Structural and functional largescale brain network dynamics: Examples from mental disorders

Hjernen er organisert i ulike funksjonelle og strukturelle nettverk. Til tross for omfattende forskning, er fremdeles ikke funksjonen og dynamikken i slike nettverk godt forstått. En økt innsikt kan være avgjørende for å forstå symptomer, og mekanismene som kontrollerer disse, hos pasienter med psykiske lidelser som schizofreni. Avhandlingen omfatter tre studier som hver adresserer ulike delmål i forskningen. Den første studien undersøker endringer i strukturelle nettverk hos en gruppe pasienter med schizofreni. Studien viser på gruppenivå at det er dels utbredte strukturelle forskjeller i hvit substans hos pasienter med schizofreni som opplever hørselshallusinasjoner sammenlignet med pasienter som ikke opplever disse hallusinasjonen. For å undersøke mulig samsvarende funksjonelle endringer har det vært behov for først å utvikle en ny tilnærming for å måle forskjeller i dynamikken mellom hjernens nettverk i hvile (DMN) og i aktiv oppgaveløsing av krevende kognitive oppgaver (EMN) hos en gruppe friske frivillige deltakere. I korte trekk, ble tre ulike visuelle, kognitive oppgaver presentert for deltakerne gjennom et fMRI blokk design. Resultatene i studien viste en antikorrelasjon i tid i områder som er involvert i henholdsvis hvile (DMN) og aktiv tilstand (EMN). For å gjøre undersøkelser hos pasienter med psykiske lidelser mindre tidkrevende, beskrives i avhandlingen også en studie som undersøker om hvileområder i hjernen (DMN) som er aktivert nettopp som del av en fMRI blokk design studier overlapper med en tilleggsundersøkelse med femminutters kontinuerlig hvile («resting state»). Sammenligningen er også interessant fra et mer basalforskningsperspektiv fordi en rask endring mellom aktiv tilstand og hvile kanskje bedre reflekterer en realistisk hviletilstand enn den kontinuerlige undersøkelsen som i dag representerer «gullstandarden» i denne type forskning. Resultatene fra studien viste stor grad av overlapp mellom aktiverte områder og at den foreslåtte tilnærmingen dermed kan ha et stort potensial i videre undersøkelser. I sum beskriver forskningen i avhandlingen muligheter for å undersøke strukturelle og funksjonelle nettverk hos pasienter med psykiske lidelser. Avhandlingen viser første resultater hos pasienter med schizofreni som strukturelle forskjeller i hvit substans mellom pasientgrupper avhengig om de opplever hørselshallusinasjoner eller ikke. Slike undersøkelser kan og bør komplementeres med undersøkelser av funksjonelle nettverk slik som foreslått i de andre studiene i avhandlingen, og i sum bidra til et godt rammeverk for videre undersøkelser hos pasienter.The human brain is organized in various networks both functionally and structurally. However, despite the extensive research on brain connectivity, which was made possible due to the development of in vivo brain imaging techniques, the neuroscientific field is still far from fully comprehending networks function and dynamics. Detailed knowledge about the relationship between various brain networks is essential for understanding the function of the healthy brain. However, many studies on mental disorders such as schizophrenia suggest that it might be caused by abnormal brain network functioning and structural aberrations. Therefore, the knowledge of the brain network's dynamics and structure might be critical for revealing the underpinnings of mental disorders such as schizophrenia. The presented thesis had three main goals, resulting in three structural and functional imaging studies. Firstly, the brain's structural connectivity affected by schizophrenia has been investigated to determine the nature and extent of its changes. Hence, Diffusion Tensor Imaging (DTI) and tract-based spatial statistics (TBSS) were employed to explore white matter differences between subtypes of schizophrenia patients compared to healthy controls. This study revealed widespread FA-value reduction in the hallucinating schizophrenia subjects' white matter compared to non-hallucinating ones. Since widespread aberrations of the white matter should affect the function of the large-scale brain networks, the second goal was to explore the two main functional brain networks, Default Mode Network (DMN) and Extrinsic Mode Network (EMN). This is because dysfunction of DMN and EMN networks has been previously suggested to be significant for the generation of symptoms of schizophrenia disorder, such as Auditory Verbal Hallucinations (AVH). Since the concept of EMN is relatively new and not yet deeply explored, and additionally protocol used in that study has not been previously utilized to study EMN and DMN, it was first necessary to test the design in a group of healthy participants. This study used the novel protocol based on the classic block design fMRI experiment with three different visual tasks: mental rotation, working memory, and mental arithmetic. The results of study II proved the existence of the EMN that is anti-correlated with the DMN and is domain-general. Lastly, the neuroimaging studies of the participants suffering from mental disorders such as schizophrenia require relatively short and effective examination protocols. Therefore, the last project investigated both similarities and differences in DMN activity between two experimental designs: block design and resign state. A classic block design experiment would be a good candidate for the investigation reflecting the fluctuating activity of the brain during typical daily activity. The results of Study III showed that the activity of the DMN was generally similar in the two experiments, though with some discrepancies. These differences were in the DMN architecture itself and concerning the relations of the DMN with other brain networks. These findings, in combination with the results of study number two suggest that the block design experiment could be the most effective for studying the function of the brain in schizophrenia. The studies incorporated in that thesis add to the current findings on the white matter alterations in schizophrenia disorder and contribute to a better understanding of the function and dynamics of the large-scale brain networks: EMN and DMN. Last but not least, the performed studies give a good background for future clinical studies on schizophrenia disorder.Doktorgradsavhandlin

