Cortical structure: Linking MRI and cytoarchitecture

MRI provides a powerful tool to investigate brain structure in living humans. However a major challenge is interpreting the biological underpinnings of changes at this scale. This dissertation describes investigations into the problem of linking microscale post mortem cortical cytoarchitecture with millimeter-scale measures of cortical anatomy accessible through in vivo MRI. Chapter 1 introduces the problem and previous work done to address it. The following two chapters apply classical atlases of cortical cytoarchitecture to understanding morphological changes both in health (Chapter 2) and in disease (Chapter 3). Chapter 2 demonstrates that sensory processing hierarchies exhibit increasing gradients of cortical thickness, related to changes in cortical cytoarchitecture. In Chapter 3, cytoarchitectonically described differences in gyral and sulcal laminar structure were used to create markers of laminar change from MRI changes in schizophrenia. Classical measurements of histology have limitations; they are observer dependent, two-dimensional with limited coverage of the cortex. To address these issues, Chapters 4-6 document work carried on BigBrain, a 3D 20$\mu$m resolution histological dataset. I created a high-resolution 3D atlas of laminar cytoarchitecture, which was mapped to MRI-compatible cortical surface reconstructions. Chapter 4 records the development of an automated 1D profile-based approach to laminar analysis, revealing basic principles of cortical cytoarchitecture. In Chapter 5 this approach was extended to identify 6 cortical layers throughout the isocortex. These tools can be used to segment 1D cortical intensity profiles derived from any modality. In Chapter 6, the analysis of cortical gradients initially identified using MRI cortical thickness in Chapter 2 was replicated and extended using novel histological data. First histological cortical thicknesses were tested for the same patterns organization measured on in vivo MRI in Chapter 2. These analyses were extended to test which layers contributed most to overall thickness. High-resolution, complete maps of cortical cytoarchitecture mapped to MRI-template cortical surface reconstructions, are a powerful tool and dataset for the neuroimaging community. They offer new possibilities for linking cortical microstructure to in vivo neuroimaging

Cortical thickness gradients in structural hierarchies.

MRI, enabling in vivo analysis of cortical morphology, offers a powerful tool in the assessment of brain development and pathology. One of the most ubiquitous measures used-the thickness of the cortex-shows abnormalities in a number of diseases and conditions, but the functional and biological correlates of such alterations are unclear. If the functional connotations of structural MRI measures are to be understood, we must strive to clarify the relationship between measures such as cortical thickness and their cytoarchitectural determinants. We therefore sought to determine whether patterns of cortical thickness mirror a key motif of the cortex, specifically its structural hierarchical organisation. We delineated three sensory hierarchies (visual, somatosensory and auditory) in two species-macaque and human-and explored whether cortical thickness was correlated with specific cytoarchitectural characteristics. Importantly, we controlled for cortical folding which impacts upon thickness and may obscure regional differences. Our results suggest that an easily measurable macroscopic brain parameter, namely, cortical thickness, is systematically related to cytoarchitecture and to the structural hierarchical organisation of the cortex. We argue that the measurement of cortical thickness gradients may become an important way to develop our understanding of brain structure-function relationships. The identification of alterations in such gradients may complement the observation of regionally localised cortical thickness changes in our understanding of normal development and neuropsychiatric illnesses.We thank Claus Hilgetag and Sarah Beul for valuable input and Helen Barbas for providing macaque data. Human data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investi- gators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, and by the McDonnell Center for Systems Neuroscience at Washington University. KW is supported by the Wellcome Trust and the University of Cambridge MB/PhD Programme, IG by a Wellcome Trust Strategic Award (RNAG/260) and LR and PF by the Bernard Wolfe Health Neuroscience Fund and Wellcome Trust.This is the final published manuscript. It first appeared at http://www.sciencedirect.com/science/article/pii/S1053811915001378

IDEAL approach to the evaluation of machine learning technology in epilepsy surgery: protocol for the MAST trial

