Solmujen sisäinen konnektiviteetti ja topologiset roolit toiminnallisissa aivoverkoissa

Many real-life phenomena consist of a number of interacting elements and can thus be modeled as a complex network. The human brain is an example of such a system where the neuronal information processing of the brain is characterized by interaction and information exchange between different brain regions. In this Thesis, we examine functional brain networks estimated from functional magnetic resonance imaging (fMRI) data. When defining network nodes, the small measurement units, voxels, are grouped to larger entities that represent supposedly functionally homogeneous brain regions referred to as Regions of Interest (ROIs). Despite their assumed homogeneity, it has been demonstrated that the voxels within a ROI exhibit spatially and temporally varying correlation structure. This gives rise to a concept referred to as internal connectivity. On the larger scale, the ROIs form a brain network where each ROI has its role in the structure of the network topology, i.e., a topological role. Topological roles have been suggested to be indicative of the node's functional specialization. On the other hand, it has been argued that internal connectivity may relate to the mechanisms the ROI uses to interact with its neighbors in the functional brain network. This Thesis combines these two ideas. To this end, we aim to predict the ROI's topological role from its internal connectivity features. We find that using internal connectivity features as model variables increases the classification accuracy in comparison to a baseline classifier. These results suggest that there is a relationship between internal connectivity and the ROI's topological role. This link provides a basis for faster and more computationally efficient topological role estimation. Further, it helps to better understand the mechanisms brain regions use to interact with each other. Both of these factors importantly increase our knowledge on brain function under different tasks and circumstances.Monet todellisen maailman ilmiöt koostuvat useista vuorovaikutuksessa olevista elementeistä, ja niitä voidaan mallintaa kompleksisina verkostoina. Ihmisaivot ovat esimerkki tällaisesta järjestelmästä, jossa aivojen hermosolutason tiedonkäsittely perustuu aivoalueiden väliseen vuorovaikutukseen ja tiedonvaihtoon. Diplomityössäni tutkin toiminnallisesta magneettikuvausdatasta rakennettuja toiminnallisia aivoverkkoja. Verkon solmuja määritettäessä pienet mittauselementit, vokselit, ryhmitellään isommiksi kokonaisuuksiksi, jotka edustavat toiminnallisesti yhtenäisiksi oletettuja aivoalueita (engl. Region of Interest, ROI). On kuitenkin osoitettu, että oletetusta yhtenäisyydestään huolimatta ROIden sisällä on monimuotoisia sekä paikallisesti että ajallisesti vaihtelevia korrelaatiorakenteita. Tästä syntyy sisäisen konnektiviteetin käsite, joka kuvaa ROI:n sisäistä korrelaatiorakennetta ja sen vaihtelua. Laajemmassa mittakaavassa ROI:t muodostavat aivoverkon, jossa jokaisella ROI:lla on verkon rakenteessa oma roolinsa, n.s. topologinen rooli. Topologisten roolien ajatellaan liittyvän ROI:den toiminnalliseen erikoistumiseen. On myös esitetty, että sisäinen konnektiviteetti liittyy niihin mekanismeihin, joiden avulla ROI vuorovaikuttaa naapureidensa kanssa toiminnallisessa aivoverkossa. Tämä diplomityö yhdistää nämä kaksi ajatusta: ROI:n topologista roolia pyritään ennustamaan sen sisäisen konnektiviteetin tekijöiden avulla. Tulokset osoittavat, että sisäisen konnektiviteetin tekijät parantavat ennustustarkkuutta verrattuna valistuneeseen arvaukseen perustuvaan pohjatasoluokittimeen. Tulokset osoittavat, että ROI:n sisäisen konnetiviteetin ja topologisten roolien välillä on yhteys. Tämä yhteys tarjoaa pohjan topologisten roolien nopeammalle ja laskennallisesti tehokkaammalle määrittämiselle ja lisää ymmärrystä niistä mekanismeista, joita ROI:t käyttävät vuorovaikuttaakseen toistensa kanssa. Nämä tekijät lisäävät tietoa aivojen toiminnasta eri tilanteissa ja tehtävissä

Fine-grained functional parcellation maps of the infant cerebral cortex

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field

Strength-dependent perturbation of whole-brain model working in different regimes reveals the role of fluctuations in brain dynamics

