Longitudinal analysis of the preterm cortex using multi-modal spectral matching

Extremely preterm birth (less than 32 weeks completed gestation) overlaps with a period of rapid brain growth and development. Investigating longitudinal brain changes over the preterm period in these infants may allow the development of biomarkers for predicting neurological outcome. In this paper we investigate longitudinal changes in cortical thickness,cortical fractional anisotropy and cortical mean diffusivity in a groupwise space obtained using a novel multi-modal spectral matching technique. The novelty of this method consists in its ability to register surfaces with very little shape complexity,like in the case of the early developmental stages of preterm infants,by also taking into account their underlying biology. A multi-modal method also allows us to investigate interdependencies between the parameters. Such tools have great potential in investigating in depth the regions affected by preterm birth and how they relate to each other

Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available. Keywords: Functional Connectivity, Cortical Surface, Task Activation, Target Subject, Intrinsic ConnectivityCongressionally Directed Medical Research Programs (U.S.) (Grant PT100120)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (R01HD067312)Neuroimaging Analysis Center (U.S.) (P41EB015902)Oesterreichische Nationalbank (14812)Oesterreichische Nationalbank (15929)Seventh Framework Programme (European Commission) (FP7 2012-PIEF-GA-33003

Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces

The human cerebral cortex is marked by great complexity as well as substantial dynamic changes during early postnatal development. To obtain a fairly comprehensive picture of its age-induced and/or disorder-related cortical changes, one needs to match cortical surfaces to one another, while maximizing their anatomical alignment. Methods that geodesically shoot surfaces into one another as currents (a distribution of oriented normals) and varifolds (a distribution of non-oriented normals) provide an elegant Riemannian framework for generic surface matching and reliable statistical analysis. However, both conventional current and varifold matching methods have two key limitations. First, they only use the normals of the surface to measure its geometry and guide the warping process, which overlooks the importance of the orientations of the inherently convoluted cortical sulcal and gyral folds. Second, the ‘conversion’ of a surface into a current or a varifold operates at a fixed scale under which geometric surface details will be neglected, which ignores the dynamic scales of cortical foldings. To overcome these limitations and improve varifold-based cortical surface registration, we propose two different strategies. The first strategy decomposes each cortical surface into its normal and tangent varifold representations, by integrating principal curvature direction field into the varifold matching framework, thus providing rich information of the orientation of cortical folding and better characterization of the complex cortical geometry. The second strategy explores the informative cortical geometric features to perform a dynamic-scale measurement of the cortical surface that depends on the local surface topography (e.g., principal curvature), thereby we introduce the concept of a topography-based dynamic-scale varifold. We tested the proposed varifold variants for registering 12 pairs of dynamically developing cortical surfaces from 0 to 6 months of age. Both variants improved the matching accuracy in terms of closeness to the target surface and the goodness of alignment with regional anatomical boundaries, when compared with three state-of-the-art methods: (1) diffeomorphic spectral matching, (2) conventional current-based surface matching, and (3) conventional varifold-based surface matching

Brain Transfer: Spectral Analysis of Cortical Surfaces and Functional Maps

International audienceThe study of brain functions using fMRI often requires an accuratematching of cortical surface data for comparing brain activation acrossa population. In this context, several tasks are critical, such as surface in-flation for cortical visualizations and measurements, surface matching andalignment of functional data for group-level analyses. Present methods typicallytreat each task separately and can be computationally expensive. It takesfor example several hours to smooth and match a single pair of cortical surfaces.Furthermore, conventional methods rely on anatomical features to drivethe alignment of functional data across individuals, whereas their relation tofunction can vary across a population. To address these issues, we proposeBrain Transfer, a spectral framework that unifies cortical smoothing, pointmatching with confidence regions, and transfer of functional maps, all withinminutes of computation. Spectral methods have the advantage of decomposingshapes into intrinsic geometrical harmonics, but suffer from the inherentinstability of these harmonics. This limits their direct comparison in surfacematching, and prevents the spectral transfer of functions. Our contributionsconsist of, first, the optimization of a spectral transformation matrix, whichcombines both, point correspondence and change of eigenbasis, and second,a localized spectral decomposition of functional data, via focused harmonics.Brain Transfer enables the transfer of surface functions across interchangeablecortical spaces, accounts for localized confidence, and gives a new way toperform statistics on surfaces. We illustrate the benefits of spectral transfersby exploring the shape and functional variability of retinotopy, which remainschallenging with conventional methods. We find a higher degree of accuracyin the alignment of retinotopy, exceeding those of conventional methods

Performing group-level functional image analyses based on homologous functional regions mapped in individuals

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the localizer to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations. Author summary No two individuals are alike. The size, shape, position, and connectivity patterns of brain functional regions can vary drastically between individuals. While interindividual differences in functional organization are well recognized, to date, standard procedures for functional neuroimaging research still rely on aligning different subjects' data to a nominal average brain based on global brain morphology. We developed an approach to reliably identify homologous functional regions in each individual and demonstrated that aligning data based on these homologous functional regions can significantly improve the study of resting state functional connectivity, task-fMRI activations, and brain-behavior associations. Moreover, we showed that individual differences in size, position, and connectivity of brain functional regions are dissociable, and each can provide nonredundant information in explaining human behavior

Metric Optimization for Surface Analysis in the Laplace-Beltrami Embedding Space

In this paper we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigen-system, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a cross-validation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects

Longitudinal analysis of extreme prematurity: a neuroimage investigation of early brain development

Brain development is a complex process, and disruptions from its normal course may affect the later neurological outcome of an individual. Preterm infants are at higher risk of disability, since a substantial part of brain development happens outside the mother’s womb, making it vulnerable to a range of insults. Understanding the early brain development during the preterm period is of critical importance and magnetic resonance imaging (MRI) allows us to investigate this. Methodologically, this is a challenging task, as classical approaches of studying longitudinal development over this period do not cope with the large changes taking place. This thesis focuses on the development of tools to study the changes in cortical folding, shape of different brain structures and microstructural changes over the preterm period from longitudinal data of extremely preterm-born infants. It describes a tissue segmentation pipeline, optimised on a postmortem fetal dataset, and then focuses on finding longitudinal correspondences between the preterm and termequivalent brain regions and structures in extremely preterm-born infants using MRI. Three novel registration techniques are proposed for longitudinal registration of this challenging data. These are based on matching the spectral components associated with either the cortical surfaces, diffusion tensor images, or both. These allow us to quantify longitudinal changes in different brain regions and structures. We investigated changes in cortical folding of different lobes, microstructural changes and tracts in the white matter, cortical thickness and changes in cortical fractional anisotropy and mean diffusivity. We used cortical surface registration to look at shape differences between controls and extremely preterm-born young adults to gain an insight into the long-term impact of prematurity. This research may contribute to the development of early biomarkers for predicting the neurological outcome of preterm infants and illuminate our understanding of brain development during this crucial period