Imaging schizophrenia: data fusion approaches to characterize and classify

Schizophrenia is a complex, chronic and disabling mental disorder that affects about one percent of the adult population. The etiology of schizophrenia remains elusive and to date there are no image based tools to diagnose it. Advancements in magnetic resonance imaging (MRI) have enabled researchers to develop less invasive and in vivo techniques, such as structural MRI (sMRI), functional MRI (fMRI) and diffusion tensor imaging (DTI), to construct theories about the neural underpinnings of schizophrenia. With sMRI, fMRI and DTI the distribution of tissues, the functional activity and the brain network are imaged respectively. Subjects with schizophrenia (SZ) and healthy controls (HC) are scanned with different modalities to identify differences, but the analysis of each modality has traditionally been carried out separately. Data fusion of multimodal data and an analysis of the joint information may hold the key to reveal hidden traces of this subtle disorder. In this work we develop techniques to correlate sMRI with fMRI, fMRI with other fMRI and DTI with symptom scores. The brain is a highly interconnected organ and local morphology can influence functional activity at distant regions. Through our methods it is possible to perform a cross correlation analysis between modalities incorporating all brain voxels. By reducing the large cross correlation matrix to useful statistics new aspects of schizophrenia are revealed. The methods introduced are simple, easy to implement and efficient. In another effort we modify canonical correlation analysis (CCA) to fuse two sets of brain data to locate brain regions with significant correlations. The new differential features identified through our fusion methods are used to classify subjects. The sMRI–fMRI fusion indicates that the linkage between gray matter and functional activity probed by a sensorimotor task is weaker in SZ than in HC. Linkages between functional activity and structural regions in the cerebellum and the prefrontal cortex are found to be aberrant in SZ. The pair wise fusion of four different fMRI tasks shows that SZ activate to different tasks less uniquely than do HC. The above results support the ‘disconnection hypothesis’ of schizophrenia and the ‘theory of cognitive dysmetria’. DTI–symptom score fusion indicates that regions in the superior longitudinal fasciculus have high DTI–symptom correlations. Our preliminary classification efforts show high success rates in the leave–one–out scheme. The results presented in this work reveal several novel and interesting findings to better understand schizophrenia. The methods introduced are general, and can be easily applied to healthy and other pathological brain data to explore brain behavior

Integration of multi-shell diffusion imaging derived metrics in tractography reconstructions of clinical data

