163 research outputs found

    The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis.

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    Late-life depression (LLD) has been associated with both generalized and focal neuroanatomical changes including gray matter atrophy and white matter abnormalities. However, previous literature has not been consistent and, in particular, its impact on the topology organization of brain networks remains to be established. In this multimodal study, we first examined cortical thickness, and applied graph theory to investigate structural covariance networks in LLD. Thirty-three subjects with LLD and 25 controls underwent T1-weighted, fluid-attenuated inversion recovery and clinical assessments. Freesurfer was used to perform vertex-wise comparisons of cortical thickness, whereas the Graph Analysis Toolbox (GAT) was implemented to construct and analyze the structural covariance networks. LLD showed a trend of lower thickness in the left insular region (p < 0.001 uncorrected). In addition, the structural network of LLD was characterized by greater segregation, particularly showing higher transitivity (i.e., measure of clustering) and modularity (i.e., tendency for a network to be organized into subnetworks). It was also less robust against random failure and targeted attacks. Despite relative cortical preservation, the topology of the LLD network showed significant changes particularly in segregation. These findings demonstrate the potential for graph theoretical approaches to complement conventional structural imaging analyses and provide novel insights into the heterogeneous etiology and pathogenesis of LLD.This work was supported by the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge, and the NIHR Biomedical Research Unit in Dementia and the Biomedical Research Centre awarded to Newcastle upon Tyne Hospitals NHS Foundation Trust and the Newcastle University. Elijah Mak was in receipt of a Gates Cambridge, PhD studentship.This is the author accepted manuscript. It first appeared from Elsevier at http://dx.doi.org/10.1016/j.neurobiolaging.2016.08.013

    Network Scaling Effects in Graph Analytic Studies of Human Resting-State fMRI Data

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    Graph analysis has become an increasingly popular tool for characterizing topological properties of brain connectivity networks. Within this approach, the brain is modeled as a graph comprising N nodes connected by M edges. In functional magnetic resonance imaging (fMRI) studies, the nodes typically represent brain regions and the edges some measure of interaction between them. These nodes are commonly defined using a variety of regional parcellation templates, which can vary both in the volume sampled by each region, and the number of regions parcellated. Here, we sought to investigate how such variations in parcellation templates affect key graph analytic measures of functional brain organization using resting-state fMRI in 30 healthy volunteers. Seven different parcellation resolutions (84, 91, 230, 438, 890, 1314, and 4320 regions) were investigated. We found that gross inferences regarding network topology, such as whether the brain is small-world or scale-free, were robust to the template used, but that both absolute values of, and individual differences in, specific parameters such as path length, clustering, small-worldness, and degree distribution descriptors varied considerably across the resolutions studied. These findings underscore the need to consider the effect that a specific parcellation approach has on graph analytic findings in human fMRI studies, and indicate that results obtained using different templates may not be directly comparable

    Solmujen sisäinen konnektiviteetti ja topologiset roolit toiminnallisissa aivoverkoissa

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    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

    Organization and hierarchy of the human functional brain network lead to a chain-like core

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    The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming ahighly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions

    The enduring impact of childhood maltreatment on grey matter development

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    Childhood maltreatment doubles an individual’s risk of developing a psychiatric disorder, yet the neurobiological nature of the enduring impact of childhood maltreatment remains elusive. This thesis explores the long-term effect of childhood maltreatment on grey matter. The primary aims of this thesis are to discern the spatial extent, temporal profile and physiological breadth of the developmental impact of childhood maltreatment amongst young people with emerging mental disorder. Chapter II comprises of a meta-analysis of thirty-eight published articles and demonstrates that adults with a history of childhood maltreatment most commonly exhibit reduced grey matter in the hippocampus, amygdala and right dorsolateral prefrontal cortex, compared to non-maltreated adults. Chapters III-V contain three original studies, involving a cohort of 123 young people, aged 14-26, with emerging mental illness. Chapter III bridges a gap between cross-sectional child and adult studies by longitudinally mapping the developmental trajectory of the hippocampus and amygdala following childhood maltreatment. This study provided the first direct evidence that childhood maltreatment stunts hippocampal development into young adulthood. Chapter IV assesses the utility of the cumulative stress and mismatch hypotheses in understanding the contribution of childhood abuse and recent stress to the structure and function of the limbic system. Chapter V extends on recent advances in connectome research to examine the effect of childhood maltreatment on structural covariance networks. Investigation of the correspondence of structural covariance with structural connectivity and functional connectivity revealed that reduced grey matter across the network is likely related to deceased functional coactivation following childhood maltreatment. Chapter VI discusses the significance of these studies in understanding how maltreatment shapes brain development and increases the risk of psychiatric illness

    Poor sleep quality associates with decreased functional and structural brain connectivity in normative aging: A MRI multimodal approach

