Extra-Visual Functional and Structural Connection Abnormalities in Leber's Hereditary Optic Neuropathy

We assessed abnormalities within the principal brain resting state networks (RSNs) in patients with Leber's hereditary optic neuropathy (LHON) to define whether functional abnormalities in this disease are limited to the visual system or, conversely, tend to be more diffuse. We also defined the structural substrates of fMRI changes using a connectivity-based analysis of diffusion tensor (DT) MRI data. Neuro-ophthalmologic assessment, DT MRI and RS fMRI data were acquired from 13 LHON patients and 13 healthy controls. RS fMRI data were analyzed using independent component analysis and SPM5. A DT MRI connectivity-based parcellation analysis was performed using the primary visual and auditory cortices, bilaterally, as seed regions. Compared to controls, LHON patients had a significant increase of RS fluctuations in the primary visual and auditory cortices, bilaterally. They also showed decreased RS fluctuations in the right lateral occipital cortex and right temporal occipital fusiform cortex. Abnormalities of RS fluctuations were correlated significantly with retinal damage and disease duration. The DT MRI connectivity-based parcellation identified a higher number of clusters in the right auditory cortex in LHON vs. controls. Differences of cluster-centroid profiles were found between the two groups for all the four seeds analyzed. For three of these areas, a correspondence was found between abnormalities of functional and structural connectivities. These results suggest that functional and structural abnormalities extend beyond the visual network in LHON patients. Such abnormalities also involve the auditory network, thus corroborating the notion of a cross-modal plasticity between these sensory modalities in patients with severe visual deficits

The Connectome Visualization Utility: Software for Visualization of Human Brain Networks

In analysis of the human connectome, the connectivity of the human brain is collected from multiple imaging modalities and analyzed using graph theoretical techniques. The dimensionality of human connectivity data is high, and making sense of the complex networks in connectomics requires sophisticated visualization and analysis software. The current availability of software packages to analyze the human connectome is limited. The Connectome Visualization Utility (CVU) is a new software package designed for the visualization and network analysis of human brain networks. CVU complements existing software packages by offering expanded interactive analysis and advanced visualization features, including the automated visualization of networks in three different complementary styles and features the special visualization of scalar graph theoretical properties and modular structure. By decoupling the process of network creation from network visualization and analysis, we ensure that CVU can visualize networks from any imaging modality. CVU offers a graphical user interface, interactive scripting, and represents data uses transparent neuroimaging and matrix-based file types rather than opaque application-specific file formats

Imaging-based parcellations of the human brain

A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions — is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies

A structural connectivity convergence zone in the ventral and anterior temporal lobes: Data-driven evidence from structural imaging

The hub-and-spoke model of semantic cognition seeks to reconcile embodied views of a fully distributed semantic network with patient evidence, primarily from semantic dementia, who demonstrate modality-independent conceptual deficits associated with atrophy centred on the ventrolateral anterior temporal lobe. The proponents of this model have recently suggested that the temporal cortex is a graded representational space where concepts become less linked to a specific modality as they are processed farther away from primary and secondary sensory cortices and towards the ventral anterior temporal lobe. To explore whether there is evidence that the connectivity patterns of the temporal lobe converge in its ventral anterior end the current study uses three dimensional Laplacian eigenmapping, a technique that allows visualisation of similarity in a low dimensional space. In this space similarity is encoded in terms of distances between data points. We found that the ventral and anterior temporal lobe is in a unique position of being at the centre of mass of the data points within the connective similarity space. This can be interpreted as the area where the connectivity profiles of all other temporal cortex voxels converge. This study is the first to explicitly investigate the pattern of connectivity and thus provides the missing link in the evidence that the ventral anterior temporal lobe can be considered a multi-modal graded hub

