Population-Average Brain Templates and Application to Automated Voxel-Wise Analysis Pipelines for Cynomolgus Macaque
This study aimed to create population-averaged structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) templates for the cynomolgus macaques and apply the templates in fully automated voxel-wise analyses. We presented the development of symmetric and asymmetric MRI and DTI templates from a sample of 63 young male cynomolgus monkeys with the use of optimized template creation approaches. We also generated the associated average tissue probability maps and Diffeomorphic Anatomical Registration using Exponentiated Lie Algebra templates for use with the Statistical Parametric Mapping (SPM), as well as th...
Source: Neuroinformatics - September 14, 2021 Category: Neuroscience Source Type: research
Controlling for Spurious Nonlinear Dependence in Connectivity Analyses
AbstractRecent analysis methods can capture nonlinear interactions between brain regions. However, noise sources might induce spurious nonlinear relationships between the responses in different regions. Previous research has demonstrated that traditional denoising techniques effectively remove noise-inducedlinear relationships between brain areas, but it is unknown whether these techniques can remove spurious nonlinear relationships. To address this question, we analyzed fMRI responses while participants watched the filmForrest Gump. We tested whether nonlinear Multivariate Pattern Dependence Networks (MVPN) outperform lin...
Source: Neuroinformatics - September 14, 2021 Category: Neuroscience Source Type: research
Correction to: High ‑throughput Analysis of Synaptic Activity in Electrically Stimulated Neuronal Cultures
(Source: Neuroinformatics)
Source: Neuroinformatics - September 10, 2021 Category: Neuroscience Source Type: research
A Comparison of Cranial Cavity Extraction Tools for Non-contrast Enhanced CT Scans in Acute Stroke Patients
This study included data from a demographically representative sample of 428 patients who had completed NECT scans following hospitalisation for stroke. In a subset of the scans (n = 20), the intracranial spaces were segmented using automated tools and compared to the gold standard of manual delineation to calculate accuracy, precision, recall, and dice similarity coefficient (DSC) values. Further, three readers independently performed regional visual comparisons of the qu ality of the results in a larger dataset (n = 428). Three tools were found; one of these had unreliable performance so subsequent evaluation was d...
Source: Neuroinformatics - September 6, 2021 Category: Neuroscience Source Type: research
Intelligible Models for HealthCare: Predicting the Probability of 6-Month Unfavorable Outcome in Patients with Ischemic Stroke
AbstractEarly prediction of unfavorable outcome after ischemic stroke is significant for clinical management. Machine learning as a novel computational modeling technique could help clinicians to address the challenge. We aim to investigate the applicability of machine learning models for individualized prediction in ischemic stroke patients and demonstrate the utility of various model-agnostic explanation techniques for machine learning predictions. A total of 499 consecutive patients with Unfavorable [modified Rankin Scale (mRS) score 3 –6,n = 140] and favorable (mRS score 0–2,n = 359) outcome after 6-month f...
Source: Neuroinformatics - August 26, 2021 Category: Neuroscience Source Type: research
Modelling Cortical Laminar Connectivity in the Macaque Brain
AbstractIn 1991, Felleman and Van Essen published their seminal study regarding hierarchical processing in the primate cerebral cortex. Their work encompassed a widescale analysis of connections reported through tracing between 35 regions in the macaque visual cortex, extending from cortical regions to the laminar level. In this work, we revisit laminar-level connectivity in the macaque brain using a whole-brain MRI-based approach. We use multimodal ex-vivo MRI imaging of the macaque brain in both white and grey matter, which are then integrated via a simple model of laminar connectivity. This model uses a granularity-base...
Source: Neuroinformatics - August 14, 2021 Category: Neuroscience Source Type: research
Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals
AbstractIn this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominat...
Source: Neuroinformatics - August 10, 2021 Category: Neuroscience Source Type: research
High-throughput Analysis of Synaptic Activity in Electrically Stimulated Neuronal Cultures
AbstractSynaptic dysfunction is a hallmark of various neurodegenerative and neurodevelopmental disorders. To interrogate synapse function in a systematic manner, we have established an automated high-throughput imaging pipeline based on fluorescence microscopy acquisition and image analysis of electrically stimulated synaptic transmission in neuronal cultures. Identification and measurement of synaptic signal fluctuations is achieved by means of an image analysis algorithm based on singular value decomposition. By exploiting the synchronicity of the evoked responses, the algorithm allows disentangling distinct temporally c...
Source: Neuroinformatics - August 10, 2021 Category: Neuroscience Source Type: research
Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org)
We report on our experiences developing one such data-sharing ecosystem focusing on ‘long-tail’ preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, no n-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommo...
Source: Neuroinformatics - August 4, 2021 Category: Neuroscience Source Type: research
Comparing Automated Morphology Quantification Software on Dendrites of Uninjured and Injured Drosophila Neurons
In this study, we tested how well automated image analysis software performed on class IVDrosophila neurons, which have several hundred individual dendrite branches. We applied each software to automatically quantify features of uninjured neurons and neurons that regenerated new dendrites after injury. Regenerated arbors exhibit defects across multiple features of dendrite morphology, which makes them challenging for automated pipelines to analyze. We compared the performances of three automated pipelines against manual quantification using Simple Neurite Tracer in ImageJ: one that is commercially available (Imaris) and tw...
Source: Neuroinformatics - August 3, 2021 Category: Neuroscience Source Type: research
Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda
AbstractSimulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for pred...
Source: Neuroinformatics - July 19, 2021 Category: Neuroscience Source Type: research
FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics
This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the ...
Source: Neuroinformatics - July 15, 2021 Category: Neuroscience Source Type: research
Automated Brain Masking of Fetal Functional MRI with Open Data
AbstractFetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convoluti...
Source: Neuroinformatics - June 15, 2021 Category: Neuroscience Source Type: research
Multiple Functional Brain Networks Related to Pain Perception Revealed by fMRI
In this study our main objective was to identify functional brain networks related to pain perception by examining whole-brain activation, avoiding the need for a priori selection of regions. We applied a data-driven technique—Constrained Principal Component Analysis for fMRI (fMRI-CPCA)—that identifies networks without assuming their anatomical or temporal properties. Open-source fMRI data collected during a thermal pain task (33 healthy participants) were subjected to fMRI-CPCA for network extraction, and networks were associated with pain perception by modelling subjective pain ratings as a function of network activ...
Source: Neuroinformatics - June 8, 2021 Category: Neuroscience Source Type: research
DTI Atlases Evaluations
AbstractThe cerebral atlas of diffusion tensor magnetic resonance image (DT-MRI, shorted as DTI) is one of the key issues in neuroimaging research. It is crucial for comparisons of neuronal structural integrity and connectivity across populations. Usually, the atlas is constructed by iteratively averaging the registered individual image. In tradition, the fuzzy group average image is easily generated in the initial stage, which is harmful to providing clear guidance for subsequent registration, to the performance of the final atlas. To solve this problem, an improved unbiased DTI atlas construction algorithm based on adapt...
Source: Neuroinformatics - June 4, 2021 Category: Neuroscience Source Type: research