Tese de mestrado integrado Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2019Nos últimos anos, com o rápido avanço das técnicas imagiológicas, a oportunidade de mapear o cérebro humano in vivo com uma resolução sem precedentes tornou-se realidade, permanecendo ainda hoje como uma das áreas de maior interesse da neurociência. Sabendo que o movimento natural das moléculas de água nos tecidos biológicos é altamente influenciado pelo ambiente microestrutural envolvente, e que a anisotropia que este processo aleatório assume na matéria branca pode ser explorada com o intuito de inferir características importantes associadas ao tecido neuronal, a ressonância magnética ponderada por difusão (dMRI, do inglês “Diffusion-Weighted Magnetic Resonance Imaging") afirmou-se como a técnica de imagem mais amplamente utilizada para a investigação in vivo e não invasiva da conectividade cerebral. A primeira técnica padrão de dMRI foi a imagiologia por tensor de difusão (DTI, do inglês "Diffusion Tensor Imaging"). Implementada com a capacidade de fornecer sensibilidade à microestrutura do tecido, esta técnica permite extrair informação acerca da tridimensionalidade da distribuição da difusão de moléculas de água através da aplicação de seis gradientes de difusão não colineares entre si. Além da difusividade média (MD, do inglês "Mean Diffusivity"), é também possível extrair outros índices microestruturais, como a anisotropia fraccional (FA, do inglês "Fractional Anisotropy"), que fornece informação acerca da percentagem de difusão anisotrópica num determinado voxel. Ambas as métricas são amplamente utilizadas como medidas de alterações microestruturais, todavia, apesar da sua sensibilidade, estes marcadores não são específicos quanto às características individuais da microestrutura tecidual. Regiões com reduzida FA podem camuflar regiões de conformação de cruzamento de fibras, ou fibras muito anguladas, que a DTI não consegue resolver. A razão para esta limitação reside no número reduzido de diferentes direções de difusão que são exploradas, assim como no pressuposto de que a distribuição das moléculas de água é gaussiana, o que não é necessariamente verdade. De forma alternativa e com o intuito de tais limitações serem ultrapassadas, é possível implementar uma representação matemática do sinal adquirido de forma a explorar o propagador de difusão, da qual a imagiologia por ressonância magnética do propagador aparente médio (MAP-MRI, do inglês “Mean Apparent Propagator Magnetic Resonance Imaging”) é exemplo. Esta técnica analítica caracteriza-se pelo cálculo da função de densidade de probabilidade associada ao deslocamento de spin, o que permite descrever o caráter não-gaussiano do processo de difusão tridimensional e quantificar índices escalares inerentes ao processo de difusão, os quais sublinham as características complexas intrínsecas à microestrutura do tecido. Estes parâmetros incluem o deslocamento médio quadrático (MSD, em inglês “mean square displacement”), a probabilidade de retorno à origem (RTOP, do inglês “return-to-the origin probability”) e suas variantes de difusão em uma e duas dimensões – a probabilidade de retorno ao plano (RTPP, do inglês “return-to-the plane probability”) e a probabilidade de retorno ao eixo (RTAP, do inglês “return-to-the axis probability”), respetivamente. Em resposta às limitações da DTI associadas à falta de especificidade para distinguir características microestruturais dos tecidos, surgiu ainda o modelo de Dispersão de Orientação de Neurite e Imagem de Densidade (NODDI, do inglês “Neurite Orientation Dispersion and Density Imaging”), o qual utiliza o processo de difusão para estimar a morfologia das neurites. Tendo como premissa subjacente que o sinal de difusão pode ser definido pela soma da contribuição dos sinais de diferentes compartimentos, este modelo biofísico diferencia o espaço intra e extracelular o que, por sua vez, permite quantificar a dispersão e densidade das neurites. Deste modo, dois parâmetros intrínsecos à microestrutura envolvente podem ser calculados: a densidade neurítica e o índice de dispersão da orientação das neurites. No entanto, de forma a garantir a viabilidade clínica do modelo, este pode ser aplicado por meio do método AMICO (do inglês “Accelerated Microstructure Imaging via Convex Optimization”) através do seu ajuste linear, o que permite o cálculo do índice de dispersão da orientação das neurites (ODI, do inglês “Orientation Dispersion Index”), da fração de volume intracelular (ICVF do inglês, “Intracellular Volume Fraction”), e da fração de volume isotrópico (ISOVF, do inglês “Isotropic Volume Fraction”). O estudo da configuração arquitetural das estruturas cerebrais in vivo, por meio da dMRI associada aos métodos de tractografia, permitiu a reconstrução não invasiva das fibras neuronais e a exploração da informação direcional inerente às mesmas, sendo que o seu estudo tem revelado uma enorme expansão por meio do estabelecimento de marcadores biológicos perante a presença de diversas condições patológicas. O objetivo principal desta dissertação prende-se com existência de uma variação proeminentenas métricas de difusão ao longo dos tratos de matéria branca no cérebro humano. Atualmente, a maioriados estudos de tractografia tem por base uma abordagem que se resume à análise do valor escalar médio da métrica de difusão para a estrutura cerebral em estudo, pelo que se tem verificado um crescente interesse na utilização de métodos que considerem a extensão da variabilidade nas métricas de difusão ao longo dos tratos de modo a providenciarem um maior nível de detalhe ao nível do processo de difusão, evitando interpretações erróneas dos parâmetros microestruturais. Desta forma, em primeiro lugar, foi desenvolvido uma análise ao longo dos tratos de matéria branca, tendo por base a variação dos valores assumidos pelos parâmetros microestruturais acima mencionados. No presente estudo foi possível demonstrar a eficácia de tal abordagem ao longo de três tratos de matéria de branca (fascículo arqueado, trato corticoespinhal, e corpo caloso), para além de permitir, através da variância assumida pelos diversos parâmetros microestruturais, o estudo detalhado de regiões anatómicas que assumem uma distribuição complexa de múltiplos conjuntos populacionais de fibras, como é o caso do centro semioval, o qual constitui uma região de cruzamento de fibras provenientes dos três tratos de matéria branca em estudo. De seguida, esta técnica foi utilizada com sucesso na identificação de diferenças microestruturais por meio do estudo dos diversos parâmetros de difusão em pacientes com diagnóstico prévio de epilepsia no lobo temporal (TLE, do inglês “Temporal Lobe Epilepsy”), com foco epiléptico localizado no hemisfério esquerdo, e controlos. O estudo do ambiente microestrutural por meio dos múltiplos mapas escalares permitiu averiguar a alteração do processo de difusão e/ou anisotropia, associadas ao efeito fisiopatológico da TLE na organização da matéria branca. Os resultados revelaram diferenças localizadas, as quais se traduziram num aumento da