Reflections on Data Sharing Practices in Spinal Cord Injury Research
AbstractThere are few pharmacological therapeutics available for spinal cord injury despite years of preclinical and clinical research. This brief editorial discusses some of the shortcomings of translational research efforts. In addition, we comment on our previous experiences with data curation and highlight evolving efforts by the spinal cord injury research community to improve prospects for future therapeutic development, especially pertaining to preclinical data sharing through the Open Data Commons for Spinal Cord Injury (ODC-SCI). (Source: Neuroinformatics)
Source: Neuroinformatics - January 16, 2021 Category: Neuroscience Source Type: research
Data-Theoretical Synthesis of the Early Developmental Process
AbstractBiological development is often described as a dynamic, emergent process. This is evident across a variety of phenomena, from the temporal organization of cell types in the embryo to compounding trends that affect large-scale differentiation. To better understand this, we propose combining quantitative investigations of biological development with theory-building techniques. This provides an alternative to the gene-centric view of development: namely, the view that developmental genes and their expression determine the complexity of the developmental phenotype. Using the model systemCaenorhabditis elegans, we exami...
Source: Neuroinformatics - January 15, 2021 Category: Neuroscience Source Type: research
Automatic Denoising of Single-Trial Event-Related Potentials
Source: Neuroinformatics - January 10, 2021 Category: Neuroscience Source Type: research
AbstractRhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehav...
Source: Neuroinformatics - January 7, 2021 Category: Neuroscience Source Type: research
RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection
AbstractHippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typ...
Source: Neuroinformatics - January 4, 2021 Category: Neuroscience Source Type: research
Causal Network Inference for Neural Ensemble Activity
AbstractInteractions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality of the variable space. A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series. CAIM combines neural recording, Bayesian n...
Source: Neuroinformatics - January 4, 2021 Category: Neuroscience Source Type: research
Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors
AbstractBrain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification methods. We propose a deep learning framework to generate longitudinal datasets by deforming T1-w brain magnetic resonance imaging scans as requested through segmentation maps. Our proposal incorporates a cascaded multi-path U-Net optimised with a multi-objective loss which allows its paths to generate different brain regions accurately. We provided our model with base...
Source: Neuroinformatics - January 2, 2021 Category: Neuroscience Source Type: research
Improving Covariance Matrices Derived from Tiny Training Datasets for the Classification of Event-Related Potentials with Linear Discriminant Analysis
AbstractElectroencephalogram data used in the domain of brain –computer interfaces typically has subpar signal-to-noise ratio and data acquisition is expensive. An effective and commonly used classifier to discriminate event-related potentials is the linear discriminant analysis which, however, requires an estimate of the feature distribution. While this inf ormation is provided by the feature covariance matrix its large number of free parameters calls for regularization approaches like Ledoit–Wolf shrinkage. Assuming that the noise of event-related potential recordings is not time-locked, we propose to decoupl...
Source: Neuroinformatics - December 14, 2020 Category: Neuroscience Source Type: research
Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography
AbstractBrain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate “regions” rather than as white matter “bundles” or fascicles, which are well-known to ov...
Source: Neuroinformatics - November 16, 2020 Category: Neuroscience Source Type: research
An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy
In conclusion, we established an optimized reference atlas for more precise mapping of fluoresc ent markers, including c-Fos, in mouse brains processed for LSFM. (Source: Neuroinformatics)
Source: Neuroinformatics - October 16, 2020 Category: Neuroscience Source Type: research
Deep Representational Similarity Learning for Analyzing Neural Signatures in Task-based fMRI Dataset
AbstractSimilarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by different cognitive states. This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality — such as whole-brain images. Unlike the previous methods, DRSL is not limited by a linear transformation or a restricted fixed nonlinear kernel function — such as...