Morphogenetic Principles of Brain Organisation in Health and Disease

Non-invasive neuroimaging methods, such as MRI, provide a window into the structure of the mammalian brain. However, despite the ubiquity of these methods, the biological interpretation of the information obtained using these tools remains elusive. In order to accurately link this macroscale data to microscale measurements, it is critical that the construct validity is high. This thesis provides novel analyses, pipelines and methods to: i) generate and validate maps of brain organisation obtained via MRI, and ii) demonstrate the utility of these methods in capturing elements of cognition and psychopathology. First, in Chapter 1, I review some of the neuroscientific context for the new methods presented, from cytoarchitecture to gene expression to connectomes. Chapters 2-4 introduce a new method, “Morphometric Similarity Mapping”, which captures the brain organisation of an individual by mapping the relationships of multiple features of the cerebral cortex. Chapter 2 focuses on the development of the analysis pipeline and the graph theoretical features of the resulting morphometric similarity networks (MSNs), with an emphasis on reproducibility. Chapter 3 highlights the generalisability of MSNs to the macaque monkey, linking MSNs to ex vivo tract tracing experiments and presenting new tools for processing non-human imaging data; as well as evidence that MSN topography is organised by cytoarchitectonic features. Chapter 4 is focused on determining the transcriptomic correlates of MSNs using publicly available gene expression maps, and on applying MSNs to examine the relationship between brain organisation and intelligence. Chapter 5 is dedicated to rigorous evaluation of the applicability of MSNs to measure specific disease-relevant phenotypes in 8 rare genetic disorder cohorts. This includes the validation of novel methods for utilising data from both single-cell sequencing technologies and differential gene expression experiments (in multiple tissue types) in analysing neuroimaging and bulk transcriptomic brain maps. Chapter 6 provides a brief summary and presents some ongoing and future projects expanding on this original work. It also importantly discusses a general framework of comparing brain maps, including MSNs and gene expression, as well as other canonical maps of brain structure and function. Altogether, this thesis presents and evaluates novel methods and applications for integrating multimodal neuroimaging data with genetic data derived from multiple tissue types and through various acquisition strategies. It also includes tools for performing these analyses in non-human primates, and pipelines for statistically comparing brain maps. These results not only provide insight into the manifestation of brain-related changes due to various components of human variation, but also provides a framework for evaluating this variation at multiple biological scales purely from non-invasive neuroimaging data

Whole-brain cortical parcellation: A hierarchical method based on dMRI tractography

In den modernen Neurowissenschaften ist allgemein anerkannt, dass die Gehirnfunktionen auf dem Zusammenwirken von verschiedenen Regionen in Netzwerken beruhen und die strukturelle Konnektivität daher großer Bedeutung ist. Daher kann die Abgrenzung funktioneller Hirnbereiche auf der Grundlage der Diffusions-Magnet-Resonanz-Tomographie (dMRT) und der Traktografie zu wertvollen Hirnkarten führen.Existierende Verfahren versuchen eine fest vorgegebene Anzahl von Regionen zu finden und/oder sind auf kleine Bereiche der grauen Substanz beschränkt. Im Allgemeinen ist es jedoch unwahrscheinlich, dass eine einzelne Parzellierung des Kortex, eine ausreichende Darstellung der funktio- anatomischen Organisation des Gehirns erlaubt. In dieser Arbeit schlagen wir eine hierarchische Clusteranalyse vor um diese Einschränkungen zu überwinden und das gesamte Gehirn zu parzellieren. Wir zeigen, dass dieses Verfahren die Eigenschaften der zugrundeliegenden Struktur auf allen Granularitätstufen des hierarchischen Baums (Dendrogramm) kodieren kann. Weiterhin entwickeln wir eine optimale Verarbeitungspipeline zur Erstellung dieses Baums, die dessen Komplexität mit minimalem Informationsverlust reduziert. Wir zeigen wie diese Datenstrukturen verwendet werden können um die Ähnlichkeitstruktur von verschiedenen Probanden oder Messungen zu vergleichen und wie man daraus verschiedene Parzellierungen des Gehirns erhalten kann.Unser neuer Ansatz liefert eine ausführlichere Analyse der anatomischen Strukturen und bietet eine Methode zur Parzellierung des ganzen Gehirns.In modern neuroscience there is general agreement that brain function relies on networks and that connectivity is therefore of paramount importance for brain function. Accordingly, the delineation of functional brain areas on the basis of diffusion magnetic resonance imaging (dMRI) and tractography may lead to highly relevant brain maps.Existing methods typically aim to find a predefined number of areas and/or are limited to small regions of grey matter. However, it is in general not likely that a single parcellation dividing the brain into a finite number of areas is an adequate representation of the function-anatomical organization of the brain. In this work, we propose hierarchical clustering as a solution to overcome these limitations and achieve whole-brain parcellation. We demonstrate that this method encodes the information of the underlying structure at all granularity levels in a hierarchical tree or dendrogram. We develop an optimal tree building and processing pipeline that reduces the complexity of the tree with minimal information loss. We show how these trees can be used to compare the similarity structure of different subjects or recordings and how to extract parcellations from them.Our novel approach yields a more exhaustive representation of the real underlying structure and successfully tackles the challenge of whole-brain parcellation

Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender