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)
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 structural...
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
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 reported, explained cl...
Source: Neuroinformatics - February 17, 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 from...
Source: Neuroinformatics - February 14, 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 and ...
Source: Neuroinformatics - February 12, 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 26, 2020 Category: Neuroscience Source Type: research