Source: Neuroinformatics - October 15, 2020 Category: Neuroscience Source Type: research
3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials
AbstractIntracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. X-ray computed tomography (CT) scans allow clinicians and researchers to qualitatively and quantitatively diagnose hemorrhagic stroke, guide interventions and determine inclusion criteria of patients in clinical trials. There is no currently available open source, validated tool to quickly segment hemorrhage. Using an automated pipeline and 2D and 3D deep neural networks, we show that we can quickly and accurately ...
Source: Neuroinformatics - September 26, 2020 Category: Neuroscience Source Type: research
Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma
This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, backgro...
Source: Neuroinformatics - September 24, 2020 Category: Neuroscience Source Type: research
An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity
This study offers a broad and simplified view of histological and microscopical knowledge in laminar research that is applicable to the limitations of MRI methodologies, primarily regarding specificity and resolution. (Source: Neuroinformatics)
Source: Neuroinformatics - September 18, 2020 Category: Neuroscience Source Type: research
Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting
AbstractIn certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors. In settings where only few of those predictors are expected to be informative, a sparse model fit can be obtained via L1-regularization. However, estimating L1-regularized models requires an iterative fitting proc...
Source: Neuroinformatics - September 14, 2020 Category: Neuroscience Source Type: research
Musclesense: a Trained, Artificial Neural Network for the Anatomical Segmentation of Lower Limb Magnetic Resonance Images in Neuromuscular Diseases
Source: Neuroinformatics - September 4, 2020 Category: Neuroscience Source Type: research
Tractography Processing with the Sparse Closest Point Transform
AbstractWe propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative ...
Source: Neuroinformatics - August 28, 2020 Category: Neuroscience Source Type: research
Cross-Sectional Volumes and Trajectories of the Human Brain, Gray Matter, White Matter and Cerebrospinal Fluid in 9473 Typically Aging Adults
AbstractAccurate knowledge of adult human brain volume (BV) is critical for studies of aging- and disease-related brain alterations, and for monitoring the trajectories of neural and cognitive functions in conditions like Alzheimer ’s disease and traumatic brain injury. This scoping meta-analysis aggregates normative reference values for BV and three related volumetrics—gray matter volume (GMV), white matter volume (WMV) and cerebrospinal fluid volume (CSFV)—from typically-aging adults studied cross-sectionally using mag netic resonance imaging (MRI). Drawing from an aggregate sample of 9473 adults, this ...
Source: Neuroinformatics - August 26, 2020 Category: Neuroscience Source Type: research
Addressing Pitfalls in Phase-Amplitude Coupling Analysis with an Extended Modulation Index Toolbox
AbstractPhase-amplitude coupling (PAC) is proposed to play an essential role in coordinating the processing of information on local and global scales. In recent years, the methods able to reveal trustworthy PAC has gained considerable interest. However, the intrinsic features of some signals can lead to the identification of spurious or waveform-dependent coupling. This prompted us to develop an easily accessible tool that could be used to differentiate spurious from authentic PAC. Here, we propose a new tool for more reliable detection of PAC named the Extended Modulation Index (eMI) based on the classical Modulation Inde...
Source: Neuroinformatics - August 25, 2020 Category: Neuroscience Source Type: research
GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population
In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software....
Source: Neuroinformatics - August 24, 2020 Category: Neuroscience Source Type: research
Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies
AbstractLarge-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In...
Source: Neuroinformatics - August 20, 2020 Category: Neuroscience Source Type: research
DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks
AbstractThe extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain images are normally not naturally aligned even when they are imaged with the same setup, let alone under the differing resolutions and dataset sizes used in mesoscopic imaging. As a result, it is difficult to achieve high-throughput automatic registration without manual intervention. Here, we propose a deep learning-based registration method called DeepMapi to predict ...
Source: Neuroinformatics - August 3, 2020 Category: Neuroscience Source Type: research
Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability
AbstractHuman brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Conten...
Source: Neuroinformatics - July 28, 2020 Category: Neuroscience Source Type: research
RT-NET: real-time reconstruction of neural activity using high-density electroencephalography
AbstractHigh-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET...
