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; Petroni, Agustín; Cohen, Saul B.; Marcelle, Enitan T; Parra, Lucas C.; Milham, Michael P.; Kelly, Simon P.

doi:10.1038/sdata.2017.40

Cited by 69 publications

(39 citation statements)

References 110 publications

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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 30 . See Table 6 for the specific paradigms and brief descriptions of each.…”

Section: Methodsmentioning

confidence: 99%

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

Alexander¹,

Escalera²,

Ai³

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).

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Section: Methodsmentioning

confidence: 99%

“… *The listed references suggest further reading regarding research questions that can be asked using each paradigm, which is not necessarily used in the cited studies. See Langer et al 30 for the precise details of all paradigms run as part of this initiative. …”

Section: Figurementioning

confidence: 99%

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

Alexander¹,

Escalera²,

Ai³

et al. 2017

Sci Data

Self Cite

490

359

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“…In the main study, ages ranged from 6 to 44 yr ( n = 114, 14.2 ± 8.0 yr, 46 females; see Fig. 1 A for a full age and sex distribution) as part of the Child Mind Institute–Multimodal Resource for Studying Information Processing in the Developing Brain ( http://fcon_1000.projects.nitrc.org/indi/cmi_eeg/ ; Langer et al, 2017 ). In the replication study, ages ranged from 5 to 21 yr ( n = 303, 11.3 ± 3.9 yr, 135 females; see Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Stimuli were presented on a 17-inch CRT monitor (SONY Trinitron Multiscan G220, display dimensions 330 × 240 mm, resolution 800 × 600 pixels, vertical refresh rate of 100 Hz). Note that some subjects did not experience all stimuli due to time limitations ( Langer et al, 2017 ). Additionally, as explained below, poor data quality for some recordings caused additional data loss.…”

Section: Methodsmentioning

confidence: 99%

The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex

Petroni

Cohen

et al. 2018

eNeuro

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Neural development is generally marked by an increase in the efficiency and diversity of neural processes. In a large sample (n = 114) of human children and adults with ages ranging from 5 to 44 yr, we investigated the neural responses to naturalistic video stimuli. Videos from both real-life classroom settings and Hollywood feature films were used to probe different aspects of attention and engagement. For all stimuli, older ages were marked by more variable neural responses. Variability was assessed by the intersubject correlation of evoked electroencephalographic responses. Young males also had less-variable responses than young females. These results were replicated in an independent cohort (n = 303). When interpreted in the context of neural maturation, we conclude that neural function becomes more variable with maturity, at least during the passive viewing of real-world stimuli.

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“…These biomarker initiatives have assessed various sets of neuropsychological, lifestyle phenotypes and brain-imaging measures, predominantly using structural and functional magnetic resonance imaging (s/fMRI). One aspect, which is at first sight surprising, is that with the exception of one notable initiative, the Healthy Brain Network study (Alexander et al, 2017;Langer et al, 2017a) , electroencephalography (EEG) measures have not been included in any large biomarker initiative.…”

Section: Introductionmentioning

confidence: 99%

Automagic: Standardized preprocessing of big EEG data

2019

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Electroencephalography (EEG) recordings have been rarely included in large-scale studies. This is arguably not due to a lack of information that lies in EEG recordings but mainly on account of methodological issues. In many cases, particularly in clinical, pediatric and aging populations, the EEG has a high degree of artifact contamination and the quality of EEG recordings often substantially differs between subjects. Although there exist a variety of standardized preprocessing methods to clean EEG from artifacts, currently there is no method to objectively quantify the quality of preprocessed EEG. This makes the commonly accepted procedure of excluding subjects from analyses due to exceeding contamination of artifacts highly subjective. As a consequence, P-hacking is fostered, the replicability of results is decreased, and it is difficult to pool data from different study sites. In addition, in large-scale studies, data are collected over years or even decades, requiring software that controls and manages the preprocessing of ongoing and dynamically growing studies. To address these challenges, we developed AUTOMAGIC, an open-source MATLAB toolbox that acts as wrapper to run currently available preprocessing methods and offers objective standardized quality assessment for growing studies. The software is compatible with the Brain Imaging Data Structure (BIDS) standard and hence facilitates data sharing. In the present paper we outline the functionality of AUTOMAGIC and examine the effect of applying combinations of methods on a sample of resting EEG data. This examination suggests that a applying a pipeline of algorithms to detect artifactual channels in combination with Multiple Artifact Rejection Algorithm (MARA), an independent component analysis (ICA)-based artifact correction method, is sufficient to reduce a large extent of artifacts.

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A resource for assessing information processing in the developing brain using EEG and eye tracking

Cited by 69 publications

References 110 publications

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

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

The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex

Automagic: Standardized preprocessing of big EEG data

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