Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, ‘ground truth’ validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how “functional connectivity” from fMRI and “tractographic connectivity” from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.
|Original language||English (US)|
|State||Published - Apr 1 2021|
Bibliographical noteFunding Information:
This study is supported by a grant Brain/MINDS‐beyond from Japan Agency of Medical Research and Development ( AMED ) ( JP20dm0307006h0002 ) (T.H., M.I.M.), grant KAKENHI from MEXT (19H04904) (M.I.M.), NIH Grant MH060974 (D.C.V.E.), RF1 MH116978 (E.Y.), NIH Grant P50NS098573 (E.Y.) and Wellcome Trust (S.M.S.). The grants from SBRI are LABEX CORTEX ANR‐11‐LABX‐0042; Université de Lyon ANR‐11‐IDEX‐0007, A2P2MC ANR‐17‐NEUC‐0004, CORTICITY ANR‐17‐HBPR‐0003, Région Auvergne‐Rhône‐Alpes SCUSI 1700933701 (H.K.), Chinese Academy of Sciences President's International Fellowship Initiative. Grant No. 2018VBA0011 (H.K.), and DUAL_STREAMS ANR‐19‐CE37‐0025 (K.K.). The authors appreciate technical contributions from Yuki Hori, Atsushi Yoshida, Kantaro Nishigori, Chihiro Yokoyama, Takayuki Ose, Masahiro Ohno, Chihiro Takeda, Akihiro Kawasaki, Kenji Mitsui, Sumika Sagawa, Reiko Kobayashi, Takuro Ikeda, Toshihiko Aso, Yuki Matsumoto, Takashi Azuma, Masahiko Takada, Chad Donahue, John Harwell, Erin Reid.
© 2021 The Authors
- Diffusion MRI
- Functional MRI
- Retrograde tracer