Reproducibility and sensitivity of brain network backbones: a demonstration in Small Vessel Disease

Mestrado integrado em Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa; Faculdade de Ciências, 2020Whole-brain networks have been used to study the connectivity paths within the brain, constructed using information from diffusion magnetic resonance imaging (dMRI) data and white matter fiber tractography (FT). These techniques can detect alterations in the white matter integrity and changes in axonal connections, whose alterations can be due to the presence of small vessel disease (SVD). However, there is a lack of consensus in network reconstruction methods and therefore no gold-standard model of the human brain network. Moreover, dMRI data are affected by methodological issues such as scan noise, presence of false-positive and false-negative connections. Consequently, the reproducibility and the reliability of these networks is normally very low. A potential solution to deal with the low reproducibility of brain networks is to analyze only its backbone structure. This backbone is assumed to represent the building blocks of structural brain networks and thus composed by a set of strong connections and voided of spurious connections. Such backbone should be reproducible in scan-rescan scenarios and relatively consistent between healthy subjects, while still being sensitive to disease-related changes. Several types of backbones have been proposed, constructed using white matter tractography, with dMRI data. However, no study has directly compared these backbones in terms of reproducibility, consistency, or sensitivity to disease effects in a patient population. In this project, we examined: (1) whether the proposed backbones can be applied to clinical datasets by testing if they are reproducible over two time-points and consistent between groups; (2) if they are sensitive to disease effects both in a cross-sectional and longitudinal analysis. We evaluated our research questions on a longitudinal cohort of patients with cerebral SVD and age matched controls, as well as a validation dataset of healthy young adults. Our cohort contained 87 elderly memory clinic patients with SVD recruited via the UMC Utrecht, scanned twice with an inter-scan time between baseline and follow-up of 27 ± 4 months. We also included baseline scans of 45 healthy elderly, matched in age, sex and education level, to be used as controls. Data from 44 healthy young adults was used as validation data. For each subject, we reconstructed brain structural networks from the diffusion MRI data. Subsequently, we computed 4 types of network backbone, previously described in literature: the Minimal Spanning Tree (MST), the Disparity Core, the K-Core, and Hub-Core. We compared these backbones and tested their reproducibility within subjects, and their consistency across subjects and across groups. Moreover, we performed a cross-sectional analysis between controls and patients at baseline, to evaluate if these backbones can detect disease effects and a longitudinal analysis with patient data over time, to test if they can detect disease progression. Regarding our first objective, our results show that overall MST is the backbone that shows the best reproducibility between repeated scans, as well as the highest consistency among subjects, for all of the three brain templates that we used. Secondly, the MST was also sensitive to network alterations both on a cross-sectional analysis (patients vs. controls) and on a longitudinal analysis (baseline vs. follow-up). We therefore conclude that, the use of these network backbones, as an alternative of the whole-brain network analysis, can successfully be applied to clinical datasets as a novel and reliable way to detect disease effects and evaluate disease progression.A demência vascular cerebral (SVD) é a segunda principal causa de demência, depois da doença de Alzheimer. Este tipo de demência está relacionado com patologias vasculares cerebrais, assim como com perda de funcionalidades cognitivas. Vários estudos explicam que a degradação da atividade cognitiva característica desta doença pode dever-se à diminuição da integridade da substância branca e a alterações nas conexões axonais. O estudo da conectividade cerebral tem sido uma forte aposta no estudo das causas e da forma como a demência vascular cerebral evolui. A construção de mapas neuronais é uma das práticas que mais tem sido usada para estudar e entender os mecanismos principais da conectividade cerebral: representar o cérebro como um conjunto de regiões e as ligações entre elas. Para isso, utiliza-se informação proveniente de imagens de ressonância magnética por difusão (dMRI), especificamente de imagens por tensor de difusão (DTI), capazes de medir a magnitude de difusão das moléculas de água no cérebro in vivo, através de gradientes aplicados em pelo menos seis direções no espaço. Desta forma, é possível estimar a direção principal do movimento das moléculas de água que compõem as microfibras da substância branca cerebral, e reconstruir os percursos de neurónios que conectam as várias regiões do cérebro. Este processo é chamado de tractografia de fibras (FT), que proporciona um modelo a três dimensões da arquitetura tecidular cerebral, permitindo a visualização e o estudo da conectividade e da continuidade dos percursos neuronais. Assim, é possível obter informação quantitativa acerca do sistema nervoso in vivo e contruir mapas de conectividade cerebral. No entanto, existe falta de consenso sobre as regras de reconstrução destes mapas neuronais, fazendo com que não haja um modelo-base para o estudo dos mesmos. Além disto, os dados provenientes das imagens de dMRI são facilmente afetados e podem diferir da realidade. Alguns exemplos mais comuns são a presença de ruído e existência tanto de conexões falsas como a ausência de conexões que deviam estar presentes, chamadas respetivamente de falsos-positivos