GeodesicSlicer: a Slicer Toolbox for Targeting Brain Stimulation
AbstractNonInvasive Brain Stimulation (NIBS) is a potential therapeutic tool with growing interest, but neuronavigation-guided software and tools available for the target determination are mostly either expensive or closed proprietary applications. To address these limitations, we propose GeodesicSlicer, a customizable, free, and open-source NIBS therapy research toolkit. GeodesicSlicer is implemented as an extension for the widely used 3D Slicer medical image visualization and analysis application platform. GeodesicSlicer uses cortical stimulation target from either functional or anatomical images to provide functionality...
Source: Neuroinformatics - March 3, 2020 Category: Neuroscience Source Type: research
Regularized Partial Least Square Regression for Continuous Decoding in Brain-Computer Interfaces
AbstractContinuous decoding is a crucial step in many types of brain-computer interfaces (BCIs). Linear regression techniques have been widely used to determine a linear relation between the input and desired output. A serious issue in this technique is the over-fitting phenomenon. Partial least square (PLS) is a well-known and popular method which tries to overcome this problem. PLS calculates a set of latent variables which are maximally correlated to the output and determines a linear relation between a low-rank estimation of the input and output data. However, this method has shown its potential to overfit the training...
Source: Neuroinformatics - February 27, 2020 Category: Neuroscience Source Type: research
NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity
AbstractNeuron shape and connectivity affect function. Modern imaging methods have proven successful at extracting morphological information. One potential path to achieve analysis of this morphology is through graph theory. Encoding by graphs enables the use of high throughput informatic methods to extract and infer brain function. However, the application of graph-theoretic methods to neuronal morphology comes with certain challenges in term of complex subgraph matching and the difficulty in computing intermediate shapes in between two imaged temporal samples. Here we report a novel, efficacious graph-theoretic method th...
Source: Neuroinformatics - February 27, 2020 Category: Neuroscience Source Type: research
Ontological Dimensions of Cognitive-Neural Mappings
In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that ‘task paradigm’ categories explain more variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, ‘study ID’, or the study from which each activation map was report...
Source: Neuroinformatics - February 18, 2020 Category: Neuroscience Source Type: research
Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change
AbstractAccurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer ’s (AD) participants. Data were downloaded ...
Source: Neuroinformatics - February 15, 2020 Category: Neuroscience Source Type: research
Understanding Computational Costs of Cellular-Level Brain Tissue Simulations Through Analytical Performance Models
AbstractComputational modeling and simulation have become essential tools in the quest to better understand the brain ’s makeup and to decipher the causal interrelations of its components. The breadth of biochemical and biophysical processes and structures in the brain has led to the development of a large variety of model abstractions and specialized tools, often times requiring high performance computing resour ces for their timely execution. What has been missing so far was an in-depth analysis of the complexity of the computational kernels, hindering a systematic approach to identifying bottlenecks of algorithms ...
Source: Neuroinformatics - February 13, 2020 Category: Neuroscience Source Type: research
Automatic Brain Extraction for Rodent MRI Images
AbstractRodent models are increasingly important in translational neuroimaging research. In rodent neuroimaging, particularly magnetic resonance imaging (MRI) studies, brain extraction is a critical data preprocessing component. Current brain extraction methods for rodent MRI usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. Here we propose a novel method, termed SHape descriptor selected Extremal Regions after Morphologically filtering (SHERM), which only requires a brain template mask as the input and is capable of automatically and reliably extracting the bra...
Source: Neuroinformatics - January 27, 2020 Category: Neuroscience Source Type: research
Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks
AbstractHybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling,...