Epilepsy and epilepsy surgery lend themselves well to the application of machine learning (ML) and artificial intelligence (AI) technologies. This is evidenced by the plethora of tools developed for applications such as seizure detection and analysis of imaging and electrophysiological data. However, few of these tools have been directly used to guide patient management. In recent years, the Idea, Development, Exploration, Assessment, Long-Term Follow-Up (IDEAL) collaboration has formalised stages for the evaluation of surgical innovation and medical devices, and, in many ways, this pragmatic framework is also applicable to ML/AI technology, balancing innovation and safety. In this protocol paper, we outline the preclinical (IDEAL stage 0) evaluation and the protocol for a prospective (IDEAL stage 1/2a) study to evaluate the utility of an ML lesion detection algorithm designed to detect focal cortical dysplasia from structural MRI, as an adjunct in the planning of stereoelectroencephalography trajectories in children undergoing intracranial evaluation for drug-resistant epilepsy

The natural axis of transmitter receptor distribution in the human cerebral cortex

Transmitter receptors constitute a key component of the molecular machinery for intercellular communication in the brain. Recent efforts have mapped the density of diverse transmitter receptors across the human cerebral cortex with an unprecedented level of detail. Here, we distill these observations into key organizational principles. We demonstrate that receptor densities form a natural axis in the human cerebral cortex, reflecting decreases in differentiation at the level of laminar organization and a sensory-to-association axis at the functional level. Along this natural axis, key organizational principles are discerned: progressive molecular diversity (increase of the diversity of receptor density); excitation/inhibition (increase of the ratio of excitatory-to-inhibitory receptor density); and mirrored, orderly changes of the density of ionotropic and metabotropic receptors. The uncovered natural axis formed by the distribution of receptors aligns with the axis that is formed by other dimensions of cortical organization, such as the myelo- and cytoarchitectonic levels. Therefore, the uncovered natural axis constitutes a unifying organizational feature linking multiple dimensions of the cerebral cortex, thus bringing order to the heterogeneity of cortical organization

Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python

Increasingly available high-resolution brain imaging data require specialized processing tools that can leverage their anatomical detail and handle their size. Here, we present user-friendly Python tools for cortical depth resolved analysis in such data. Our implementation is based on the CBS High-Res Brain Processing framework, and aims to make high-resolution data processing tools available to the broader community

Obesity associated with increased brain age from midlife.

Common mechanisms in aging and obesity are hypothesized to increase susceptibility to neurodegeneration, however, direct evidence in support of this hypothesis is lacking. We therefore performed a cross-sectional analysis of magnetic resonance image-based brain structure on a population-based cohort of healthy adults. Study participants were originally part of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) and included 527 individuals aged 20-87 years. Cortical reconstruction techniques were used to generate measures of whole-brain cerebral white-matter volume, cortical thickness, and surface area. Results indicated that cerebral white-matter volume in overweight and obese individuals was associated with a greater degree of atrophy, with maximal effects in middle-age corresponding to an estimated increase of brain age of 10 years. There were no similar body mass index-related changes in cortical parameters. This study suggests that at a population level, obesity may increase the risk of neurodegeneration.This work was supported by the Bernard Wolfe Health Neuroscience Fund and the Wellcome Trust (grant number RNAG/259). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) research was supported by the Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neurobiolaging.2016.07.01

Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach

Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FCD detection methods are limited by high numbers of false positive predictions, primarily due to vertex- or patch-based approaches that lack whole-brain context. Here, we propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions. To address the specific challenges of FCD identification, our proposed model includes an auxiliary loss to predict distance from the lesion to reduce false positives and a weak supervision classification loss to facilitate learning from uncertain lesion masks. On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0.74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison to 49% when using the MLP. This improvement in specificity is vital for clinical integration of lesion-detection tools into the radiological workflow, through increasing clinical confidence in the use of AI radiological adjuncts and reducing the number of areas requiring expert review.Comment: accepted at MICCAI 202

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

Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy.