e falsos-negativos. Consequentemente, os modelos de conectividade não podem ser comparados entre diferentes aparelhos de ressonância, nem mesmo entre diferentes momentos temporais, por terem uma baixa reprodutibilidade, tornando estes métodos poucos fiáveis. As soluções propostas para lidar com esta falta de consenso quanto à reconstrução de mapas ou redes neuronais e a presença de conexões falsas podem ser agrupadas em duas categorias: normalização e redução da rede neuronal através da aplicação de um limiar (threshold, em inglês). Contudo, os processos de normalização para remover certas tendências erradas destas redes não eram suficientes e, por vezes, introduziam outras dependências. Quanto à aplicação de limiares, mesmo que alguns estudos mostrem que a sua utilização no mapa neuronal do cérebro todo pode efetivamente eliminar alguns efeitos, a própria escolha de um limiar pode conduzir a modificações nas redes neuronais através de eliminação de certas comunicações fundamentais. Como uma extensão da redução destas redes neuronais com o objetivo de lidar com a sua baixa reprodutibilidade, vários estudos têm proposto uma nova abordagem: analisar apenas uma espécie de esqueleto das mesmas. O objetivo deste “esqueleto-neuronal” é o de representar as ligações mais importantes e estruturais e estar isento de falsas conexões. Idealmente, este “esqueleto-neuronal” seria reprodutível entre diferentes dispositivos e consistente entre indivíduos saudáveis, enquanto se manteria fiel às diferenças causadas pela presença de doenças. Assim sendo, o estudo da extração de um esqueleto-neuronal, visa encontrar estruturas fundamentais que evitem a perda de propriedades topológicas. Por exemplo, considerando pacientes com SVD, estes esqueletos-neuronais devem fornecer uma melhor compreensão das alterações da conectividade cerebral ao longo do tempo, permitindo uma comparação sólida entre diferentes pontos no tempo e a identificação de alterações que sejam consequência inegável de doença. Alguns tipos destas redes neuronais foram já propostos em diversas publicações científicas, que podem ser construídos utilizando FT de substância branca com informação proveniente de dMRI. Neste estudo, utilizamos o Minimum Spanning Tree (MST), o K-Core, o Disparity Core e o Hub-Core, que são redes-esqueleto já existentes na literatura. A eficácia tanto do uso do MST como do K-Core já foram comprovadas tanto a nível de deteção de alterações da conectividade cerebral, como na medida em que conseguem manter as conexões mais importantes do esqueleto cerebral, eliminando conexões que podem ser consideradas duvidosas. No entanto, até agora, nenhum estudo se focou na comparação dos diferentes esqueletos-neuronais existentes quanto à sua reproducibilidade, consistência ou sensibilidade aos efeitos de doença ao longo do tempo. Neste estudo, utilizamos os quatro modelos-esqueletos mencionados anteriormente, avaliando: (1) se estes esqueletos-neuronais podem ser efetivamente aplicados a dados clínicos, testando a sua reproducibilidade entre dois pontos de tempos distintos e a sua consistência entre grupos de controlos saudáveis; (2) se são sensíveis a efeitos causados por doença, tanto entre controlos e pacientes, como na evolução de alterações de conectividade em pacientes ao longo do tempo. Os dados longitudinais utilizados provêm de imagens ponderadas em T1 de 87 pacientes idosos com SVD, assim como de um grupo controlo de 45 idosos saudáveis coincidentes em idade com estes pacientes, e de um grupo de validação constituído por 44 jovens saudáveis. Para cada um dos objetivos, testamos os 4 tipos de esqueletos-neuronais, baseados primeiramente num modelo que divide o córtex cerebral em 90 regiões de interesse (ROIs) e posteriormente em modelos de 200 e 250 regiões. No pós-processamento, foram construídas e comparadas matrizes de conectividade que representam as ligações entre as várias regiões em que dividimos o córtex. Com estas matrizes foi possível calcular valores de conectividade como a força nodal (NS) e a eficiência global (GE). Também foram comparadas matrizes que diziam respeito a parâmetros específicos de DTI como a anisotropia fracionada (FA) e a difusividade média (MD). A análise estatística foi feita utilizando testes paramétricos t-test e ANOVA. Os resultados indicam que, no geral, estas redes podem ser utilizadas como forma de analisar e estudar mapas de conectividade cerebral de forma mais precisa, pois são reprodutíveis entre controlos saudáveis em tempos diferentes, e conseguem detetar as diferenças de conectividade devidas a doença. Além disso, representam as ligações mais importantes da rede de conectividade neuronal, criando uma base para comparações fiáveis. A maior parte dos esqueletos-neuronais mostraram ser consistentes dentro de cada grupo de estudo, e apresentaram diferenças de conectividade entre controlos e pacientes. Neste caso, comparando sujeitos saudáveis com pacientes, os valores das componentes de FA e de MD destes esqueletos neuronais, e as suas alterações, vão de encontro com a literatura sobre a evolução do estado das ligações neuronais no desenvolvimento de demência. Quanto à análise longitudinal dos pacientes, concluímos que a NS representa uma medida mais fiável de análise das alterações ao longo do tempo da doença do que a GE. Finalmente, e ainda que algumas destes esqueletos-neuronais tenham mostrado melhor desempenho do que outros em diferentes abordagens, concluímos que o MST é a rede-esqueleto que dispõe dos melhores resultados utilizando o modelo de 90 e 200 ROIs, do cérebro todo, assim como usando o modelo de 250 ROIs aplicado só ao hemisfério esquerdo. Em suma, conclui-se que a utilização destes tipos de redes-esqueleto pode vir a tornar-se uma melhor alternativa em relação à utilização das redes neuronais originais do cérebro completo, visto que podem ser eficazmente aplicadas à análise de dados clínicos como forma fiável de detetar presença e evolução de doenças

