The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection
Poster No:
2400
Submission Type:
Abstract Submission
Authors:
Michelle Jansen1, Marcel Zwiers1, Jose Marques1, Kwok-Shing Chan1, Jitse Amelink2, Mareike Altgassen3, Joukje Oosterman1, David Norris1
Institutions:
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Gelderland, 2Max Planck Institute for Psycholinguistics, Radboud University, Nijmegen, Gelderland, 3Johannes Gutenberg-University Mainz, Mainz, Rheinland-Pfalz
First Author:
Michelle Jansen
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Co-Author(s):
Marcel Zwiers
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Jose Marques
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Kwok-Shing Chan
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Joukje Oosterman
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
David Norris
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Donders Institute for Brain, Cognition and Behaviour, Radboud University
Nijmegen, Gelderland
Introduction:
Ageing individuals display a marked variability in cognitive functioning. Recent scientific efforts have led to the growing availability of imaging cohort studies to further our understanding of the underlying mechanisms of normal cognitive aging, including the role of brain structure, brain function, and other factors relating to risk and resilience in ageing (e.g., education, sleep).
To further understand healthy cognitive aging, a comprehensive characterization of brain health parameters is required in conjunction with other factors that contribute to individual risk and resilience. The Advanced BRain Imaging on ageing and Memory (ABRIM) study aims to add to previously published datasets by offering researchers access to a cross-sectional, normative database of individuals aged between 18-80 years old, including numerous neuroimaging and behavioural parameters.
To further understand healthy cognitive aging, a comprehensive characterization of brain health parameters is required in conjunction with other factors that contribute to individual risk and resilience. The Advanced BRain Imaging on ageing and Memory (ABRIM) study aims to add to previously published datasets by offering researchers access to a cross-sectional, normative database of individuals aged between 18-80 years old, including numerous neuroimaging and behavioural parameters.
Methods:
We included data of 295 participants from the general population in the Netherlands (median age 52, IQR 36-66, 53.2% females).
The MRI protocol consisted of T1-weighted, T2-weighted, MP2RAGE and B1 mapping, multi-echo gradient echo (MEGRE), diffusion-weighted, and resting-state fMRI sequences. All images were defaced prior to further processing. All raw and preprocessed data, as well as applied code, was saved in accordance with the BIDS standard. We used BIDScoin for data management and generation of visual quality control reports (Zwiers et al., 2022).
We processed our data using several standard BIDS-compliant pipelines, including MRIqc for quality assessment reports (Esteban et al., 2017). We used several MP2RAGE-related scripts to generate quantitative R1 maps (https://github.com/Donders-Institute/MP2RAGE-related-scripts), SEPIA to generate quantitative susceptibility maps from the MEGRE images (Chan et al., 2020), QSIprep to preprocess diffusion-weighted images and for reconstruction (Cieslak et al., 2021), fMRIprep to preprocess functional data (Esteban et al., 2019), with and without previous NORDIC denoising (Moeller et al., 2020). For a complete overview of the methodological steps that were applied to each imaging modality, see Figure 1.
Participants underwent several neuropsychological tests tapping on global cognition, verbal intelligence, processing speed, executive functions, and memory. The Cognitive Reserve Index questionnaire was used to obtain measures for educational attainment, leisure activities, and occupational complexity. Several self-reported questionnaires were applied to obtain information on demographics, general health (e.g., medication use), depressive symptoms, pain, psychopathic traits, memory strategy use, subjective memory failure, and subjective cognitive functioning.
In a sub-sample of 120 participants, actigraphy data was recorded for 7 consecutive days to infer sleep-wake rhythms.
The MRI protocol consisted of T1-weighted, T2-weighted, MP2RAGE and B1 mapping, multi-echo gradient echo (MEGRE), diffusion-weighted, and resting-state fMRI sequences. All images were defaced prior to further processing. All raw and preprocessed data, as well as applied code, was saved in accordance with the BIDS standard. We used BIDScoin for data management and generation of visual quality control reports (Zwiers et al., 2022).
We processed our data using several standard BIDS-compliant pipelines, including MRIqc for quality assessment reports (Esteban et al., 2017). We used several MP2RAGE-related scripts to generate quantitative R1 maps (https://github.com/Donders-Institute/MP2RAGE-related-scripts), SEPIA to generate quantitative susceptibility maps from the MEGRE images (Chan et al., 2020), QSIprep to preprocess diffusion-weighted images and for reconstruction (Cieslak et al., 2021), fMRIprep to preprocess functional data (Esteban et al., 2019), with and without previous NORDIC denoising (Moeller et al., 2020). For a complete overview of the methodological steps that were applied to each imaging modality, see Figure 1.
Participants underwent several neuropsychological tests tapping on global cognition, verbal intelligence, processing speed, executive functions, and memory. The Cognitive Reserve Index questionnaire was used to obtain measures for educational attainment, leisure activities, and occupational complexity. Several self-reported questionnaires were applied to obtain information on demographics, general health (e.g., medication use), depressive symptoms, pain, psychopathic traits, memory strategy use, subjective memory failure, and subjective cognitive functioning.
In a sub-sample of 120 participants, actigraphy data was recorded for 7 consecutive days to infer sleep-wake rhythms.
Results:
ABRIM data is released through the Radboud Data Repository (https://data.ru.nl/). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data in BIDS format, as well as corresponding scripts, to facilitate data usage among both expert and non-expert users. Basic demographics (e.g., age, sex) are also included. The ABRIM behavioural collection includes the detailed demographics (e.g., general health), outcomes of all neuropsychological tests, self-reported questionnaires and actigraphy data. While the ABRIM MRI collection is estimated to be released in November 2023 (https://doi.org/10.34973/7q0a-vj19), the behavioural collection will be released in November 2028. Data access is available for registered users (Data use agreement for identifiable human data – scientific use).
Conclusions:
ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including an extensive characterization of both the brain as well as cognitive and behavioural characteristics. With ABRIM, we hope to further facilitate research into the underlying mechanisms of healthy cognitive ageing.
Higher Cognitive Functions:
Higher Cognitive Functions Other
Lifespan Development:
Aging
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 2
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 1
Keywords:
Aging
Cognition
FUNCTIONAL MRI
Memory
MRI
Open Data
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Workflows
1|2Indicates the priority used for review
Provide references using author date format
Cieslak M., Cook P.A., He X., Yeh F.C., Dhollander T., Adebimpe A., et al. (2021), ‘QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data.’ Nature Methods, vol 18, no. 7, pp. 775-778.
Esteban O., Birman D., Schaer M., Koyejo O.O, Poldrack R.A., Gorgolewski K.J. (2017), ‘MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One, vol 12, no. 9, pp. e0184661.
Esteban O., Markiewicz C.J., Blair R.W., Moodie C.A., Isik A.I., Erramuzpe A., et al. (2019), ‘fMRIPrep: a robust preprocessing pipeline for functional MRI.’ Nature Methods, vol 16, no. 1, pp. 111-116.
Moeller S., Pisharady P.K., Ramanna S., Lenglet C., Wu X., Dowdle L., et al. (2021), ‘NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing.’ NeuroImage, vol 226, pp. 117539.
Zwiers M.P, Moia S., Oostenveld R. (2022), ‘BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure’. Frontiers Neuroinformatics, vol 15, pp. 770608.