White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences.

Dramatic advances in the last six months in High Definition Fiber Tracking (HDFT) make it possible to image the fiber connectivity from source to destination mapping hundreds of thousands fiber tracks with sufficient resolution to identify the cable level circuit diagram of the human brain. Brain activity imaging studies using functional Magnetic Resonance Imagining (fMRI) identify differential activation patterns as a function of task and level of practice. These data show subnetworks with communication of high bandwidth vector associations, scalar priority and control signals, and interactions with control and meta cognition. The connectivity and activity data support a triarchic cognitive architecture. Processing is the synergistic interaction of three interlinked cognitive computational systems with differential computation role and evolutionary history. These data provided a detailed diagram to guide reverse engineering of the systems levels of the human brain.