Feasibility of diffusion and probabilistic white matter analysis in patients implanted with a deep brain stimulator.

Deep brain stimulation (DBS) for Parkinson\u27s disease (PD) is an established advanced therapy that produces therapeutic effects through high frequency stimulation. Although this therapeutic option leads to improved clinical outcomes, the mechanisms of the underlying efficacy of this treatment are not well understood. Therefore, investigation of DBS and its postoperative effects on brain architecture is of great interest. Diffusion weighted imaging (DWI) is an advanced imaging technique, which has the ability to estimate the structure of white matter fibers; however, clinical application of DWI after DBS implantation is challenging due to the strong susceptibility artifacts caused by implanted devices. This study aims to evaluate the feasibility of generating meaningful white matter reconstructions after DBS implantation; and to subsequently quantify the degree to which these tracts are affected by post-operative device-related artifacts. DWI was safely performed before and after implanting electrodes for DBS in 9 PD patients. Differences within each subject between pre- and post-implantation FA, MD, and RD values for 123 regions of interest (ROIs) were calculated. While differences were noted globally, they were larger in regions directly affected by the artifact. White matter tracts were generated from each ROI with probabilistic tractography, revealing significant differences in the reconstruction of several white matter structures after DBS. Tracts pertinent to PD, such as regions of the substantia nigra and nigrostriatal tracts, were largely unaffected. The aim of this study was to demonstrate the feasibility and clinical applicability of acquiring and processing DWI post-operatively in PD patients after DBS implantation. The presence of global differences provides an impetus for acquiring DWI shortly after implantation to establish a new baseline against which longitudinal changes in brain connectivity in DBS patients can be compared. Understanding that post-operative fiber tracking in patients is feasible on a clinically-relevant scale has significant implications for increasing our current understanding of the pathophysiology of movement disorders, and may provide insights into better defining the pathophysiology and therapeutic effects of DBS