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    Sleep is a ubiquitous phenomenon, essential to the organism homeostasis. Notwithstanding, there has been an increasing concern with its disruption, not only within the context of pathological conditions, such as neurologic and psychiatric diseases, but also in health. In fact, sleep complaints are becoming particularly common, especially in middle-aged and older adults, which may suggest an underlying susceptibility to sleep quality loss and/or its consequences. Thus, a whole-brain modeling approach to study the shifts in the system can cast broader light on sleep quality mechanisms and its associated morbidities. Following this line, we sought to determine the association between the standard self-reported measure of sleep quality, the Pittsburgh Sleep Quality Index (PSQI) and brain correlates in a normative aging cohort. To this purpose, 86 participants (age range 52-87 years) provided information regarding sociodemographic parameters, subjective sleep quality and associated psychological variables. A multimodal magnetic resonance imaging (MRI) approach was used, with whole-brain functional and structural connectomes being derived from resting-state functional connectivity (FC) and probabilistic white matter tractography (structural connectivity, SC). Brain regional volumes and white matter properties associations were also explored. Results show that poor sleep quality was associated with a decrease in FC and SC of distinct networks, overlapping in right superior temporal pole, left middle temporal and left inferior occipital regions. Age displayed important associations with volumetric changes in the cerebellum cortex and white matter, thalamus, hippocampus, right putamen, left supramarginal and left lingual regions. Overall, results suggest that not only the PSQI global score may act as a proxy of changes in FC/SC in middle-aged and older individuals, but also that the age-related regional volumetric changes may be associated to an adjustment of brain connectivity. These findings may also represent a step further in the comprehension of the role of sleep disturbance in disease, since the networks found share regions that have been shown to be affected in pathologies, such as depression and Alzheimer's disease.Financial support was provided by FEDER funds through the Operational Programme Competitiveness Factors-COMPETE and National Funds through FCT-Foundation for Science and Technology under the project POCI-01-0145-FEDER-007038, by the project NORTE-01-0145-FEDER-000013 [supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 (P2020) Partnership Agreement, through the European Regional Development Fund (FEDER)], by POCI-01-0145-FEDER-016428 [supported by the Operational Programme Competitiveness and Internationalization (COMPETE 2020) and the Regional Operational Program of Lisbon and National Funding through Portuguese Foundation for Science and Technology (FCT, Portugal)], and by the Portuguese North Regional Operational Programme [ON.2 – O Novo Norte, under the National Strategic Reference Framework (QREN), through FEDER]. The work was also developed under the scope of the projects SwitchBox (European Commission, FP7; contract HEALTH-F2-2010-259772) and TEMPO-Better mental health during aging based on temporal prediction of individual brain aging trajectories (Fundação Calouste Gulbenkian; Contract grant number P-139977). LA, TC, RM, PSM, and CP-N were supported by FCT PhD scholarships [SFRH/BD/101398/2014 to LA; SFRH/BD/90078/2012 to TC; PDE/BDE/113604/2015 from the PhD-iHES Programme to RM; PDE/BDE/113601/2015 to PSM; PD/BD/106050/2015 from the Inter-University Doctoral Programme in Aging and Chronic Disease (PhDOC) to CP-N] and AC by a scholarship from the project NORTE-08-5639-FSE-000041 (NORTE 2020; UMINHO/BD/51/2017). NCS was a recipient of a Research Assistantship by the through the FCT Investigator Programme 200∞ Ciência.info:eu-repo/semantics/publishedVersio

    The Electrophysiology of Resting State fMRI Networks

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    Traditional research in neuroscience has studied the topography of specific brain functions largely by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. Recently, investigations of brain activity in the resting state, most frequently using functional magnetic resonance imaging (fMRI), have revealed spontaneous correlations within widely distributed brain regions known as resting state networks (RSNs). Variability in RSNs across individuals has found to systematically relate to numerous diseases as well as differences in cognitive performance within specific domains. However, the relationship between spontaneous fMRI activity and the underlying neurophysiology is not well understood. This thesis aims to combine invasive electrophysiology and resting state fMRI in human subjects to better understand the nature of spontaneous brain activity. First, we establish an approach to precisely coregister intra-cranial electrodes to fMRI data (Chapter 2). We then created a novel machine learning approach to define resting state networks in individual subjects (Chapter 3). This approach is validated with cortical stimulation in clinical electrocorticography (ECoG) patients (Chapter 4). Spontaneous ECoG data are then analyzed with respect to fMRI time-series and fMRI-defined RSNs in order to illustrate novel ECoG correlates of fMRI for both local field potentials and band-limited power (BLP) envelopes (Chapter 5). In Chapter 6, we show that the spectral specificity of these resting state ECoG correlates link classic brain rhythms with large-scale functional domains. Finally, in Chapter 7 we show that the frequencies and topographies of spontaneous ECoG correlations specifically recapitulate the spectral and spatial structure of task responses within individual subjects

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

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    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

    Genetics of functional brain networks

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