High-accuracy individual identification using a “thin slice” of the functional connectome

Connectome fingerprinting—a method that uses many thousands of functional connections in aggregate to identify individuals—holds promise for individualized neuroimaging. A better characterization of the features underlying successful fingerprinting performance—how many and which functional connections are necessary and/or sufficient for high accuracy—will further inform our understanding of uniqueness in brain functioning. Thus, here we examine the limits of high-accuracy individual identification from functional connectomes. Using ∼3,300 scans from the Human Connectome Project in a split-half design and an independent replication sample, we find that a remarkably small “thin slice” of the connectome—as few as 40 out of 64,620 functional connections—was sufficient to uniquely identify individuals. Yet, we find that no specific connections or even specific networks were necessary for identification, as even small random samples of the connectome were sufficient. These results have important conceptual and practical implications for the manifestation and detection of uniqueness in the brain. Patterns of functional connectivity are so distinct between different people that they can be used to predict individual identity with high accuracy. Here, we show that a strikingly small fraction of the functional connectome is actually needed to predict individual identity (as few as 40 functional connections from 64,620). We further show that although certain functional connections may be most informative, even small fractions of the connectome selected at random can be used to identify individuals, and that no specific connections or even networks are actually necessary. The results indicate that uniquely identifying signatures of brain functioning are widely distributed throughout the brain and can be detected in a much more compact manner than previously appreciated

Integrating Temporal and Spatial Scales: Human Structural Network Motifs Across Age and Region of Interest Size

Human brain networks can be characterized at different temporal or spatial scales given by the age of the subject or the spatial resolution of the neuroimaging method. Integration of data across scales can only be successful if the combined networks show a similar architecture. One way to compare networks is to look at spatial features, based on fiber length, and topological features of individual nodes where outlier nodes form single node motifs whose frequency yields a fingerprint of the network. Here, we observe how characteristic single node motifs change over age (12–23 years) and network size (414, 813, and 1615 nodes) for diffusion tensor imaging structural connectivity in healthy human subjects. First, we find the number and diversity of motifs in a network to be strongly correlated. Second, comparing different scales, the number and diversity of motifs varied across the temporal (subject age) and spatial (network resolution) scale: certain motifs might only occur at one spatial scale or for a certain age range. Third, regions of interest which show one motif at a lower resolution may show a range of motifs at a higher resolution which may or may not include the original motif at the lower resolution. Therefore, both the type and localization of motifs differ for different spatial resolutions. Our results also indicate that spatial resolution has a higher effect on topological measures whereas spatial measures, based on fiber lengths, remain more comparable between resolutions. Therefore, spatial resolution is crucial when comparing characteristic node fingerprints given by topological and spatial network features. As node motifs are based on topological and spatial properties of brain connectivity networks, these conclusions are also relevant to other studies using connectome analysis

AN EDGE-CENTRIC PERSPECTIVE FOR BRAIN NETWORK COMMUNITIES

Thesis (Ph.D.) - Indiana University, Department of Psychological and Brain Sciences and Program in Neuroscience, 2021The brain is a complex system organized on multiple scales and operating in both a local and distributed manner. Individual neurons and brain regions participate in specific functions, while at the same time existing in the context of a larger network, supporting a range of different functionalities. Building brain networks comprised of distinct neural elements (nodes) and their interrelationships (edges), allows us to model the brain from both local and global perspectives, and to deploy a wide array of computational network tools. A popular network analysis approach is community detection, which aims to subdivide a network’s nodes into clusters that can used to represent and evaluate network organization. Prevailing community detection approaches applied to brain networks are designed to find densely interconnected sets of nodes, leading to the notion that the brain is organized in an exclusively modular manner. Furthermore, many brain network analyses tend to focus on the nodes, evidenced by the search for modular groupings of neural elements that might serve a common function. In this thesis, we describe the application of community detection algorithms that are sensitive to alternative cluster configurations, enhancing our understanding of brain network organization. We apply a framework called the stochastic block model, which we use to uncover evidence of non-modular organization in human anatomical brain networks across the life span, and in the informatically-collated rat cerebral cortex. We also propose a framework to cluster functional brain network edges in human data, which naturally results in an overlapping organization at the level of nodes that bridges canonical functional systems. These alternative methods utilize the connection patterns of brain network edges in ways that prevailing approaches do not. Thus, we motivate an alternative outlook which focuses on the importance of information provided by the brain’s interconnections, or edges. We call this an edge-centric perspective. The edge-centric approaches developed here offer new ways to characterize distributed brain organization and contribute to a fundamental change in perspective in our thinking about the brain

A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain.

The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico-cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico-cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior-posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals

The individuality of shape asymmetries of the human cerebral cortex

Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences

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

Towards naturalistic scanning environments for wearable magnetoencephalography

Magnetoencephalography (MEG) is a neuroimaging technique that probes human brain function, by measuring the magnetic fields generated at the scalp by current flow in assemblies of neurons. A direct measure of neural activity, MEG offers high spatiotemporal resolution, but limitations imposed by superconducting sensor technologies impede its clinical utility. Specifically, neuromagnetic fields are up to a billion times smaller than that of the Earth, meaning MEG must be performed inside a magnetically shielded room (MSR), which is typically expensive, heavy, and difficult to site. Furthermore, current MEG systems employ superconducting quantum interference devices (SQUIDs) to detect these tiny magnetic fields, however, these sensors require cryogenic cooling with liquid helium. Consequently, scanners are bulky, expensive, and the SQUIDs must be arranged in a fixed, one-size-fits-all array. Any movement relative to the fixed sensors impacts data quality, meaning participant movement in MEG is severely restricted. The development of technology to enable a wearable MEG system allowing free participant movement would generate a step change for the field. Optically-pumped magnetometers (OPMs) are an alternative magnetic field detector recently developed with sufficient sensitivity for MEG measurements. Operating at body temperature, in a small and lightweight sensor package, OPMs offer the potential for a wearable MEG scanner that allows participant movement, with sensors mounted on the scalp in a helmet or cap. However, OPMs operate around a zero-field resonance, resulting in a narrow dynamic range that may be easily exceeded by movement of the sensor within a background magnetic field. Enabling a full range of participant motion during an OPM-MEG scan therefore presents a significant challenge, requiring precise control of the background magnetic field. This thesis describes the development of techniques to better control the magnetic environment for OPM-MEG. This includes greater reduction of background magnetic fields over a fixed region to minimise motion artefacts and facilitate larger movements, and the application of novel, multi-coil active magnetic shielding systems to enable flexibility in participant positioning within the MSR. We outline a new approach to map background magnetic fields more accurately, reducing the remnant magnetic field to <300 pT and yielding a five-fold reduction in motion artefact, to allow detection of a visual steady-state evoked response during continuous head motion. Employing state-of-the-art, triaxial OPMs alongside this precision magnetic field control technique, we map motor function during a handwriting task involving naturalistic head movements and investigate the advantages of triaxial sensitivity for MEG data analysis. Using multi-coil active magnetic shielding, we map motor function consistently in the same participant when seated and standing, and demonstrate the first OPM-MEG hyperscanning experiments. Finally, we outline how the integration of a multi-coil system into the walls of a lightweight MSR, when coupled with field control over a larger volume, provides an open scanning environment. In sum, these developments represent a significant step towards realising the full potential of OPM-MEG as a wearable functional neuroimaging technology