Deep Learning ‐based Classification of Resting‐state fMRI Independent‐component Analysis
AbstractFunctional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and...
Source: Neuroinformatics - February 5, 2021 Category: Neuroscience Source Type: research
Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs
We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N = 528 subjects for ICV,N = 501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and h igh white matter hyperintensity burden. Two of...
Source: Neuroinformatics - February 1, 2021 Category: Neuroscience Source Type: research
Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction
AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1)image pre-processing– the raw MR image is ...
Source: Neuroinformatics - January 27, 2021 Category: Neuroscience Source Type: research
A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility
AbstractThere is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and su...
Source: Neuroinformatics - January 27, 2021 Category: Neuroscience Source Type: research
Signaleeg
This article analyses state-of-the-art EEG signal processing tools and proposes a new one: Signaleeg. This addresses the limitations of previous tools. It has been designed with the aim of helping users to build predictive models from EEG signals in a process that is called signal-data mining (DM). Moreover, Signaleeg is user friendly and multi-threaded, with optimisation facilities for finding the best predictive model. It has been implemented and tested in three scenarios: schizophrenia diagnosis, alcoholism detection, and emotion recognition. The tool provided good results in each case, thus demonstrating its versatilit...
Source: Neuroinformatics - January 21, 2021 Category: Neuroscience Source Type: research
Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis
AbstractThere has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with...
Source: Neuroinformatics - January 18, 2021 Category: Neuroscience Source Type: research
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)
Source: Neuroinformatics - January 10, 2021 Category: Neuroscience Source Type: research
Dream
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
We examined whether changes induced with our framework were detected by FAST, SPM, SIENA, SIENAX, and the Jacobian integration method. We observed that induced and detected changes were highly correlated (Adj.R2> 0.86). Our preliminary results on harmonised datasets showed the potential of our framework to be applied to various data collections without further adjustment. (Source: Neuroinformatics)
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 decouple the ti...
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 overlap throughout...
Source: Neuroinformatics - November 16, 2020 Category: Neuroscience Source Type: research
