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 Gaussia...
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)
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 study provid...
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
