CMIG Atlases

Overview The ABCD atlas was synthesized using the Multimodal Image Normalisation Tool (MINT) developed by the Data, Analysis and Informatics Resource Center of the ABCD study using adolescent brain MRI data from the ABCD Study®. The MINT ABCD atlas is generated from 1000 observations from baseline and two-year follow up imaging data. Below is a brief overview, for more details please see Pecheva et al. (2022). Intended use The ABCD atlas was created to facilitate analyses of multimodal MRI data using the statistical package FEMA (Parekh et al., 2024). All available scans from all observations included in ABCD Data Release 6.0 were registered to the ABCD atlas. For details on how to use the ABCD atlas along with ABCD Release 6.0 data for whole-brain voxelwise linear mixed effects analyses in FEMA, please see the description of the ABCD concatenated data and the FEMA package. What’s included These sections provide overviews of the contents of each .zip file. Please see the detailed file descriptions for more information. Data are provided as .mat and NIfTI files where possible. ABCD3_cor10 The ABCD atlas includes group representative volumes of the following modalities: T1w T2w Spherical harmonics coefficients derived using the restricted sprectrum imaging (RSI) model for three compartments: restricted, hindered and free water RGB colour-coded FA map Anatomical labels of cortical and subcortical structures from Freesurfer’s Deskian/Killiany parcellation (Desikan et al., Neuroimage, 2006) white matter tract parcellation from AltasTrack (Hagler et al., Hum. Brain Mapp., 2009). abcd3_rel6.0 In addition to these atlas files, all available scans from all observations included in Data Release 6.0 of the ABCD study were registered to the ABCD atlas. Participants' cortical, subcortical and white matter parcellations were warped to atlas space and averaged to create probabilistic anatomical labels of cortical, subcortical and white matter structures specific to the ABCD Release 6.0 data. These probabilistic labels differ from the labels in ABCD3_cor10 only in the observations included in generating them. external_parcellations We incorporated two independent brain parcellations into the MINT ABCD space to provide parcellations of subcortical structures not available from Freesurfer or AtlasTrack: Subcortical atlas that was generated using T1 and T2 scans from 168 typical adults from the Human Connectome Project (HCP) (Pauli et al., 2018), Thalamic nuclei atlas that was generated from adult HCP data from 70 participants (Najdenovska et al., 2018). showVolData Here are included atlas files in the formats required by the MATLAB-based MR volume viewer distributed as part of the FEMA package. Atlas creation Images were registered using rigid body, affine and nonlinear transformations. In total eleven multimodal channels were used to align scans and create the MINT ABCD atlas, however the input channels varied according to the registration step. Three structural MRI (sMRI) channels were included: T1w images, white matter segmentation, and grey matter segmentation; and eight diffusion MRI (dMRI) channels: the zeroth and second order spherical harmonics (SH) coefficients of the restricted orientation distribution function (ODF) and the zeroth order SH coefficients from the hindered and free water ODFs from the restriction spectrum imaging (RSI) model. sMRI and dMRI channels were first aligned within-subject using rigid body registration transforms. Then inter-subject anatomical alignment was achieved using rigid body, affine and nonlinear transforms. A group average was computed for each channel across the 1000 scans best aligned with the registration target, based on the mean Pearson correlation across all eleven input channels. The registration target was updated with the new multimodal average and the procedure was repeated three times, producing the iteratively refined multimodal ABCD atlas. References Pecheva, D., Iversen, J. R., Palmer, C. E., Watts, R., Jernigan, T. L., Hagler, D. J., & Dale, A. M. (2022). Multimodal Image Normalisation Tool (MINT) for the Adolescent Brain and Cognitive Development study: The MINT ABCD Atlas. bioRxiv. https://doi.org/10.1101/2022.08.09.503395 Parekh, P., Fan, C. C., Frei, O., Palmer, C. E., Smith, D. M., Makowski, C., Iversen, J. R., Pecheva, D., Holland, D., Loughnan, R., Nedelec, P., Thompson, W. K., Hagler, D. J. Jr, Andreassen, O. A., Jernigan, T. L., Nichols, T. E., & Dale, A. M. (2024). FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data. Human Brain Mapping, 45(2), e26579. https://doi.org/10.1002/hbm.26579 Najdenovska, E., Alemán-Gómez, Y., Battistella, G., Descoteaux, M., Hagmann, P., Jacquemont, S., Maeder, P., Thiran, J.-P., Fornari, E., & Bach Cuadra, M. (2018). Scientific Data, 5(1), 180270. https://doi.org/10.1038/sdata.2018.270 Pauli, W. M., Nili, A. N., & Tyszka, J. M. (2018). Scientific Data, 5(1), 180063. https://doi.org/10.1038/sdata.2018.63