DRT: A new toolbox for the Standard EEG Data Structure in large-scale EEG applications

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

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SPARC Data Structure: Rationale and Design of a FAIR Standard for Biomedical Research Data

10.1101/2021.02.10.430563 ◽

2021 ◽

Author(s):

Anita Bandrowski ◽

Jeffrey S. Grethe ◽

Anna Pilko ◽

Tom Gillespie ◽

Gabi Pine ◽

...

Keyword(s):

Data Structure ◽

Biomedical Research ◽

Large Scale ◽

Open Data ◽

Cell Types ◽

Research Data ◽

Imaging Data ◽

Organ Specific ◽

The Rich ◽

Automated Tools

AbstractThe NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) initiative is a large-scale program that seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. Integral to the SPARC program are the rich anatomical and functional datasets produced by investigators across the SPARC consortium that provide key details about organ-specific circuitry, including structural and functional connectivity, mapping of cell types and molecular profiling. These datasets are provided to the research community through an open data platform, the SPARC Portal. To ensure SPARC datasets are Findable, Accessible, Interoperable and Reusable (FAIR), they are all submitted to the SPARC portal following a standard scheme established by the SPARC Curation Team, called the SPARC Data Structure (SDS). Inspired by the Brain Imaging Data Structure (BIDS), the SDS has been designed to capture the large variety of data generated by SPARC investigators who are coming from all fields of biomedical research. Here we present the rationale and design of the SDS, including a description of the SPARC curation process and the automated tools for complying with the SDS, including the SDS validator and Software to Organize Data Automatically (SODA) for SPARC. The objective is to provide detailed guidelines for anyone desiring to comply with the SDS. Since the SDS are suitable for any type of biomedical research data, it can be adopted by any group desiring to follow the FAIR data principles for managing their data, even outside of the SPARC consortium. Finally, this manuscript provides a foundational framework that can be used by any organization desiring to either adapt the SDS to suit the specific needs of their data or simply desiring to design their own FAIR data sharing scheme from scratch.

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Large-scale neocortical dynamic function and EEG: Use of theory and methods in clinical research on children with Attention Deficit Hyperactivity Disorder

Behavioral and Brain Sciences ◽

10.1017/s0140525x00343254 ◽

2000 ◽

Vol 23 (3) ◽

pp. 411-411

Author(s):

Michael Murias ◽

James M. Swanson

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Attention Deficit ◽

Large Scale ◽

Dynamic Theory ◽

Clinical Population ◽

Hyperactivity Disorder ◽

Age Related ◽

Dynamic Function ◽

Eeg Data ◽

Regional Specificity

We used Nunez's physiologically based dynamic theory of EEG to make predictions about a clinical population of children with Attention Deficit Hyperactivity Disorder (ADHD) known to have neuronanatomical abnormalities. Analysis of high-density EEG data (long-range coherence) showed expected age-related differences and surprising regional specificity that is consistent with some of the literature in this clinical area.

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Simultaneous EEG Acquisition System for Multiple Users: Development and Related Issues

Sensors ◽

10.3390/s19204592 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4592

Author(s):

Sunghan Lee ◽

Hohyun Cho ◽

Kiseong Kim ◽

Sung Chan Jun

Keyword(s):

Large Scale ◽

Acquisition System ◽

Signal Acquisition ◽

Human Interactions ◽

Multiple Users ◽

Simultaneous Acquisition ◽

Eeg Data ◽

Reasonable Cost ◽

Average Loss ◽

Crowd Psychology

Social interaction is one of humans’ most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. Most studies to date have investigated dyadic interactions, although multilateral interactions are more common in reality. However, it is believed that most studies have focused on such interactions because of methodological limitations, in that it is very difficult to design a well-controlled experiment for multiple users at a reasonable cost. Accordingly, there are few simultaneous acquisition systems for multiple users. In this study, we propose a design framework for an acquisition system that measures EEG data simultaneously in an environment with 10 or more people. Our proposed framework allowed us to acquire EEG data at up to 1 kHz frequency from up to 20 people simultaneously. Details of our acquisition system are described from hardware and software perspectives. In addition, various related issues that arose in the system’s development—such as synchronization techniques, system loads, electrodes, and applications—are discussed. In addition, simultaneous visual ERP experiments were conducted with a group of nine people to validate the EEG acquisition framework proposed. We found that our framework worked reasonably well with respect to less than 4 ms delay and average loss rates of 1%. It is expected that this system can be used in various hyperscanning studies, such as those on crowd psychology, large-scale human interactions, and collaborative brain–computer interface, among others.

