Detailed T1-Weighted Profiles from the Human Cortex Measured in Vivo at 3 Tesla MRI

7T MP2RAGE for cortical myelin segmentation: Impact of aging

BACKGROUND

Myelin and iron are major contributors to the cortical MR signal. The aim of this study was to investigate 1. Can MP2RAGE-derived contrasts at 7T in combination with k-means clustering be used to distinguish between heavily and sparsely myelinated layers in cortical gray matter (GM)? 2. Does this approach provide meaningful biological information?

METHODS

The following contrasts were generated from the 7T MP2RAGE images from 45 healthy controls (age: 19-75, f/m = 23/22) from the ATAG data repository: 1. T1 weighted image (UNI). 2. T1 relaxation image (T1map). 3. INVC/T1map ratio (RATIO). K-means clustering identified 6 clusters/tissue maps (csf, csf/gm-transition, wm, wm/gm transition, heavily myelinated cortical GM (dGM), sparsely myelinated cortical GM (sGM)). These tissue maps were then processed with SPM/DARTEL (volume-based analyses) and Freesurfer (surface-based analyses) and dGM and sGM volume/thickness of young adults (n = 27, 19-27 years) compared to those of older adults (n = 18, 42-75 years) at p<0.001 uncorrected.

RESULTS

The resulting maps showed good agreement with histological maps in the literature. Volume- and surface analyses found age-related dGM loss/thinning in the mid-posterior cingulate and parahippocampal/entorhinal gyrus and age-related sGM losses in lateral, mesial and orbitofrontal frontal, insular cortex and superior temporal gyrus.

CONCLUSION

The MP2RAGE derived UNI, T1map and RATIO contrasts can be used to identify dGM and sGM. Considering the close relationship between cortical myelo- and cytoarchitecture, the findings reported here indicate that this new technique might provide new insights into the nature of cortical GM loss in physiological and pathological conditions.

Cortical depth profiles in primary visual cortex for illusory and imaginary experiences

Visual illusions and mental imagery are non-physical sensory experiences that involve cortical feedback processing in the primary visual cortex. Using laminar functional magnetic resonance imaging (fMRI) in two studies, we investigate if information about these internal experiences is visible in the activation patterns of different layers of primary visual cortex (V1). We find that imagery content is decodable mainly from deep layers of V1, whereas seemingly 'real' illusory content is decodable mainly from superficial layers. Furthermore, illusory content shares information with perceptual content, whilst imagery content does not generalise to illusory or perceptual information. Together, our results suggest that illusions and imagery, which differ immensely in their subjective experiences, also involve partially distinct early visual microcircuits. However, overlapping microcircuit recruitment might emerge based on the nuanced nature of subjective conscious experience.

Widespread cortical dyslamination in epilepsy patients with malformations of cortical development

PURPOSE

Recent research in epilepsy patients confirms our understanding of epilepsy as a network disorder with widespread cortical compromise. Here, we aimed to investigate the neocortical laminar architecture in patients with focal cortical dysplasia (FCD) and periventricular nodular heterotopia (PNH) using clinically feasible 3 T MRI.

METHODS

Eighteen epilepsy patients (FCD and PNH groups; n = 9 each) and age-matched healthy controls (n = 9) underwent T1 relaxation 3 T MRI, from which component probability T1 maps were utilized to extract sub-voxel composition of 6 T1 cortical layers. Seventy-eight cortical areas of the automated anatomical labeling atlas were divided into 1000 equal-volume sub-areas for better detection of cortical abnormalities, and logistic regressions were performed to compare FCD/PNH patients with healthy controls with the T1 layers composing each sub-area as regressors. Statistical significance (p < 0.05) was determined by a likelihood-ratio test with correction for false discovery rate using Benjamini-Hochberg method.

RESULTS

Widespread cortical abnormalities were observed in the patient groups. Out of 1000 sub-areas, 291 and 256 bilateral hemispheric cortical sub-areas were found to predict FCD and PNH, respectively. For each of these sub-areas, we were able to identify the T1 layer, which contributed the most to the prediction.

CONCLUSION

Our results reveal widespread cortical abnormalities in epilepsy patients with FCD and PNH, which may have a role in epileptogenesis, and likely related to recent studies showing widespread structural (e.g., cortical thinning) and diffusion abnormalities in various human epilepsy populations. Our study provides quantitative information of cortical laminar architecture in epilepsy patients that can be further targeted for study in functional and neuropathological studies.

Assessing reproducibility in association studies

Research that links brain structure with behavior needs more data, better analyses, and more intelligent approaches.

Mapping Cortical Laminar Structure in the 3D BigBrain

Histological sections offer high spatial resolution to examine laminar architecture of the human cerebral cortex; however, they are restricted by being 2D, hence only regions with sufficiently optimal cutting planes can be analyzed. Conversely, noninvasive neuroimaging approaches are whole brain but have relatively low resolution. Consequently, correct 3D cross-cortical patterns of laminar architecture have never been mapped in histological sections. We developed an automated technique to identify and analyze laminar structure within the high-resolution 3D histological BigBrain. We extracted white matter and pial surfaces, from which we derived histologically verified surfaces at the layer I/II boundary and within layer IV. Layer IV depth was strongly predicted by cortical curvature but varied between areas. This fully automated 3D laminar analysis is an important requirement for bridging high-resolution 2D cytoarchitecture and in vivo 3D neuroimaging. It lays the foundation for in-depth, whole-brain analyses of cortical layering.