Characterizing Intra-soma Diffusion with Spherical Mean Spectrum Imaging

Huynh, Khoi Minh; Wu, Ye; Thung, Kim-Han; Ahmad, Sahar; Taylor, Hoyt Patrick; Shen, Dinggang; Yap, Pew Thian

doi:10.1007/978-3-030-59728-3_35

Cited by 4 publications

(2 citation statements)

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“…Specifically, cortical thickness is defined as the Euclidean distance between two correspondent vertices on the white and pial surfaces and myelination is quantified using the ratio between T1w and T2w images 67 . Spherical Mean Spectrum Imaging (SMSI) 68, 69 was used to calculate intra-cellular volume fraction, extra-cellular volume fraction, intra-soma volume fraction, microscopic fractional anisotropy, microscopic mean diffusivity, microscopic anisotropy index, and orientation coherence index. Diffusion Tensor Imaging (DTI) was used to calculate fractional anisotropy and mean diffusivity.…”

Section: Methodsmentioning

confidence: 99%

Functional Hierarchy of the Human Neocortex from Cradle to Grave

Taylor,

Thung,

Huynh

et al. 2024

Preprint

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Recent evidence indicates that the organization of the human neocortex is underpinned by smooth spatial gradients of functional connectivity (FC). These gradients provide crucial in-sight into the relationship between the brain’s topographic organization and the texture of human cognition. However, no studies to date have charted how intrinsic FC gradient architecture develops across the entire human lifespan. In this work, we model developmental trajectories of the three primary gradients of FC using a large, high-quality, and temporally-dense functional MRI dataset spanning from birth to 100 years of age. The gradient axes, denoted as sensorimotor-association (SA), visual-somatosensory (VS), and modulation-representation (MR), encode crucial hierarchical organizing principles of the brain in development and aging. By tracking their evolution throughout the human lifespan, we provide the first ever comprehensive low-dimensional normative reference of global FC hierarchical architecture. We observe significant age-related changes in global network features, with global markers of hierarchical organization increasing from birth to early adulthood and decreasing there-after. During infancy and early childhood, FC organization is shaped by primary sensory processing, dense short-range connectivity, and immature association and control hierarchies. Functional differentiation of transmodal systems supported by long-range coupling drives a convergence toward adult-like FC organization during late childhood, while adolescence and early adulthood are marked by the expansion and refinement of SA and MR hierarchies. While gradient topographies remain stable during late adulthood and aging, we observe decreases in global gradient measures of FC differentiation and complexity from 30 to 100 years. Examining cortical microstructure gradients alongside our functional gradients, we observed that structure-function gradient coupling undergoes differential lifespan trajectories across multiple gradient axes.

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Section: Methodsmentioning

confidence: 99%

Functional Hierarchy of the Human Neocortex from Cradle to Grave

Taylor,

Thung,

Huynh

et al. 2024

Preprint

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“…On the other hand, SMSI gives more biologically feasible results with lower isotropic volume fraction in white matter than gray matter. Note that isotropic diffusion in gray matter is in part intra-soma diffusion [52,53].…”

Section: Diffusion Indicesmentioning

confidence: 99%

Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging

Huynh

et al. 2020

IEEE Trans. Med. Imaging

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During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b-values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine-to coarse-scale diffusion processes, characterizing restricted and hindered diffusion stemming respectively from intra-and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy (μFA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.

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