Source: Neuroinformatics - July 27, 2020 Category: Neuroscience Source Type: research
Constructing Connectome Atlas by Graph Laplacian Learning
AbstractThe recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a “common” brain connectivity map (also calledconnectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structu...
Source: Neuroinformatics - July 24, 2020 Category: Neuroscience Source Type: research
Supervised Multidimensional Scaling and its Application in MRI-Based Individual Age Predictions
In this study, we advanced a novel supervised DR technique for regression purposes, namely, supervised multidimensional scaling (SMDS). The implementation of SMDS includes two steps: (1) evaluating pairwise distances among entities based on their labels and constructing a new space through a distance-preserving projection; (2) establishing an explicit linear relationship between the feature space and the new space. Based on this linear relationship, DR for test entities can be performed. We evaluated the performance of SMDS first on a synthetic dataset, and the results indicate that (1) SMDS is relatively robust to Gaussia...
Source: Neuroinformatics - July 15, 2020 Category: Neuroscience Source Type: research
MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity
We present hereMEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings.MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expectMEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms. (Source: Neuroinformatics)
Source: Neuroinformatics - July 8, 2020 Category: Neuroscience Source Type: research
DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data
In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data. (Source: Neuroinformatics)
Source: Neuroinformatics - July 4, 2020 Category: Neuroscience Source Type: research
SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance
AbstractSpike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby ...
Source: Neuroinformatics - July 1, 2020 Category: Neuroscience Source Type: research
DeepNeuro: an open-source deep learning toolbox for neuroimaging
We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces. (Source: Neuroinformatics)
Source: Neuroinformatics - June 22, 2020 Category: Neuroscience Source Type: research
supFunSim : Spatial Filtering Toolbox for EEG
AbstractBrain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. ThesupFunSim library is a newMatlab toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) measure. ThesupFunSim library is based on the we...
Source: Neuroinformatics - June 20, 2020 Category: Neuroscience Source Type: research
Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI
AbstractThe firstgeneration of brain-computer interfaces (BCI) classifies multi-channel electroencephalographic (EEG) signals, enhanced by optimized spatial filters.The second generation directly classifies covariance matrices estimated on EEG signals, based on straightforward algorithms such as the minimum-distance-to-Riemannian-mean (MDRM). Classification results vary greatly depending on the chosen Riemannian distance or divergence, whose definitions and reference implementations are spread across a wide mathematical literature. This paper reviews all the Riemannian distances and divergences to process covariance matric...
Source: Neuroinformatics - June 18, 2020 Category: Neuroscience Source Type: research
NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data
AbstractThe development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade. Collaborative analysis techniques emerge as the idea developed. In this paper, we present NEURO-LEARN, a solution for collaborative pattern analysis of neuroimaging data. Its collaboration scheme consists of four parts: p...
Source: Neuroinformatics - June 9, 2020 Category: Neuroscience Source Type: research
Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer ’s Disease
AbstractDiffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer ’s disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV ) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previou...
Source: Neuroinformatics - June 9, 2020 Category: Neuroscience Source Type: research
Regularized Bagged Canonical Component Analysis for Multiclass Learning in Brain Imaging
AbstractA fundamental problem of supervised learning algorithms for brain imaging applications is that the number of features far exceeds the number of subjects. In this paper, we propose a combined feature selection and extraction approach for multiclass problems. This method starts with a bagging procedure which calculates the sign consistency of the multivariate analysis (MVA) projection matrix feature-wise to determine the relevance of each feature. This relevance measure provides a parsimonious matrix, which is combined with a hypothesis test to automatically determine the number of selected features. Then, a novel MV...
Source: Neuroinformatics - June 4, 2020 Category: Neuroscience Source Type: research
BVAR-Connect : A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks
AbstractIn this paper we proposeBVAR-connect, a variational inference approach to a Bayesian multi-subject vector autoregressive (VAR) model for inference on effective brain connectivity based on resting-state functional MRI data. The modeling framework uses a Bayesian variable selection approach that flexibly integrates multi-modal data, in particular structural diffusion tensor imaging (DTI) data, into the prior construction. The variational inference approach we develop allows scalability of the methods and results in the ability to estimate subject- and group-level brain connectivity networks over whole-brain parcellat...
