The HBN-EEG Dataset

This is Release 5 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from participants involved in the HBN project.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the _events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Copyright and License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0). Please cite the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181) as well as the dataset paper (https://doi.org/10.1101/2024.10.03.615261).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

The HBN-EEG Dataset

This is Release 9 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

1. Resting State: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

Release NC | --NOT FOR COMMERCIAL USE-- This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

The HBN-EEG Dataset

This is Release 2 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

1. Resting State: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

Release NC | --NOT FOR COMMERCIAL USE-- This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

The HBN-EEG Dataset

This is Release 1 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

1. Resting State: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

Release NC | --NOT FOR COMMERCIAL USE-- This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

CMI Healthy Brain Network. Contains various dimensional psychiatric scales, cognitive testing, physical performance measures, EEG (raw and pre-processed) and eye tracking data, T1w, fMRI (rest and movie), DKI, field map, and magnetization transfer data.

The HBN-EEG Dataset

This is Release 11 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

1. Resting State: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

Release NC | --NOT FOR COMMERCIAL USE-- This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

Abstract copyright UK Data Service and data collection copyright owner.

The COVID-19 pandemic has caused major disruptions to families' lives in many ways, including through lockdowns, social distancing, home learning requirements, and restrictions. This resulted in a rapidly changing situation where different pressures have arisen for children, young people and their families over time. Understanding the psychological effects of the COVID-19 pandemic on children and young people, through the collection of high quality data and in a way that could directly inform policy, was set out as an immediate research priority in a Lancet position paper (Holmes et al., 2020) at the start of the pandemic. The Co-SPACE study was launched on 30th March 2020, a week after the first national lockdown was implemented in the UK, with the purpose of using the findings to inform resources and support for families. It was then extended in 2022 under the project on 'Learning from the trajectories of mental health challenges for children, young people and parents over the course of the Covid-19 pandemic' in collaboration with the CORONA x CODOMO project in Japan (run by Dr Naho Morisaki at the National Center for Child Health and Development).

The Co-SPACE project aimed to:

track participating children and young people’s mental health throughout the COVID-19 crisis,

identify what protects children and young people from deteriorating mental health (over time, and at particular stress points),

determine how this varies according to child, family and environmental characteristics.

The Co-SPACE study, overall, involved an online longitudinal survey completed monthly from March 2020 to July 2021 by (i) UK-based parents/carers of children and young people (aged 4-16 years, at the start of the study), and (ii) their children (if aged 11-16 years, at the start of the study). Additional, longer-term follow-ups were then completed 6-monthly in March/April 2022, October 2022, and March/April 2023 by parents who took part in the original Co-SPACE survey. To develop a richer understanding of people’s experiences, qualitative interviews were also conducted with parents/carers, young people, and people who work with them. The current data available includes parent/carer reported survey data only.

The study was designed and conducted with rapid and meaningful stakeholder involvement, including through in-depth discussion with advisory groups of experts, young people, and parents/carers. Parent/carer and young people's involvement was facilitated through the UKRI Emerging Minds Research Network Plus.

In addition to the Principal Investigators (PW, CC, & SS), contributors to the study were as follows: Praveetha Patalay, UCL; Helen Dodd, University of Exeter; Pete Lawrence, University of Southampton; Simona Skripkauskaite, University of Oxford; Samantha Pearcey, University of Oxford; Adrienne Shum, University of Oxford; Amy McCall, University of Oxford; Olly Robertson, University of Oxford; Bettina Moltrecht, UCL; Eoin McElroy, Ulster University; Lowrie Hilladakis (nee Burgess), University of Oxford; Ning Ding, University of Oxford; Martha Oakes, University of Oxford; Naho Morisaki, National Center for Child Health and Development .

Further information, including research reports, are available from the Co-SPACE project website.

Latest edition information

For the second edition (January 2025), the study has been updated to include three new waves of data collection conducted between March 2022 and March 2023. The data and documentation files have been replaced with new versions.

The survey covered: demographic details; health history; Covid-19 experiences; employment changes; school attendance and concerns about it; family relationships, routines, communication; children's mental health/behavioural difficulties; parental stress, mental health, and needs.