difusividade e redução da anisotropia do processo de difusão ao longo dos tratos em estudo dos pacientes com TLE, sugerindo deste modo uma perda na organização das diversas estruturas anatómicas e a expansão do espaço extracelular face aos controlos. Verificou-se ainda que pacientes com esta condição neurológica sofrem de alterações microestruturais que afetam redes cerebrais em grande escala, envolvendo regiões temporais e extratemporais de ambos os hemisférios. Adicionalmente, aplicada como técnica capaz de investigar padrões de mudança na matéria branca, procedeu-se à realização de um estudo assente na estatística espacial baseada no trato (TBSS, do inglês “Tract-Based Spatial Statistics”). Após a exploração das diversas métricas de difusão, os pacientes com TLE (com lateralização à esquerda) demonstraram alterações no processo de difusão, ilustradas pelos diversos padrões de mudança microestrutural de cada métrica em estudo, concordantes com os resultados anteriormente aferidos pela análise ao longo do trato. Por fim, uma análise baseada em fixel (FBA, do inglês “Fixel-Based Analysis”) foi realizada, a qual permitiu uma análise estatística abrangente de medidas quantitativas da matéria branca, com o intuito de detetar alterações no volume intra-axonal por variação na densidade intra-voxel e/ou reorganização da morfologia macroscópica. Para identificar tais diferenças entre pacientes e controlos, três parâmetros foram considerados: densidade das fibras (FD, do inglês “Fibre Density”), seção transversal do feixe de fibras (FC, do inglês “Fibre-bundle Cross-section”), e densidade de fibras e seção transversal (FDC, do inglês “Fibre Density and Cross-section). Reduções na FD, FC e FDC foram identificadas em pacientes com TLE (com lateralização à esquerda) em comparação com os controlos, o que está de acordo com as mudanças microestruturais que resultam do processo de degeneração que afeta as estruturas de matéria branca com a perda de axónios na presença de uma condição neuropatológica como a TLE. Apesar do resultado final positivo, tendo em conta a meta previamente estabelecida, está aberto o caminho para o seu aperfeiçoamento, tendo em vista as direções futuras que emergem naturalmente desta dissertação. Como exemplo disso, poder-se-á recorrer ao estudo pormenorizado das metodologias técnicas associadas à abordagem apresentada que tem por base a análise das métricas de difusão ao longo dos tratos de matéria branca, uma vez que o desvio padrão associado a cada valor atribuído pelas diversas métricas foi significativo, o que de alguma forma poderá ter influenciado os resultados e, consequentemente, as conclusões deles extraídas, tendo em vista a sua viabilidade enquanto aplicação clínica. Como nota final, gostaria apenas de salientar que a imagiologia por difusão e, em particular, a tractografia têm ainda muito espaço para progredir. A veracidade desta afirmação traduz-se pela existência de uma grande variedade de modelos e algoritmos implementados, bem como de técnicas e metodologias de análise à informação microestrutural retida tendo por base o perfil de difusão que carateriza cada trato em estudo, sem que no entanto, exista consenso na comunidade científica acerca da melhor abordagem a seguir.Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging method which has been successfully applied to study white matter (WM) in order to determine physiological information and infer tissue microstructure. The human body is filled with barriers affecting the mobility of molecules and preventing it from being constant in different directions (anisotropic diffusion). In the brain, the sources for this anisotropy arise from dense packing axons and from the myelin sheath that surrounds them. Diffusion Tensor Imaging (DTI) is widely used to extract fibre directions from diffusion data, but it fails in regions containing multiple fibre orientations. The constrained spherical deconvolution technique had been proposed to address this limitation. It provides an estimate of the fibre orientation distribution that is robust to noise whilst preserving angular resolution. As a noninvasive technique that generates a three-dimensional reconstruction of neuronal fibres, tractography is able to map in vivo the human WM based on the reconstruct of the fibre orientations from the diffusion profile. Most of the tractography studies use a “tract-averaged” approach to analysis, however it is well known that there is a prominent variation in diffusion metrics within WM tracts. In this study we address the challenge of defining a microstructural signature taking into account the potentially rich anatomical variation in diffusion metrics along the tracts. Therefore, a workflow to conduct along-tract analysis of WM tracts (namely, arcuate fasciculus, corticospinal and corpus callosum) and integrate not only DTI derived measures, but also more advanced parameters from Mean Apparent Propagator-Magnetic Resonance Imaging (MAP-MRI) and Neurite Orientation Dispersion and Density Imaging (NODDI) model, was developed across healthy controls and patients with Temporal Lobe Epilepsy (TLE). Beyond the true biological variation in diffusion properties along tracts, this technique was applied to show that it allows a more detailed analysis of small regions-of-interest extracted from the tract in order to avoid fibres from WM pathways in the neighbourhood, which might lead to equivocal biological interpretations of the microstructural parameters. Consequently, the along-tract streamline distribution from the centrum semiovale, which is known to be a complex fibre geometry with multiple fibres populations from arcuate fasciculus, corticospinal and corpus callosum, was investigated. Finally, to validate our approach and highlight the strength of this extensible framework, two other methods were implemented in order to support the conclusions derived from the along-tract analysis computed between-groups. Firstly, a tract-based spatial statistics (TBSS) analysis was performed to study the WM change patterns across the whole brain in patients with TLE, and explore the alteration of multiple diffusion metrics. This voxel-based technique provides a powerful and objective method to perform multi-subject comparison, based on voxel-wise statistics of diffusion metrics but simultaneous aiming to minimize the effects of misalignment using a conventional voxel-based analysis method. With this in mind, the results showed increased diffusivity and reduced diffusion anisotropy, suggesting a loss of structural organization and expansion of the extracellular space in the presence of neuropathological condition as TLE. Secondly, the fixel-based analysis (FBA) was performed allowing a comprehensive statistical analysis of WM quantitative measures in order to have access to changes that may result within WM tracts in the presence of TLE. The microstructural/macrostructural changes in WM tracts of TLE patients were observed in temporal and extratemporal regions of both hemispheres, which agrees with the concept that epilepsy is a network disorder

