A spatial imaging-transcriptomics paradigm for deciphering the molecular basis of microscopic MRI in the normal brain and Alzheimer's disease.

Imaging genomics offers powerful links between genetics and neuroimaging phenotypes, but conventional methods often lack spatial correspondence. We introduce spatial imaging-transcriptomics, a paradigm that directly integrates magnetic resonance imaging (MRI) and spatially resolved transcriptomics (ST) with matched resolution from the same brain through spatially co-registered maps. We apply this framework to wild-type and Alzheimer's disease (AD) mouse models to uncover the molecular underpinnings of diffusion MRI.

7 Tesla multimodal MRI dataset of ex-vivo human brain.

Ex-vivo MRI offers invaluable insights into the complexity of the human brain, enabling high-resolution anatomical delineation and integration with histopathology, and thus, contributes to both basic and clinical studies on normal and pathological brains. However, ex-vivo MRI is challenging in sample preparation, acquisition, and data analysis, and existing ex-vivo MRI datasets are often single image modality and lack of ethnic diversity. In our study, we aimed to address these limitations by constructing a comprehensive multimodal MRI database acquired from six ex-vivo Chinese human brains.

Layer-Dependent Effect of Aβ-Pathology on Cortical Microstructure With Ex Vivo Human Brain Diffusion MRI at 7 Tesla.

The laminar-specific distributions of Aβ and Tau deposition in the neocortex of Alzheimer's disease (AD) have been established. However, direct evidence about the effect of AD pathology on cortical microstructure is lacking in human studies. We performed high-resolution T2-weighted and diffusion-weighted MRI (dMRI) on 15 ex vivo whole-hemisphere specimens, including eight cases with low AD neuropathologic change, three cases with primary age-related tauopathy (PART), and four healthy controls (HCs).

Developmental changes in connectivity-based parcellation in typical cortical areas during early life.

The human brain undergoes profound developmental changes in the cortex during early life. Functional parcellation of the cortex may also be altered, but there was a limited methodology for quantification. Here, we employed connectivity-based parcellation of sub-regions as a marker to characterize the heterogeneous development of the cerebral cortex.

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging.

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s.

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies.

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients.

Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity.

The neonatal period is a critical window for the development of the human brain and may hold implications for the long-term development of cognition and disorders. Multi-modal connectome studies have revealed many important findings underlying the adult brain but related studies were rare in the early human brain. One potential challenge is the lack of an appropriate and unbiased parcellation that combines structural and functional information in this population.

Layer-specific microstructural patterns of anterior hippocampus in Alzheimer's disease with ex vivo diffusion MRI at 14.1 T.

High-resolution ex vivo diffusion MRI (dMRI) can provide exquisite mesoscopic details and microstructural information of the human brain. Microstructural pattern of the anterior part of human hippocampus, however, has not been well elucidated with ex vivo dMRI, either in normal or disease conditions. The present study collected high-resolution (0.

Microstructural Alterations in Projection and Association Fibers in Neonatal Hypoxia-Ischemia.

Background: Diffusion MRI (dMRI) is known to be sensitive to hypoxic-ischemic encephalopathy (HIE). However, existing dMRI studies used simple diffusion tensor metrics and focused only on a few selected cerebral regions, which cannot provide a comprehensive picture of microstructural injury.

Purpose: To systematically characterize the microstructural alterations in mild, moderate, and severe HIE neonates compared to healthy neonates with advanced dMRI using region of interest (ROI), tract, and fixel-based analyses.

A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene-neuroanatomy connections.

The embryonic mouse brain undergoes drastic changes in establishing basic anatomical compartments and laying out major axonal connections of the developing brain. Correlating anatomical changes with gene-expression patterns is an essential step toward understanding the mechanisms regulating brain development. Traditionally, this is done in a cross-sectional manner, but the dynamic nature of development calls for probing gene-neuroanatomy interactions in a combined spatiotemporal domain.

Extracting and Selecting Robust Radiomic Features from PET/MR Images in Nasopharyngeal Carcinoma.

Objectives: This work aims to study the variation, robustness, and feature redundancy of PET/MR radiomic features in the primary tumor of nasopharyngeal carcinoma (NPC).

Procedures: PET/MR scans of 21 NPC patients were used in this study. The primary tumor volumes were defined using PET, T2-weighted-MR (T2-MR), and diffusion-weighted MR (DW-MR) images.

A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis.

The purpose of this study was to investigate the predictive performance of 2D and 3D image features across multi-organ cancers using multi-modality images in radiomics studies. In this retrospective study, we included 619 patients with three different cancer types (intrahepatic cholangiocarcinoma (ICC), high-grade osteosarcoma (HOS), pancreatic neuroendocrine tumors (pNETs)) and four clinical end points (early recurrence (ER), lymph node metastasis (LNM), 5-year survival and histologic grade). The image features included fifty-eight 2D image features and fifty-eight 3D image features.