Multimodal Image Normalisation Tool (MINT) for the Adolescent Brain and Cognitive Development study: the MINT ABCD Atlas
Pecheva, D.; Iversen, J. R.; Palmer, C. E.; Watts, R.; Jernigan, T. L.; Hagler, D. J.; Dale, A. M.
Show abstract
The Adolescent Brain and Cognitive Development (ABCD) study aims to measure the trajectories of brain, cognitive, and emotional development. Cognitive and behavioural development during late childhood and adolescence have been associated with a myriad of microstructural and morphological alterations across the brain, as measured by magnetic resonance imaging (MRI). These associations may be strongly localised or spatially diffuse, therefore, it would be advantageous to analyse multimodal MRI data in concert, and across the whole brain. The ABCD study presents the unique challenge of integrating multimodal data from tens of thousands of scans at multiple timepoints, within a reasonable computation time. To address the need for a multimodal registration and atlas for the ABCD dataset, we present the synthesis of an ABCD atlas using the Multimodal Image Normalisation Tool (MINT). The MINT ABCD atlas was generated from baseline and two-year follow up imaging data using an iterative approach to synthesise a cohort-specific atlas from linear and nonlinear deformations of eleven channels of diffusion and structural MRI data. We evaluated the performance of MINT against two widely used methods and show that MINT achieves comparable alignment to current state-of-the-art multimodal registration, at a fraction of the computation time. To validate the use of the ABCD MINT atlas in whole brain, voxelwise analysis, we replicate and expand on previously published region-of-interest analysis between diffusion MRI-derived measures and body mass index (BMI). We also report novel association between BMI and brain morphology derived from the registration deformations. We present the ABCD MINT atlas as a publicly available resource to facilitate whole brain voxelwise analyses for the ABCD study.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Reliability Assessment of Tissue Classification Algorithms for Multi-Center and Multi-Scanner Data 98%
- The macaque brain ONPRC18 template with combined gray and white matter labelmap for multimodal neuroimaging studies of nonhuman primates 98%
- CIVET-Macaque: an automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques 98%
Similar papers in this journal
- A series of five population-specific Indian brain templates and atlases spanning ages 6 to 60 years 97%
- PhiPipe: a multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments 97%
- Voxel-wise Intermodal Coupling Analysis of Two or More Modalities using Local Covariance Decomposition 97%
Similar papers in this journal
- Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation 96%
- High resolution atlasing of the venous brain vasculature from 7T quantitative susceptibility 96%
- Morphological and functional variability in central and subcentral motor cortex of the human brain 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.