Investigating white matter changes underlying overactive bladder in multiple sclerosis with diffusion MRI

Lower urinary tract symptoms (LUTS) are presented in more than 80% of multiple sclerosis (MS) patients. Current understanding of LUT control is based on studies exploring activities in grey matter (GM) and investigating functional correlations with LUTS. The relationship between white matter (WM) changes and overactive bladder (OAB) symptoms are limited to findings in small vessel disease, and the nature of the association between WM changes and OAB symptoms is poorly understood. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques provide non-invasive techniques to study WM abnormalities and correlates to clinical observations. The overarching objectives of this work are to explore WM abnormalities subtending OAB symptoms in MS, and to reconstruct the structural network underpinning the working model of lower urinary tract (LUT) control. Using Tract-Based Spatial Statistics (TBSS), OAB symptoms related WM abnormalities in MS can be identified, and a structural network subtending OAB symptoms in MS can be subsequently created. The findings of this work illustrate the correlation between OAB symptoms severity and WM abnormalities in MS. These were observed in regions in frontal lobes and non-dominant hemisphere, including corpus callosum, anterior corona radiata bilaterally, right anterior thalamic radiation, superior longitudinal fasciculus bilaterally, and right inferior longitudinal fasciculus. The structural network created for OAB symptoms in MS connected regions known to be involved in the working model of LUT control, and the network identified connectivity between insula and frontal lobe, which is the key circuit for perception of bladder fullness. Moreover, structural connectivity between insula-temporal lobe and insula-occipital lobe were observed, which may underpin changes seen in functional MRI (fMRI) studies. The novel findings of this study present WM abnormalities and structural connectivity subtending LUTS in MS with diffusion-weighted imaging (DWI). The techniques used in this work can be applied to other patterns of LUTS and other neurological diseases