Source: Neuroinformatics - January 13, 2020 Category: Neuroscience Source Type: research
A Rational Approach to Understanding and Evaluating Responsive Neurostimulation
AbstractClosed-loop brain stimulation is increasingly used in level 4 epilepsy centers without an understanding of how the device behaves on a daily basis. This lack of insight is a barrier to improving closed-loop therapy and ultimately understanding why some patients never achieve seizure reduction. We aimed to quantify the accuracy of closed-loop seizure detection and stimulation on the RNS device through extrapolating information derived from manually reviewed ECoG recordings and comprehensive device logging information. RNS System event logging data were obtained, reviewed, and analyzed using a custom-built software p...
Source: Neuroinformatics - January 9, 2020 Category: Neuroscience Source Type: research
Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics
AbstractElectro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are currently limited to single- or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.Here, we proposePorthole andSt...
Source: Neuroinformatics - January 5, 2020 Category: Neuroscience Source Type: research
Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition
AbstractCanonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification of ICAs as representatives of RSN time-consuming and largely dependent on visual inspection. Here, we present Personode, a user-friendly and open source MATLAB-based toolbox that semi-automatically performs the classifi...
Source: Neuroinformatics - January 3, 2020 Category: Neuroscience Source Type: research
FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation
AbstractSegmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the po...
Source: Neuroinformatics - January 2, 2020 Category: Neuroscience Source Type: research
Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI
In this study, a novel approach was proposed to accurately parcellate the whole cerebral cortex at the individual level using resting-state functional magnetic resonance image (rs-fMRI). To examine the functional homogeneity in parcellation, a new evaluation criterion, similarity of cluster (SC) coefficient, was proposed. The parcellation results demonstrated the high consistency between two resting-state sessions (Dice>0.72). The most consistent parcellation appeared in the frontal cortex and the least consistent parcellation appeared in the occipital cortex. The functional homogeneity of subregions was high in frontal...
Source: Neuroinformatics - December 4, 2019 Category: Neuroscience Source Type: research
Web Application for Quantification of Traumatic Brain Injury-Induced Cortical Lesions in Adult Mice
AbstractDisabilities resulting from traumatic brain injury (TBI) strongly correlate with the cytoarchitectonic part of the brain damaged, lesion area, and type of lesion. We developed a Web application to estimate the location of the lesion on mouse cerebral cortex caused by TBI induced by lateral fluid-percussion injury. The application unfolds user-determined TBI lesion measurements, e.g., from histologic sections to a reference template, and estimates the total lesion area, including the percentage of cortex damaged in different cytoarchitectural cortical regions. The resulting lesion can be visualized on a two-dimensio...
Source: Neuroinformatics - December 4, 2019 Category: Neuroscience Source Type: research
Diverse Community Structures in the Neuronal-Level Connectome of the Drosophila Brain
In this study, we constructed a neuron-to-neuron network of theDrosophila brain based on the 28,573 fluorescence images of single neurons in the newly releasedFlyCircuit v1.2 (http://www.flycircuit.tw) database. By performing modularity and centrality analyses, we identified eight communities (right olfaction, left olfaction, olfactory core, auditory, motor, pre-motor, left vision, and right vision) in the brain-wide network. Further investigation on information exchange and structural stability revealed that the communities of different functions dominated different types of centralities, suggesting a correlation between ...
Source: Neuroinformatics - December 3, 2019 Category: Neuroscience Source Type: research
A Novel 2D Standard Cartesian Representation for the Human Sensorimotor Cortex
AbstractFor some experimental approaches in brain imaging, the existing normalization techniques are not always sufficient. This may be the case if the anatomical shape of the region of interest varies substantially across subjects, or if one needs to compare the left and right hemisphere in the same subject. Here we propose a new standard representation, building upon existing normalization methods: Cgrid (Cartesian geometric representation with isometric dimensions). Cgrid is based on imposing a Cartesian grid over a cortical region of interest that is bounded by anatomical (atlas-based) landmarks. We applied this new re...