Despite decades of research, there is still a lack of understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used whole-brain computational models capable of presenting different dynamical regimes to reproduce empirical data's turbulence level. We showed that the model's fluctuations regime fitted to turbulence more faithfully reproduces the empirical functional connectivity compared to oscillatory and noise regimes. By applying global and local strength-dependent perturbations and subsequently measuring the responsiveness of the model, we revealed each regime's computational capacity demonstrating that brain dynamics is shifted towards fluctuations to provide much-needed flexibility. Importantly, fluctuation regime stimulation in a brain region within a given resting state network modulates that network, aligned with previous empirical and computational studies. Furthermore, this framework generates specific, testable empirical predictions for human stimulation studies using strength-dependent rather than constant perturbation. Overall, the whole-brain models fitted to the level of empirical turbulence together with functional connectivity unveil that the fluctuation regime best captures empirical data, and the strength-dependent perturbative framework demonstrates how this regime provides maximal flexibility to the human brain

Diffusion Models for Medical Image Analysis: A Comprehensive Survey

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired noise-free data from noisy data samples. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples despite their known computational burdens. Capitalizing on the advances in computer vision, the field of medical imaging has also observed a growing interest in diffusion models. To help the researcher navigate this profusion, this survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis. Specifically, we introduce the solid theoretical foundation and fundamental concepts behind diffusion models and the three generic diffusion modelling frameworks: diffusion probabilistic models, noise-conditioned score networks, and stochastic differential equations. Then, we provide a systematic taxonomy of diffusion models in the medical domain and propose a multi-perspective categorization based on their application, imaging modality, organ of interest, and algorithms. To this end, we cover extensive applications of diffusion models in the medical domain. Furthermore, we emphasize the practical use case of some selected approaches, and then we discuss the limitations of the diffusion models in the medical domain and propose several directions to fulfill the demands of this field. Finally, we gather the overviewed studies with their available open-source implementations at https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging.Comment: Second revision: including more papers and further discussion

Layers Of Maturation In Cortical Hierarchies

Hierarchies form critical scaffolds for top-down processing but are often multiplex. In the brain, multiple layers of complex hierarchies intersect, dissociate, and re-converge over the lifespan. Although aspects of local hierarchical organizations are well-mapped for sensory systems, the fashion by which hierarchical organization extends globally is unknown. Human neuroimaging provides a means by which to observe both the developmental emergence and functions of global neurohierarchical organization. Here, we leveraged these advances to distill multiple layers of hierarchical formation across diverse brain-tissue quantifications. We demonstrate that these layers form common and dissociable biomarkers of the developmental emergence of complex cognition. Our results indicate that multiplex neurocognitive development both processes across a normative hierarchical pattern and contributes to engraining the pattern into cortical function. Further, our results suggest that neurocognitive development is largely contemporaneous with neurocognitive aging in an integrated, flexible lifespan sequence

Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy

In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible. To this end, recently developed disease progression models can be used to perform early diagnosis, as well as predict the subjects' disease stages and future evolution. However, these models have not yet been applied to rare neurodegenerative diseases, are not suitable to understand the complex dynamics of biomarkers, work only on large multimodal datasets, and their predictive performance has not been objectively validated. In this work I developed novel models of disease progression and applied them to estimate the progression of Alzheimer's disease and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing visual deficits. My first contribution is a study on the progression of Posterior Cortical Atrophy, using models already developed: the Event-based Model (EBM) and the Differential Equation Model (DEM). My second contribution is the development of DIVE, a novel spatio-temporal model of disease progression that estimates fine-grained spatial patterns of pathology, potentially enabling us to understand complex disease mechanisms relating to pathology propagation along brain networks. My third contribution is the development of Disease Knowledge Transfer (DKT), a novel disease progression model that estimates the multimodal progression of rare neurodegenerative diseases from limited, unimodal datasets, by transferring information from larger, multimodal datasets of typical neurodegenerative diseases. My fourth contribution is the development of novel extensions for the EBM and the DEM, and the development of novel measures for performance evaluation of such models. My last contribution is the organization of the TADPOLE challenge, a competition which aims to identify algorithms and features that best predict the evolution of AD.Comment: PhD thesis; Defended in Jan 2019 at University College Londo

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

The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging