Shamo: A Tool for Electromagnetic Modeling, Simulation and Sensitivity Analysis of the Head
AbstractAccurate electromagnetic modeling of the head of a subject is of main interest in the fields of source reconstruction and brain stimulation. Those processes rely heavily on the quality of the model and, even though the geometry of the tissues can be extracted from magnetic resonance images (MRI) or computed tomography (CT), their physical properties such as the electrical conductivity are difficult to measure with non intrusive techniques. In this paper, we propose a tool to assess the uncertainty in the model parameters, the tissue conductivity, as well as compute a parametric forward models for electroencephalogr...
Source: Neuroinformatics - March 10, 2022 Category: Neuroscience Source Type: research
Design and Application of Automated Algorithms for Diagnosis and Treatment Optimization in Neurodegenerative Diseases
AbstractNeurodegenerative diseases represent a growing healthcare problem, mainly related to an aging population worldwide and thus their increasing prevalence. In particular, Alzheimer ’s disease (AD) and Parkinson’s disease (PD) are leading neurodegenerative diseases. To aid their diagnosis and optimize treatment, we have developed a classification algorithm for AD to manipulate magnetic resonance images (MRI) stored in a large database of patients, containing 1,200 images. T he algorithm can predict whether a patient is healthy, has mild cognitive impairment, or already has AD. We then applied this classification al...
Source: Neuroinformatics - March 9, 2022 Category: Neuroscience Source Type: research
nGauge: Integrated and Extensible Neuron Morphology Analysis in Python
AbstractThe study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. The packages, however, provide relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developednGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API),nGauge can be referenced ...
Source: Neuroinformatics - March 5, 2022 Category: Neuroscience Source Type: research
A Practical Workflow for Organizing Clinical Intraoperative and Long-term iEEG Data in BIDS
We describe the 6 steps in the pipeline that are essential to structure the data from these clinical iEEG recordings into BIDS and the challenges during this process. These proposed workflow enable centers performing clinical iEEG recordings to structure their data to improve accessibility, reusability and interoperability of clinical data. (Source: Neuroinformatics)
Source: Neuroinformatics - March 4, 2022 Category: Neuroscience Source Type: research
Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM
This study proposes a dynamic hyperalignment algorithm (dHA) to functionally align the inter-subject neural activity. The hidden Markov model (HMM) was used to study how the brain dynamics responds to emotion during long-time movie-viewing activity. The results showed that dHA significantly improved inter-subject consistency and allowed more consistent temporal HMM states across participants. Afterward, grouping the emotions in a clustering dendrogram revealed a hierarchical grouping of the HMM states. Further emotional sensitivity and specificity analyses of ordered states revealed the most significant differences in happ...
Source: Neuroinformatics - March 4, 2022 Category: Neuroscience Source Type: research
Neuroimaging-ITM: A Text Mining Pipeline Combining Deep Adversarial Learning with Interaction Based Topic Modeling for Enabling the FAIR Neuroimaging Study
AbstractSharing various neuroimaging digital resources have received widespread attention in FAIR (Findable, Accessible, Interoperable and Reusable) neuroscience. In order to support a comprehensive understanding of brain cognition, neuroimaging provenance should be constructed to characterize both research processes and results, and integrates various digital resources for quick replication and open cooperation. This brings new challenges to neuroimaging text mining, including fragmented information, lack of labelled corpora, and vague topics. This paper proposes a text mining pipeline for enabling the FAIR neuroimaging s...
Source: Neuroinformatics - March 2, 2022 Category: Neuroscience Source Type: research
Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains
AbstractRecent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somat...
Source: Neuroinformatics - February 19, 2022 Category: Neuroscience Source Type: research
A Learning Based Framework for Disease Prediction from Images of Human-Derived Pluripotent Stem Cells of Schizophrenia Patients
AbstractHuman induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new available information though, there is still a critical need to establish quantifiable and accessible molecular markers that can be used to reveal the biological causality of the disease. In this paper, we introduce a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of hiPSCs of schizophrenia (SCZ...
Source: Neuroinformatics - January 22, 2022 Category: Neuroscience Source Type: research
Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data
AbstractIn this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as theInternational Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data beFindable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collab...
Source: Neuroinformatics - January 21, 2022 Category: Neuroscience Source Type: research
Correction to: Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy
(Source: Neuroinformatics)
Source: Neuroinformatics - January 13, 2022 Category: Neuroscience Source Type: research
Learning Low-Dimensional Semantics for Music and Language via Multi-Subject fMRI
AbstractEmbodied Cognition (EC) states that semantics is encoded in the brain as firing patterns of neural circuits, which are learned according to the statistical structure of human multimodal experience. However, each human brain is idiosyncratically biased, according to its subjective experience, making this biological semantic machinery noisy with respect to semantics inherent to media, such as music and language. We propose to represent media semantics using low-dimensional vector embeddings by jointly modeling the functional Magnetic Resonance Imaging (fMRI) activity of several brains via Generalized Canonical Correl...
Source: Neuroinformatics - January 7, 2022 Category: Neuroscience Source Type: research
pyPheWAS: A Phenome-Disease Association Tool for Electronic Medical Record Analysis
AbstractAlong with the increasing availability of electronic medical record (EMR) data, phenome-wide association studies (PheWAS) and phenome-disease association studies (PheDAS) have become a prominent, first-line method of analysis for uncovering the secrets of EMR. Despite this recent growth, there is a lack of approachable software tools for conducting these analyses on large-scale EMR cohorts. In this article, we introducepyPheWAS, an open-source python package for conducting PheDAS and related analyses. This toolkit includes 1) data preparation, such as cohort censoring and age-matching; 2) traditional PheDAS analy...
Source: Neuroinformatics - January 3, 2022 Category: Neuroscience Source Type: research
Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED)
AbstractHuman electrophysiological and related time series data are often acquired in complex, event-rich environments. However, the resulting recorded brain or other dynamics are often interpreted in relation to more sparsely recorded or subsequently-noted events. Currently a substantial gap exists between the level of event description required by current digital data archiving standards and the level of annotation required for successful analysis of event-related data across studies, environments, and laboratories. Manifold challenges must be addressed, most prominently ontological clarity, vocabulary extensibility, ann...
Source: Neuroinformatics - December 30, 2021 Category: Neuroscience Source Type: research
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