Source: Neuroinformatics - December 3, 2019 Category: Neuroscience Source Type: research
Neurosense: deep sensing of full or near-full coverage head/brain scans in human magnetic resonance imaging
Source: Neuroinformatics - November 21, 2019 Category: Neuroscience Source Type: research
A Large Deformation Diffeomorphic Framework for Fast Brain Image Registration via Parallel Computing and Optimization
AbstractIn this paper, we proposed an efficient approach for large deformation diffeomorphic metric mapping (LDDMM) for brain images by utilizing GPU-based parallel computing and a mixture automatic step size estimation method for gradient descent (MAS-GD). We systematically evaluated the proposed approach in terms of two matching cost functions, including the Sum of Squared Differences (SSD) and the Cross-Correlation (CC). The registration accuracy and computational efficiency on two datasets inducing respective 120 and 1,560 registration maps were evaluated and compared between CPU-based LDDMM-SSD and GPU-based LDDMM-SSD...
Source: Neuroinformatics - November 7, 2019 Category: Neuroscience Source Type: research
Zeffiro User Interface for Electromagnetic Brain Imaging: a GPU Accelerated FEM Tool for Forward and Inverse Computations in Matlab
This article introduces theZeffiro interface (ZI) version 2.2 for brain imaging. ZI aims to provide a simple, accessible and multimodal open source platform for finite element method (FEM) based and graphics processing unit (GPU) accelerated forward and inverse computations in the Matlab environment. It allows one to (1) generate a given multi-compartment head model, (2) to evaluate a lead field matrix as well as (3) to invert and analyze a given set of measurements. GPU acceleration is applied in each of the processing stages (1) –(3). In its current configuration, ZI includes forward solvers for electro-/magnetoenc...
Source: Neuroinformatics - October 9, 2019 Category: Neuroscience Source Type: research
Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data
AbstractMulti-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to different regions of the brain. However, its performance depends on the size of the sphere, which can overestimate the region of activation when a large sphere size is employed. In the current study, we examined the validity of two different alternatives to Searchlight: an atlas-based local averaging method (ABLA, Schrouff et al.Neuroinformatics 16, 117 –143,2013a) and a...
Source: Neuroinformatics - August 11, 2019 Category: Neuroscience Source Type: research
Brain-Wide Shape Reconstruction of a Traced Neuron Using the Convex Image Segmentation Method
In this study, we proposed a convex image segmentation model for neuronal shape reconstruction that segments a neurite into cross sections along its traced skeleton. Both the sparse nature of gradient images and the rule that fuzzy neurites usually have a small radius are utilized to improve neuronal shape reconstruction in regions with fuzzy neurites. Because the model is closely related to the traced skeleton point, we can use this relationship for identifying neurite with crossover regions. We demonstrated the performance of our model on various datasets, including those with fuzzy neurites and neurites with crossover r...
Source: Neuroinformatics - August 8, 2019 Category: Neuroscience Source Type: research
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 t...
Source: Neuroinformatics - August 3, 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 2, 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 4, 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 28, 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 25, 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 r...
Source: Neuroinformatics - June 24, 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). ...
Source: Neuroinformatics - June 11, 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 regressio...
Source: Neuroinformatics - May 16, 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 250). (Source: Neuroinformatics)
Source: Neuroinformatics - May 4, 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 23, 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 17, 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 16, 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 13, 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 10, 2019 Category: Neuroscience Source Type: research
Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging
AbstractAn important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means ...
Source: Neuroinformatics - March 27, 2019 Category: Neuroscience Source Type: research
Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics
AbstractMultivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (...
Source: Neuroinformatics - March 22, 2019 Category: Neuroscience Source Type: research
Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning
AbstractAutomatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, offering both superior accuracy and fast result. The proposed method is based on deep Convolutional Neural Networks (CNNs) and incorporates distinct segmentation and error correction steps. Segmentation masks are produced by an ensemble of three independent models, operating with orthogonal slices of the input volume, while erroneous labels are subsequently corrected by ...
Source: Neuroinformatics - March 15, 2019 Category: Neuroscience Source Type: research
Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment
AbstractLittle is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this metric could provide supplementary information to traditional FC for early Alzheimer ’s disease (AD) detection. However, whether such findings apply to network-level brain functional integration is unknown. In this paper, we propose an extended HOFC method, termed int...
