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Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors
The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults.
Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1–90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session.
Significant MRI site and vendor effects (p < .05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman’s r correlations >0.43, p < 1.39E-36). In particular, volumes larger than 200 mm3 (for amygdalar nuclei) and 300 mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε < 5% and DICE > 0.80).
Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.
•Differences in MRI site/vendor had a limited effect on volume reproducibility.•Differences in MRI site/vendor had an extensive effect on spatial accuracy.•Reliability is good for larger amygdalar and hippocampal structures.•Automated volumetry is reliable in multicenter MRI studies.
Journal Article
Data analytics in professional soccer : performance analysis based on spatiotemporal tracking data
Daniel Link explores how data analytics can be used for studying performance in soccer. Based on spatiotemporal data from the German Bundesliga, the six individual studies in this book present innovative mathematical approaches for game analysis and player assessment. The findings can support coaches and analysts to improve performance of their athletes and inspire other researchers to advance the research field of sports analytics. Contents Individual Ball Possession in Soccer Real Time Quantification of Dangerousity A Topography of Free Kicks Match Importance Affects Activity Effect of Ambient Temperature on Pacing Depends on Skill Level Vanishing Spray Reduces Extent of Rule Violations Target Groups Lecturers and students of sports science, data analytics, computer science Experts in sports data, bookmakers, media companies, sports reporting, coaches and sports analysts The Author Dr. Daniel Link has been a lecturer and researcher at the Department of Sports and Health Sciences at the Technical University of Munich (TUM) since 2010. His research focuses on performance analysis in team sports, including technological aspects of data acquisition as well as the modeling of phenomena in sports. He supports top level teams and sport federations in implementing new approaches in match analysis.
Book
A complete data processing workflow for cryo-ET and subtomogram averaging
Electron cryotomography is currently the only method capable of visualizing cells in three dimensions at nanometer resolutions. While modern instruments produce massive amounts of tomography data containing extremely rich structural information, data processing is very labor intensive and the results are often limited by the skills of the personnel rather than the data. We present an integrated workflow that covers the entire tomography data processing pipeline, from automated tilt series alignment to subnanometer resolution subtomogram averaging. Resolution enhancement is made possible through the use of per-particle per-tilt contrast transfer function correction and alignment. The workflow greatly reduces human bias, increases throughput and more closely approaches data-limited resolution for subtomogram averaging in both purified macromolecules and cells.
Journal Article
Data-driven computational neuroscience : machine learning and statistical models
\"Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered\"-- Provided by publisher.
BOOK
Techniques and applications of hyperspectral image analysis
Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions.Other imaging topics that are covered are segmentation, regression and classification.
eBook
Tribological characteristics and advanced processing methods of textured surfaces: a review
Surface texture is one of the hot spots in the field of surface tribology. It promotes friction by storing lubricating oil and abrasive particles, and in some cases it can also improve hydrodynamic effects. Since it has been widely used in mechanical parts, tribological characteristics and surface quality cannot be ignored. Nowadays, there are many ways to fabricate surface texture. Several classification methods based on different processing principles are introduced in this paper. It includes direct laser ablation, mechanical processing, EDM and ECM in material reduction processing, laser cladding, deposition method, and electroforming in additive processing and laser shock processing in deformation processing. The surface texture with good quality can be obtained by selecting proper machining method and proper machining parameters. The machining principle of each method, the research status of surface morphology of surface texture, and the advantages and disadvantages of each method are summarized. Finally, potential hybrid processing methods including their advantages and disadvantages as well as examples are presented.
Journal Article
The practice of reproducible research : case studies and lessons from the data-intensive sciences
\"The Practice of Reproducible Research presents concrete examples of how researchers in the data-intensive sciences are working to improve the reproducibility of their research projects. Each of the thirty-one case studies in this volume describes the workflow that an author used to complete a real-world research project, highlighting how particular tools, ideas, and practices have been combined to support reproducibility. Authors emphasize the very practical how, rather than the why or what, of conducting reproducible research. Part 1 contains an accessible introduction to reproducible research, a basic reproducible research project template, and a synthesis of lessons learned from across the thirty-one case studies. Parts 2 and 3 focus on the case studies. The Practice of Reproducible Research is an invaluable resource for students and researchers who wish to better understand the practice of data-intensive sciences and learn how to make their own research more reproducible.\"--Provided by publisher.
Book
Benchmarking of the BITalino biomedical toolkit against an established gold standard
The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device. This work followed a methodical experimental protocol to acquire data from the two devices simultaneously. Four physiological signals were acquired: electrocardiography, electromyography, electrodermal activity and electroencephalography. Root mean square error and coefficient of determination were computed to analyse differences between BITalino and BioPac. Electrodermal activity signals were very similar for the two devices, even without applying any major signal processing techniques. For electrocardiography, a simple morphological comparison also revealed high similarity between devices, and this similarity increased after a common segmentation procedure was followed. Regarding electromyography and electroencephalography data, the approach consisted of comparing features extracted using common post-processing methods. The differences between BITalino and BioPac were again small. Overall, the results presented here show a close similarity between data acquired by the BITalino and by the reference device. This is an important validation step for all researchers working with this multimodal platform.
Journal Article