Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain

Diffusion magnetic resonance imaging has been widely used in both clinical and preclinical studies to characterize tissue microstructure and structural connectivity. The diffusion MRI protocol for the Human Connectome Project (HCP) has been developed and optimized to obtain high-quality, high-resolution diffusion MRI (dMRI) datasets. However, such efforts have not been fully explored in preclinical studies, especially for rodents. In this study, high quality dMRI datasets of mouse brains were acquired at 9.4T system from two vendors. In particular, we acquired a high-spatial resolution dMRI dataset (25 um isotropic with 126 diffusion encoding directions), which we believe to be the highest spatial resolution yet obtained; and a high-angular resolution dMRI dataset (50 um isotropic with 384 diffusion encoding directions), which we believe to be the highest angular resolution compared to the dMRI datasets at the microscopic resolution. We systematically investigated the effects of three important parameters that affect the final outcome of the connectome: b value (1000 s/mm2 to 8000 s/mm2), angular resolution (10 to 126), and spatial resolution (25 um to 200 um). The stability of tractography and connectome increase with the angular resolution, where more than 50 angles are necessary to achieve consistent results. The connectome and quantitative parameters derived from graph theory exhibit a linear relationship to the b value (R2 > 0.99); a single-shell acquisition with b value of 3000 s/mm2 shows comparable results to the multi-shell high angular resolution dataset. The dice coefficient decreases and both false positive rate and false negative rate gradually increase with coarser spatial resolution. Our study provides guidelines and foundations for exploration of tradeoffs among acquisition parameters for the structural connectome in ex vivo mouse brain.

A Riemannian approach to predicting brain function from the structural connectome

Ongoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono- as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.

Resting brain activity emerges from wave propagating along spatiotemporal varying hyper-structural connectome

How spontaneous brain activities emerge from the structural connectivity (SC) has puzzled researchers for a long time. The underlying mechanism still remains largely unknown. Previous studies on modeling the resting-state human brain functional connectivity (FC) are normally based on the relatively static structural connectome directly and very few of them concern about the dynamic spatiotemporal variability of FC. Here we establish an explicit wave equation to describe the spontaneous cortical neural activities based on the high-order hypergraph representation of SC. Theoretical solution shows that the dynamic couplings between brain regions fluctuates in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of the hyper-structural connectome (hSC), which orchestrates the cortical activities propagating in both space and time. Ultimately, we present a possible mechanism of how negative correlations emerge during the fluctuation of the hypergraph Laplacian of SC, which helps to further understand the fundamental role of SC in shaping the entire pattern of FC with a new perspective. Comprehensive tests on four connectome datasets with different resolutions confirm our theory and findings.

A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer’s Disease

The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer’s disease, i.e., early signs of subnetworks impairment due to Alzheimer’s disease.3. The sample in this study consisted of 123 first-degree Alzheimer’s disease relatives and 61 non-relatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer’s disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer’s disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer’s disease structural-connectome-trait presents a posterior–posterior and posterior-temporal pattern, supplying new evidences to the cascading network failure model.