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 26, 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 21, 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 14, 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 inter-n...
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)
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 stochastic...
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 performed with FSL A...
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
An End-to-end System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging
AbstractTraumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. Detailed studies of the microglial response after TBI require high throughput quantification of changes in microglial count and morphology in histological sections throughout the brain. In this paper, we present a fully automated end-to-end system that is capable of assessing microglial activation in white matter regions on whole slide images of Iba1 stained sections. Our approach involves the division of the full brain slides into smaller image patches that are subsequently automatically classified into white and grey matt...
Source: Neuroinformatics - November 8, 2018 Category: Neuroscience Source Type: research
Automated Metadata Suggestion During Repository Submission
AbstractKnowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these categories risks reducing completeness in the other because manually curating metadata is time consuming and is restricted by familiarity with both the data and the metadata annotation scheme. The diverse interests of a research community may drive a resource to have hundreds of metadata tags with few examples for each making it challenging for humans or machine learni...
Source: Neuroinformatics - October 31, 2018 Category: Neuroscience Source Type: research