Enabling New Functionally Embedded Mechanical Systems Via Cutting, Folding, and 3D Printing

Traditional design tools and fabrication methods implicitly prevent mechanical engineers from encapsulating full functionalities such as mobility, transformation, sensing and actuation in the early design concept prototyping stage. Therefore, designers are forced to design, fabricate and assemble individual parts similar to conventional manufacturing, and iteratively create additional functionalities. This results in relatively high design iteration times and complex assembly strategies

The topology of structural brain connectivity in diseases and spatio-temporal connectomics

The brain is a complex system, composed of multiple neural units interconnected at different spatial and temporal scales. Diffusion MRI allows probing in vivo the anatomical connectivity between different cortical areas through white matter tracts. In parallel, functional MRI records neural-related signals of brain activity. Particularly, during rest (in absence of specific external task) reproducible dynamical patterns of functional synchronization have been shown across different brain areas. This rich information can be conveniently represented in the form of a graph, a mathematical object where nodes correspond to cortical regions and are connected by edges representing anatomical connections. On the top of this structural network, or brain connectome, individual nodes are associated to functional signals representing neural activity over observation periods. Network science has fundamentally contributed to the characterization of the human connectome. The brain is a small-world network, able to combine segregation and integration aspects. These properties allow functional specialization on the one side, and efficient communication between distant brain areas on the other side, supporting complex cognitive and executive functions. Graph theoretical methods quantify brain topological properties, and allow their comparison between different populations and conditions. In fact, brain connectivity patterns and interdependences between anatomical substrate and functional synchronization have been proved to be impaired in a variety of brain disorders, and to change across human development and aging. Despite these important advancements in the understanding of the brain structure and functioning, many questions are currently unanswered. It is not clear for instance how structural connectivity features are related to individual cognitive capabilities and deficits, and if they have the concrete potential to distinguish pathological subgroups for early diagnosis of brain diseases. Most importantly, it is not yet understood how the connectome topology relates to specific brain functions, and how the transmission of information happens on the top of the structural connectivity infrastructure in order to generate observed functional dynamics. This thesis was motivated by these interdisciplinary inputs, and is the result of a strong interaction between biological and clinical questions on the one hand, and methodological development needs on the other hand. First, we have contributed to the characterization of the human connectome in health and pathologies by adapting and developing network measures for the description of the brain architecture at different scales. Particularly, we have focused on the topological characterization of subnetworks role within the overall brain network. Importantly, we have shown that the topological alteration of distinct brain subsystems may be a biomarker for different brain disorders. Second, we have proposed an original network model for the joint representation of brain structural and functional connectivity properties. This flexible spatio-temporal framework allows the investigation of functional dynamics at multiple temporal scales. Importantly, the investigation of spatio-temporal graphs in healthy subjects have allowed to disclose temporal relationships between local brain activations in resting state recordings, and has highlighted functional communication principles across the brain structural network

The Functional, Ecological, and Evolutionary Morphology of Sea Lampreys (Petromyzon marinus)

