Optimization of graph construction can significantly increase the power of structural brain network studies

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network

Purpose: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. / Methods: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A “traveling brains” dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. / Results: The results show an inter-center and inter-subject variability of <10%, except for “clustering coefficient” (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners’ hardware. / Conclusions: The results show low variability of connectivity topological metrics across sites running a harmonised protocol

Network diffusion modeling predicts neurodegeneration in traumatic brain injury

Objective Traumatic brain injury (TBI) is a heterogeneous disease with multiple neurological deficits that evolve over time. It is also associated with an increased incidence of neurodegenerative diseases. Accordingly, clinicians need better tools to predict a patient’s long‐term prognosis. Methods Diffusion‐weighted and anatomical MRI data were collected from 17 adolescents (mean age = 15y8mo) with moderate‐to‐severe TBI and 19 healthy controls. Using a network diffusion model (NDM), we examined the effect of progressive deafferentation and gray matter thinning in young TBI patients. Moreover, using a novel automated inference method, we identified several injury epicenters in order to determine the neural degenerative patterns in each TBI patient. Results We were able to identify the subject‐specific patterns of degeneration in each patient. In particular, the hippocampus, temporal cortices, and striatum were frequently found to be the epicenters of degeneration across the TBI patients. Orthogonal transformation of the predicted degeneration, using principal component analysis, identified distinct spatial components in the temporal–hippocampal network and the cortico‐striatal network, confirming the vulnerability of these networks to injury. The NDM model, best predictive of the degeneration, was significantly correlated with time since injury, indicating that NDM can potentially capture the pathological progression in the chronic phase of TBI. Interpretation These findings suggest that network spread may help explain patterns of distant gray matter thinning, which would be consistent with Wallerian degeneration of the white matter connections (i.e., “diaschisis”) from diffuse axonal injuries and multifocal contusive injuries, and the neurodegenerative patterns of abnormal protein aggregation and transmission, which are hallmarks of brain changes in TBI. NDM approaches could provide highly subject‐specific biomarkers relevant for disease monitoring and personalized therapies in TBI

Accuracy and reliability of diffusion imaging models

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain\u27s white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL\u27s BedpostX [BPX], DSI Studio\u27s Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3\u27s Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI

Increased structural connectivity in high schizotypy

AbstractThe link between brain structural connectivity and schizotypy was explored in two healthy participant cohorts, collected at two different neuroimaging centres, comprising 140 and 115 participants, respectively. The participants completed the Schizotypal Personality Questionnaire (SPQ), through which their schizotypy scores were calculated. Diffusion-MRI data were used to perform tractography and to generate the structural brain networks of the participants. The edges of the networks were weighted with the inverse radial diffusivity. Graph theoretical metrics of the default mode, sensorimotor, visual, and auditory subnetworks were derived and their correlation coefficients with the schizotypy scores were calculated. To the best of our knowledge, this is the first time that graph theoretical measures of structural brain networks are investigated in relation to schizotypy. A positive correlation was found between the schizotypy score and the mean node degree and mean clustering coefficient of the sensorimotor and the default mode subnetworks. The nodes driving these correlations were the right postcentral gyrus, the left paracentral lobule, the right superior frontal gyrus, the left parahippocampal gyrus, and the bilateral precuneus, that is, nodes that exhibit compromised functional connectivity in schizophrenia. Implications for schizophrenia and schizotypy are discussed

Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization

PURPOSE: To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets. METHODS: Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference. RESULTS: In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97). CONCLUSION: We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks

The impact of genetic risk for Alzheimer’s disease on the structural brain networks of young adults

INTRODUCTION: We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer’s disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. METHODS: Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant’s genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks. RESULTS: In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = –0.19, p = 1.4 × 10(–3)), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10(–3)), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = –0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = –0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = –0.16, p = 0.035; r = –0.15, p = 0.036). DISCUSSION: We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer’s disease in later life

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

Brain Dynamics as Confirmatory Biomarker of Dementia with Lewy Bodies Versus Alzheimer’s Disease - an Electrophysiological Study

PhD ThesisIntroduction Dementia with Lewy bodies (DLB), Parkinson’s disease dementia (PDD) and Alzheimer’s disease dementia (AD) are associated with different pathologies. Nevertheless, symptomatic overlap between these conditions may lead to misdiagnosis. Resting-state functional connectivity features in DLB as assessed with electroencephalography (EEG) are emerging as diagnostic biomarkers. However, their pathological significance is still questioned. This study aims to further investigate this aspect and to infer functional and structural sources of EEG abnormalities in DLB. Methods Graph theory analysis was first performed to assess EEG network differences between healthy controls (HC) and dementia groups. Source localisation and Network Based Statistics (NBS) were used to infer EEG cortical network and dominant frequency (DF) alterations in DLB compared with AD. Further analysis aimed to assess the subnetwork associated with visual hallucination (VH) symptom in DLB and PDD, i.e. LBD, compared with not-hallucinating (NVH) patients. Finally, probabilistic tractography was performed on diffusion tensor imaging (DTI) data between cortical regions, thalamus, and basal forebrain (NBM). Correlation between structural and functional connectivity was tested. Results EEG α-band (7-13.5 Hz) network features were affected in LBD compared with HC, whilst DLB β-band network (14-20.5 Hz) was weaker and more segregated when compared with AD. This scenario replicated in the source domain. DF was significantly lower in DLB compared with AD, and positively correlated with structural connectivity strength between NBM and the cortex. Functional visual ventral network connectivity and cholinergic projections towards the cortex were affected in VH compared with NVH, and significantly correlated in NVH. Conclusions Functional connectivity as assessed with EEG is more affected in DLB compared with AD. Moreover, the visual ventral network is functionally altered in VH compared with NVH. Results from structural analysis provide empirical evidence on the role of cholinergic dysfunctions in DLB and PDD pathology and corresponding functional correlates