The HBN-EEG Dataset

This is Release 8 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from participants involved in the HBN project.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the _events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Copyright and License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0). Please cite the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181) as well as the dataset paper (https://doi.org/10.1101/2024.10.03.615261).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

The HBN-EEG Dataset

This is Release 4 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from participants involved in the HBN project.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the _events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Copyright and License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0). Please cite the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181) as well as the dataset paper (https://doi.org/10.1101/2024.10.03.615261).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

The HBN-EEG Dataset

This is Release 7 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

1. Resting State: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

Release NC | --NOT FOR COMMERCIAL USE-- This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

Background: Cognitive frailty, characterized by the coexistence of cognitive impairment and physical frailty, is a significant predictor of cognitive decline. However, few studies integrate both cognitive and physical assessments alongside hormonal markers, such as cortisol, that may influence frailty and cognitive function. To address this gap, our study combines non-invasive physical, cognitive, and cortisol markers to assess frailty in aging adults.

Methods: Data were collected from four sites as part of the Healthy Minds for Life (HML) longitudinal cohort, a project within the Precision Aging Network. Baseline data included cognitive evaluation using the Montreal Cognitive Assessment (MoCA); frailty assessment using a validated 20-second elbow flexion-extension test analyzed by AI under single-task and dual-task conditions; cortisol measurement in eccrine sweat samples via Direct Analysis in Real Time Mass Spectrometry (DART-MS); and demographic information.

Results: Of 202 participants completing all assessments, 60 were identified with mild cognitive impairment (MCI). The dual-task frailty index (FI) derived from the 20-second test significantly differentiated individuals with MCI from cognitively robust participants and correlated strongly with MoCA scores (p = 0.015). The dual-task FI showed superior model fit compared to the single-task FI when predicting cognitive function. A significant correlation between the dual-task FI and cortisol by age interaction was observed (p = 0.0042) highlighting the potential impact of cortisol to moderate the relationship between frailty and age in an otherwise healthy aging population. By contrast, no significant correlation was found between dual-task FI and aging outside of the presence of cortisol (p = 0.116) in this study.

Conclusions: This study highlights practical and efficient methods for assessing frailty emphasizing the value of dual-task testing and cortisol measures in identifying individuals at higher risk for cognitive and physical decline. The findings underscore the importance of integrating hormonal markers with cognitive and physical assessments to enhance risk stratification and intervention planning in aging populations.

The HBN-EEG Dataset

This is Release 3 of HBN-EEG, the EEG and (soon-released) Eye-Tracking Section of the Child Mind Network Healthy Brain Network (HBN) Project, curated into the Brain Imaging Data Structure (BIDS) format. This dataset is part of a larger initiative to advance the understanding of child and adolescent mental health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et al., Sci Data 2017).

Data Description

This dataset comprises electroencephalogram (EEG) data and behavioral responses collected during EEG experiments from >3000 participants (5-21 yo) involved in the HBN project. The data has been released in 11 separate Releases, each containing data from a different set of participants.

Tasks

The HBN-EEG dataset includes EEG recordings from participants performing six distinct tasks, which are categorized into passive and active tasks based on the presence of user input and interaction in the experiment.

Passive Tasks

1. Resting State: Participants rested with their heads on a chin rest, following instructions to open or close their eyes and fixate on a central cross.

2. Surround Suppression: Participants viewed flashing peripheral disks with contrasting backgrounds, while event markers and conditions were recorded.

3. Movie Watching: Participants watched four short movies with different themes, with event markers recording the start and stop times of presentations.

Active Tasks

1. Contrast Change Detection: Participants identified flickering disks with dominant contrast changes and received feedback based on their responses.

2. Sequence Learning: Participants memorized and repeated sequences of flashed circles on the screen, designed for different age groups.

3. Symbol Search: Participants performed a computerized symbol search task, identifying target symbols from rows of search symbols.