Lampreys (Petromyzontiformes) are jawless vertebrates with an evolutionary history lasting at least 360 million years and are often used in comparisons with jawed vertebrates because some of their morphological aspects, such as the segmented trunk musculature with curved myosepta and a non-mineralized skeleton fibrous skeleton, are thought to resemble the condition of early vertebrates before the evolution of jaws. Although earlier authors studied the morphology of the skeleto-muscular system of the trunk of lampreys, their studies are not detailed and complete enough to allow a functional and biomechanical analysis that is needed as a basis for modeling the mechanics of lamprey locomotion and for understanding the causal roles played by the anatomical structures within the trunk. Questions remain, such as what is the architecture of the trunk fibroskeleton, and how does it function with the musculature to bend the trunk? This dissertation studied the functional, ecological and evolutionary morphology of the trunk of Sea Lampreys (Petromyzon marinus) as well as its relevance in understanding the environmental history of landlocked lamprey populations. Functional morphology revealed that the fibroskeleton of the trunk is a self-supporting concatenated system of fibers, which creates a scaffold for the musculature and transmits forces to bend the trunk during swimming. Ecological morphology demonstrated the adaptive advantage of the fibroskeleton’s architecture, which enables the movements that are performed during migration and spawning and gives lampreys the capacity to colonize upstream realms. These results help explain the evolutionary morphology of lampreys, which likely originated in freshwater as algal feeders and evolved into parasites after going through an intermediary scavenging stage. When these insights are applied to the evolution of landlocked Sea Lampreys, it becomes evident that their entry into freshwater lakes occurred as soon as they were able to reach them and that populations likely became established in Lake Ontario, Lake Champlain, and the Finger Lakes thousands of years ago. This insight undermines the current status of landlocked Sea Lampreys as invasive species in these lakes and the case for their eradication. Hence, this dissertation provides a comprehensive and integrative analysis of lamprey biology from their anatomy to environmental policy

Mapping connections in the neonatal brain with magnetic resonance imaging

The neonatal brain undergoes rapid development after birth, including the growth and maturation of the white matter fibre bundles that connect brain regions. Diffusion MRI (dMRI) is a unique tool for mapping these bundles in vivo, providing insight into factors that impact the development of white matter and how its maturation influences other developmental processes. However, most studies of neonatal white matter do not use specialised analysis tools, instead using tools that have been developed for the adult brain. However, the neonatal brain is not simply a small adult brain, as differences in geometry and tissue decomposition cause considerable differences in dMRI contrast. In this thesis, methods are developed to map white matter connections during this early stage of neurodevelopment. First, two contrasting approaches are explored: ROI-constrained protocols for mapping individual tracts, and the generation of whole-brain connectomes that capture the developing brain's full connectivity profile. The impact of the gyral bias, a methodological confound of tractography, is quantified and compared with the equivalent measurements for adult data. These connectomes form the basis for a novel, data-driven framework, in which they are decomposed into white matter bundles and their corresponding grey matter terminations. Independent component analysis and non-negative matrix factorisation are compared for the decomposition, and are evaluated against in-silico simulations. Data-driven components of dMRI tractography data are compared with manual tractography, and networks obtained from resting-state functional MRI. The framework is further developed to provide corresponding components between groups and individuals. The data-driven components are used to generate cortical parcellations, which are stable across subjects. Finally, some future applications are outlined that extend the use of these methods beyond the context of neonatal imaging, in order to bridge the gap between functional and structural analysis paradigms, and to chart the development of white matter throughout the lifespan and across species

Mapping connections in the neonatal brain with magnetic resonance imaging

The neonatal brain undergoes rapid development after birth, including the growth and maturation of the white matter fibre bundles that connect brain regions. Diffusion MRI (dMRI) is a unique tool for mapping these bundles in vivo, providing insight into factors that impact the development of white matter and how its maturation influences other developmental processes. However, most studies of neonatal white matter do not use specialised analysis tools, instead using tools that have been developed for the adult brain. However, the neonatal brain is not simply a small adult brain, as differences in geometry and tissue decomposition cause considerable differences in dMRI contrast. In this thesis, methods are developed to map white matter connections during this early stage of neurodevelopment. First, two contrasting approaches are explored: ROI-constrained protocols for mapping individual tracts, and the generation of whole-brain connectomes that capture the developing brain's full connectivity profile. The impact of the gyral bias, a methodological confound of tractography, is quantified and compared with the equivalent measurements for adult data. These connectomes form the basis for a novel, data-driven framework, in which they are decomposed into white matter bundles and their corresponding grey matter terminations. Independent component analysis and non-negative matrix factorisation are compared for the decomposition, and are evaluated against in-silico simulations. Data-driven components of dMRI tractography data are compared with manual tractography, and networks obtained from resting-state functional MRI. The framework is further developed to provide corresponding components between groups and individuals. The data-driven components are used to generate cortical parcellations, which are stable across subjects. Finally, some future applications are outlined that extend the use of these methods beyond the context of neonatal imaging, in order to bridge the gap between functional and structural analysis paradigms, and to chart the development of white matter throughout the lifespan and across species