Test-retest reliability of structural brain networks from diffusion MRI

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test–retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test–retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test–retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability

Reproducibility of graph metrics of human brain structural networks

Recent interest in human brain connectivity has led to the application of graph theoretical analysis to human brain structural networks, in particular white matter connectivity inferred from diffusion imaging and fiber tractography. While these methods have been used to study a variety of patient populations, there has been less examination of the reproducibility of these methods. A number of tractography algorithms exist and many of these are known to be sensitive to user-selected parameters. The methods used to derive a connectivity matrix from fiber tractography output may also influence the resulting graph metrics. Here we examine how these algorithm and parameter choices influence the reproducibility of proposed graph metrics on a publicly available test-retest dataset consisting of 21 healthy adults. The dice coefficient is used to examine topological similarity of constant density subgraphs both within and between subjects. Seven graph metrics are examined here: mean clustering coefficient, characteristic path length, largest connected component size, assortativity, global efficiency, local efficiency, and rich club coefficient. These reproducibility of these network summary measures is examined using the intraclass correlation coefficient (ICC). Graph curves are created by treating the graph metrics as functions of a parameter such as graph density. Functional data analysis techniques are usedto examine differences in graph measures that result from the choice of fiber tracking algorithm. The graph metrics consistently showed good levels of reproducibility as measured with ICC, with the exception of some instability at low graph density levels. The global and local efficiency measures were the most robust to the choice of fiber tracking algorithm

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

Harmonization; MRI; Multiple sclerosisHarmonització; Ressonància magnètica; Esclerosi múltipleArmonización; Resonancia magnética; Esclerosis múltipleThere is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resource

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Magnetic resonance imaging of brain tissue abnormalities: transverse relaxation time in autism and Tourette syndrome and development of a novel whole-brain myelin mapping technique

The transverse relaxation time (T2) is a fundamental parameter of magnetic resonance imaging sensitive to tissue microstructure and water content, thus offering a non-invasive approach to evaluate abnormalities of brain tissue in-vivo. Prevailing hypotheses of two childhood psychiatric disorders were tested using quantitative T2 imaging and automated region of interest (ROI) analyses. In autism, the under-connectivity theory, which proposes aberrant connectivity within white matter (WM) was assessed, finding T2 to be eleveted in the frontal and parietal lobes, while dividing whole brain data into neurodevelopmentally relevant WM ROIs found increased T2 in bridging and radiate WM. In Tourette syndrome, tissue abnormalities of deep gray matter structures implicated in the symptomology of this disorder were evaluated and increased T2 of the caudate was found. Despite the sensitivity of quantitative T2 measurements to underlying pathophysiology, interpretation remain difficult. However, in WM, the compartmentalization of distinct water environments may lead to the detection of multi-exponential T2 decay. The metric of interest is principally the myelin water fraction (MWF), which is the proportion of the MRI signal arising from water trapped within layers of the myelin sheath. As a proof of concept study, the ability to measure the MWF based on T2* decay was evaluated and compared to a MWF measurements obtained from T2 decay. Data were analysed using both non-negative least squares and a two-pool model. Signal losses near sources of magnetic field inhomogeneity, such as the sinuses, rendered T2* components inseparable, invalidating this approach for whole brain MWF measurements. However, this study demonstrated the suitability of a two-pool model to calculate the MWF in WM. A novel approach, based on the multi-component gradient echo sampling of spin echoes (mcGESSE) and a two-pool model of WM, is proposed and its feasibility demonstrated using simulations. The in-vivo implementation of mcGESSE followed, with reproducibility of MWF measurements being assessed and the potential of an accelerated protocol using parallel imaging being investigated. While further work is needed to assess data quality, this approach shows great potential to obtain whole brain MWF data within a clinically relevant scan time