A Resource for Assessing Information Processing in the Developing Brain Using EEG and Eye Tracking

Langer, Nicolas; Ho, Erica J.; Alexander, Lindsay; Xu, Helen; Jozanovic, Renee K.; Henin, Simon; Cohen, Saul B.; Marcelle, Enitan T; Parra, Lucas C.; Milham, Michael P.; Kelly, Simon P.

doi:10.1101/092213

Cited by 14 publications

(31 citation statements)

References 97 publications

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“…The tutorial data consists of EEG from 4 subjects (with the original IDs: A00062279001, A00062578001, A00062842001, A00062219001) of the age group 25-44 from the data sharing paper by Langer et al (2017) 10 . The datasets have been preprocessed using the Automagic 11 artefact correction (with the standard settings) and have been filtered with a highpass filter of 1 Hz and a lowpass filter of 30 Hz using the standard settings of pop_firfiltnew() from EEGLAB 14.0.0b.…”

Section: Description Of the Tutorial Dataset And Completed Preprocessmentioning

confidence: 99%

Microstate EEGlab toolbox: An introductory guide

Pedroni

Langer

et al. 2018

Preprint

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EEG microstate analysis offers a sparse characterisation of the spatio-temporal features of large-scale brain network activity. However, despite the concept of microstates is straight-forward and offers various quantifications of the EEG signal with a relatively clear neurophysiological interpretation, a few important aspects about the currently applied methods are not readily comprehensible. Here we aim to increase the transparency about the methods to facilitate widespread application and reproducibility of EEG microstate analysis by introducing a new EEGlab toolbox for Matlab. EEGlab and the Microstate toolbox are open source, allowing the user to keep track of all details in every analysis step. The toolbox is specifically designed to facilitate the development of new methods. While the toolbox can be controlled with a graphical user interface (GUI), making it easier for newcomers to take their first steps in exploring the possibilities of microstate analysis, the Matlab framework allows advanced users to create scripts to automatise analysis for multiple subjects to avoid tediously repeating steps for every subject. This manuscript provides an overview of the most commonly applied microstate methods as well as a tutorial consisting of a comprehensive walk-through of the analysis of a small, publicly available dataset.

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Section: Description Of the Tutorial Dataset And Completed Preprocessmentioning

confidence: 99%

Microstate EEGlab toolbox: An introductory guide

Pedroni

Langer

et al. 2018

Preprint

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131

192

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“…For each participant, EEG and eye-tracking data are obtained during a battery that was previously assembled to examine attention, perception, inhibitory control, and decision-making 29 . See Table 6 for the specific paradigms and brief descriptions of each.…”

Section: Electroencephalography (Eeg) and Eye Trackingmentioning

confidence: 99%

“…Langer N et al (2017) A resource for assessing information processing in the developing brain using EEG and eye tracking Sci Data, 4: 170040…”

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confidence: 99%

An open resource for transdiagnostic research in pediatric mental health and learning disorders

Alexander

Escalera

et al. 2017

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Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics, and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n = 664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).. CC-BY-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/149369 doi: bioRxiv preprint first posted online Jun. 13, 2017; PURPOSE OF DATA COLLECTIONPsychiatric and learning disorders are among the most common and debilitating illnesses across the lifespan. Epidemiologic studies indicate that 75% of all diagnosable psychiatric disorders begin prior to age 241 . This underscores the need for increased focus on studies of the developing brain 2 . Beyond improving our understanding of the pathophysiology that underlies the emergence of psychiatric illness throughout development, such research has the potential to identify clinically useful markers of illness that can improve the early detection of pathology and guide interventions. Although the use of neuroimaging, neuropsychology, neurophysiology and genetics has made significant strides in revealing biological correlates for a broad array of illnesses, findings have been lacking in specificity 3 . Consequently, progress in finding clinically useful brain-based biomarkers has been disappointing 4,5 .Given the slow pace in biomarker identification, investigators have been prompted to rethink research paradigms and practices. Most notably, the emphasis on mapping diagnostic labels from a clinically defined nosology (e.g., the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases) to varying biological indices has proven to be problematic, as it assumes consistent biological relationships with broad constellations of signs and symptoms 6,7 . Epidemiologists, psychopathologists, geneticists and neuroscientists are reconsidering the relevance of diagnostic boundaries due to the lack of specif...

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“…Data for this study was taken from a publicly available dataset provided by the Child Mind Institute (CMI) [17]. Our study focused on pre-adolescent children (< 11 y.o.…”

Section: B Datasetsmentioning

confidence: 99%

“…), resulting in a sample size of n = 45 children. EEG data was captured using a high-density 129-electrode hydro-cel EEG cap, with the pre-processing handled by the CMI prior to the point of data distribution (for details on EEG pre-processing see [17]). Through utilizing the already pre-processed EEG offered by the CMI, we aimed to promote easier replication of the methodology outlined in this manuscript.…”

Section: B Datasetsmentioning

confidence: 99%

Introducing the Joint EEG-Development Inference (JEDI) Model: A Multi-Way, Data Fusion Approach for Estimating Paediatric Developmental Scores via EEG

Kinney‐Lang

Ebied

Auyeung

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Accounting for developmental changes in children is a key consideration for adapting neurorehabilitation technologies to paediatric populations. Using well-established clinical tests and questionnaires can be resource and time intensive. With many data-driven rehabilitation approaches relying on EEG data, a means to rapidly assess and infer developmental status of children directly from these recordings could be critical. This manuscript proposes a new model for estimating classic developmental diagnostic scores by exploiting data fusion in a joint tensor-matrix decomposition of the EEG and score data. We have designated this model the Joint EEG-Development Inference (JEDI) model. The proposed model is illustrated using a common EEG task (button press) via publicly available paediatric data from pre-adolescent children. Using 3 distinct recording blocks for training, validation and testing and a 10-fold cross-validation scheme, a robust experimental design was used to evaluate the JEDI model under various conditions. Results indicate that the JEDI model can estimate the developmental scores of children while maintaining a high degree of similarity at a population level. These results highlight the JEDI model as a potential evolving tool for rapidly assessing child's development. Clinically, the proposed model could provide useful developmental information in a convenient and low resource manner.

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A Resource for Assessing Information Processing in the Developing Brain Using EEG and Eye Tracking

Cited by 14 publications

References 97 publications

Microstate EEGlab toolbox: An introductory guide

Microstate EEGlab toolbox: An introductory guide

An open resource for transdiagnostic research in pediatric mental health and learning disorders

Introducing the Joint EEG-Development Inference (JEDI) Model: A Multi-Way, Data Fusion Approach for Estimating Paediatric Developmental Scores via EEG

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