Dataset Information

High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor Low-Rank Reconstruction.

ABSTRACT: The goals of fMRI acquisition include high spatial and temporal resolutions with a high signal to noise ratio (SNR). Oscillating Steady-State Imaging (OSSI) is a new fMRI acquisition method that provides large oscillating signals with the potential for high SNR, but does so at the expense of spatial and temporal resolutions. The unique oscillation pattern of OSSI images makes it well suited for high-dimensional modeling. We propose a patch-tensor low-rank model to exploit the local spatial-temporal low-rankness of OSSI images. We also develop a practical sparse sampling scheme with improved sampling incoherence for OSSI. With an alternating direction method of multipliers (ADMM) based algorithm, we improve OSSI spatial and temporal resolutions with a factor of 12 acquisition acceleration and 1.3 mm isotropic spatial resolution in prospectively undersampled experiments. The proposed model yields high temporal SNR with more activation than other low-rank methods. Compared to the standard grad- ient echo (GRE) imaging with the same spatial-temporal resolution, 3D OSSI tensor model reconstruction demonstrates 2 times higher temporal SNR with 2 times more functional activation.

SUBMITTER: Guo S

PROVIDER: S-EPMC7751316 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor Low-Rank Reconstruction.

Guo Shouchang S Fessler Jeffrey A JA Noll Douglas C DC

IEEE transactions on medical imaging 20201130 12

The goals of fMRI acquisition include high spatial and temporal resolutions with a high signal to noise ratio (SNR). Oscillating Steady-State Imaging (OSSI) is a new fMRI acquisition method that provides large oscillating signals with the potential for high SNR, but does so at the expense of spatial and temporal resolutions. The unique oscillation pattern of OSSI images makes it well suited for high-dimensional modeling. We propose a patch-tensor low-rank model to exploit the local spatial-tempo ...[more]

PMID: 32809938

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Project description:The gut represents a potential entry site for a wide range of pathogens including protozoa, bacteria, viruses, or fungi. Consequently, it is protected by one of the largest and most diversified population of immune cells of the body. Its surveillance requires the constant sampling of its encounters by dedicated sentinels composed of follicles and their associated epithelium located in specialized area. In the small intestine, Peyer's patches (PPs) are the most important of these mucosal immune response inductive sites. Through several mechanisms including transcytosis by specialized epithelial cells called M-cells, access to the gut lumen is facilitated in PPs. Although antigen sampling is critical to the initiation of the mucosal immune response, pathogens have evolved strategies to take advantage of this permissive gateway to enter the host and disseminate. It is, therefore, critical to decipher the mechanisms that underlie both host defense and pathogen subversive strategies in order to develop new mucosal-based therapeutic approaches. Whereas penetration of pathogens through M cells has been well described, their fate once they have reached the subepithelial dome (SED) remains less well understood. Nevertheless, it is clear that the mononuclear phagocyte system (MPS) plays a critical role in handling these pathogens. MPS members, including both dendritic cells and macrophages, are indeed strongly enriched in the SED, interact with M cells, and are necessary for antigen presentation to immune effector cells. This review focuses on recent advances, which have allowed distinguishing the different PP mononuclear phagocyte subsets. It gives an overview of their diversity, specificity, location, and functions. Interaction of PP phagocytes with the microbiota and the follicle-associated epithelium as well as PP infection studies are described in the light of these new criteria of PP phagocyte identification. Finally, known alterations affecting the different phagocyte subsets during PP stimulation or infection are discussed.

Project description:Cortical parcellation has long been a cornerstone in the field of neuroscience, enabling the cerebral cortex to be partitioned into distinct, non-overlapping regions that facilitate the interpretation and comparison of complex neuroscientific data. In recent years, these parcellations have frequently been based on the use of resting-state fMRI (rsfMRI) data. In parallel, methods such as independent components analysis have long been used to identify large-scale functional networks with significant spatial overlap between networks. Despite the fact that both forms of decomposition make use of the same spontaneous brain activity measured with rsfMRI, a gap persists in establishing a clear relationship between disjoint cortical parcellations and brain-wide networks. To address this, we introduce a novel parcellation framework that integrates NASCAR, a three-dimensional tensor decomposition method that identifies a series of functional brain networks, with state-of-the-art graph representation learning to produce cortical parcellations that represent near-homogeneous functional regions that are consistent with these brain networks. Further, through the use of the tensor decomposition, we avoid the limitations of traditional approaches that assume statistical independence or orthogonality in defining the underlying networks. Our findings demonstrate that these parcellations are comparable or superior to established atlases in terms of homogeneity of the functional connectivity across parcels, task contrast alignment, and architectonic map alignment. Our methodological pipeline is highly automated, allowing for rapid adaptation to new datasets and the generation of custom parcellations in just minutes, a significant advancement over methods that require extensive manual input. We describe this integrated approach, which we refer to as Untamed, as a tool for use in the fields of cognitive and clinical neuroscientific research. Parcellations created from the Human Connectome Project dataset using Untamed, along with the code to generate atlases with custom parcel numbers, are publicly available at https://untamed-atlas.github.io.

Dataset Information

High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor Low-Rank Reconstruction.

Publications

High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor Low-Rank Reconstruction.

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