An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases.
Yiming XiaoJonathan C LauTaylor AndersonJordan DeKrakerD Louis CollinsTerry PetersAli R KhanPublished in: Scientific data (2019)
Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, amygdala, and hippocampus for all mentioned atlases. The dataset intends to provide a bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: https://osf.io/xkqb3/ .
Keyphrases
- magnetic resonance imaging
- contrast enhanced
- resting state
- high resolution
- single cell
- diffusion weighted imaging
- cerebral ischemia
- functional connectivity
- white matter
- electronic health record
- computed tomography
- big data
- healthcare
- magnetic resonance
- deep learning
- mental health
- machine learning
- multiple sclerosis
- artificial intelligence
- blood brain barrier
- mass spectrometry
- data analysis