Response to “Fallacies of Mice Experiments”
AbstractIn a recent Editorial, De Schutter commented on our recent study on the roles of a cortico-cerebellar loop in motor planning in mice (De Schutter 2019,Neuroinformatics, 17, 181 –183, Gao et al. 2018,Nature, 563, 113 –116). Two issues were raised. First, De Schutter questions the involvement of the fastigial nucleus in motor planning, rather than the dentate nucleus, given previous anatomical studies in non-human primates. Second, De Schutter suggests that our study design did not delineate different component s of the behavior and the fastigial nucleus might play roles in sensory discrimination rather than moto...
Source: Neuroinformatics - August 2, 2019 Category: Neuroscience Source Type: research
Automated Brain Region Segmentation for Single Cell Resolution Histological Images Based on Markov Random Field
AbstractThe brain consists of massive regions with different functions and the precise delineation of brain region boundaries is important for brain region identification and atlas illustration. In this paper we propose a hierarchical Markov random field (MRF) model for brain region segmentation, where a MRF is applied to the downsampled low-resolution images and the result is used to initialize another MRF for the original high-resolution images. A fractional differential feature and a gray level co-occurrence matrix are extracted as the observed vector for the MRF and a new potential energy function, which can capture th...
Source: Neuroinformatics - August 1, 2019 Category: Neuroscience Source Type: research
MEAnalyzer – a Spike Train Analysis Tool for Multi Electrode Arrays
AbstractDespite a multitude of commercially available multi-electrode array (MEA) systems that are each capable of rapid data acquisition from cultured neurons or slice cultures, there is a general lack of available analysis tools. These analysis gaps restrict the efficient extraction of meaningful physiological features from data sets, and limit interpretation of how experimental manipulations modify neural network activity. Here, we present the development of a user-friendly, publicly-available software called MEAnalyzer. This software contains several spike train analysis methods including relevant statistical calculati...
Source: Neuroinformatics - July 3, 2019 Category: Neuroscience Source Type: research
Fully-Automated Identification of Imaging Biomarkers for Post-Operative Cerebellar Mutism Syndrome Using Longitudinal Paediatric MRI
This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that ar e related to the development of POPCMS following tumor resection in the posterior fossa. (Source: Neuroinformatics)
Source: Neuroinformatics - June 27, 2019 Category: Neuroscience Source Type: research
A Data Structure for Real-Time Aggregation Queries of Big Brain Networks
AbstractRecent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of the key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, these tend to exceed the past state-of-the art in size and complexity by several orders of magnitude. Since current analytical ...
Source: Neuroinformatics - June 24, 2019 Category: Neuroscience Source Type: research
A Comprehensive Framework to Capture the Arcana of Neuroimaging Analysis
We present a novel software framework,Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, “end-to-end” workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retri eves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by ...
Source: Neuroinformatics - June 23, 2019 Category: Neuroscience Source Type: research
DisConICA: a Software Package for Assessing Reproducibility of Brain Networks and their Discriminability across Disorders
In this study, we present DisConICA or “DiscoverConfirmIndependentComponentAnalysis ”, a software package that implements the methodology in support of our hypothesis. It relies on a “discover-confirm” approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generali zed Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique featu...
Source: Neuroinformatics - June 10, 2019 Category: Neuroscience Source Type: research
3D-Deep Learning Based Automatic Diagnosis of Alzheimer ’s Disease with Joint MMSE Prediction Using Resting-State fMRI
AbstractWe performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer ’s disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (L...
Source: Neuroinformatics - May 15, 2019 Category: Neuroscience Source Type: research
Imputation Strategy for Reliable Regional MRI Morphological Measurements
We examined our approach of correcting segmentation outputs on a cohort of 970 subjects, which were undergone an extensive, time-consuming, manual post-segmentation correction. Arandom forest imputation technique recovered thegold standard results with a significant accuracy (r = 0.93,p < 0.0001; when 30% of the segmentations were considered incorrect in a non-random fashion). Therandom forest technique proved to be most effective forbig data studies (N > 250). (Source: Neuroinformatics)
Source: Neuroinformatics - May 3, 2019 Category: Neuroscience Source Type: research
Hierarchical Structured Sparse Learning for Schizophrenia Identification
AbstractFractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured ...
Source: Neuroinformatics - April 22, 2019 Category: Neuroscience Source Type: research
Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU
In this study, cellular automata technique is applied to the geodesic tractography problem and the algorithm is implemented on a graphics processing unit. Cellular automaton based method is preferable to current techniques due to its parallel nature and ability to solve the connectivity based segmentation problem with the same computational complexity, which has important applications in neuroimaging. An application to prior-less tracking and connectivity based segmentation of corpus callosum fibers is presented as an example. A geodesic tractography based corpus callosum atlas is provided, which reveals high projections t...
Source: Neuroinformatics - April 16, 2019 Category: Neuroscience Source Type: research
Correction to: PyPNS: Multiscale Simulation of a Peripheral Nerve in Python
The original version of this article unfortunately contained a mistake. The following text: “This project has received funding from European Research Council (ERC) Synergy Grant no. 319818.” is missing in the Acknowledgments. (Source: Neuroinformatics)
Source: Neuroinformatics - April 15, 2019 Category: Neuroscience Source Type: research
Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification
AbstractFunctional connectivity networks, derived from resting-state fMRI data, have been found as effective biomarkers for identifying mild cognitive impairment (MCI) from healthy elderly. However, the traditional functional connectivity network is essentially a low-order network with the assumption that the brain activity is static over the entire scanning period, ignoring temporal variations among the correlations derived from brain region pairs. To overcome this limitation, we proposed a new type of sparse functional connectivity network to precisely describe the relationship of temporal correlations among brain region...
Source: Neuroinformatics - April 12, 2019 Category: Neuroscience Source Type: research
NengoDL: Combining Deep Learning and Neuromorphic Modelling Methods
AbstractNengoDL is a software framework designed to combine the strengths of neuromorphic modelling and deep learning. NengoDL allows users to construct biologically detailed neural models, intermix those models with deep learning elements (such as convolutional networks), and then efficiently simulate those models in an easy-to-use, unified framework. In addition, NengoDL allows users to apply deep learning training methods to optimize the parameters of biological neural models. In this paper we present basic usage examples, benchmarking, and details on the key implementation elements of NengoDL. More details can be found...
Source: Neuroinformatics - April 9, 2019 Category: Neuroscience Source Type: research
Fallacies of Mice Experiments
(Source: Neuroinformatics)
Source: Neuroinformatics - April 1, 2019 Category: Neuroscience Source Type: research