Source: Neuroinformatics - February 9, 2019 Category: Neuroscience Source Type: research
Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling
AbstractHere we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the ...
Source: Neuroinformatics - January 16, 2019 Category: Neuroscience Source Type: research
Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites
AbstractTracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to cla...
Source: Neuroinformatics - January 11, 2019 Category: Neuroscience Source Type: research
What Is Old Is New Again: Investigating and Analyzing the Mysteries of the Claustrum
Source: Neuroinformatics - January 7, 2019 Category: Neuroscience Source Type: research
Stochastic Rank Aggregation for the Identification of Functional Neuromarkers
AbstractThe main challenge in analysing functional magnetic resonance imaging (fMRI) data from extended samples of subject (N > 100) is to extract as much relevant information as possible from big amounts of noisy data. When studying neurodegenerative diseases with resting-state fMRI, one of the objectives is to determine regions with abnormal background activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. In this work, we propose a novel approach based on clustering and ...
Source: Neuroinformatics - January 2, 2019 Category: Neuroscience Source Type: research
Small Animal Multivariate Brain Analysis (SAMBA) – a High Throughput Pipeline with a Validation Framework
AbstractWhile many neuroscience questions aim to understand the human brain, much current knowledge has been gained using animal models, which replicate genetic, structural, and connectivity aspects of the human brain. While voxel-based analysis (VBA) of preclinical magnetic resonance images is widely-used, a thorough examination of the statistical robustness, stability, and error rates is hindered by high computational demands of processing large arrays, and the many parameters involved therein. Thus, workflows are often based on intuition or experience, while preclinical validation studies remain scarce. To increase thro...
Source: Neuroinformatics - December 19, 2018 Category: Neuroscience Source Type: research
Automatic Thalamus Segmentation on Unenhanced 3D T1 Weighted Images: Comparison of Publicly Available Segmentation Methods in a Pediatric Population
AbstractThe anatomical structure of the thalamus renders its segmentation on 3DT1 images harder due to its low tissue contrast, and not well-defined boundaries. We aimed to investigate the differences in the precision of publicly available segmentation techniques on 3DT1 images acquired at 1.5 T and 3 T machines compared to the thalamic manual segmentation in a pediatric population. Sixty-eight subjects were recruited between the ages of one and 18 years. Manual segmentation of the thalamus was done by three junior raters, and then corrected by an experienced rater. Automated segmenta tion was then performe...
Source: Neuroinformatics - December 14, 2018 Category: Neuroscience Source Type: research
Automated Neuron Reconstruction from 3D Fluorescence Microscopy Images Using Sequential Monte Carlo Estimation
AbstractMicroscopic images of neuronal cells provide essential structural information about the key constituents of the brain and form the basis of many neuroscientific studies. Computational analyses of the morphological properties of the captured neurons require first converting the structural information into digital tree-like reconstructions. Many dedicated computational methods and corresponding software tools have been and are continuously being developed with the aim to automate this step while achieving human-comparable reconstruction accuracy. This pursuit is hampered by the immense diversity and intricacy of neur...
Source: Neuroinformatics - December 12, 2018 Category: Neuroscience Source Type: research
Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering
In this study, the ALS disease progression is measured by the change of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) score over time. The study aims to provide clinical decision support for timely forecasting of the ALS trajectory as well as accurate and reproducible computable phenotypic clustering of participants. Patient data are extracted from DREAM-Phil Bowen ALS Prediction Prize4Life Challenge data, most of which are from the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) archive. We employed model-based and model-free machine-learning methods to predict the change of the ALSFRS ...
Source: Neuroinformatics - November 20, 2018 Category: Neuroscience Source Type: research
A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience
AbstractThe curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of en...
Source: Neuroinformatics - November 15, 2018 Category: Neuroscience Source Type: research