We are developing a novel diffusion MRI-based pulse sequence, acquisition, and signal modeling framework to enable us to "see" or detect fine-scale structures in the living human brain that are three orders of magnitude smaller than the underlying voxel size, and which are currently invisible using clinical MRI techniques. To be concrete, we acquire images with isotropic voxels that are about 1.5 mm on each side, but attempt to observe features of microscopic objects, such as axon diameters and axon diameter distributions, etc., which require a spatial resolution of about 2 microns. One way we accomplish this feat is to develop advanced mathematical/physical models describing the relationship between the observed MR signal and the various microstructural parameters under investigation. It is also important to correct for various artifacts that can blur or corrupt these images, leading to incorrect estimates of imaging quantities. Then, we attempt to infer the biophysical basis of these signals. One method to translate to the Connectome 2.0 is AxCaliber MRI, an approach we invented and developed at the NIH, but which was limited in its resolution on conventional MRI scanners. The new Connectome 2.0 scanner allows one to detect axons with finer axon diameters. Another approach we are migrating is mean apparent propagator (MAP) MRI, a method that measures the net displacement distribution or average propagator of diffusing water molecules in tissue. This provides information about the different microenvironments water finds itself in within living brain tissue. Another approach we translating to the Connectome scanner is Time-Scaling MRI, which entails obtaining Mean Apparent Propagator (MAP) MRI data at different diffusion times. This approach allows us to infer certain features of hierarchical tissue organizations, such as the possible fractal dimension of tissues, that we can exploit to provide mesoscopic and microscale information. We also are investigating various multiple-pulsed field gradient (mPFG) MRI methods for clinical translation, some of which we have previously developed in our lab, which we are extensively vetting, and working to migrate to this powerful new clinical scanning platform. A new mPFG methodology we have been pioneering is a way to estimate the diffusion tensor distribution (DTD) within each voxel, which can be used to study the heterogeneity of water transport processes. This has been greatly enhanced in terms of experimental design, computational speed of processing, and shoring up its mathematical underpinnings. We have also been developing NHP atlases that allow histological image data to be merged and compared with MRI data to enable us to test and vet various MRI methods we develop. In the coming years, much additional vetting and testing will be required to ensure the accuracy and precision of our acquisition and modeling pipelines so that they are ready for clinical implementation and testing in the "out years" of this grant, when the prototype Connectome 2.0 scanner will be completed and ready for scanning of normal volunteers and clinical subjects.