SPECTRAL CLUSTERING BASED PARCELLATION OF FETAL BRAIN MRI

Many neuroimaging studies are based on the idea that there are distinct brain regions that are functionally or micro-anatomically homogeneous. Obtaining such regions in an au-tomatic way is a challenging task for fetal data due to the lack of strong and consistent anatomical features at the early stages of brain development. In this paper we propose the use of an automatic approach for parcellating fetal cerebral hemi-spheric surfaces into K regions via spectral clustering. Unlike previous methods, our technique has the crucial advantage of only relying on intrinsic geometrical properties of the corti-cal surface and thus being unsupervised. Results on a data-set of fetal brain MRI acquired in utero demonstrated a convinc-ing parcellation reproducibility of the cortical surfaces across fetuses with varying gestational ages and folding magnitude

Time resolved functional brain networks : a novel method and developmental perspective

Functional neuroimaging has helped elucidating the complexity of brain function in ever more detail during the last 30 years. In this time the concepts used to understand how the brain works has also developed from a focus on regional activation to a network based whole brain perspective (Deco et al., 2015). The understanding that the brain is not just merely responding to external demands but is itself a co-creator of its perceived reality is now the default perspective (Buzsáki and Fernández-Ruiz, 2019). This means that the brain is never resting and its intrinsic architecture is the basis for any task related modulation (Cole et al., 2014). As often in science, understanding and technological advances go hand in hand. For the advancement of the functional neuroimaging field during the last decade, methods that are able to track, capture and model time resolved connectivity changes has been essential (Lurie et al., 2020). This development is an ongoing process. Part of the work presented in this thesis is a small contribution to this collective endeavor. The first theme in the thesis is time resolved connectivity of functional brain networks. This theme is present in Study I which presents a novel method for analysis of time resolved connectivity using BOLD fMRI data. With this method, subnetworks in the brain are defined dynamically. It allows for connectivity changes to be tracked from time point to time point while respecting the temporal ordering of the data. It also provides relational properties in terms of differences in phase coherence between simultaneously integrated networks and their gradual change. The method can be used see how whole brain connectivity configurations recure in quasi-cyclic patterns. Finally, the method is able to estimate flexibility and modularity of individual brain areas. The method is applied in Study III in order to understand how premature birth effects flexibility and modularity of intrinsic functional brain networks. Beyond the purely scientific endeavor to understand how the brain creates cognition, consciousness, perception and supports motor function, neuroimaging research has also been helpful in elucidating normal brain development and neurodevelopmental disorders. The second theme in this thesis is brain development in extremely preterm born children at school age. This theme is the focus of Study II & III. Study II investigates the prevalence of discrete white matter abnormalities at school age in children born extremely preterm and the relationship to neuro-motor outcome. The prevalence of white matter abnormalities was high but there was no relationship to an unfavorable outcome. Also, a longitudinal association to neonatal white matter injury was seen. While discrete white matter abnormalities were not correlated to quantitative measures of white matter volume and white matter integrity, neonatal white matter injury was associated with lower volume and integrity at age 8- 11 years. Moreover, neonatal white matter injury was associated with lower processing speed at 12 years. The third and final study investigated flexibility and modularity as well as lateralization of intrinsic networks in children born extremely preterm at age 8-11 years. No significant differences in either flexibility or modularity was seen for any intrinsic network after correcting for multiple comparisons. However, at the level of individual brain areas, preterm children showed decreased flexibility in both the basal ganglia and thalamus. Also, children born extremely preterm had a decreased level of lateralization in most networks

Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and understand the underlying subnetworks which contribute towards these observed holistic connectomic alterations. One organizational principle is the rich-club - a core subnetwork of brain regions that are strongly connected, forming a high-cost, high-capacity backbone that is critical for effective communication in the network. Investigations primarily focus on its alterations with disease and age. Here, we present a systematic analysis of not only the rich-club, but also other subnetworks derived from this backbone - namely feeder and seeder subnetworks. Our analysis is applied to structural connectomes in a normal cohort from a large, publicly available lifespan study. We demonstrate changes in rich-club membership with age alongside a shift in importance from 'peripheral' seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as increased efficiency in the feeder subnetwork and decreased measures of network integration and segregation in the seeder subnetwork. These results demonstrate the different developmental patterns when analyzing the connectome stratified according to its rich-club and the potential of utilizing this subnetwork analysis to reveal the evolution of brain architectural alterations across the life-span

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen