Neurite Exchange Imaging ((NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Karger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.

Exploring dynamic functional connectivity by incorporating prior knowledge of brain structure

Anjali Bagunu Tarun

The synchronized firing of distant neuronal populations gives rise to a wide array of functional brain networks that underlie human brain function. Given the enormous perception, learning, and cognition potential of the human brain, it is not surprising that network-level representations of brain function reveal intrinsically rich organizational structure. What remains puzzling is how the human brain maintains its vast repertoire of functional connectivity (FC) states despite being constrained by the underlying fixed anatomical substrate. Advances in modern neuroimaging technologies, such as diffusion-weighted magnetic resonance imaging (DW-MRI), have made it possible to map the brain's anatomical scaffold, while functional MRI (fMRI) provides complementary information on neural activity. In this thesis, we develop and apply methods for combining DW-MRI and fMRI data into an integrated framework to analyze the interplay between brain structure and function. We first explored the dynamics of brain function during wakefulness and across the different non-rapid eye movement (NREM) sleep stages. We applied the innovation-driven co-activation pattern (iCAP) analysis to uncover the spatial and temporal organization of overlapping large-scale brain networks. Our results reveal new spatial patterns covering regions that support the physiological organization of sleep and arousal. Contrary to the previously observed decreasing FC that accompanies increasing sleep depth, we instead observe a surge of network activity and cross-network interactions during NREM stage 2, followed by an abrupt decrease in NREM stage 3. In the next step of the thesis, we propose a new method that models all brain voxels as nodes of a high-resolution voxel-level brain graph. We provide two ways to construct the graph and we characterize their properties by performing a spectral analysis of the graph Laplacian operator. Our findings show that despite the huge dimensionality of the proposed brain graphs, the majority of the structural information is captured by the Laplacian's lowest frequency eigenmodes. The lower end of the Laplacian spectra also captures about 85% of the energy content of functional MRI, suggesting that functional patterns are overall smooth over the structure, thus providing, for the first time, a direct and quantitative measure of how much brain function is shaped by the anatomy. Going beyond a scalar measure of the SC-FC link, we introduce a new framework that interpolates gray matter signals onto the white matter using the structure embedded in the voxel-level brain grid to guide the process. This enables visualization of key white matter structures that link temporally coherent gray matter areas. We found whole-brain structure-function networks that extend currently known spatial patterns that are limited within the gray matter only. Finally, we assessed the collective mediation of white matter pathways by giving a quantitative measure of the overall anatomical range between temporally coherent gray matter areas. We utilized a canonical model of graph diffusion to extract the anatomical range of functional network interactions. We find that this measure meaningfully differentiates brain regions according to a behaviorally relevant macroscale gradient that divides the cortex between low-level primary sensory areas and high-level cognitive functions.

EPFL2020

Exploring dynamic functional connectivity by incorporating prior knowledge of brain structure

Anjali Bagunu Tarun

EPFL2020