Source: Neuroinformatics - June 4, 2020 Category: Neuroscience Source Type: research
QFib: Fast and Efficient Brain Tractogram Compression
AbstractDiffusion MRI fiber tracking datasets can contain millions of 3D streamlines, and their representation can weight tens of gigabytes of memory. These sets of streamlines are called tractograms and are often used for clinical operations or research. Their size makes them difficult to store, visualize, process or exchange over the network. We propose a new compression algorithm well-suited for tractograms, by taking advantage of the way streamlines are obtained with usual tracking algorithms. Our approach is based on unit vector quantization methods combined with a spatial transformation which results in low compressi...
Source: Neuroinformatics - May 29, 2020 Category: Neuroscience Source Type: research
Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data
AbstractReconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational m...
Source: Neuroinformatics - May 23, 2020 Category: Neuroscience Source Type: research
A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination
AbstractQuantitative analysis of neuronal morphologies usually begins with choosing a particular feature representation in order to make individual morphologies amenable to standard statistics tools and machine learning algorithms. Many different feature representations have been suggested in the literature, ranging from density maps to intersection profiles, but they have never been compared side by side. Here we performed a systematic comparison of various representations, measuring how well they were able to capture the difference between known morphological cell types. For our benchmarking effort, we used several curat...
Source: Neuroinformatics - May 3, 2020 Category: Neuroscience Source Type: research
Colocalization Colormap –an ImageJ Plugin for the Quantification and Visualization of Colocalized Signals
Source: Neuroinformatics - May 1, 2020 Category: Neuroscience Source Type: research
Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification
AbstractNeurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal an...
Source: Neuroinformatics - April 28, 2020 Category: Neuroscience Source Type: research
FEMfuns: A Volume Conduction Modeling Pipeline that Includes Resistive, Capacitive or Dispersive Tissue and Electrodes
In this study, this need is addressed by introducing the first open-source pipeline, FEMfuns (finite element method for useful neuroscience simulations), that allows neuroscientists to solve the forward problem in a variety of different geometrical domains, including different types of source models and electrode properties, such as resistive and capacitive materials. FEMfuns is based on the finite element method (FEM) implemented in FEniCS and includes the geometry tessellation, several electrode-electrolyte implementations and adaptive refinement options. ThePython code of the pipeline is available under the GNU General ...
Source: Neuroinformatics - April 17, 2020 Category: Neuroscience Source Type: research
Human Brain Atlases in Stroke Management
AbstractStroke is a leading cause of death and a major cause of permanent disability. Its management is demanding because of variety of protocols, imaging modalities, pulse sequences, hemodynamic maps, criteria for treatment, and time constraints to promptly evaluate and treat. To cope with some of these issues, we propose novel, patented solutions in stroke management by employing multiple brain atlases for diagnosis, treatment, and prediction. Numerous and diverse CT and MRI scans are used: ARIC cohort, ischemic and hemorrhagic stroke CT cases, MRI cases with multiple pulse sequences, and 128 stroke CT patients, each wit...
Source: Neuroinformatics - April 14, 2020 Category: Neuroscience Source Type: research
Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data
In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray ...
Source: Neuroinformatics - April 12, 2020 Category: Neuroscience Source Type: research
Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study
AbstractChanges in cognitive performance due to neurodegenerative diseases such as Alzheimer ’s disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biom arkers of this type have been developed, especially those that are robust to image noise and applicable to clinical analyses. In this paper, we introduce a variational framework to compute optimal transportation (OT) on brain struct...
Source: Neuroinformatics - April 5, 2020 Category: Neuroscience Source Type: research
NeAT: a Nonlinear Analysis Toolbox for Neuroimaging
AbstractNeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzh...
Source: Neuroinformatics - March 23, 2020 Category: Neuroscience Source Type: research
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 2, 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 26, 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 26, 2020 Category: Neuroscience Source Type: research