Magnetic Resonance Imaging (MRI)

The periaqueductal gray (PAG) is known to play a crucial role in pain modulation and has shown a strong interaction with anterior cingulate cortex in previous functional imaging studies. We investigated the intrinsic functional connectivity of PAG using resting fMRI data from 100 subjects. The results showed that PAG is functionally connected to ACC (rostral and pregenual ACC) and also rostral ventromedial medulla (RVM), forming a core ACC-PAG-RVM network for pain modulation even no pain stimulus is applied.

The study of the mechanism of acupuncture action was revolutionized by the use of functional magnetic resonance imaging (fMRI). Over the past decade, our fMRI studies of healthy subjects have contributed substantially to elucidating the central effect of acupuncture on the human brain. These studies have shown that acupuncture stimulation, when associated with sensations comprising deqi, evokes deactivation of a limbic-paralimbic-neocortical network, which encompasses the limbic system, as well as activation of somatosensory brain regions.

Understanding the neurophysiology of human cognitive development relies on methods that enable accurate comparison of structural and functional neuroimaging data across brains from people of different ages. A fundamental question is whether the substantial brain growth and related changes in brain morphology that occur in early childhood permit valid comparisons of brain structure and function across ages.

The vessel caliber index (VCI), a magnetic resonance imaging biomarker of the average blood vessel diameter, is increasingly being used as a tool for assessing tumor angiogenesis and response to antiangiogenic therapy. However, although the VCI has been correlated with histological vessel diameters, good quantitative agreement with histology has been lacking. In addition, no VCI validation studies have been performed in vivo where the structural deformations frequently associated with histological tissue preparation are not present.

Advanced cerebrovascular β-amyloid deposition (cerebral amyloid angiopathy, CAA) is associated with cerebral microbleeds, but the precise relationship between CAA burden and microbleeds is undefined. We used T2*-weighted magnetic resonance imaging (MRI) and noninvasive amyloid imaging with Pittsburgh Compound B (PiB) to analyze the spatial relationship between CAA and microbleeds.

A critical goal of Alzheimer disease research is to identify disease biomarkers that can be used in clinical trials to assist in the adjudication of treatment effects. While clinical validation remains a goal for many potential Alzheimer disease biomarkers, the rapid proliferation of markers has sparked comparative efforts as well.

Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria.

BACKGROUND: Alzheimer's disease (AD) and its transitional state mild cognitive impairment (MCI) are characterized by amyloid plaque and tau neurofibrillary tangle (NFT) deposition within the cerebral neocortex and neuronal loss within the hippocampal formation. However, the precise relationship between pathologic changes in neocortical regions and hippocampal atrophy is largely unknown.

The prediction of individuals with mild cognitive impairment (MCI) destined to develop Alzheimer's disease (AD) is of increasing clinical importance. In this study, using baseline T1-weighted MRI scans of 324 MCI individuals from two cohorts and automated software tools, we employed factor analyses and Cox proportional hazards models to identify a set of neuroanatomic measures that best predicted the time to progress from MCI to AD.