Bridging the gap between system and cell: The role of ultra-high field MRI in human neuroscience

Closing the mechanistic gap: the value of microarchitecture in understanding cognitive networks

Cognitive neuroscience aims to provide biologically relevant accounts of cognition. Contemporary research linking spatial patterns of neural activity to psychological constructs describes 'where' hypothesised functions occur, but not 'how' these regions contribute to cognition. Technological, empirical, and conceptual advances allow this mechanistic gap to be closed by embedding patterns of functional activity in macro- and microscale descriptions of brain organisation. Recent work on the default mode network (DMN) and the multiple demand network (MDN), for example, highlights a microarchitectural landscape that may explain how activity in these networks integrates varied information, thus providing an anatomical foundation that will help to explain how these networks contribute to many different cognitive states. This perspective highlights emerging insights into how microarchitecture can constrain network accounts of human cognition.

The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

Brainglance: Visualizing Group Level MRI Data at One Glance

The vast majority of studies using functional magnetic resonance imaging (fMRI) are analyzed on the group level. Standard group-level analyses, however, come with severe drawbacks: First, they assume functional homogeneity within the group, building on the idea that we use our brains in similar ways. Second, group-level analyses require spatial warping and substantial smoothing to accommodate for anatomical variability across subjects. Such procedures massively distort the underlying fMRI data, which hampers the spatial specificity. Taken together, group statistics capture the effective overlap, rendering the modeling of individual deviations impossible – a major source of false positivity and negativity. The alternative analysis approach is to leave the data in the native subject space, but this makes comparison across individuals difficult. Here, we propose a new framework for visualizing group-level information, better preserving the information of individual subjects. Our proposal is to limit the use of invasive data procedures such as spatial smoothing and warping and rather extract regional information from the individuals. This information is then visualized for all subjects and brain areas at one glance – hence we term the method brainglance. Additionally, our method incorporates a means for clustering individuals to further identify common traits. We showcase our method on two publicly available data sets and discuss our findings.

Apparent thinning of human visual cortex during childhood is associated with myelination

Human cortex appears to thin during childhood development. However, the underlying microstructural mechanisms are unknown. Using functional magnetic resonance imaging (fMRI), quantitative MRI (qMRI), and diffusion MRI (dMRI) in children and adults, we tested what quantitative changes occur to gray and white matter in ventral temporal cortex (VTC) from childhood to adulthood, and how these changes relate to cortical thinning. T1 relaxation time from qMRI and mean diffusivity (MD) from dMRI provide independent and complementary measurements of microstructural properties of gray and white matter tissue. In face- and character-selective regions in lateral VTC, T1 and MD decreased from age 5 to adulthood in mid and deep cortex, as well as in their adjacent white matter. T1 reduction also occurred longitudinally in children's brain regions. T1 and MD decreases 1) were consistent with tissue growth related to myelination, which we verified with adult histological myelin stains, and 2) were correlated with apparent cortical thinning. In contrast, in place-selective cortex in medial VTC, we found no development of T1 or MD after age 5, and thickness was related to cortical morphology. These findings suggest that lateral VTC likely becomes more myelinated from childhood to adulthood, affecting the contrast of MR images and, in turn, the apparent gray-white boundary. These findings are important because they suggest that VTC does not thin during childhood but instead gets more myelinated. Our data have broad ramifications for understanding both typical and atypical brain development using advanced in vivo quantitative measurements and clinical conditions implicating myelin.

Visualizing the Human Subcortex Using Ultra-high Field Magnetic Resonance Imaging

With the recent increased availability of ultra-high field (UHF) magnetic resonance imaging (MRI), substantial progress has been made in visualizing the human brain, which can now be done in extraordinary detail. This review provides an extensive overview of the use of UHF MRI in visualizing the human subcortex for both healthy and patient populations. The high inter-subject variability in size and location of subcortical structures limits the usability of atlases in the midbrain. Fortunately, the combined results of this review indicate that a large number of subcortical areas can be visualized in individual space using UHF MRI. Current limitations and potential solutions of UHF MRI for visualizing the subcortex are also discussed.