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Basic numerical competences in large-scale assessment data: Structure and long-term relevance

Journal of Experimental Child Psychology ◽

10.1016/j.jecp.2017.09.015 ◽

2018 ◽

Vol 167 ◽

pp. 32-48 ◽

Cited By ~ 3

Author(s):

Stefa Hirsch ◽

Katharina Lambert ◽

Karien Coppens ◽

Korbinian Moeller

Keyword(s):

Data Structure ◽

Large Scale ◽

Assessment Data ◽

Large Scale Assessment ◽

Scale Assessment

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BRLO-Tree: A Data Structure Used for 3D GIS Dynamic Scene Rendering

Cybernetics and Information Technologies ◽

10.1515/cait-2015-0059 ◽

2015 ◽

Vol 15 (4) ◽

pp. 124-137 ◽

Cited By ~ 2

Author(s):

Wenju Wang ◽

Zhang Xuan ◽

Liujie Sun ◽

Zhongmin Jiang ◽

Jingjing Shang

Keyword(s):

Data Structure ◽

Large Scale ◽

Three Dimensional ◽

Level Of Detail ◽

Dynamic Scene ◽

3D Gis ◽

Dynamic Scenes ◽

Complex Dynamic ◽

3D Objects ◽

System A

Abstract BRLO-Tree (Block-R-Tree-Loose-Octree) is presented in this paper based on the R-Tree and Loose-Octree. The aim of the structure is to visualize the large scale and complex dynamic scenes in a 3D (three-dimensional) GIS (Geographic Information System). A new method of clustering rectangles to construct R-tree based on an improved K-means algorithm is put forward. Landform in 3D GIS is organized by R-Tree. The block is used as the basic rendering unit. The 3D objects of each block are respectively organized by a Loose-Octree. A series of techniques, based on this data structure, such as LOD (Level of Detail), relief impostors are integrated. The results of the tests show that BRLO-Tree cannot only support the large scale 3D GIS scene exhibition with wandering and fighting, but it can also efficiently manage the models in a dynamic scene. At the same time, a set of integrated techniques based on BRLO-Tree can make the rendering pictures more fluence and the rendering time vastly improved.

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An Automated Open-Source Workflow for Standards-Compliant Integration of Small Animal Magnetic Resonance Imaging Data

10.1101/667980 ◽

2019 ◽

Cited By ~ 1

Author(s):

Horea-Ioan Ioanas ◽

Markus Marks ◽

Clément M. Garin ◽

Marc Dhenain ◽

Mehmet Fatih Yanik ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Data Structure ◽

Magnetic Resonance ◽

Open Source ◽

Large Scale ◽

Free Field ◽

Small Animal ◽

Field Operator ◽

Imaging Data ◽

Resonance Imaging

AbstractLarge-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. In this article we present an open-source workflow which automatically converts and reposits data from the ParaVision structure into the widely supported and openly documented Brain Imaging Data Structure (BIDS). Complementing this workflow we also present operator guidelines for appropriate ParaVision data input, and a programmatic walk-through detailing how preexisting scans with uninterpretable metadata records can easily be made compliant after the acquisition.

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An Improved Spatial Data Structure for Web-based Large-scale 3D Scene

2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) ◽

10.1109/icsess47205.2019.9040768 ◽

2019 ◽

Author(s):

Jiaxi Chen ◽

Wei Fang

Keyword(s):

Data Structure ◽

Spatial Data ◽

Large Scale ◽

Web Based ◽

Spatial Data Structure ◽

3D Scene

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A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets

10.31234/osf.io/aqckx ◽

2021 ◽

Author(s):

Natalie Marie Saragosa-Harris ◽

Natasha Chaku ◽

Niamh MacSweeney ◽

Victoria Guazzelli Williamson ◽

Maximilian Scheuplein ◽

...

Keyword(s):

Data Structure ◽

Longitudinal Study ◽

Brain Development ◽

Cognitive Neuroscience ◽

Large Scale ◽

Diversity And Inclusion ◽

Data Release ◽

Methodological Considerations ◽

And Behavior ◽

Adolescent Brain Development

As the largest longitudinal study of adolescent brain development and behavior to date, the Adolescent Brain Cognitive Development (ABCD) Study® has provided immense opportunities for researchers across disciplines since its first data release in 2018. The size and scope of the study also present a number of hurdles, which range from becoming familiar with the study design and data structure to employing rigorous and reproducible analyses. The current paper is intended as a guide for researchers and reviewers working with ABCD data, highlighting the features of the data (and the strengths and limitations therein) as well as relevant analytical and methodological considerations. Additionally, we explore justice, equity, diversity, and inclusion efforts as they pertain to the ABCD Study and other large-scale datasets. In doing so, we hope to increase both accessibility of the ABCD Study and transparency within the field of developmental cognitive neuroscience.

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