Magnetic Resonance Imaging (MRI)

The myelin g-ratio is defined as the ratio of the inner to the outer diameter of the myelin sheath. This ratio provides a measure of the myelin thickness that complements axon morphology (diameter and density) with high specificity for assessment of demyelination in diseases such as multiple sclerosis. Previous work has shown that an aggregate g-ratio map can be computed using a formula that combines axon and myelin density measured with quantitative MRI. In this work, we computed g-ratio weighted maps in the cervical spinal cord of nine healthy subjects.

Extensive efforts are devoted to understand the functional (FC) and structural connections (SC) of the brain. FC is usually measured by functional magnetic resonance imaging (fMRI), and conceptualized as degree of synchronicity in brain activity between different regions. SC is typically indexed by measures of white matter (WM) properties, for example, by diffusion weighted imaging (DWI). FC and SC are intrinsically related, in that coordination of activity across regions ultimately depends on fast and efficient transfer of information made possible by structural connections.

Cerebral amyloid angiopathy is a common neuropathological finding in the ageing human brain, associated with cognitive impairment. Neuroimaging markers of severe cerebral amyloid angiopathy are cortical microbleeds and microinfarcts. These parenchymal brain lesions are considered key contributors to cognitive impairment. Therefore, they are important targets for therapeutic strategies and may serve as surrogate neuroimaging markers in clinical trials.

The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units.

Despite recent advances in auditory neuroscience, the exact functional organization of human auditory cortex (AC) has been difficult to investigate. Here, using reversals of tonotopic gradients as the test case, we examined whether human ACs can be more precisely mapped by avoiding signals caused by large draining vessels near the pial surface, which bias blood-oxygen level dependent (BOLD) signals away from the actual sites of neuronal activity.

Oxygen is delivered to brain tissue by a dense network of microvessels, which actively control cerebral blood flow (CBF) through vasodilation and contraction in response to changing levels of neural activity. Understanding these network-level processes is immediately relevant for (1) interpretation of functional Magnetic Resonance Imaging (fMRI) signals, and (2) investigation of neurological diseases in which a deterioration of neurovascular and neuro-metabolic physiology contributes to motor and cognitive decline.

When visual objects are located in the lower visual field, human observers perceive objects to be nearer than their real physical location. Conversely, objects in the upper visual field are viewed farther than their physical location. This bias may be linked to the statistics of natural scenes, and perhaps the ecological relevance of objects in the upper and lower visual fields (Previc, 1990; Yang & Purves, 2003). However, the neural mechanisms underlying such perceptual distortions have remained unknown.

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans.