Magnetic Resonance Imaging (MRI)

Amide proton transfer (APT) MRI is sensitive to ischemic tissue acidosis and has been increasingly used as a research tool to investigate disrupted tissue metabolism during acute stroke. However, magnetization transfer asymmetry (MTR(asym)) analysis is often used for calculating APT contrast, which only provides pH-weighted images. In addition to pH-dependent APT contrast, in vivo MTR(asym) is subject to a baseline shift (ΔMTR'(asym)) attributable to the slightly asymmetric magnetization transfer (MT) effect. Additionally, APT contrast approximately scales with T(1) relaxation time.

PURPOSE: To improve clinical three-dimensional (3D) MR spectroscopic imaging with more accurate localization and faster acquisition schemes.

OBJECTIVE: New preclinical Alzheimer disease (AD) diagnostic criteria have been developed using biomarkers in cognitively normal (CN) adults. We implemented these criteria using an MRI biomarker previously associated with AD dementia, testing the hypothesis that individuals at high risk for preclinical AD would be at elevated risk for cognitive decline.

The presence of pericytes in brain regions undergoing repair is evident of the recruitment of bone marrow-derived multipotent regenerative cells to the neurovascular unit during angiogenesis. At present, post mortem sampling is the only way to identify them. Therefore, such cell typing is inadequate for preserving neural progenitor cells for any meaningful stem cell therapy. We aimed to target cerebral pericytes in vivo using dual gene transcript-targeted MRI (GT-tMRI) in male C57black6 mice after a 60-min bilateral carotid artery occlusion (BCAO).

Magnetic resonance imaging (MRI) and spectroscopy (MRS) are versatile diagnostic techniques capable of characterizing the complex stroke pathophysiology, and hold great promise for guiding stroke treatment. Particularly, tissue viability and salvageability are closely associated with its metabolic status. Upon ischemia, ischemic tissue metabolism is disrupted including altered metabolism of glucose and oxygen, elevated lactate production/accumulation, tissue acidification and eventually, adenosine triphosphate (ATP) depletion and energy failure.

Ultra-high field MRI (≥ 7 T) has recently shown great sensitivity to depict patterns of tissue microarchitecture. Moreover, recent studies have demonstrated a dependency between T₂* and orientation of white matter fibers with respect to the main magnetic field B₀. In this study we probed the potential of T₂* mapping at 7 T to provide new markers of cortical architecture. We acquired multi-echo measurements at 7 T and mapped T₂* over the entire cortex of eight healthy individuals using surface-based analysis.

The perirhinal cortex (Brodmann's area 35) is a multimodal area that is important for normal memory function. Specifically, perirhinal cortex is involved in the detection of novel objects and manifests neurofibrillary tangles in Alzheimer's disease very early in disease progression. We scanned ex vivo brain hemispheres at standard resolution (1 mm × 1 mm × 1 mm) to construct pial/white matter surfaces in FreeSurfer and scanned again at high resolution (120 μm × 120 μm × 120 μm) to determine cortical architectural boundaries.

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks.

Neuroimaging studies have suggested the presence of alterations in the anatomo-functional properties of the brain of patients with chronic pain. However, investigation of the brain circuitry supporting the perception of clinical pain presents significant challenges, particularly when using traditional neuroimaging approaches. While potential neuroimaging markers for clinical pain have included resting brain connectivity, these cross-sectional studies have not examined sensitivity to within-subject exacerbation of pain.

Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data.