We present Connectome 2.0, the next-generation human MRI scanner for imaging structural anatomy and connectivity spanning the microscopic, mesoscopic and macroscopic scales. This work builds upon our expertise in engineering the first human Connectome MRI scanner with 300 mT/m maximum gradient strength (Gmax), the highest ever achieved for a human system, for the Human Connectome Project (HCP). The goal of the HCP was to map the macroscopic structural connections of the in vivo healthy adult human brain using diffusion tractography. While this instrument has made important contributions to our understanding of macroscale connectional topology, our experience with the scanner over the last seven years has taught us that dedicated high-gradient performance scanners can also acquire a rich array of diffusion measurements that provide unparalleled in vivo assessment of neural tissue microstructure, such as the relative size and packing density of cells and axons. However, the current Connectome instrument is limited in its ability to resolve the full range of length scales needed to probe the microscopic and mesoscopic structure of the brain, due to basic design limitations, important technical elements, and biological interactions with the large rapidly switching gradients. Our experience with the first generation Connectome scanner and realization of its limitations motivates our multi-site proposal for the next generation human Connectome MRI scanner (Connectome 2.0) to achieve sensitivity to a broader range of cellular and axonal size scales, morphologies, and interconnections represented throughout the brain. Our goal here is to translate our initial experience into building a one-of-a-kind high-slew rate, ultra- high-gradient strength MRI scanner that is optimized for the study of neural tissue microstructure and neural circuits across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current Gmax to 500 mT/m and tripling the maximum slew rate to 600 T/m/s; (2) pushing the limits of the RF receive coils and gradient characterization to enable maximum sensitivity with greatly reduced artifacts using real-time eddy current corrected dMRI acquisitions; (3) developing new pulse sequences to achieve the highest diffusion- and spatial-resolution ever achieved in vivo; and (4) calibrating the measurements obtained from this next generation instrument through systematic validation of the diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales. We envision creating the ultimate diffusion MRI machine capable of addressing the BRAIN 2025 mandate to image across scales, from the microscopic scale needed to probe cellular heterogeneity and plasticity, to the mesoscopic scale for enumerating the distinctions in cortical structure and connectivity that define cyto- and myeloarchitechtonic boundaries, to improvements in estimates of macroscopic connectivity.

Public Health Relevance

The next generation human MRI scanner for mapping the microscopic, mesoscopic and macroscopic connections in the human brain, Connectome 2.0, will dramatically change our ability to visualize the structure of the human brain. Connectome 2.0 will provide unprecedented spatial and diffusion resolution compared to the highest performance gradient systems for human imaging to date. As a new research tool, Connectome 2.0 will be applied to transform the scientific understanding of circuitry and to explore the microstructural substrate of functional plasticity in the human brain.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZEB1)
Program Officer
Wang, Shumin
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Massachusetts General Hospital
United States
Zip Code