The Amsterdam Ultra-high field adult lifespan database (AHEAD): A freely available multimodal 7 Tesla submillimeter magnetic resonance imaging database
Publication date
2020-11-01
Editors
Advisors
Supervisors
Document Type
Article
Metadata
Show full item recordCollections
License
cc_by
Abstract
Normative databases allow testing of novel hypotheses without the costly collection of magnetic resonance imaging (MRI) data. Here we present the Amsterdam Ultra-high field adult lifespan database (AHEAD). The AHEAD consists of 105 7 Tesla (T) whole-brain structural MRI scans tailored specifically to imaging of the human subcortex, including both male and female participants and covering the entire adult life span (18-80 yrs). We used these data to create probability maps for the subthalamic nucleus, substantia nigra, internal and external segment of the globus pallidus, and the red nucleus. Data was acquired at a submillimeter resolution using a multi-echo (ME) extension of the second gradient-echo image of the MP2RAGE sequence (MP2RAGEME) sequence, resulting in complete anatomical alignment of quantitative, R1-maps, R2*-maps, T1-maps, T1-weighted images, T2*-maps, and quantitative susceptibility mapping (QSM). Quantitative MRI maps, and derived probability maps of basal ganglia structures are freely available for further analyses.
Keywords
Ultra-high field 7 Tesla structural MRI, Subcortex, Probabilistic maps, Basal ganglia, Atlases as Topic, Databases, Factual, Female, Globus Pallidus/anatomy & histology, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Red Nucleus/anatomy & histology, Substantia Nigra/anatomy & histology, Subthalamic Nucleus/anatomy & histology, Young Adult
Citation
Alkemade, A, Mulder, M J, Groot, J M, Isaacs, B R, van Berendonk, N, Lute, N, Isherwood, S J, Bazin, P-L & Forstmann, B U 2020, 'The Amsterdam Ultra-high field adult lifespan database (AHEAD) : A freely available multimodal 7 Tesla submillimeter magnetic resonance imaging database', NeuroImage, vol. 221, 117200, pp. 1-7. https://doi.org/10.1016/j.neuroimage.2020.117200