running on centralized servers. In this paper we apply the SaaS approach to neuroimaging-based age prediction. Our system, named "NAPR" (Neuroanatomical Age Prediction using R), provides access to predictive modeling software running on a persistent cloud-based Amazon Web Services (AWS) compute instance...
October 20, 2017: Neuroinformatics
Guan-Wei He, Ting-Yuan Wang, Ann-Shyn Chiang, Yu-Tai Ching
Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures...
October 14, 2017: Neuroinformatics
Vito Paolo Pastore, Aleksandar Godjoski, Sergio Martinoia, Paolo Massobrio
We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis...
October 7, 2017: Neuroinformatics
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