Focal cortical dysplasia is a congenital abnormality of cortical development and the leading cause of surgically remediable drug-resistant epilepsy in children. Post-surgical outcome is improved by presurgical lesion detection on structural MRI. Automated computational techniques have improved detection of focal cortical dysplasias in adults but have not yet been effective when applied to developing brains. There is therefore a need to develop reliable and sensitive methods to address the particular challenges of a paediatric cohort. We developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort. In addition to established measures, such as cortical thickness, grey-white matter blurring, FLAIR signal intensity, sulcal depth and curvature, our novel features included complementary metrics of surface morphology such as local cortical deformation as well as post-processing methods such as the "doughnut" method - which quantifies local variability in cortical morphometry/MRI signal intensity, and per-vertex interhemispheric asymmetry. A neural network classifier was trained using data from 22 patients with focal epilepsy (mean age = 12.1 ± 3.9, 9 females), after intra- and inter-subject normalisation using a population of 28 healthy controls (mean age = 14.6 ± 3.1, 11 females). Leave-one-out cross-validation was used to quantify classifier sensitivity using established features and the combination of established and novel features. Focal cortical dysplasias in our paediatric cohort were correctly identified with a higher sensitivity (73%) when novel features, based on our approach for detecting local cortical changes, were included, when compared to the sensitivity using only established features (59%). These methods may be applicable to aiding identification of subtle lesions in medication-resistant paediatric epilepsy as well as to the structural analysis of both healthy and abnormal cortical development.This research was supported by the National Institute for Health Research Biomedical Research Centre at Great Ormond Street Hospital for Children NHS Foundation Trust and University College London. SA received funding from the Rosetrees Trust (A711). KW received funding from the James Baird Fund and the Wellcome Trust (WT095692MA). TB from Great Ormond Street Hospital Children's Charity (V1213 and V2416). LR and PCF are funded by the Wellcome Trust and the Bernard Wolfe Health Neuroscience Fund

Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks.

Human functional magnetic resonance imaging (fMRI) brain networks have a complex topology comprising integrative components, e.g. long-distance inter-modular edges, that are theoretically associated with higher biological cost. Here, we estimated intra-modular degree, inter-modular degree and connection distance for each of 285 cortical nodes in multi-echo fMRI data from 38 healthy adults. We used the multivariate technique of partial least squares (PLS) to reduce the dimensionality of the relationships between these three nodal network parameters and prior microarray data on regional expression of 20 737 genes. The first PLS component defined a transcriptional profile associated with high intra-modular degree and short connection distance, whereas the second PLS component was associated with high inter-modular degree and long connection distance. Nodes in superior and lateral cortex with high inter-modular degree and long connection distance had local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific to supragranular layers of human cortex. In contrast, primary and secondary sensory cortical nodes in posterior cortex with high intra-modular degree and short connection distance had transcriptional profiles enriched for RNA translation and nuclear components. We conclude that, as predicted, topologically integrative hubs, mediating long-distance connections between modules, are more costly in terms of mitochondrial glucose metabolism.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.PEV is supported by an MRC Bioinformatics Research Fellowship (MR/K020706/1). Functional MRI data acquisition was supported by a strategic award from the Wellcome Trust to the University of Cambridge (IMG, PBJ, ETB) and University College London (RJD, PF): the Neuroscience in Psychiatry Network (NSPN). Additional support was provided by the NIHR Cambridge Biomedical Research Centre. Access to gene expression data was provided by the Allen Institute for Brain Sciences Website: © 2015 Allen Institute for Brain Science. Allen Human Brain Atlas [Internet]. Available from: http://human.brain-map.org. FV is supported by a Gates Cambridge PhD studentship. KW is supported by the University of Cambridge MB/PhD Programme and the Wellcome Trust. We thank Gita Prabu, Roger Tait, Cinly Ooi, John Suckling and Becky Inkster for fMRI data collection and storage. ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline(GSK).This is the final version of the article. It first appeared from Royal Society Publishing via http://dx.doi.org/10.1098/rstb.2015.036