Contents

• EEG Data: High-resolution EEG recordings capture a wide range of neural activity during various tasks.
• Behavioral Responses: Participant responses during EEG tasks, including reaction times and accuracy. This data was originally recorded within the behavior directory of the HBN data. The data is now included with the EEG data within the events.tsv files.

Special Features

• Hierarchical Event Descriptors (HED): Events, including the original EEG events and the included behavioral events, have clear explanations, including proper HED annotation suitable for systematic meta and mega analysis of the data.
• P-Factor, Attention, Internalization and Externalization: Derived from the CBCL questionnaire, these factors provide valuable insights into the psychopathology of the participants, adding a rich layer of interpretation to the EEG and behavioral data.
• Data quality and availability: We performed minimal quality control to ensure that the data was not corrupted, each task had its necessary events, and was ready for preprocessing. The results of this quality control are available in the participants.tsv file.
• Future Releases: We are committed to enhancing this dataset with additional, valuable features in its next stages, including:
  • Personalized EEG Electrode Locations: To offer more detailed insights into individual neural activity patterns.
  • Personalized Lead Field Matrix: Enabling better understanding and interpretation of EEG data.
  • Eye-Tracking Data: Providing a window into the visual attention and processing mechanisms during EEG experiments.

Other HBN-EEG Datasets

For access all releases of the HBN-EEG dataset, follow this link on NEMAR.org. The links to the individual releases are below:

Release 1 | DS005505

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R1
• Total subjects: 136

Release 2 | DS005506

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R2
• Total subjects: 152

Release 3 | DS005507

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R3
• Total subjects: 183

Release 4 | DS005508

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R4
• Total subjects: 324

Release 5 | DS005509

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R5
• Total subjects: 330

Release 6 | DS05510

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R6
• Total subjects: 134

Release 7 | DS005511

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R7
• Total subjects: 381

Release 8 | DS005512

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R8
• Total subjects: 257

Release 9 | DS005514

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R9
• Total subjects: 295

Release 10 | DS005515

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R10
• Total subjects: 533

Release 11 | DS005516

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_R11
• Total subjects: 430

Release NC | --NOT FOR COMMERCIAL USE-- This dataset is intended for research purposes only under the CC-BY-NC-SA-4.0 License and is not currently hosted on OpenNeuro/NEMAR. Any commercial use is prohibited.

• S3 URI: s3://fcp-indi/data/Projects/HBN/BIDS_EEG/cmi_bids_NC
• Total subjects: 458

Copyright and License

The HBN-EEG dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY SA 4.0), except for the Not-for-Commercial-Use dataset. Please cite the dataset paper (https://doi.org/10.1101/2024.10.03.615261) as well as the original HBN publication (https://dx.doi.org/10.1038/sdata.2017.181).

Acknowledgments

We would like to express our gratitude to all participants and their families, whose contributions have made this project possible. We also thank our dedicated team of researchers and clinicians for their efforts in collecting, processing, and curating this data.

• proportion of patients with no country of origin recorded in the administrative database. of note all patients had US postal codesSocio-demographic data.

Background: Cognitive frailty, the concurrent presence of mild cognitive impairment (MCI) and physical frailty, poses a significant risk for adverse outcomes in older adults. Traditional assessments that rely on extensive walking tests or specialized equipment, are impractical for routine or remote evaluations. This study evaluated a 20-second video-based Upper Frailty Meter (vFM) test, incorporating dual-task conditions, as a feasible tool for identifying cognitive frailty. Methods: Data from 413 participants aged 50–79 years in the Healthy Minds for Life cohort were analyzed across four sites: the University of Arizona, Johns Hopkins University, Emory University, and the University of Miami. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA), whereas frailty indices were derived from the vFM test. Participants performed repetitive elbow flexion-extension under single-task (physical task only) and dual-task (physical task with concurrent cognitive exercise) conditions. Frailty phenotypes, including slowness, weakness, and exhaustion, were quantified using AI-based video kinematic analysis. Logistic regression and receiver operating characteristic (ROC) analyses evaluated the model's predictive accuracy for cognitive frailty. Results: Participants classified as cognitive frailty group (n=53, 12.8%) demonstrated significantly higher frailty index scores compared to robust individuals (p