Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes
AbstractElectron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy, compared to previous approaches on public mitochondria segmentation datasets. Unfortunately, many of these publications make neither the code nor the full training details public, leading to reproducibility issues and dubious model comparisons. Thus, following a recent code of best practices in the field, we present an...
Source: Neuroinformatics - December 2, 2021 Category: Neuroscience Source Type: research
ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability
AbstractThe FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research productsFindable,Accessible,Interoperable, andReusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymized Computation (COINSTAC) platform in the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium combines the technological approach of decentralized analyses with the sociological approach of sharing data. In addition, ENIGMA + COINSTAC provides a platform to facilitate the use of machine-actionable data objects. We first present how ENIGMA and COINSTA...
Source: Neuroinformatics - November 30, 2021 Category: Neuroscience Source Type: research
Assessing Computational Methods to Quantify Mother-Child Brain Synchrony in Naturalistic Settings Based on fNIRS Signals
AbstractMother-child brain-to-brain synchrony captures the temporal similarities in brain signals between dyadic partners, and has been shown to emerge during the display of joint behaviours. Despite the rise in the number of studies that investigate synchrony in naturalistic contexts, the use of varying methodological approaches to compute synchrony remains a central problem. When dyads engage in unstructured social interactions, the wide range of behavioural cues they display contribute to the use of varying lengths of signals to compute synchrony. The present functional Near-infrared Spectroscopy (fNIRS) study investiga...
Source: Neuroinformatics - November 30, 2021 Category: Neuroscience Source Type: research
Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer ’s Disease
AbstractGraph theory has been extensively used to investigate brain network topology and its changes in disease cohorts. However, many graph theoretic analysis-based brain network studies focused on the shortest paths or, more generally, cost-efficiency. In this work, we use two new concepts, connectedness and 2-connectedness, to measure different global properties compared to the previously widely adopted ones. We apply them to unravel interesting characteristics in the brain, such as redundancy design and further conduct a time-varying brain functional network analysis for characterizing the progression of Alzheimer ’s...
Source: Neuroinformatics - November 26, 2021 Category: Neuroscience Source Type: research
Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas
AbstractHuman brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, m...
Source: Neuroinformatics - November 26, 2021 Category: Neuroscience Source Type: research
Federated Analysis of Neuroimaging Data: A Review of the Field
AbstractThe field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperabil...
Source: Neuroinformatics - November 22, 2021 Category: Neuroscience Source Type: research
TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis
AbstractAccurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To add...
Source: Neuroinformatics - November 11, 2021 Category: Neuroscience Source Type: research
3dSpAn: An interactive software for 3D segmentation and analysis of dendritic spines
AbstractThree-dimensional segmentation and analysis of dendritic spine morphology involve two major challenges: 1) how to segment individual spines from the dendrites and 2) how to quantitatively assess the morphology of individual spines. To address these two issues, we developed software called 3dSpAn (3-dimensional Spine Analysis), based on implementing a previously published method, 3D multi-scale opening algorithm in shared intensity space. 3dSpAn consists of four modules: a) Preprocessing and Region of Interest (ROI) selection, b) Intensity thresholding and seed selection, c) Multi-scale segmentation, and d) Quantita...
Source: Neuroinformatics - November 7, 2021 Category: Neuroscience Source Type: research
Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy
AbstractDespite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temp...
Source: Neuroinformatics - October 29, 2021 Category: Neuroscience Source Type: research
Farewell, Neuroinformatics!
(Source: Neuroinformatics)
Source: Neuroinformatics - October 29, 2021 Category: Neuroscience Source Type: research
A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience
AbstractNeuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problem...
Source: Neuroinformatics - October 28, 2021 Category: Neuroscience Source Type: research
Convolutional Neural Network Based Frameworks for Fast Automatic Segmentation of Thalamic Nuclei from Native and Synthesized Contrast Structural MRI
Abstract
Thalamic nuclei have been implicated in several neurological diseases. Thalamic nuclei parcellation from structural MRI is challenging due to poor intra-thalamic nuclear contrast while methods based on diffusion and functional MRI are affected by limited spatial resolution and image distortion. Existing multi-atlas based techniques are often computationally intensive and time-consuming. In this work, we propose a 3D convolutional neural network (CNN) based framework for thalamic nuclei parcellation using T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) images. Transformation of images to an effic...
Source: Neuroinformatics - October 9, 2021 Category: Neuroscience Source Type: research
A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling
AbstractWith advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, the Neuromorphological File Specification, available atwww.mbfbioscience.com/filespecification (Angstman et al.,2020). The format, created and maintained by MBF Bioscience, is broadly utilized by the neuroscience community. The data format ’s structure and capabilities have evolved since its inception, with modifications made to kee...
Source: Neuroinformatics - October 2, 2021 Category: Neuroscience Source Type: research
Extreme Learning Machine Design for Dealing with Unrepresentative Features
AbstractExtreme Learning Machines (ELMs) have become a popular tool for the classification of electroencephalography (EEG) signals for Brain Computer Interfaces. This is so mainly due to their very high training speed and generalization capabilities. Another important advantage is that they have only one hyperparameter that must be calibrated: the number of hidden nodes. While most traditional approaches dictate that this parameter should be chosen smaller than the number of available training examples, in this article we argue that, in the case of problems in which the data contain unrepresentative features, such as in EE...
Source: Neuroinformatics - September 29, 2021 Category: Neuroscience Source Type: research
Cross-validated Adaboost Classification of Emotion Regulation Strategies Identified by Spectral Coherence in Resting-State
AbstractIn the present study, quantitative relations between Cognitive Emotion Regulation strategies (CERs) and EEG synchronization levels have been investigated for the first time. For this purpose, spectral coherence (COH), phase locking value and mutual information have been applied to short segments of 62-channel resting state eyes-opened EEG data collected from healthy adults who use contrasting emotion regulation strategies (frequently and rarely use of rumination&distraction, frequently and rarely use of suppression&reappraisal). In tests, the individuals are grouped depending on their self-responses to both...
Source: Neuroinformatics - September 18, 2021 Category: Neuroscience Source Type: research
